International Journal of Accounting Research

Factor Influencing Rural Women’s Loan Repayment Performance in Sheka Zone, Southwest Ethiopia People’s Region

Endale Emiru Ayano^* Department of Accounting and Finance, Mizan Tepi University, Tepi, Ethiopia
^*Correspondence: Endale Emiru Ayano, Department of Accounting and Finance, Mizan Tepi University, Tepi, Ethiopia, Email:

Received: 06-Jul-2022, Manuscript No. IJAR-22-17342; Editor assigned: 08-Jul-2022, Pre QC No. IJAR-22-17342 (QC); Reviewed: 22-Jul-2022, QC No. IJAR-22-17342; Revised: 27-Dec-2023, Manuscript No. IJAR-22-17342 (R); Published: 03-Jan-2024, DOI: 10.35248/2472-114X.24.12.371

INTRODUCTION

Most deprived people manage to mobilize resources to develop their enterprises and their dwellings slowly over time. Financial services could enable the poor to influence their initiative, accelerating the process of building incomes, assets and economic security. In developing countries largest number of the people lives in rural areas and they can’t get access to credit. To reduce these challenges recently micro finances institutions established and provides credit services largely for poor and deprived household in rural. This institution to improve the livelihood of the poor different countries implements the microfinances services. Among these, our country Ethiopia is implement different micro financial institution by naming different name in different region. Micro finances are the critical element in poverty reduction strategy [1,2].

Microfinance intervenes in empowering women in economic aspect by providing loan to them. If women self-capable by income, they can participate in decision making at household level. Enhancing women economically also change social attitude that underestimation of women’s contribution to national economic development. Rural household is the target user of this credit services from micro institution as to one of the rural community. Even if the loan amount provided is relatively low compared to men’s, they lend from microfinance institution. In our country Ethiopia due to conservative view of norm and culture the women engaged in limited area of business and get limited access of credit. Due to the institution provides the services to make a profit; it suspects the women borrower not to be loan non performer [3,4].

By taking these in to account the researcher intended to assess the factor influencing loan repayment of rural women borrower. For this study purposes, only the women borrower which is found in South West part of Ethiopia in this zones Omo microfinance borrower. This repayment performance is important to make sure that the MFI are operated in a sustainable basis. Therefore, this paper is focus on the factors affecting loan repayment of rural women’s in Omo microfinance institution in this zone’s branches [5].

Problem statement

The key activities of the MFI are to provide the financial services to both urban and rural household without collateral to those have financial constraints. The objectives of the institutions are to alleviate the poverty by providing lending and saving services. Due to other financial institution like bank are concentrated in urban area and requires the collateral to lend, MFI focuses to fill these gap by providing the services for rural household and without collateral. Now a day, in our country Ethiopia more than 80 percent of the population live in rural areas and no access to get formal financial institution other than MFI. However, the services provided to the rural household are not sufficient as it intended. Different factors might be determining this institution to provide the financial services [6].

Among the significant factor loan repayment performance is critical one. Our culture is not creating opportunities to women’s to engage in different public affairs. And this determines the rural women are not benefited from those services like MFI lending and saving. These indirectly affect the service provided by MFI. The institution fear of nonperforming loan doesn’t provide the services as its potential. In study area also rural women’s have no regular basis of income like urban. Urban women’s can get regular income for instance from rent, pension and so on. But when we came to rural women’s particularly in Sheka zone they haven’t regular basis of income to repay the loan they received. One reason to focus on rural women’s in the study area [7].

To come up with this problem the researcher aimed to assess the factor influencing rural women loan repayment in Sheka zone of South west Ethiopia. In addition to above real problem there is different study conducted and to reconcile the inconsistency of finding. For instance, Girma findings show that large family size resulted to high probability of loan repayment. Gathoni and Saied found that age of the respondent have negative relation with loan repayment. Study shows that the frequency of visit by the microfinance officials has no significant effect on repayment performance when the loan repayment date matured. And also no prior study conducted on rural women’s loan repayment in south west Ethiopia particularly in this study area. By including this variable the researcher will try to see the association between the family size, the frequency MFI agent contact borrower and rural women’s loan repayment [8-10].

Research questions

The study was expected to answer the following inquiries

• What are the demographic factor determine rural women’s loan repayment performance?

• How social and economics factor affect the loan repayment of rural women?

