Medires Publishers - Article

Abstract

The general objective of this study was to identify the major factors that influence the loan repayment performance of urban women in the case of vision fund micro finance institution, in Fiche town. The study was employed both qualitative and quantitative research methods. Surveys were conducted among urban women who have taken out loans from vision fund micro finance institutions. To select the desired sample from population of the study, stratified random sampling technique was used. Both descriptive statistics and econometric analyses particularly logistic regression (binary logit) was employed to present the results and findings of the research. Inferential statistics were used to identify significant correlations between different factors and loan repayment performance. The descriptive statistics results showed that out of 80 women borrowers 45 (56%) women borrowers were performing well or pay their loan on time while the remaining 35 (44%) were non-performing in their ability to pay their loan on time. In addition, the binary logit model results indicate that 8 variables were considered for the model out of which, 4 variables were found to significantly influence loan repayment performance of women borrowers at less than 5 percent level of significance. Education level of women, experience in credit use, and grace period positively and significantly influenced loan repayment performance of women borrowers while, engagement in social ceremonies negatively influenced the loan repayment performance of women borrowers in the study area. Finally, the researcher recommends that financial institution must provide educational training service related to loan, asses past credit experience of borrower, longer grace period while women borrowers must prioritize their spending pattern to improve their loan repayment performance.

Background of the Study

The major causes of low economic growth and high incidence of poverty in Ethiopia include lack of income, asset, employment opportunities, skills, education, health, social infrastructure and inappropriate policies. Microcredit helps the poor to be involved in income generating activities that allow them to accumulate capital and improve their standard of living. However, the women are perpetually marginalized in the institutionalized credit programmers.

Recently, many development programs have been extending reasonable amount of credits to urban women. Still, formal financial institutions have not been interested in delivering credit to the poor women without collateral requirements (Abafita, 2013). But, however, many of the credit users cannot repay (payback) the entire received loan at a right time and this has been a source of a great discourages for credit provider institutions. The loan repayment performance of the beneficiaries was found to be very low (Abdullah, 2012). Therefore, this study aims to assess the factors that affect the loan repayment performance of urban women.

Statement of the Problem

In Ethiopia, urban women have emerged as key contributors to the country's economic growth and poverty reduction efforts. MFIs provide microcredit to the borrowers on the basis of financial discipline in that clients were expected to display the required effort to repay their loans on time and must invest the borrowed loans for productive purposes. However, most MFIs in Ethiopia were experiencing default problems with declining repayment rates (Abafita, 2013). Fortunately, group-based microfinance system that involves peer pressure and joint liability were implemented. Still, the loan repayment performance of women clients was low and unable to repay their loan on the right time (Mengistu, 2012).

Many socio-economic and institutional factors such as borrower side factors, lender side factors like the size and maturity of loan, interest rate charged and timing of loan disbursement have an impact on the repayment rates (Oke, et al., 2010). Onyeagocha et al., (2013) states that although, loan repayment is determined by the ability of the borrowers, businesses characteristics and characteristics of the lending institutions may influence loan repayment capacity among borrowers. So, understanding the factors that affect loan repayment performance would be essential for designing targeted interventions and policies that can enhance loan repayment rates and promote financial inclusion among urban women. Therefore, this study was aimed to assess the factors that affect loan repayment performance of urban women.

Objectives of the Study

The general objective of the study is to assess factors affecting loan repayment performance of urban women, in Fiche Town.

Specific objectives

To identify borrower specific factors affecting loan repayment performance of urban women borrowers?

To identify institutional factors affecting loan repayment performance of urban women borrowers?

To identify business specific factors affecting loan repayment performance of urban women borrowers?

Research Methodology

Research Design

This study employed an explanatory research.

Research Approach

The researcher employed quantitative research approach to analyse the factors that affect loan repayment performance of urban women borrowers.

Source of data and methods of data collection

For this study, both primary and secondary data were used. Primary data was collected from sample of urban women who have taken loans through structured questionnaires. Secondary data were collected from vision microfinance institutions found in the town.

