Journal of Agricultural Economics and Rural Development

Vol. 5(3), pp. 648-655, December, 2019. © www.premierpublishers.org, ISSN: 2167-0477

Research Article

Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman woredas of Horoguduru- Wollega Zone, Region,

*1Amsalu File, 2Oliyad Sori 1Wollega University, The Campus’s Finance Head, P.O. Box 38, Ethiopia 2Wollega University, Department of Agricultural Economics, P.O. Box 38, Ethiopia

Credit repayment is one of the dominant importance for viable financial institutions. This study was aimed to identify determinants of loan repayment capacity of smallholder farmers in Horro and Abay-Chomen Woredas. The study used primary data from a sample of formal credit borrower farmers in the two woredas through structured questionnaire. A total of 120 farm households were interviewed during data collection and secondary data were collected from different organizations. The logit model results indicated that a total of fourteen explanatory variables were included in the model of which six variables were found to be significant.; among these variables, family size and expenditure in social ceremonies negatively while, credit experience, livestock, extension contact and income from off-farm activities positively influenced the loan repayment performance of smallholder farmers in the study areas. Based on the result, the study recommended that the lending institution should give attention on loan supervision and management while the borrowers should give attention on generating alternative source of income to pay the loans which is vital as it provides information that would enable to undertake effective measures with the aim of improving loan repayment in the study area.

Key words: Loan repayment performance, Smallholder farmers, logit model, Horro and Abbay Chomen Woredas

INTRODUCTION

The economic growth of developing countries depends to productivity among the resource poor farmers. However, a great extent on the growth of the agricultural sector. lack of financial resource is one of the major problems Ethiopia is one example of a developing country, facing poor households. Formal financial institutions are characterized by a predominantly subsistence agrarian inefficient and inaccessible in providing credit facilities to economy. The nature of farming in Ethiopia is dominated the poor. Delivering productive credit, low cost, efficient by traditional micro holdings of the subsistence type, with credit services and recovering a high percentage of loans less than two hectares of land being the average holding granted are the ideal aims in rural finance (Wenner, 2015). (CSA, 2015). Over the last four decades the international donor agencies and governments of less developing countries The use of credit has been envisaged as one way of have spent billions of dollars on projects, rapidly promoting technology transfer, while the use of expanding the volume of agricultural loan and the number recommended farm inputs is regarded as key to of rural institutions (Adams and Graham, 2011). agricultural development (Tomoya M. and Takashi, 2010). (Medhin, 2015 and Million, 2014) have indicated that credit is the largest source of farm capital in Ethiopia. Agricultural credit has a key role for the development of different sectors (Sileshi 2014, Tomoya and Takashi, 2010). *Corresponding Author: Amsalu File, Wollega University, The Campus’s Finance Head, P.O. Box 38, The provision of sustainable formal credit for agricultural Ethiopia. E-mail: [email protected] inputs is one of the most effective strategies for improving Co-Author Email: [email protected]

Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman woredas of HoroguduruWollega Zone, Oromia Region, Ethiopia File and Sori 649

