An Empirical Analysis of Microcredit Repayment in Southwestern Nigeria

Transcription

1 Humanity & Social Sciences Journal 2 (1): 63-74, 2007 ISSN IDOSI Publications, 2007 An Empirical Analysis of Microcredit Repayment in Southwestern Nigeria J.T.O. Oke, R. Adeyemo and M.U. Agbonlahor 1 Department of Agricultural Economics, Obafemi Awolowo University, Ile-Ife, Nigeria 2 Department of Agricultural Economics, University of Agriculture, Abeokuta, Nigeria Abstract: This paper provides empirical analysis of microcredit repayment in Southwestern Nigeria. Multi-stage stratified random sampling procedure was used to collect data from 200 members of microfinance institutions (MFIs) in the study area. Linear multiple regression was used to determine the variables that affected microcredit repayment. The mean weekly contribution of the members of microfinance nongovernmental organizations (NGOs) in groups wasx and the mean borrowing wasx 23, per annum. On the average, they pay 90 percent of their microcredit as at when due. The F-values indicated that the microcredit repayment model had good fit (p = 0.01) with an adjusted 2 R of o.36. The variables that significantly influence repayment: income, distance between dwelling place and bank, amount of business investment, socio-cultural expenses, amount of loan borrowed, access to business information, penalty for lateness to group meetings, membership of cooperative society, number of days between loan application and disbursement and poverty indicator were analyzed. Poverty was found to hamper repayment. Nevertheless, the study supports findings that members of microfinance groups are creditworthy. The study concluded that belonging to microfinance institutions will improve the status of members. Key words: Microfinance institutions % microcredit % savings % borrowing and repayment INTRODUCTION International organizations are coming to the realization that Non-Governmental Organizations (NGOs) are veritable and effective channels to ensure programme implementation effectiveness, particularly, in poverty projects in view of their on the ground presence and first hand knowledge of the needs and interest of the poor (Okumadewa, 1998). Thus, microfinance intermediaries comprise mostly of NGOs. According to Dichter (1999), the World Bank sustainable Banking with the poor project (SBP) in mid-1996 estimated that there were more than 1,000 microfinance institutions over 100 countries, each having a minimum of 1,000 members and with 3 years of experience. In a survey of 206 of such institutions, 73per cent were NGOs, 13.6per cent credit unions, 7.8 per cent banks and the rest savings unions. Most microfinance NGOs in Nigeria took off as credit-first financial institutions. They obtained resources from donor agencies, which, they loan to their Members at the grassroots. For instance, external donor funds accounted for about 77 per cent of their funding between 1992 and 1996 (Ogundipe, 1999). This is supported by the report of Adetunmbi (1999) that over 80 per cent of the aggregate loan funds available in the semi-formal microcredit institution in Nigeria is from donor and governmental sources while about 20 per cent is self-imposed tariffs. This development has serious implications for sustainability of the system. Corresponding Author: Dr. J.T.O.Oke, Department of Agricultural Economics, Obafemi Awolowo University, Ile-Ife, Nigeria 63