Targeted population, sample size and sampling technique

The target population was urban women who have received loans from vision fund microfinance institution. Stratified sampling techniques were used to draw representative sample. The main reason for stratification is because the targeted credit users were selected based on the diversified business activities they are caring on. Therefore, based on proportional stratified random sampling a total (sample) of 80 women borrowers were selected. The desired sample size was determined by employing the formula given by Yamane (1967):

n=N1+N(e2), n=1301+1300.072= 80

Where: n= the sample size, N= the total number of women borrower and e = level of precision or margin of error (7%)

Methods of Data Analysis

Descriptive statistics, such as frequencies, percentages, mean, and standard deviation were used to summarize the loan repayment performance of the sample. In addition, econometric analysis, specifically binary logistic regression model was used to identify factors affecting loan repayment performance of urban women.

Model Specification

Binary logistic regression model (logit) was used to analyze the factors that affect loan repayment ability of the women borrowers.

Assume the basic model, Yi=β1+β2Xi+εi

The cumulative Logistic distributive function can be econometrically specified as follows:

Pi=EY=1Xi=11+e-(β1+β2Xi)

There is a problem with non-linearity in the previous expression, but this can be solved by creating the odds ratio Pi1-Pi and its log-transformation.

Pi1-Pi=prob(y=1|Xirob(y=0|Xi =1+eyi1+e-yi=eyi

Li=lnPi1-Pi= yi=β1+β2Xi

Li is called the logit, thus, the log-odds is a linear function of the explanatory variables.

Variable definitions

Dependent variable

The dependent variable of the study, loan repayment performance, was encoded as dummy variables. Women’s who borrowed but did not repay the full amount of money based on the agreement are considered as non-performing (i.e., the value of the repayment performance in this case is zero). On the other hand, women’s that repaid back all the money that they had borrowed within the stated time are considerd as performing loan (it takes one).

Explanatory variables

Based on empirical literature, the following demographic, socio-economic and institutional factors were hypothesized to explain loan repayment performance of urban women in the study area. The researcher considered age of the borrower, education level, family size, past credit experience, loan size, follow up, grace period, lending interest rate, timeliness of loan, collateral requirement, business type, celebration of social ceremonies and business form as independent variables that likely affect loan repayment performance of urban women.

Major research findings

Borrowers loan repayment classification

As shown in table 2 below, out of 80 women borrowers 45(56%) were performing loans and the remaining 35 (44%) were non-performing. From this we have loan repayment category of performing and non-performing for analysis purpose.

Table 1:  Repayment classification of borrowers

Source own survey result, 2016

Borrowers Related Factors

Borrower specific factors are the first most important factor related with personal characteristics of the borrower and it’s important in determining performing and nonperforming loans based on the personal behavior of the borrower. Under this research, age, education level, credit experience and family size were identified to evaluate their contribution in loan repayment performances of the borrower. So, now let us see effect of all variables from loan repayment performances perspectives.

Table 2: Descriptive statistics of borrower specific factors by the repayment performance

Source own survey result, 2016

Age: The average age of the whole sampled women borrowers was around 48 years with the minimum and maximum ages of 24 and 67 years, respectively. The average age of women borrowers having good repayment performance was 47 years while that of women borrowers performing less was 48 years (Table 4.1).

Family size: As indicated in the above table, the average size of the the whole sampled women borrowers are around 4.7 members. In addition, the mean family size of loan performing women borrower is around 4.8 members while the average family size of non-performing women borrowers is around 4.5 members. This indicat that as family size increases, the likelihood of loan repayment performance increases.

Education level of women borrowers: Education is an important determinant of loan repayment performance. The average years of schooling of the whole sampled women borrowers was around 3 years with the minimum and maximum of 1 and 4 years of schooling, respectively with statistically significant mean difference between the two groups at 1% (Table 4.1). This result indicates as education level increases, women borrower’s loan repayment performance also increases.