The Loans taken from credit institutions vary from country woredas, from East by woreda, to country, region to region, sector to sector. But farmers from the West by woreda. and in the developing countries have been identified as the Fincha town. Horro and woredas are most defaulting group of credit beneficiaries. While credit comprised of the three main agro-ecological zones remains the largest source of farm capital, prospective namely, Woina Dega (moderate), Dega (cool) and Kola. borrowers are denied access to credit by financial Woina Dega Zone lies almost at the middle of the Woredas institutions as a result of high loan delinquency among itself and having the average elevation between 1500- farmers. This phenomenon does not only reduce farmer 2400 meters above the sea levels. There are different productivity but contributes also to dwindling household crops produced in the study area’s agro-ecological zone income and food security. In order to improve agricultural like maize, Teff, bean, wheat, sorghum, pea, barley (Zonal credit within financial institutions, it is very important to Agricultural office report, 2015). examine the loan repayment capacity of farmers (Million, 2014). The main economic activity of the Woredas is agriculture, which is based on land resource. However, due to rapid Hunte (1996) argued that default problems destroy lending population growth, per capita land holding is declining and capacity as the flow of repayment declines, transforming this result in a very intensive agriculture that degraded the lenders into welfare agencies and loan default is a disaster quality of the soil (Zonal agricultural office report, 2015). because failing to implement appropriate lending The decline on the quality of the soil adversely affected the strategies and credible credit policies often result in land productivity. Rapid population growth also results in termination of credit institutions. Farmers incapable to high exploitation of the scarce water and forest resources. repay loans timely or they face a serious problem to repay The excessive deforestation and soil erosion caused by which is a problem for both agricultural credit institutions very intensive agricultural system are some of the densely and smallholder farmers (Million, 2014 and Amare, 2006). populated part of the area has reached the stage where According to Horro Guduru Wollega Rural Development the land resource can no longer support animal and human Office second Quarter Report (2015/2016), about 24.3 lives (CSA, 2010). million birr loan which was given from 2010 to 2014, has not been repaid in general and according to data obtained b. Data Sources and Type from the institutions in Horro and Abay chomen districts in (2016/2017), about 5.75 million birr loan, which was not In order to under-take this study both primary and repaid in particular. Similarly, since farmers use loan for secondary data were used. The primary data were non-productive purposes, they become unable to repay it collected through personal interview and focused group and even they borrow it for agricultural product which is discussion through semi-structured questionnaires, which climate dependent, they fail to generate more profit. was prepared for the study. The secondary data were Although there are such like problems that affect loan collected from available books, magazines, articles, repayment performance of small holders, there is no detail relevant research papers, annual reports and internet study conducted which is related with detrminants of loan sources. repayment performances of smallholder farmers in the study area. Therefore, this study was aimed at examining c. Sample Size and Sampling procedure the loan repayment performance of farm households in Horro and Abay Choman woredas of Horro Guduru In this study, two -stage random sampling procedure was Wollega administrative zone. employed for the selection of the respondents. In the first step of the sampling, In the first stage, forty-two kebeles in Research Methodology the Woredas are listed and six kebeles (three from each district) were selected using simple random sampling a. Description of the Study Area technique.

The study was conducted in the oromia region, Horro In the second stage, from 2720 the total household in the Guduru Wollega zone specifically Horro and Abay six kebeles were stratified in to two groups. These are 582 Chomen woredas. Shambu is the capital town of Horro credit participants and 2138 non-participants of formal Guduru Wollega zone which is located at 315km away source of financial institutions based on the household lists from the capital city of Ethiopia Addis Ababa in Western which are obtained from the office of the kebeles and part of the country. Horro and Abay Chomen woredas are formal financial institutions. among 9 Woreda’s of Horro Guduru Wollega zone. According to CSA population projection, Horro and Abay Finally, the list of farmers who have obtained loans from Chomen woredas have 97296 and 59371 total population, formal credit sources were recorded from each kebeles respectively (CSA, 2015). and a total of 120 farm households were selected randomly using probability proportional to size sampling The woredas are bounded from the North by Jardaga Jarte technique. and Hababo Guduru woreda, in South by Jima Geneti and

Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman woredas of HoroguduruWollega Zone, Oromia Region, Ethiopia J. Agric. Econ. Rural Devel. 650

푁 approximately equal to 2.718; Xi is the ith explanatory The study used a simplified equation: 푛 = , 1+푁푒2 variables; and α and βi are parameters to be estimated. where n is sample size, N is population size and e is level Hosmer and Lemeshew (2013) pointed out that the logistic of precision provided by Yamane (1967) to determine the model could be written in terms of the odds and log of required sample size at 95% confident level. odds, which enables one to understand the interpretation of the coefficients. Table 1: Sampled Households 1 (1 − 푃 ) = (2) No. Name of Kebele No. of borrowers of No. of 푖 1+푒푍푖 formal financial sampled Therefore, 푍푖 institutions in the borrowers 푃푖 1+푒 푍푖 study area (in the ( ) = ( −푍푖) = 푒 (3) 1−푃푖 1+푒 year 2017) 푍푖 푃푖 1+푒 ∑ 훽푖푥푖 1. Didibe Kistana 247 51 ( ) = ( −푍푖) = 푒(훼+ ) (4) 2. Doyo Bariso 121 25 1−푃푖 1+푒

3. Kombolcha Chanco 58 12 Taking the natural logarithm of equation (4) 4. Homi 68 14

5. Dembal Gobaya 44 9 푃푖 6. Digga Arbas 44 9 푍푖 = 퐿푛 ( ) = 훼 + 훽1푋1 + 훽2푋2+...... 훽푚푋푚 (5) 1−푃푖 Total 582 120 Source: own calculation from total sample households. If the disturbance term (ui) is taken into account, the logit model becomes d. Methods of Data Analysis m