• What are the institutional factor affect the loan repayment performance of rural women?

Objective of the study

The general objective of the study is to identify and examine the factor influencing rural women’s loan repayment performance in study area.

Specific objectives

In line with above general objective the study has the following specific objectives;

• To examine the demographic factor determine rural women’s loan repayment performance.

• To identify social and economic factor affect the loan repayment performance of rural women.

• To investigate the institutional factor affect the loan repayment performance of rural women.

Empirical review

Norhaziah and Noor tried to investigate factor that affect the repayment performance of MFI programs in Malaysia and come with some findings. Their finding show that factor like gender, formal religious education, distance to the lender office, business formality, total sales per month, total loan received, loan monitoring and loan disbursement lag significantly affect the clients loan repayment performance. Finally, they concluded that no pressure from the institution to repay the loan disbursed [11].

Conducted on factor affecting loan repayment performance of banks in Garowe district, Punt land in Somalia. They investigate by having three objectives; to find out the extent by which the characteristics of the loan borrowers affect the repayment performance, to find out the loan characteristics effects on loan repayment and to establish the effects of purpose of the loan to the loan repayment concerning the borrowers. Finally they concluded that there is a significant relationship seen between the performance of loan repayment and the characteristics of the borrowers. And there is also a significant relationship seen between the loan characteristics and loan repayment performance as well as a significant relationship between the purpose and performance of banks [12].

Girma tried to investigate on determinant factor of loan repayment performance of MFIs around Gedeo zone, southern Ethiopia. The author analysed by using ten explanatory variables through binary logistic regression. And the finding shows that educational level, method of lending, nearness of borrower’s residence to the institutions, family size, and income from activities financed by loan and training is significantly affecting the profitability of the loan repayment. Depending on his finding the researcher suggest that to reduce loan repayment problem proper training, continuous supervision, enough loan officers or committee and technical support for borrowers have to be provided [13].

Study that on factors affect the loan repayment performance in MFI in Kenya revealed that, education level, number of dependents, and hobbies of borrowers as individual characteristic. Business characteristics like length of operation, management and type of business. While the lenders characteristics were: Groups handled, period taken to qualify new members and the criteria used to evaluate credit worthiness are influencing factor of loan repayment [14].

Gathoni and Sayeed assess determinant of loan repayment in small scale enterprises in developing country case of Kenya. Their study found that personal characteristics like; education level, family size, amount of loan applied, business experience of the respondent has positive relation to loan repayment. However, age, interest rate and change in gender have inverse relationship to loan repayment [15].

In south Wollo zone, Kalu district tried to investigate on small farmer’s loan repayment performance. By using different explanatory variables they concluded that total land holding size of the family in hectare, total livestock holding, expenditure on social festivals, number of years of experience in agricultural extension services, purpose of borrowing and source of credit are statistically significant factor determine the loan repayment performance in the area. Conducted in Eastern part of Ethiopia loan repayment performance in Harar MFI on three kebeles sample. He found that saving habit of clients, loan size, perception of clients on repayment period, source of income, availability of training, business experiences, business type, family size, and purpose of saving were significantly influencing factor on loan repayment performance. Investigated on factor affecting women’s effectiveness in use of micro finance and microcredit services in Jimma zone. His finding shows that family size, health status, loan diversion, time of loan application and distance from credit institution are significant factor that affect loan repayment performance of borrower. Also investigated on determinant of smallholder farmer’s loan repayment performance in Assosa district, western Ethiopia. He concluded that livestock ownership, age, family size, income from crop product, get extension agent, off farm income, and sex were significantly determine loan repayment performance of small holder farmer’s. Tried to assess factor affecting loan repayment performance of borrower on selected MFI in Oromia region. The result of his study shows that sex, income from other sources, monitoring utilization of other group members, credit timeliness, repayment time suitability, repayment trend on monthly basis and training adequacy are significant factor affect the loan repayment performance [16].

Recently conducted on factors affecting loan repayment performance of smallholder farmer’s in Ethiopia. They used only three districts as a sample those are Sidama, South Gonder, and Amhara regional state. However, this sample is not representative to conclude at national level by using 150 respondents [17].

Materials and Methods

This section of the proposal describes description of study area, type and source of data, methods of data collection, econometric model of the study and methods of data analysis, definition of variables, and finally model diagnostic approaches were presented at this point [18].