Credit Experience: Another borrower related factor is credit experiences of women respondents expressed in terms of years. It helps borrowers in utilizing the loan for intended purpose and on how to prepare payments as per the schedules. As shown in table 4.1 above, the whole sampled women borrowers have on average 2-year experience in credit use with significant mean difference between the two groups at 5% significance level (Table 4.1). This indicates that, experienced borrowers are better in repaying their loan than those who didn’t have any experience.

Social Ceremonies: The analysis indicated that 51 (64 %) respondents had celebrated one or more ceremonies during the year preceding the survey and the remaining 29 (36%) the respondents were not celebrate any social ceremonies. Of the total respondents who had participated in a ceremony 36 (45%) respondents were loan performing while 15 (19%) respondents were non-performing. Furthermore, of the total respondents who did not participated in social ceremony 9 (11%) respondents were loan performing while 20 (25%) respondents were non-performing. Chi square tests indicated that there is significant association between loan repayment performance and social festival. This reveals that borrowers celebrating more social ceremony may use the borrowed money for non-productive activities that may hinder their loan repayment ability.

Table 3: Respondents response on social ceremonies

Source own survey result, 2016

Business Related Factors

Other factors that affects loan repayment performances of women borrowers emanates from the business itself. Thus, in this research business form and business type were selected as business related factors.

Table 4: Business related factors

Source own survey result, 2016

Business form: for this research, borrowers were categorized under three business forms; sole proprietorships, private limited companies and share companies. Based on the results of the survey indicated in the above table, 40 (50%) borrowers were sole owners/ private borrowers of their business, while 21(26%) borrowers were Private Limited Company (PLC) and the remaining 19 (24%) of borrowers were Share Companies. In terms of their Loan repayment performances, from total private borrowers/sole owners as shown in the table, 30% of them were performing loans and 20% were nonperforming. In cases of Private Limited company borrowers, 11% were performing loans while the remaining 15% were non-performing loans. Lastly 15 % and 9 were performing and non-performing loans respectively in case of Share Company.

Business type: this variable evaluates which economic sector from agricultural, service and industrial sector of the economy affects loan repayment performances of the borrowers. With respect to the business sector on which loans were invested, majority of them were agricultural related business and service providers. As shown in table 4.5 above, 25 (31%) borrowers took the loan to engage in agricultural and service type business, whereas, 20 (15%) borrowers took the loan to invest on industrial sector of the economy and the remaining 10 (12%) invested on trade. Now, out agricultural loans 20% were performing and 11% of them were nonperforming. On the other hand, from among loans invested on service sector of the economy, 17% loans were performing and 14% were nonperforming loans. The loans invested on industry sector indicate that 15% were performing and the remaining 10% was non-performing loans.

Institutional Related Factors

Among institutional related factors, collateral and timeliness of loan were taken as factors of loan repayment performance. These variables was encoded as as dummy variables having their own features.

Table 5: Institutional factors

Source own survey result, 2016

Collateral: this is a dummy variable taking 0 if the collateral is sufficient and 1 if the collateral is not sufficient. The value of such collateral is believed to be more or equal to the amount of money permitted for the borrower. As shown in the above table, 67 (84%) respondents confirm that vision fund micro-finance institution requires sufficient collateral to grantee loan provision and the remaining 13(16%) believes the institution has no sufficient collateral. In terms of performances, out of those who believe the bank has no sufficient collateral, 9% of them were performing their duty of repaying their loan and 7% were non-performing. From those who believe the bank has sufficient collateral, 48% were performing well while 36% were non-performing. The chi-square result reveals no significant association between collateral and loan repayment performance.