2 Loans are either given to groups or individual members of the Microfinance Institutions (MFIs). The Community Development Foundation (CDF), for instance, gave 90 per cent of all loans approved for on lending by groups to their individual members while the remaining 10per cent was for direct loan to individual entrepreneurs. Ogundipe (1999) gave the distribution of NGOs members by activity as 25 per cent - farmers, 23 per cent - food processors, 25 per cent - produce marketers, 17 per cent - storage operators while the rest engage in diverse form of activities. Chirwa (1997) specified a probit model to assess the determinants of the probability of credit repayment among smallholders in Malawi. The model allows for analysis of borrowers as being defaulters or non-defaulters. Various specifications of the X-vector were explored by step-wise elimination. However, only five factors (sales of crops, size of group, degree of diversification, income transfer and the quality of information) were consistently significant determinants of agricultural credit repayment. The explanatory power of the model is plausible with the log likelihood statistically significant at 1- percent. Four independent variables gender, amount of loan, club experience and household size were not statistically significant in various specifications. Oni (1999) studied the proportion of loan repayment by smallholder farmers in Osun State. His explanatory variables were: amount of loan collected, expenditure on farm, interest rate, extent of farmers contact with bank, disbursement lag, cultivated land area and years of experience in farming. The result of linear and log form equations showed that the regression coefficients associated with amount of loan (+), disbursement lag (-) and extent of farmers contact with banks (+) had expected signs and were statistically significant at 5 per cent. This study was undertaken to critically examine the factors that influence microcredit repayment among members of microfinance institutions in Southwestern Nigeria. MATERIALS AND METHODS Data were obtained from a field survey of rural communities Members of MFIs. Questionnaire for the survey were amended based on experience acquired from a pre-testing exercise. Information was sought on age, sex, occupation and education of respondents, involvement of the respondent in microfinance group, family size, income of respondent, consumption expenditure (on food, medical care, children education and energy), social expenses (on ceremonies, religious obligations, social clubs, extended family), source(s) of microcredit, amount of such credit and its uses, microcredit disbursement lag, borrowing experience, microcredit repayment and interaction with lending institutions. Multistage stratified random sampling procedure was used in this study. Microfinance Institutions were selected from those belonging to Community Development and Microfinance Roundtable (CDMR) which is the umbrella body for the duly registered MFIs in Nigeria (Olomola, 1999; Women s World Banking, 2003). They featured prominently in the United Nations Capital Development Fund microfinance report on Nigeria (UNCDF, 1997). A preliminary investigation was carried out on the MFIs in Oyo and Ondo States. The investigation revealed that COWAN and FADU are the two topmost microfinance MFIs in terms of outreach (number of Members served and spatial coverage). This study therefore purposively selected COWAN in Ondo State and FADU for Oyo State. Investigation revealed that both COWAN and FADU operate in all the Local Government Areas in Ondo and Oyo State respectively. Lists of Members were obtained from the records of the MFIs. One hundred (100) Members were randomly selected from the list of Members in each of the MFIs. This added up to two hundred (200) Members of MFIs. The survey was carried out between January and June The descriptive statistics such as frequency table, mean, median, standard deviation, coefficient if variation and percentages were used to summarize socio-demographic variables of the respondents. A multiple regression model was used to analyze the factors affecting savings of respondents. 64

3 Since the objective of this study was to establish causal relationship between the dependent and the independent variables in the model, the linear form of the regression was run. Ordinary least square technique was used to estimate the parameters of the model. This was because with the normality assumption for e, the OLS estimators are normally i distributed and they are said to be best unbiased estimators (BUE) (Gujarati, 1995). The regression analyses were run stepwise using the SPSS package so as to determine the order of importance of the explanatory variables in explaining the variations observed in the dependent variable. The t-test was performed to test the significance of each of the explanatory variables at alpha levels of one, five and ten percent. Chow test was performed to test for significant difference between equations for Members and non-members savings models. The model is specified as follows: MRP = $ + $ IHH + $ CSIZE + $ AOL + $ GEXP +$ SEX + $ DIV $ ICT + $ HHS + $ NSR+ $ INFO +$ HHO + $ VISITS +$ EDUC + $ MEETING +$ ABI + $ DISLAG $ BEC + $ DDB + $ SCA + $ MTHDUE +$ MEMCTCS + $ PLATE + $ MOPO_EXP + e $, $, $, $, $, $, $ $ $ $ $, $ > , 13, 14, 15, $, $, $ $ ± , 17, = $, $ $ $, $, $, $, $, $ < 0 (1) 2 4, 14, Where, MRP = proportion of microcredit repaid on the date when repayment falls due IHH = income of respondent in 2003 (N) CSIZE = Size of the borrowers club/group. AOL = Amount of loan obtained in year 2003 (naira) GEXP = group membership experience (years) SEX = Gender of the respondent (0, male, 1 = female) DIV = Degree of diversification of loan investment (i. e. the number of inputs bought) ICT = Repayment of loan from transfer income (0 - repayment without transfer income, 1 = partial/ full repayment with transfer income) NSR = Number of spouse of respondent HHS = Adult equivalent family size of the respondents. INFO = Quality of business information (0 = no/poor information, 1 = Good information). HHO = Enterprise of the respondent (farmer = 1, non farmer = 0) VISITS = Number of visits by loan officials per month EDUC = number of years of formal education of the respondent. MEETING = Number of days microcredit group members meet / month ABI = Amount of business investment in the year 2003 (naira) DISLAG = Loan disbursement lag defined as the number of days between the submission of loan application and actual loan collection. BEC = Business enterprise combination (respondent is engaged in more than one business = 1, otherwise =0) DDB = Distance between the dwelling unit of respondent and the nearest bank (km) SCA = Expenses on socio-cultural activities (naira) MOPO_EXP = Poverty indicator (MOPO_EXP = 1 if expenditure is less than two-third of mean expenditure and 0 otherwise) MTHDUE = Number of months for which the loan is due for repayment. MEMCTCS = Membership of cooperative society (member = 1, non-member = 0) 65