Description of the study area

Sheka is a zone in the Ethiopian Southern Nations and Nationalities Regional State, bordered on the south by Bench Sheko, on west Gambela Region, on north Oromia region, and on east Keffa. Based on the 2007 Census report by the Central Statistics Agency, this zone has a total population of 199,314 of these 101,059 are men and 98,255 women. From this 34,227 or 17.17% are urban inhabitants. The administration center of Sheka is Tepi. Sheka is an administrative zone in Southern Nations Nationalities and peoples region where one of the few remaining wet forest are found in Ethiopia, which covers more than 47% of the total area and far away 675 km from Addis Ababa in southwest direction. It has two town administration and three woreda. The two town administrations are Masha and Tepi town administration. Those woreda are namely Masha, Andracha and Yeki. Majority of this zone covered by dense forest and the population settled sparsely. And such micro finance to render and address the client make difficult. And also the rural women’s can’t pay loan they received when compared as urban women within stated time and settlement schedule [19].

Research design

For this study the researcher was used cross sectional type of data. For the purpose of this study the researcher used both qualitative and quantitative approach. The research design selected for this study used a cross sectional survey design. And research considered both descriptive and analytical aspects. Descriptive studies are non-experimental researches that describe the characteristics of a particular individual, or of a group. It deals with the relationship between variables and development of generalizations and use of theories that have universal validity.

Source of data and collection technique

The data source to conduct the study the researcher was used both primary and secondary data. The primary data was collected from the rural women’s borrower from Omo microfinance institution in Sheka zone. This primary data was collected through structured questionnaire which is both closed and open ended. The secondary source of data will be different published and unpublished research on related topic as well as internet sources [20].

Population and sample size determination

Target population for this study rural women’s household which get loan service from their surrounding micro finance institution. According to Omo micro finance institution Masha District rural women’s which borrow from the institution are 2068. However, the target population for this study excludes the urban women’s borrower. And exclude the two town administration in this zone which is Masha town and Tepi town. Therefore, for this study only woman who borrowed from Omo micro finance institution which lived in rural areas are target population.

Sampling techniques and sample size determination

Appropriate sample size depends on various factors relating to the subject under investigation including time, cost, data and degree of accuracy. In the first stage, Sheka zone was selected purposively. Then the respondent was selected randomly with proportionating to each woreda. The selections of respondents from sample population for questionnaires were based on stratified random sampling. Stratified random sampling was used as the total population of the study is heterogeneous across woreda.

This method of sampling produces characteristics in the sample that are proportional to the overall population. The target population which is 2068 is proportionated to each woreda after sample size determined. Hence, the sample size will be using Yamane’s (1967).

Where,

n=sample size

e=error term

In this study the researcher estimated at 95 percent confidence level and the remaining 5 percent as error term then the sample (n) of the study will be:

Therefore sample will be assigned to each woreda according to their proportion from the target population in Table 1 below.

Table 1: The stratified sampling for three woreda.

Methods of data analysis and presentation

As data analysis is very important aspect of any study, it basically involves the analysis of all the data that were collected. The data that were collected needs to be analyzed so that can be easily inferring in order to provide the justification for the work was done during research. Data analysis helps to describe facts, detect patterns, develop explanations and test hypothesis.

In addition to this the researcher was used descriptive data analysis method to achieve the objective which is mentioned under specific objective section. Due to only the econometrics analysis cannot achieve our objective. Further the data set were analyzed by using the statistical software program namely STATA version 14.

Econometric model specifications

To meet the objective of the study, the researcher was adopting binary logistic regression as the outcome variable has two outcomes in the sense that it can be either non-defaulter or defaulter. The explanatory variables that included in the investigation are presented in Tables 2 and 3 as follows:

Table 2: Description of variables and expected signs.

Definition of variables

Dependent variable

RWLRP: Rural women’s loan repayment performance

Independent variables

AGC: Age of the Client

EDL: Education Level of Client

FASZ: Family Size

OFFI: Off Farm Income

FARSZ: Farm Size

INTR: Interest

DLI: Distance from Lending Institution

FARI: Farm Income

TRN: Training Access

CSC: Celebration of Social Ceremony

Table 3: Variables used in the model and summery of their expected sign.