Timeliness of loan: this variable assess whether the lending institution deliver its services within a shortest possible time or otherwise. Time horizon is a dummy variable encoded as 1 for timely release of loan 2 for delayed services. Timeliness of loan disbursement is important when loans are used for seasonal activities such as agriculture. The survey result indicated in the table above shows that 43(54%) borrower’s believes that the loan was served within a reasonable time, while 37(46%) borrowers get delayed loan service. Out of timely served borrowers, 53% were performing their loan, while the 1% failed. On the other hand out of borrowers who got service after some dalliance, 4% were repaying their loan as per the requirements and 42% were non-performing. The chi-square result reveals the strong and significant association between time and loan repayment at significant level 1% (X2=64.38, at P = 0.000). The statistics results in this survey indicate the fact that getting service within the shortest possible time contributes to well performance and vice versa.

Diagnostic test Results

According to (Gujarati, 1995), for the econometric estimation to bring about best, unbiased and consistent result, it has to fulfill the basic linear classical assumptions. Therefore, if these assumptions do not hold well on what so ever account, the estimators derived may not be efficient. Accordingly, the most important tests such as heteroscedasticity and multicollinearity test are tested and the results are presented in the following sections.

Heteroscedasticity Test

The Breusch-Pagan test was used to check for the presence of heteroscedasticity in the residuals. The regression models used in this study proved that the test statistics is not significant and the variance of the error term is constant or homoscedastic and we had sufficient evidence to accept the null hypothesis of Homoscedasticity. Therefore it can be concluded that the CLRM assumption of heteroscedasticity is not violated.

Breusch–Pagan test for heteroscedasticity

Multicollinearity Test

If an independent variable has exact linear combination with the other independent variables, then we say the model suffers from perfect collinearity. This assumption is concerned with the relationships which exist between explanatory variables. One of the assumptions of the CLRM is that there is no exact linear relationship exists between any of the explanatory variables. When this assumption is violated, there is perfect MC. If all explanatory variables are uncorrelated with each other, there is absence of MC. The technique of variance inflation factor (VIF) was employed to detect the problem of multicollinearity among the continuous variables. According to Gujarati (2003), larger value of VIF shows co-linearity across variables, thus if VIF exceeds 10 indicates the problem of multicollinearity within variables. As shown in the table below the result shows that no explanatory variables which have variance inflation factor near to 10 i.e. the maximum value among those explanatory variables was 1.14 in case of celebration of social ceremony while 1.09 was an average VIF of all variables. Therefore, the results reveal that there was no serious problem of association among all independent variables.

Table 6: VIF of the explanatory variables used in the model

In addition to VIF, contingency coefficients were computed to check the existence of multicollinearity problem among the discrete explanatory variables. The contingency coefficient is calculated following the formula provided by (Garson, 2008 cited in Fikirte, 2011). Hair et al (2006) argued that correlation coefficient range between -0.9 or 0.9 indicates no serious collinearity problem. Therefore, as shown the table below, it can be concluded that in this study the problem of multicollinearity did not exist between independent variables in the model. Hence all the variables were retained for use in the estimations.

Table 7: Correlation Matrix (only discrete explanatory variables)

Econometric Model Results

Binary Logistic regression model was used to determine the explanatory variables which are good predictors of the loan repayment performance of women borrowers. The Maximum Likelihood estimates of the Logistic regression model indicate that 8 variables were considered in the econometric model out of which, 4 variables were found to significantly influence loan repayment performance of women borrowers.

Table 8: Maximum likelihood estimates of the Binary Logit model

Source own survey result, 2016

As shown in the above regression table, the dependent variable “Loan repayment Performances” (RPS) was regressed as a function of eight independent variables. The regression output shows that variables like education level of women borrowers, experience in credit use, celebration of social ceremonies and grace period were statistically significant factors affecting the repayment performances of women borrowers. The detail analyses of the results for each significant explanatory variables and their importance in loan repayment performances were discussed as below.

Educational level of women: Education level of the borrower positively affect the loan repayment performance at 1% significance level. Thus, as education level of the borrower improved by one grade level, the likelihood of borrower’s ability to repay their loans increased by 30%. This implies that borrowers that were more educated may have access to business information, use their personal knowledge, skill and experience to properly manage their loan and repay timely. This result is also consistent with the descriptive analysis result.