4 PLATE = Amount paid as penalty for lateness (naira) e = the error term N (0, 1) $ 0 = the intercept term $ i = coefficients of explanatory variables (I = 1, 2 23) RESULTS AND DISCUSSION Members of the microfinance NGOs were mostly Yorubas (97 %) and few Edo people (3%). Respondents claim that their close relations are easier to draw into group based association than people from other ethnic groups. This corroborates the findings of Zeller et al. (2001) that nearly half of group members are related to each other. Substantial importance is placed on known information about potential members since joint liability and group cohesion, served as collateral for group members to obtain microcredit from microfinance NGOs (Zeller et al., 2001). The distribution of respondents according to genders showed that there were more female (57 per cent) than male (43 per cent). This supports the findings of UNCDF (1997), Adebayo (1997), ADB (2000), Olomola (2001), Adeyeye (2003) and ADB (2005) that members of microfinance NGOs are mostly women who, along with children, form the group that is vulnerable to poverty. This makes NGO microfinance delivery in the area a truly poverty alleviation initiative. The age of respondents ranged between 18 and 71 years with a mean of about years. The majority of the respondents were 41 years and above. The low coefficient of variation (20.01%) implied that members were within close age bracket suggesting a very strong peer influence in group formation. Also young smallholders were not many among the members of the NGOs. Respondents attributed this to migration of young enterprising youths to the urban centers in search of white collar jobs and better social life. Family size and structure: Distribution of respondents based on their family size revealed that the respondents had between three and 21 individuals in their families. The modal family size group was 4-6 individuals (59.00%) followed by 7 9 (29.00%). Over 90 per cent of the respondents had less than 10 individuals in their families. The mean value of family size was Components of the families were broken down into different age groups and gender (Table 1) based on categorization in Zeller et al. (2001). The table shows that the smallholders had per cent adult and per cent children. The mean adult equivalent consumption value (3.96) was the conversion of the mean family size to their adult equivalent after Aromolaran (2000) and Zeller et al. (2001). Level of education: The numbers of years of Western (formal) education obtained by respondents were converted to the equivalent educational levels. Most members (81%) had formal education. Years of formal education ranged from zero for no formal education to 18 years for respondents with higher degrees. The mean duration of education was 6.14years. This points to the fact that clients of microfinance NGOs were literate individuals in their rural communities. This is likely to afford them some level of managerial ability in their business pursuit Business enterprise: Respondents business enterprises were classified into farming and non-farming (Table 2). More members (52. 50%) were involved in non-farming than farming businesses (47.50%). More female Clients (32.50%) were into non-farming businesses than male while more male (26.00%) were into farming business that female (21.50%). 66