Descriptive statistics Zi =  +  i Xi +U i (6) i=1 Descriptive statistics such as mean, percentages, frequencies, chi-square test, and standard deviations was RESULTS AND DISCUSSION used to summarize data collected from a sample. Socio-Economic and Institutional Factors Econometric model (Continuous Variables)

Specification of the logit model Out of the total 120 sample interviewed farmer This study is planned to analyze which and how much the household’s borrowers 99 (82.5%) were non-defaulters hypothesized regressors was related to the loan and the remaining 21 (17.5%) were complete defaulters. repayment performance of rural households. The model The descriptive Statistics in the table 2 shows that the specifies the dependent variable is a dummy variable, average age of households’ respondents was 41.82% which take a value zero or one depending on whether or years with the maximum and minimum ages of not a borrower defaulted. However, the independent respondents observed were 65 and 24 years respectively. variables were of both types, that is, continuous or In addition, the mean of non- defaulter was 41.36 years categorical. while that of defaulters was 43.95 years with the mean difference between the two groups was statistically Hosmer and Lemeshew (2013) pointed out that a logistic significant at 1 percent. This result showed that as mean distribution (logit) has got advantage over the others in the age increase default rate decreases. analysis of dichotomous outcome variable in that it is extremely flexible. Hence, the logistic model was selected As we observed in below table 2, the average family size for this study. Therefore, the cumulative logistic probability of the sample households was 7.42 with the maximum model is econometrically specified as follows: family size 15 and minimum 3. The mean family size of non-defaulter was 6.97 and with that of defaulters was 9.52 1 with statistically significant at 1% between means of the 푃 = 퐹(푍 ) = 퐹(훼 + ∑ 훽 푋 ) = (1) 푖 푖 푖 푖 1+푒−푍푖 two groups. Defaulters had on average slightly higher family size than non-defaulters. This implies that the higher Pi is the probability of individual certain choice given Xi; e the household size related with the higher the dependency denotes the base of natural logarithms, which is ratio for non-defaulters.

Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman woredas of HoroguduruWollega Zone, Oromia Region, Ethiopia File and Sori 651

Table 2: Summary of continuous variables for defaulter and non-defaulter for all the respondents Non-defaulters Defaulters Total Sample Variable Characteristics (N=99) (N=21) T- Value (N=120) Mean St.dev Mean St.dev Mean St.dev AGE (year) 41.36 9.08 43.95 8.92 2.623*** 41.82 2.62 FSHH (family size in no) 6.97 2.78 9.52 2.93 2.495*** 7.42 2.96 EDUCTLVL (education in class) 6.82 3.57 5.57 3.14 2.263** 6.6 3.52 DFMHH (distance in km) 2.58 1.9 2.67 2.0 2.206** 2.59 1.92 SLIHH (land in hk) 2.28 1.34 2.02 1.35 2.651*** 2.23 1.36 TLUHHH (livestock in unit) 11.91 6.28 7.29 5.84 3.217*** 11.1 6.43 ExSocr (social ceremony in birr) 1432.32 652.28 1976.19 707.75 2.534*** 1527.5 691.12 AMBOH (money borrowed in birr) 5146.77 1605 5827.62 2166 1.69* 5265.92 411.62 PKGEPRC (exp.in agri. In year) 4.11 1.33 3.14 1.25 1.833* 3.94 1.36 Excon (extn. contact in no days) 1.67 0.705 1.53 0.86 2.453*** 1.56 0.73 Source: Own Survey, 2017