Tools of data analysis

Data analysis is very important aspect of this study, as it basically involves the analysis of all the data that were collected. The data collected needs to be analyzed so that can be easily deducing in order to provide the justification for the work done during research. After accomplishment of data collection procedure, it was classified as per each variable; the qualitative data was coded to be measured quantitatively. In this research data were analyzed by the help of State Software package version 14.0 (Table 3).

Results and Discussion

This section contains two parts; the descriptive analysis in which the demographic and socio economic background of sample respondents. The second part is an econometric analysis in which results from binary logistic regression were thoroughly discussed with regard factor influencing rural women’s loan repayment performance in Sheka Zone, Southwest Ethiopia peoples regions. The total sample determined by the researcher was 335. However, the questionnaires that were collected from the respondent only 326 by having 97.31% response rate.

Descriptive analysis

Socio demographic characteristics of sample respondents: Before discussing the data related to the major items, summary of socio demographic characteristics of the respondents was presented below. Description of the socio demographic characteristics of the respondents gives some basic information about age, marital status, and education level.

Age of the respondents: The study revealed that majority (219) respondents were age 30 and above which is 67.18%. And the remaining 107 or 32.82% respondents were below age 30 (Table 4).

Table 4: Age of the respondents.

Marital status of respondents: Table 5 indicates that out of 326 respondents, 225 (69.02%) percent were married, 59 (18.10%) percent were unmarried, 19 (5.82%) were divorced, and 23 (7.06%) percent were widowed. This indicates that the majorities of the respondents were married borrower.

Table 5: Marital status of the respondents.

Education level of the respondents: The study revealed that out of 326 sample respondents, 26 (7.98%) respondents were having education level of college diploma and above, 117 (35.89%) respondents were having secondary education, 149 (45.71%) of the respondents were with primary education, and 34 (710.43%) respondents have no formal education. The number shows that respondents with elementary education followed by respondents with secondary education are more engaged in borrowing from the institution (Table 6).

Table 6: Education level of the respondents.

Econometric analysis: The general objective of this study was to examine factor influencing rural women’s loan repayment performance in Sheka zone, South West Ethiopia People’s Region. Thus, to perform this task, binary logistic regression model was used as econometric model. But, before applying logistic regression for analysis, taking model diagnostic test becomes necessary. The model diagnostic test was performed to insure that the model fits well. The model diagnostic test can be done by running OLS regression model. The rationale behind conducting diagnostic test is to check whether the model is correctly specified or not, to check the correlation between explanatory variables exist or not, and helpful in checking the overall significance of the model.

Accordingly, inclusion and exclusion of irrelevant and relevant variables respectively were tested by OV tests (Omitted Variable) and the result shows misspecification is not the problem for this model. VIF test was used to check the problem of multi collinarity and the result shows multi collinarity is not the problem and the Variance Inflation Factor (VIF) test confirmed non-existence of multi collinarity among variables in the model (VIF=1.22). The nonexistence of heteroskedasticity problem was tested by using Breusch-Pagan test.

After model diagnostic, logit model was used for analysis in which the dependent variable (RWLRP) was a binary response variable taking value of 0 and 1, for non-defaulter and its counterparts respectively. The independent variable used in the model contains dummy and categorical. Performing all the stated procedures above, the logistic regression model was run. A total of 10 explanatory were used to explain the dependent variable. From ten explanatory variables used for analysis, eight variables were found significant at less than 5% probability level. However, the result from logit model, whether it is coefficient report or odds ratio; it shows only direction of the effect of the explanatory variable on the dependent variable, but not the magnitude. Unlike slope coefficient measures directly the change in probability of an event occurring as the result of a unit change in the value of a repressor in the LPM; in logistic regression model, the slope coefficient of a variable gives the change in the log of the odds associated with a unit change in that variable, ceteris paribus. The regression output of Logit model is presented in Table 7.

Table 7: Logit model estimates for rural women’s loan repayment performance in Sheka zone

Age of the Client (AGC: Age of the client is one explanatory variable which included in the study. However, the regression result shows the age of the client is statistically insignificant to influence the loan repayment performance of rural women’s.

Education Level of the Client (EDLC: Education level of the client is one important factor that influence the loan repayment performance of rural women’s. These variables are statistically and positively significant factor that influence the repayment performance. This is significant at 1 percent. The result shows that as client more educated their repayment performance become good and vice versa.