5 Table 1: Structure of families of respondents Family members Age (years) Gender* Frequency Percentage (%) 0 1+ Male (0.31) Female (0.30) Male (0.48) Female (0.44) Male (0.58) Female (0.49) Male (0.73) Female (0.66) Male (0.87) Female (0.72) Male (1.03) Female (0.83) Male (0.68) Female (0.61) Mean adult equivalent consumption value 3.96 * - Figures in parenthesis were the adult equivalent consumption values adapted from Zeller et al., (2001). Source: Data analysis, 2004 Table 2: Breakdown of business enterprises of respondents Business type* Farming Percentage Non-farming Percentage Arable cropping Produce buying 4.25 Tree cropping 8.25 Food processing Fishing 5.25 Pot making (Iron or clay) Fish farming 4.75 Trading 4.00 Poultry 8.00 Soap making Other livestock 5.50 Weaving 5.75 Others (Bee keeping & Snailry) 1.25 Transportation 5.50 Teaching All farming All non-farming Total * - Multiple response Source: Data analysis, 2004 Non-farming businesses of members of microfinance NGOs ranged from Trading (15.25%), Food processing (13.50) to Transportation (2.00%) while farming businesses ranged from Arable cropping (14.50%) to Bee keeping and Snailry (1.25%). Members being engaged in bee keeping and snailry (1.25%) was a reflection of higher level of education among the clients plus special NGO training to impact skills that enabled them to try new business enterprises than those common in the area. Membership of cooperative societies: It is shown that more clients of MFIs were members (60.50%) of cooperative societies than non-members (39.50). Clients who belonged to cooperative societies claimed that the societies served as alternative sources of financial transactions in form of savings and loan procurement to them and that they had been playing this significant role before the advent of NGO microfinance delivery. The respondents said they were involved in ajo, esusu, and Cooperative Thrift and Credit Societies. The first two are forms of rotational group savings in the area. 67

6 Table 3: Borrowings of respondents from groups/societies in 2003 Amount borrowed in 2004 (N) Frequency % Not applicable Total Minimum 5000 Maximum Mean Median Standard Deviation Coefficient of variation Source: Data analysis, 2004 The respondents had experiences ranging from one to 23 years of group/society membership. The respondents had mean years of experience of years. Weekly contributions: Respondents said that the contributions were not made all year round but only in times of surplus like the harvesting time for farmers. Respondents contributed as low asx100 or as high asx2, 600 per week. Many of the Clients (49.50%) contributedx and less per week. Amount of contributions can be said to be low because very many members (85.50%) did not contribute beyond x1, per week. The mean weekly contribution of members wasx The fact that savings by contributions was not new to the respondents was supported by the findings of Oladeji and Ogunrinola (2001) who identified two forms of informal savings namely the fixed-fund and the rotatory fund system in the area. In the first type, regular contributions are made by members to mobile bankers for safe keeping and withdrawal form it is possible after one month while the second is such that money is contributed by group of participants and part or whole of the contribution is given to members in turn. Borrowings from groups: Many of the members (97.50%) borrowed from their groups. The very few members (2.50%) that did not borrow were new entrants into NGO groups who were not yet eligible to do so. This revealed that microfinance NGO members had ready access to loan facilities. Members borrowed betweenx5, andx90, Some respondents, especially those in trading business, claimed that they borrowed two or more times in a year depending on their business turn-over and credit requirement. The mean value of the borrowings wasx23, Business investment: The data on Table 11 shows that the amount of business investment ranged betweenx2, andx101, Only 38 per cent of the respondents invested more thanx30, in their businesses. This suggests that there was a low level of investment which Zeller, et al. (2001) identified as characterizing the economy of rural areas. Annual savings The annual savings of respondents calculated as income less expenditure is shown in Table 12. This is categorized into de-saving where a respondent s expenditure is more than the income. Mean annual savings for wasx10, which was considered too low compared to investment by an average member (N25, ). 68

7 Table 4: Annual savings of respondents in 2003 Savings (N) Frequency % # Total Minimum Maximum Mean Median Standard Deviation Coefficient of variation Source: Data analysis, 2004 Table 5: Annual income of respondents Income (N) Frequency % Total Minimum Maximum Mean Median Standard Deviation Coefficient of variation Source: Data analysis, 2004 Dwelling units (house types): Most of the respondents (47.50%) live in houses plastered with cement inside and outside. This was followed by houses plastered with cement inside only per cent. Houses of some members were painted (15.50 %) while some were without cement plastering (7.50 %). No member lived in thatched house in any of the communities visited. Annual income of respondents: Annual income of the respondents as returns from business investment and other sources is presented in Table 5. The poorest member earnedx17, 550 while the richest earnedx298, 800. Sixty-two per cent of the members earnedx100, 000 or below per annum. However, 13 per cent of them earned overx200, 000. The mean income wasx98,