The descriptive statistics result revealed in table 2 above development agents settled their debt timely as compared show that the average education level of the entire sample to those who had no or few contacts. households was about 6.6 with maximum class of 12 and minimum 0 classes. The average level of classes for The descriptive statistics in table 2 above show that the complete defaulters was 6 and for the non-defaulters was average mean of extension contact for the total sample 7. The difference between the mean values of the two households was 1.56. In case of complete defaulters, it groups was statistically significant at 5%. Possible was 1.53 and for non-defaulters it was 1.67. This result justification for this could be that more educated people shows as the mean of extension contact increase the loan can properly use the loan for increase of agricultural repayment performance increases. The mean difference production. The better agricultural product will improve the between the two groups was significant at 1% level of income of the household which contribute to better loan significance. Possible justification for this is that as the repayment. The results also show that, non-defaulters are number of contact increase the farmers could get sufficient more educated compared to defaulters which indicates the technical supports that can help him/her to adopt modern importance of education in repaying loans on time. agricultural technologies that can improve productivity. Hence, if productivity increases, the farmers can earn The descriptive statistics in the table 2 indicated that the better income from their agriculture, which can in turn average money borrowed were birr 5,265.92. The survey contribute to timely loan repayment. results also revealed that on average Birr 5,146.77 was borrowed by non-defaulters and defaulters borrowed Birr Socio-economic and Institutional Characteristics of 5,827.62 with 10% level of significance. The mean (Discrete Variables) difference between the two groups was significant at 10% level of significance. The sample was composed of both male and female- headed households. As depicted on table 3, among the Credit experience in extension package varied among the total sample household heads of 120, 89.17 percent were sample borrowers from minimum value of two to a male household heads and 10.83 percent were female maximum of 6 years’ experience. As observed from the household heads. 90.91 percent of the non-defaulters and above table 2 the average Credit experience sample 9.09 percent of the non-defaulters were male and female- house hold were 3.94, While non-defaulter participated on headed households where as 80.95 percent of the average for higher number of years (4.11) as compared to defaulters and 19.05 percent of the defaulters were male the defaulters who participated on average for 3.14 years. and female-headed households respectively. The The mean difference between the two groups was differences in terms of sex among the two groups were not statistically significant. That is, respondents who had significance. frequent in credit experience and contacts with

Table 3: Sex of the Respondent Non- default Defaulters Total No. Percent No. Percent x2-value No. percent Sex 1.778 Male 90 90.91 17 80.95 107 89.17 Female 9 9.09 4 19.05 13 10.83 Source: Own Survey, 2017

Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman woredas of HoroguduruWollega Zone, Oromia Region, Ethiopia J. Agric. Econ. Rural Devel. 652

Table 4: Source of Credit Non- default Defaulters Total No Percent No Percent x2-value No Percent Source of credit 0.123 OSCCO 56 56.57 11 52.38 67 55.83 Wasasa micro 43 43.43 10 47.62 53 44.17 Source: Own Survey, 2017

Table 5: The maximum likelihood estimates of the logit model Variable Coefficient Std.Err. Z P>z Co.Marginal effect Sex 0.199 0.054 -0.17 0.866 -0.009 Age -0.039 0.003 0.58 0.559 0.002 FSHH 0.333 0.010 -2.40 0.016** -0.026 EDUCTLVL -0.102 0.007 0.68 0.498 0.005 DFMHH 0.133 0.011 -0.55 0.582 -0.007 SLIHH -0.622 0.022 1.36 0.174 0.030 TLUHHH 0.191 0.005 1.73 0.084* 0.009 ExSocr -0.09 0.046 -1.95 0.054* -0.075 AMBOH 2.212 0.078 -1.37 0.171 -0.107 PKGEPRC 0.949 0.022 2.08 0.038** 0.046 Excon 1.023 0.028 1.75 0.080* 0.049 CRDTSRCE 0.265 0.039 -0.33 0.742 -0.012 Offr 0.000 0.000 3.97 0.00*** 0.000 PBROW 0.929 0.137 -0.29 0.769 -0.040 Logistic regresses Number of obs = 120 LR χ2 (14) =55.97 Prob > χ2 = 000 Log likelihood = -27.66456 Pseudo R2 = 0.742 Source: Own Survey, 2017