Family Size (FASZ: Family size is one of important factor that influence loan repayment performance in this study area. This is positively and significantly affects the performance of rural loan repayment (significant at 1% significance level). The study result shows that as family size increase the borrower (client) become more non-defaulter/perform well. This result is in line with finding of Berhanu, and Sileshi. But it is inconsistent with the finding of Firafis, Retta, and Abafita.

Off Farm Income (OFFI: Off farm income means the income that generated other than farm income. For this study it measured by average annual income other than farm income. The result shows that off farm income positively and significantly affects loan repayment performance in the study area. Borrowers that have off farm income able to repay loan than those haven’t. According to this result, women’s those have off farm become more non defaulter when compared to those depend only on farm income.

Farm Size (FARMS: Farm size is cultivable land that used for farming. The researcher used farm size as one explanatory variable that affect the loan repayment performance of rural women’s. The regression result shows that farm size positively and significantly affects the repayment performance of the rural women’s. This is highly significant at 1% significance level.

Interest (INTR: Interest is another variable that used in explanatory variables. This variable measured by the perception of the borrower (rural women’s). The regression result shows that statistically insignificant to affect the repayment performance.

Distance from Lending Institution (DLI: Distance from lending institution is another important factor that determines the loan repayment performance of rural women’s. For this study the distance measured by walking hour. The regression result show that distance from lending institution negatively and significantly affect the repayment performance. This implies as more far from the lending institution less probability to repay the loan as well as less willingness to borrow from institution. Thus adversely affect the small microfinance objectives addressing small scale saver and borrower. The result is negatively significant at 5% significance level.

Farm Income (FRMI: Farm income is also one important explanatory variable included in the study. The regression result shows that farm income positively and significantly affects the repayment performance. That means as farm income increase the ability to repay will be increase and vice versa. The binary logistic regression result shows the farm income is statistically significant at 1% significance level.

Training access (TRNG: Training access also another important explanatory variable included in this study. The regression result shows that training access positive and statistically significant to affect the loan repayment performance. Thus implies those have training access perform well than those have no access. This result is statistically significant at 1% of significance level.

Celebration of Social Ceremonies (CLSC: Celebration of social ceremonies included in the study. The regression result shows that social ceremony celebration has a significant negative impact on loan repayment performance. The reason is that celebration of one or more of social ceremonies need much material and financial resources, which are beyond what the borrowers could afford and worse them being defaulters.

Conclusion

Financial services could enable the poor to influence their initiative, accelerating the process of building incomes, assets and economic security. In developing countries largest number of the people lives in rural areas and they can’t get access to credit. To reduce these challenges recently micro finances institutions established and provides credit services largely for poor and deprived household in rural. The service provided by institution also affected by the repayment performance of the borrower. The performance of microfinance institutions objective is affected by repayment status of the client. For this the researcher tried to address the influencing factor of loan repayment performance particularly rural women. Since the goal of micro finance institution formed to address poor and rural areas that can’t get access to banks.

To meet its objective, this study used cross sectional data that were collected from 326 sample rural woman’s from three words (Masha, Andracha, and Yeki), while the non-response rate was reported as 2.69% (out of 335 rural women’s). The sample respondents were selected using stratified random sampling proportionally from three words women’s borrower.

Descriptive statistics and binary logistic regression method were applied for analysis of the collected data. The study was included ten explanatory variables for this investigation. Thus variables are: age of client, education level of client, family size, off farm income, farm size, interest, and distances from lending institution, farm income, training access, and celebration of social ceremonies. Among ten explanatory variables except age of client and interest all other (eight) variables are statistically significant to affect the loan repayment performance in the study area.

Recommendation

Based on the finding of the study, the following policy recommendations has been drawn for immediate improvement to be made with regard to loan repayment performance of rural women in Sheka Zone, Southwest Ethiopia Region.

• Providing appropriate and adequate formal education to those rural women’s. According to the findings of this study, those have formal education has positive link with loan performing. Formal education enables them to become non defaulter. So it’s better to provide formal education for client of the institution to upgrade from informal education, as to become non-defaulter.

• The regression result shows that farm size positively and significantly affects the repayment performance of the rural women’s. It’s better to provide for client necessary inputs to help their land they have to be cultivable as much as possible.