8 Table 6: Average annual consumption expenditure of respondents on items in 2003 Item Average % 1. Food Clothing Education Transportation House rent Health care Energy Associations/groups Religious obligations Remittances Ceremonies Total Source: Data analysis, 2004 Table 7: Microcredit repayment rate of Clients of microfinance NGOs as at when due Repayment rate Frequency Percentage # Not applicable Total Minimum -12 Maximum 100 Mean Median Standard deviation Coefficient of variation 0.19 Source: Data analysis, 2004 Annual consumption expenditure: Consumption expenditure include expenditure on food, transportation, clothing, education, house rent, health care and energy consumption (electricity, kerosene or fuel wood), association/club activities, religious obligations, remittances and ceremonies. The breakdown of average spending on each of these items is as shown table 6. Members spent betweenx21, andx263, on consumption. Only 8.50 per cent spent abovex200, The mean consumption expenditure wasx87, Socio-cultural expenses: Expenditure of respondents on ceremonies, religious activities, association/clubs and extended family members were grouped under socio-cultural spending and presented. Generally, this category of spending reduces real consumption and savings of individual which may also lower business investment. However, the NGO members were observed to spend more of their socio-cultural expenses on positive events like group/networking activities and training programmes that enhanced their productivity (items 8 and 11 in Table 6). For instance, at a focused group discussion (FGD) session, FADU members of Cocoa Farmers Association of Nigeria in Lagelu Local Government of Oyo State 70

9 Table 8: Regression output of the microcredit repayment model Variable Clients model Constant (9.38)*** IHH 5.47 E-05 (2.55)*** SEX (-1.04) NRS 0.03 (1.20) HHO (-1.94) HHS 0.03 (0.46) BEC 0.08 (0.72) DDB (-2.76)*** EDUC (-1.65) ABI 1.29 E-04 (2.30)** SCA 1.04 E-04 (2.48)** AOL 0.27 (3.38)*** INFO (5.70)*** MTHDUE -5.67(-4.18) MEMCTCS 7.05 (2.05)** PLATE (-3.09)*** MEETINGS (-1.98) GEXP 0.08 (0.68) CSIZE (-0.71) VISITS 0.08 (0.80) DIV (-1.49) ICT 0.09 (0.88) DISLAG (-3.53)*** MOPO_EXP (-2.10)** 2 R Adjusted R 0.36 F- value 12.48*** ***, ** - Significant at one and five per cent Figures in parenthesis are t-values Source: Data analysis, 2004 highlighted their programme of events to include participation in the State s Trade Fare, visitation to Research Stations and collaboration with the government in the on-going efforts to raise the production level of cocoa in Nigeria. They also had various training programmes for members and provided professional advice to members experiencing problems on their farms. In a similar session with Ifira Women Development Association, COWAN members revealed how they assist each other to improve their businesses. They also participated in training programmes organized at COWAN Head Office in Akure. The mean socio-cultural expenditure wasx15, Microcredit repayment of NGO members: The repayment rate of microcredit obtained by the members of NGOs (Table 7) showed that 88 per cent repaid more than 70 per cent of their microcredit as at when due. Two members with negative repayment added some accumulated interest to the principal which made the amount payable to be higher than the amount borrowed. One of these was having a long time illness while the other complained of serious crop failure. Mean repayment was per cent while the median was per cent. The coefficient of variation of repayment among these members of microfinance NGOs was 0.19 which indicates that the percentage repayment clustered around the mean. Not applicable on the table (2.50%) refers to those members that did not borrow from their NGOs. Empirical results of microcredit repayment model: Table 8 shows the 23 variables that went into the microcredit repayment regression. The F-value (12.24) was highly significant (p = 0.01) implying that the model was a good fit. The 2 adjusted R was 0.36 which implies that the variables in the model were able to explain 36 per cent of the variability in 71