Source of Credit Out of the total fourteen variables which were hypothesized to determine loan repayment performance of Farmers in the study area used credit from different small holder farmers six of them namely total of livestock institutions (Oromiya credit and saving Share Company unit, expenditure on social festivals, number of extensions and Wasasa micro finance). With regard to sources of contact, family size, credit experience in Extension credit out of the total 55.83 percent borrowed from OSCCO package and income from off-farm activities were found to and the remaining 44.17 percent borrowed from Wasasa be statistically significant. micro finance. The performance of credit repayment similar with respect to sources of credit. The proportion of Out of the total significant factors of loan repayment in the defaulter households (52.38 percent borrowed from study area total livestock unit (TLUHH), expenditure on OSCCO as compared to Wasasa micro finance (47.62 social festivals (ExSocr) and number of extensions contact percent). The difference between these percentage figures (Excon), were significant explanatory variables at 10 was not significant (Table, 4). percent level of significance, while family size (FHHS) and credit experience in Extension package (PKGEXPRC) Logit Model Results where significant 5 percent. Moreover, the remaining explanatory variable off-farm activities (Offr) were To determine the explanatory variables which are good significant factor at 1 percent in affecting loan repayment indicators of the loan repayment performances of the performance of small holder farmers. The significant respondents, the logit regression model was estimated explanatory variables are discussed below. using the Maximum Likelihood Estimation Method. The results of the analysis are presented in the following Table. Family Size (FHHS): The result in table 5 above shows that family size has a significant negative effect on the loan The table 5, shows determinants loan repayment repayment performance at 5 percent significant level. performances of smallholder farmers and ***, ** and * From the above table we can observe that as the family represent level of significance at1%, 5% and 10% size increase by 1 person the loan repayment rate respectively decreases by 0.026 among the total sample households.

Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman woredas of HoroguduruWollega Zone, Oromia Region, Ethiopia File and Sori 653

The result of logit model on the table 5 show that, as the repayment among the whole respondents. This implies number of the family size increases by one person the that credit experience of farmers in extension programs probability of being defaulter 0.026 percent. The possible have developed their credit utilization and management justification could be that, if family size increase food skills that helped them to pay loans timely. In addition, as requirement of the household could increase, so that most a result of their participation in credit extension for a of the agricultural product be used for consumption. number of years, these farmers are the beneficiary of the Hence, family size has negative effect on loan repayment use of improved agricultural technologies that would performance in the study area. The result is consistent with increase their income generating capacity and these repay the studies conducted by Sileshi, (2014), Daniel (2014), loans timely. The result of this study is in line with the result inconsistent to Zelalem, G., Hassen,B,(.2012). obtained by Assefa B.A. (2013) and Million (2014).

Total of Livestock unit (TLUHH): This is one of the Number of Extension Contact (Excon): The number of explanatory variables that positively affect the loan contact days that the household head has with extension repayment rate at 10 percent significant level. From the agents is another important institutional factor, which was logit result obtained in the table 5 above we can observe positively related to the dependent variable (significant at that an increase in amount of livestock holding by one 10 percent level) for all the respondents. The result of logit Tropical Livestock Unit increases the loan repayment rate model on table 5 shows that each additional contact by 0.009 units among the entire samples. An increase in increases the probability of being a non-defaulter by 4.9 TLU increases the probability of being non-defaulter by percent. This implies that, farmers with more access to .009. The implication is that, Livestock is one of the technical assistance on agricultural activities were able to important household assets that can easily be changed to repay their loan as promised, more than those who had cash. Whenever, the farmers face crop failure, the less or no assistance at all. The reason for this is that, immediate household asset they have to pay the loan is farmers who have frequent contact with development the livestock. Hence, they are forced to sale it. In addition, agents are better to informed about markets, increase as a proxy to oxen ownership the result suggests that productivity and production technologies. As a result, they farmers who have larger number of livestock have are motivated to repay their loans on time. Similar result sufficient number of oxen to plough their field timely and was also obtained by Chirwa E, (1997) and Belay (2002). as a result obtain high yield and income to repay loans. The result is also supported by findings of Sileshi (2014), Income from Off-farm Activities (Offr): This variable Daniel (2014), Amare (2006) and Abebe (2011). was positively affects the loan repayment rate at 1 percent significance level in the study area. This might be due to Expenditure on Social Festivals (ExSocr): This is a the fact that, off-farm activities were additional sources of continuous variable that shows frequency of social income for smallholders and the cash generated from celebration in the year 2016/2017. The ceremonies include these activities could back up the farmers' income to settle wedding, circumcision, funeral and engagement their debt. The logit result in the table 5 show that farmers' celebrations. It is clear that such occasions cause over participation in off-farm activity increases the probability of expenditure of the limited incomes of the households on being non-defaulter by 0.02 percent and on average practices that do not bring any income to the household. increases the rate of loan repayment by 0.002 percent for The Logit result shows that celebration of social all respondents. Possible reason is that borrowers who ceremonies has negative impact on loan repayment rate had other alternative source of income were found to be at 10 percent significance level. It revealed that an better payers relative to those who didn’t have other increase in social celebration by one unit causes an sources of income. This result is contrary to results increase in default rate by 0.075 percent among the total obtained by Bekele (2001) and Belay (2005) but is in line sample households. Furthermore, each additional social with that of Amare (2006) and Medhin (2015). festival increases the probability of being defaulter by 0.075 percent. The result of this study is consistent with the result obtained by Belay (2002) and Shimelles (2009). CONCLUSION