Experience in credit use: The result of logit regression shows that experience in credit use influence the repayment capacity positively and statistically significant at 5%. Thus, as experience of women borrowers increase by one year, the likelihood of borrower’s ability to repay their loans increased by 29.4%. The implication of this result is that those who had long credit experience have good knowledge of managing and handling the financial aspects of their business and at better position than those who never had such exposure. This result is in line with results presented under descriptive part of this research.

Celebration of social ceremonies: This is one of the explanatory variables that were negatively and significantly affected loan repayment performance of women borrowers at 1% significant level. Thus, as women borrowers had celebrated one or more ceremonies, the likelihood of repayment performance becomes decreased by 79%. This indicates that borrowers who participate more on social ceremonies found to be non-performer in their ability to repay loan because borrowers may use the loan received for personal expenditure and loans miss their intended target. Therefore, borrowers poorly perform in their loan repayment.  This result is also consistent with the results presented under descriptive analysis part.

Grace period: It is a specified time frame at the beginning of a loan during which borrowers are allowed to make no payments before the principal amount becomes due. It is significant predictor loan repayment performance. This variable was positively and significantly impact loan repayment performance of women borrowers at 5% significance level. This implies that grace period gives additional time to learn about personal finance and reduce psychological pressures by offering a temporary financial relief from full principal payments. This allows women borrower to allocate resources effectively and ultimately to improve their ability to repay their loans on time. Overall, these factors contribute to an improved ability for women borrowers to repay their loans on time.

Conclusion And Recommendations

Conclusion

The main objective of this study was to assess and identify factors influencing women loan repayment performance in the case of vision fund microfinance institution. A binary logistic regression model and descriptive statistics were used to analyze the data collected from the sample respondents. The study used both primary and secondary data. The loan repayment performance of women borrowers were influenced by a combination of borrower-specific, business-specific, and institutional factors.

The descriptive statistics results showed that out of 80 women borrowers 45 (56%) women borrowers were performing well or pay their loan on time while the remaining 35 (44%) were non-performing in their ability to pay their loan on time. In addition, the binary logit model results indicate that 8 variables were considered for the model out of which, 4 variables were found to significantly influence loan repayment performance of women borrowers at less than 5 percent level of significance. Education level of women, experience in credit use, and grace period positively and significantly influenced loan repayment performance of women borrowers while, engagement in social ceremonies negatively influenced the loan repayment performance of women borrowers in the study area. In summary, factors such as education level, credit experience, grace period duration, and expenditure on social ceremonies play significant roles in determining the loan repayment performance of urban women. By focusing on improving these factors and practicing responsible financial management, urban women borrowers can enhance their ability to repay loans effectively.

Recommendation

To improve the loan repayment performance of women borrowers, the following recommendations were suggested:

Enhance borrower education: A higher education level is strongly associated with better loan repayment performance among women. This is because higher education often correlates with improved financial literacy, better job opportunities, and a greater understanding of long-term financial planning. Financial institutions should offer educational programs aimed at improving financial literacy and management skills among women borrowers.

Asses past credit experience: Women with a history of responsible credit management, such as paying bills on time and managing credit cards effectively, tend to have better loan repayment performance. Lenders can use credit reports and credit scores to assess an individual's creditworthiness and predict their ability to repay loans.

Prioritize spending: While social ceremonies like weddings or funerals are important cultural events for many women, the amount spent on social ceremonies can negatively impact loan repayment performance. High expenditures on social ceremonies may strain an urban woman's finances, making it difficult for her to meet her loan repayments on time. Therefore, it is recommended that borrowers carefully manage their spending on social ceremonies to maintain good loan repayment performance.