• Another important factor that affects the repayment performance of loan is clients distance from lending institutions. The micro finance institutions collected this loan when they come to repay. However, their distances from this institution adversely affect repayment performance. So the institution should be adjusting the repayment procedure by assigning the agent (who collect the loan) like as of deposit collected by agent.

• It’s better to provide training for those clients in the study area. Because, those have training access is more performer than can’t get access.

REFERENCES

1. Abafita J. Microfinance and Loan Repayment Performance: A Case Study of the Oromia Credit and Saving Shares Company in Kuyu. Msc. Thesis Addis Ababa University, Addis Ababa. 2003.
2. Mohamed MA, Muturi W, Samantar MS. Factors affecting loan repayment performance of banks in garowe district, puntland, Somalia. Int J Contemp Med Res. 2019; 6(3):2308-2365.

   [Googlescholar]  

3. Abrham. Assessment of factors affecting loan repayment performance of borrowers: An empirical study on selected Microfinance Institutions in Oromia Region. Unpublished Master’s Thesis Addis Ababa University, Ethiopia. 2017.
4. Angaine F, Waari DN. Factors influencing loan repayment in Micro-Finance Institutions in Kenya. IOSR. Eur J Manag Bus. (IOSR-JBM). 2014;16(9):66-72.

   [Googlescholar]

5. Brooks C. Introductory Econometrics for Finance. Second Edition. Cambridge University Press, New York, Melbourne. 2008.
6. Firafis H. Determinants of loan repayment performance: Case study of Harari microfinance institutions. J Agric Ext Rural Dev. 2015; 7(2):56-64.

   [Googlescholar]

7. Gathoni Kiliswa N, Sayeed Bayat M. Determinants of loan repayment in small scale enterprises in developing countries. "Econ Syst Res. 2014;1(2):67-79.

   [Googlescholar]

8. Gebre-Egziabher F, Destaw K, Yasin A. Determinants of Smallholder Farmers Loan Repayment Performance in Assosa District, Western Ethiopia. "J Pov Dev. 2018;43.
9. Jote GG. Determinants of loan repayment: the case of microfinance institutions in Gedeo Zone, SNNPRS, Ethiopia. Uni J Acc Fin. 2018; 6(3):108-22.
10. Jianing, F. Why Logistic Regression Analyses Are More Reliable than Multiple Regression Analyses. J Bus Econ. 2013;4(7):620-633.
11. Nawai N, Shariff MNM. Loan Repayment Problems in Microfinance Programs that use Individual Lending Approach: A Qualitative Analysis. J Tran Entr. 2013;1(2):93-99. 

    [Googlescholar]

12. Nawai N, Shariff MN. Factors affecting repayment performance in microfinance programs in Malaysia. Procedia Soc. 2012;62:806-811.
13. Oke JT, Adeyemo R, Agbonlahor MU. An empirical analysis of microcredit repayment in Southwestern Nigeria. J Hum Behav Soc Environ. 2007;16(4):37-55.

    [Crossreff] [Googlescholar]

14. Retta G. Women and Microfinance: The case Women Fuel Wood Carries in Addis Ababa. MSc. Thesis Addis Ababa University. 2000.
15. Sengupta R, Aubuchon CP.  The microfinance revolution: An overview.: Federal Reserve Bank of st. Louis review. 2008;9-30.
16. Sileshi M. Factor Affecting Loan Repayment performance: The Case of Small Farmers in Hararghe, Ethiopia. Msc Thesis, University of Nairobi, Kenya. 2010.
17. Dubale S, Beshir H. Factors Affecting Loan Repayment Performance of Smallholder Farmers in Ethiopia. Int J Agric. 2020;9(3):75.

    [Crossreff] [Googlescholar]

18. Welderufael L, Tesfatsion S, Gedifew A. Factors Influencing MFIs Group Loan Repayment Performance: A Case of MSEs’ Service Sector in Mekelle City, Ethiopia. Res J Finance Account. 2015;6.
19. Yilkal W. Factors Affecting Women’s Effectiveness in use of Microfinance and Microcredit Services; Jimma Zone, Southwest Ethiopia. Glob J  Managa Busi Res: Economics and Commerce. 2015;15(10).
20. Gebeyehu Z, Beshire H, Haji J. Determinants of loan repayment performance of smallholder farmers: The case of Kalu district, South Wollo zone, Amhara National Regional State, Ethiopia. Int J Finance Econ. 2013;1(11):431-446.