10 percentage loan repayment (MRP). This R was high compared to 0.20 reported by Oni (1999) for loan repayment among 2 the smallholder Clients of Nigerian Agricultural and Co-operative Bank and the Union Bank of Nigeria in Osun State. Ten of these variables were significant (p = 0.01 or p = 0.05). They are: income (IHH), distance between dwelling place and bank (DDB), amount of business investment (ABI), socio-cultural expenses (SCA), amount of loan borrowed (AOL), access to business information (INFO), penalty for lateness to group meetings (PLATE), membership of cooperative society (MEMCTCS), number of days between loan application and disbursement (DISLAG) and poverty indicator (MOPO_EXP). Four variables (DDB, PLATE, DISLAG and MOPO_EXP) had negative coefficients while the remaining ones had positive coefficients. All the variables except SCA and PLATE conformed to theoretical expectations. SCA was positive rather than being negative. This implies that those that spent more on positive socio-cultural activities repaid larger percentage of their microcredit. Since SCA included spending on club/group activities, the result suggests that the Clients who spent more on such activities had more commitments to their groups and repaid microcredit better. Some Clients claimed that they detest being labeled as not being creditworthy because of the social stigma attached to such label in the area. PLATE was negative rather than being positive. This implies that Clients that had lower repayment rates belonged to groups that charged higher amounts as penalty for lateness. This indicates that high charges for lateness are borne out of lack of trust for credit worthiness. High penalty charges might even discourage attendance at group meetings so that it becomes more difficult to track down defaulting members. The positive value of IHH (5.47E-05) indicates a direct relationship between repayment and income of Clients. Anx100, increment in income will increase repayment rate by 5.47 per cent. Distance between dwelling place of Clients and bank (DDB) was inversely related to repayment such that one kilometer increase in bank distance will reduce the rate of microcredit repayment by 0.92 per cent. The nearer a Client was to a bank, the better was repayment rate. Such a Client is living in community accessible to the Loan Officer of the microfinance NGO. Educated Clients in such communities may even be instructed to make payment in specified bank accounts of the NGO. On the other hand, the NGOs may encounter difficulty or incur more cost in following up Clients in remote communities for loan recovery. This calls for increasing banking opportunities in the area. Amount of business investment is positively related to repayment. The coefficient of ABI (1.29E-04) implies that ax10, increase in business investment will increase repayment rate by 1.29 per cent. Therefore, NGOs should consider screening Clients for readiness to invest microcredit to be taken in their business before granting their application. This result is an indication of profitable use of microcredit by Clients. Therefore, NGOs should consider raising the current level of microcredit given to their Clients in the area so as to increase their business investment. SCA (1.04E04) was directly related to repayment. An additionalx10, on socio-cultural spending will increase repayment rate by 1.04 per cent. This suggests that the more a Client is committed to social activities (group networking) the better the chance of repayment of microcredit. Enhancing the social status of beneficiaries of microfinance programmes should therefore go hand in hand with its implementation. The coefficient of AOL (0.27) was positive. This means that an additional naira of loan obtained will raise repayment rate by 0.27 per cent. Thus more credit to Clients in the study will increase their productivity as they are yet to attain their optimal credit utilization capacity. The variable INFO was directly related to repayment. Having access to business related information will increase Clients loan repayment rate by per cent. This suggests that NGOs or microcredit institutions should intensify the training of their Clients on some basic enterprise management skills. This will enable the Clients to utilize the microcredit provided appropriately for increased productivity and consequently ensure prompt repayment. Membership of Cooperative Society (MEMCTCS) had a positive coefficient (7.05). The direct relationship between membership of Cooperative Society and microcredit repayment might result from the fact that the same set of people who are credit worthy in Cooperative Societies will be in microfinance groups. Being a member of Cooperative Society will improve repayment rate of a Client by 7.05 per cent. There was an inverse relationship between PLATE and repayment. 72