Grace Period: A grace period, which is the time between the end of a payment period and the due date for the next payment, can affect loan repayment performance. A longer grace period may provide urban women with more flexibility in managing their finances and could lead to improved loan repayment behavior. Financial institutions should provide enough grace period to increase the repayment ability of urban women. By implementing these recommendations, financial institutions can improve the loan repayment performance of women borrowers and contribute to economic empowerment of women.

Acknowledgement

My deepest gratitude goes to Vision fund micro finance institution to their open support during data collection.

Author information

Bogale Belay Abegaz

Department of Agricultural Economics, Salale University, Oromiya, Ethiopia

Conflict of interest

The author declares that there is no competing of interest and did not receive financial support from any organization for this article

References

1. Mijena, A. (2011). Determinants of credit repayment and fertilizer use by cooperative members in Ada District, East Shoa Zone, Oromia Region (Doctoral dissertation, Haramaya University).
   View at Publisher | View at Google Scholar
2. Al Mamun, A., Abdul Wahab, S., & Malarvizhi, C. A. (2011). Empirical investigation on repayment performance of Amanah Ikhtiar Malaysia’s hardcore poor clients. International Journal of Business and Management, 6(7), 125-133.
   View at Publisher | View at Google Scholar
3. Gebeyehu, A. (2002). Loan repayment and its determinants in small scale enterprises financing in Ethiopia (Doctoral dissertation, MSc Thesis. Addis Ababa University, Ethiopia).
   View at Publisher | View at Google Scholar
4. Berhanu, A. (2005). Determinants of formal source of credit loan repayment performance of smallholder farmers: the case of north western Ethiopia, North Gondar. Unpublished MSc. Thesis.
   View at Publisher | View at Google Scholar
5. Wubie, A. W., Desta, T. S., & Tesfahuney, R. G. (2014). Factors that affect loan repayment performance of smallholder farmer borrowers from Microfinance Institutions in Ethiopia: a survey study of Amhara Credit and Saving Institution (ACSI)-Fogera Wereda, Amhara.
   View at Publisher | View at Google Scholar
6. Okorie, A. (1986). Major determinants of agricultural smallholder loan repayment in a developing economy: Empirical evidence from Ondo State, Nigeria. Agricultural Administration, 21(4), 223-234.
   View at Publisher | View at Google Scholar
7. Million, S. H. (2012). Factors affecting loan repayment performance of smallholder farmers in Eastern Hararghe, Ethiopia (Doctoral dissertation).
   View at Publisher | View at Google Scholar
8. Belay, A. (2002). Factors influencing loan repayment of rural women in Eastern Ethiopia: The case of Dire Dawa area. Unpublished M. Sc. Thesis, Alemaya University, Ethiopia.
   View at Publisher | View at Google Scholar
9. Nanjundeswaraswamy, T. S., & Divakar, S. (2021). Determination of sample size and sampling methods in applied research. Proceedings on engineering sciences, 3(1), 25-32.
   View at Publisher | View at Google Scholar
10. Ababa, A. (2007). Ethiopia: Federal Democratic Republic of Ethiopia. Minstry of Health.
    View at Publisher | View at Google Scholar
11. Aredo, D. (1993). Informal and semi-formal financial sectors in Ethiopia: a study of the iqqub, iddir, and savings and credit co-operatives. Centre for the Study of African Economies, University of Oxford, Oxford, GB.
    View at Publisher | View at Google Scholar
12. Melles, F. (2000). Informal financial institutions: Impact analysis of ACORD's credit intervention through Iddirs in Dire Dawa. Unpublished MSc Thesis, Addis Ababa University.
    View at Publisher | View at Google Scholar
13. Kefeni, K. (2018). DETERMINANTS OF LOAN REPAYMENT PERFORMANCE IN PROJECT FINANCING: A CASE STUDY IN DEVELOPMENT BANK OF ETHIOPIA (Doctoral dissertation, St. Mary's University).