Credit Experience in Extension Package Ethiopia is one example of a developing country, (PKGEXPRC): Variables representing institutional service characterized by a predominantly subsistence agrarian have strongly influenced smallholder farmer’s loan economy. The nature of farming in Ethiopia is dominated recovery. For instance, number of years of credit by traditional micro holdings of the subsistence type, with experience in extension services (PKGEXPRC) is the less than two hectares of land being the average holding. factor, which was positively related to the dependent The study was undertaken in Horro and Abay Choman variable (significant at 5% level). Each additional year of districts of Horoguduru Wollega Zone Ethiopia. The study credit extension package experience increases the tried to identify determinants of loan repayment probability of being non-defaulter by 4.6 percent. On performance in the study area. So, in order to under-take average, one-year additional participation in credit this study both primary and secondary data were used. experience extension package increases the rate of loan The main data used for this study was collected from a

Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman woredas of HoroguduruWollega Zone, Oromia Region, Ethiopia J. Agric. Econ. Rural Devel. 654

sample of formal credit borrower farmers through semi- The econometric results also indicated that farmers who structured questionnaires, which was prepared for the engaged in off-farm activities earned more income and study. The secondary data were collected from available were able to settle their debts in a more time manner, than books, magazines, articles, relevant research papers, those who were not engaged in off farm activities. This annual reports and internet sources. A multi-stage random indicates that, rural development strategies and concerned sampling procedure was employed for the selection of the stakeholders should not only emphasize on increasing respondents. Data collected were analyzed by using agricultural production but simultaneous attention should descriptive and econometric model. be given to alternative income generation activities that promote off-farm activities in the rural areas From descriptive survey result sample households with large family size were found to more defaulters than less family size in the study area because most of the ACKNOWLEDGEMENTS dependent family members are in education that leads to the dependency ratio to be high, which requires higher We give our great and special thanks to our God who utilization rate of loan or income for other purpose. In other helped us to finish this research case the total livestock units are factors which were positive significant influence on loan repayment REFERENCES performance. That means livestock units’ increases the recovery rate of repayment, therefore small holder farmers Abebe Mijena. 2011. Determinants of credit repayment must give attention on livestock production farming system and fertilizer use by cooperative members in Ada to overcome the challenges of repayment. Although, credit district, East Shoa Zone, Oromia Region. Msc. Thesis, experience and number of extension contact has a positive Haramaya University, Ethiopia impact on loan repayment performance of smallholder Adams, D. and D. Graham, 2011. A critique of traditional farmers as compared to those who had less or no credit agricultural credit project and polices. Journal of experience and extension contact with development agent Development Economics and also with lending financial institutions. Amare Birhanu 2006. Determinants Of Formal Source Of Credit Loan Repayment Performance Of Smallholder The result of econometric model shows, out of the total Farmers: The Case Of North Western Ethiopia, North significant factors of loan repayment in the study area total Gondar,MSc thesis, Haramaya University, Ethiopia. livestock unit, expenditure on social festivals and number Assefa B.A. 2013 ‘Factors influencing loan repayment of of extensions contact, were significant explanatory rural women in Eastern Ethiopia: the case of Dire Dawa variables, while family size and credit experience in Area’, A Thesis presented to the school of graduate Extension package significantly affected loan repayment. studies, AlemayaUniversity, Ethiopia Moreover, the remaining explanatory variable off-farm Bekele Hundie, (2001). Factors Influencing the Loan activities (Offr) were significant factor at 1 percent in Repayment Performance of Smallholders in Ethiopia. affecting loan repayment performance of small holder M.Sc.Thesis,Alemaya University, Ethiopia farmers. The paper was faced the problem of well- Belay Abebe, (2002) Factors Influencing Loan Repayment organized secondary data and by primary data sometimes of Rural Women in Eastern Ethiopia: The Case of Dire farmers are not willing to give detail information about Dawa Area. M.Sc.Thesis, Alemaya University, Ethiopia credit access and usage as they might use it for Chirwa E. Wadonda,(1997) Econometric Analysis of the nonproductive purposes. So, generally this research was Determinants of Agricultural Credit Repayment in conducted to provide some knowledge bases for both Malawi, African Review of Money, Finance and lenders and borrowers of credit and can help other Banking,No. 1-2,1997. Pp. 107-123. researchers as a reference for future credit loan CSA (Central Statistical Agency), 2015. Agriculture repayment performance related researches. sample survey volume VІІ report on crop and livestock product utilization. Addis Ababa, Ethiopia Daniel & Josephine. (2014). Factors Influencing Sacco RECOMMENDATIONS Members to Seek Services of Other Financial Service Providers in Kenya. International Review of Concerned stakeholders, especially religion and Management and Business Research community leaders should teach the community under Gujarati, D. N. (1995). Basic Econometrics, 3rd Edition. their supervision about importance of family planning. It is McGraw-Hill, New York important that small holder farmers and the livestock Guduru Wollega zone Rural Development and sector should give more attention for the following area: information office second Quarter report, 2015/16). Improved feeding system and management of livestock, Horo and Abay Chomen Woreda Agricultural Office Report Genetic resource improvement, Control or preventions of 2015. animal diseases and pesticides. Hosmer D.W. and S. Lemeshew, 2013. Applied logistic Regression

Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman woredas of HoroguduruWollega Zone, Oromia Region, Ethiopia File and Sori 655

Hunte, C. K. (1996). Controlling loan default and improving Takashi and Tomoya. 2010. The Impacts of Fertilizer the lending technology in credit institution: AEMFI. Credit on Crop Production and Incomein Ethiopia. Saving and Development. National Graduate Institute for Policy Studies, M.sc Medhin Mekonnen,(2015) Determinants of Loan Thesis Tokyo, Japan Repayment Performance of Rural Women Based Wenner, M. D. (1015). Group Credit: A Menace to Improve Saving and Credit Cooperatives’ Members: The Case Information Transfer and Loan Repayment of Dire Dawa Administration Performance. Journal of Development Studies.32 (2), Million Sileshi, Rose Nyikal and Sabina Wangia 2014 Pp 263- 281 Factors Affecting Loan Repayment Performance of Yamane Taro. 1967. Statistics: An Introductory Analysis, Smallholder Farmers in East Hararghe, Ethiopia. The 2nd Ed. New York: Harper and Row journal of Developing Country Studies, 11(2), 205-213. Zelalem, G., Hassen, B., (2013). Determinants of loan Shimelles Tenaw and K.M. Zahidul I., 2009. Rural financial repayment performance of small holders’ farmers. services and effects of microfinance on agricultural International Journal of Economic Business and productivity and on poverty: Discussion Papers No: 37, Finance, 1(11), Pp 436-442 Helsinki

APPENDICES Accepted 3 December 2019 Appendix 1: Conversion Factors Citation: File A, Sori O (2019). Determinants of Loan Appendix table 1 Conversion Factors used to Compute Repayment Performance of Smallholder Farmers in Horro Tropical Livestock unit (TLU) and Abay Choman woredas of HoroguduruWollega Zone, Livestock type TLU (Tropical Livestock Unit) Oromia Region, Ethiopia. Journal of Agricultural Calf 0.2 Economics and Rural Development, 5(3): 648-655. Heifer 0.75 Cows/oxen 1 Horse/Mule 1.1 Donkey 0.7 Copyright: © 2019: File and Sori. This is an open-access Donkey (Young) 0.35 article distributed under the terms of the Creative Sheep/Goat 0.13 Commons Attribution License, which permits unrestricted Sheep/Goat (Young) 0.06 use, distribution, and reproduction in any medium, Livestock type TLU (Tropical Livestock Unit) provided the original author and source are cited.

Appendix Table 2: Variance inflation factor for continuous explanatory variable Variable VIF 1/VIF Age 2.40 0.416413 SLIHH 1.99 0.503422 TLUHHH 1.82 0.550637 ExSocr 1.71 0.583663 EDUCTLVL 1.70 0.589251 AMBOH 1.66 0.601778 FSHH 1.60 0.625184 PKGEPRC 1.50 0.664542 DFMHH 1.50 0.667884 Excon 1.15 0.871835 Mean of VIF 1.56 Source: own survey, 2017

Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman woredas of HoroguduruWollega Zone, Oromia Region, Ethiopia