    View at Publisher | View at Google Scholar
14. Haile, F. (2015). Determinants of loan repayment performance: Case study of Harari microfinance institutions. Journal of Agricultural extension and rural development, 7(2), 56-64.
    View at Publisher | View at Google Scholar
15. Gutu, E. (2014). Assessing the effectiveness of group lending and Its impact on profitability in case of Jimma zone Oromiya credit and saving share company (Doctoral dissertation, the Institute of Development Studies and partner organisations).
    View at Publisher | View at Google Scholar
16. Abafita, J. (2003). Microfinance and loan repayment performance. A Case Study of the Oromia Credit and Savings Share.
    View at Publisher | View at Google Scholar
17. Maddala, G. S., & Lahiri, K. (1992). Introduction to econometrics (Vol. 2, p. 525). New York: Macmillan.
    View at Publisher | View at Google Scholar
18. TESFAY, S. E. (2013). COLLEGE OF BUSINESS AND ECONOMICS DEPARTMENT OF ECONOMICS FACTORS INFLUENCING FORMAL LOAN REPAYMENT PERFORMANCE OF URBAN WOMEN IN TIGRAY (Doctoral dissertation, MEKELLE UNIVERSITY).
    View at Publisher | View at Google Scholar
19. Million, S. H. (2012). Factors affecting loan repayment performance of smallholder farmers in Eastern Hararghe, Ethiopia (Doctoral dissertation).
    View at Publisher | View at Google Scholar
20. Nawai, N., & Shariff, M. N. M. (2010). Determinants of repayment performance in microcredit programs: A review of literature. International Journal of Business and Social Science, 1(2).
    View at Publisher | View at Google Scholar
21. Nawai, N., & Shariff, M. N. M. (2012). Factors affecting repayment performance in microfinance programs in Malaysia. Procedia-Social and Behavioral Sciences, 62, 806-811.
    View at Publisher | View at Google Scholar
22. Nwankwo, O. (2013). Agricultural financing in Nigeria: An empirical study of Nigerian agricultural co-operative and rural development bank (NACRDB): 1990-2010. Journal of Management research, 5(2), 28-44.
    View at Publisher | View at Google Scholar
23. Yibrie, O., & Ramakrishna, R. (2017). Determinants of loan repayment performance in ACSI. International Journal of Advanced Research in Management and Social Sciences, 6(4), 151-170.
    View at Publisher | View at Google Scholar
24. Million, S. H. (2012). Factors affecting loan repayment performance of smallholder farmers in Eastern Hararghe, Ethiopia (Doctoral dissertation).
    View at Publisher | View at Google Scholar
25. AHMED, A. (2020). DEBRE BERHAN UNIVERSITY COLLEGE OF POST GRADUATE DEPARTMENT OF ACCOUNTING AND FINANCE.
    View at Publisher | View at Google Scholar
26. Sengupta, R., & Aubuchon, C. P. (2008). The microfinance revolution: An overview. Federal Reserve Bank of St. Louis Review, 90(January/February 2008).
    View at Publisher | View at Google Scholar
27. Million, S. H. (2012). Factors affecting loan repayment performance of smallholder farmers in Eastern Hararghe, Ethiopia (Doctoral dissertation).
    View at Publisher | View at Google Scholar
28. Pasha, S. A. M., & Negese, T. (2014). Performance of loan repayment determinants in Ethiopian micro finance-An analysis. Eurasian Journal of Business and Economics, 7(13), 29-49.
    View at Publisher | View at Google Scholar
29. Wongnaa, C. A., & Awunyo-Vitor, D. (2013). Factors affecting loan repayment performance among yam farmers in the Sene District, Ghana. Agris on-line Papers in Economics and Informatics, 5(2), 111-122.
    View at Publisher | View at Google Scholar
30. Gebeyehu, Z., Beshire, H., & Haji, J. (2013). Determinants of loan repayment performance of smallholder farmers: The case of Kalu district, South Wollo zone, Amhara National Regional State, Ethiopia. Ethiopian Economics Association (Eea), 189.
    View at Publisher | View at Google Scholar