11 This did not conform to theoretical expectations. A one naira increment in penalty charges for lateness reduced repayment by 0.88 per cent. Hence, penalty should not be so high as to discourage attendance at meetings. The variable DISLAG was inversely related to microcredit repayment. Delay in disbursement of credit reduced the ability of Clients to repay such loan. This conformed to the findings of Olomola (2001) that delay in credit disbursement increases delinquency in borrowers. A day s delay in microcredit disbursement will reduce repayment rate by 0.98 per cent. This calls for prompt response by NGOs to the application of Clients for microcredit so as to ensure its timely and profitable use that will enable beneficiaries to repay. MOPO_EXP was inversely related to microcredit repayment. This implies that the poorer the Clients, the more difficult it is to repay microcredit. Poverty reduced the rate of microcredit repayment by Clients of NGOs in the area by 0.17 per cent. SUMMARY AND CONCLUSION This paper analyzed the socio-economic variables that affect microcredit repayment of Clients of NGOs. Well-established microfinance NGOs such as COWAN and FADU carry out microfinance delivery in the study area with a primary objective of poverty alleviation among members. Most members (97.50 %) borrowed from their groups and invested in various farming and non-farming businesses. Repayment rate was about 90 per cent, members spending on socio-cultural activities was positively related to repayment, a naira increase in penalty for lateness will reduce repayment rate by 0.88 percent, improvement in income will increase the rate of repayment, increasing banking opportunities in the area will improve microcredit repayment, access to adequate business information will increase repayment rate by 29 per cent, Members that belong to cooperative societies are the ones who had better repayment rate. Delay of disbursement of loans reduced repayment rate by 0.98 percent. In conclusion, members of Microfinance NGOs in the area are creditworthy and they fulfill their repayment obligation as at when due. REFERENCES ADB, Finance for the poor: Microfinance development strategy. Rural Asian study: Beyond the Green Revolution. Manila: Asian Development Bank, pp: 52. ADB, Microfinance development strategy update. Manila: Asian Development Bank. Adebayo, A.A., The role of NGOs in poverty alleviation: A case study of Farmers Development Union. In poverty alleviation in Nigeria, selected papers for the 1997 Annual Conference of the Nigerian Economic Society, pp: Adetunbi, O., "Microfinance market in Nigeria, Journal of the Community Development Foundation, Vol. 1: 5-6. Adeyeye, V.A., Impact of cooperative-based NGOs on rural poverty: A case study of Farmers Development Union (FADU) in Osun State, Nigeria. NISER Monograph series No 10, Ibadan, NISER, pp: 73. Aromolaran, A.B., Food consumption pattern and women income: Implications for household food security in Nigeria. Final report presented at AERC BiannualWorkshop, Grand Regency Hotel, Nairobi, Kenya, 2ns 7 th December, pp: 46. Chirwa, E.A., An econometric analysis of the determinants of agricultural credit payment in Malawi, African Review of Money Finance and Banking, Vol. 1-2: Dichter, T.W., NGOs in microfinance: Past, present and future. In Microfinance in Africa, Breth, S. A. (Ed.) Mexico City Sasakawa Africa Association, pp:

12 rd Gujarati, D.N., Basic econometrics, 3 edition, New York, McGraw-Hill, Inc., pp: 838. NPC, Population census of the Federal Republic of Nigeria: Analytical report at the national level. Abuja- Nigeria. The National Population Commission, pp: 455. Ogundipe, E.A., The Role of non-governmental organizations in poverty alleviation in Nigeria," Bullion, publication of the Central Bank of Nigeria Vol. 23: Okunmadewa, F., Domestic and international response to poverty alleviation in Nigeria. Proceedings of the 7 th th th Annual Conference of the Zonal Research Units Organised by Research Department, CBN at Makurdi, 8-12 June, pp: Oladeji, S.I. and I.O. Ogunrinola, Determinants of informal savings in Southwestern Nigeria. Savings and Development, No XXV: Olomola, A.S., Financial innovations in Nigerian microfinance system, In: Olomola, A.S. and S.O. Akande (eds.), Agricultural Finance Issues in Nigeria Ibadan, (NISER), pp: Olomola, A.S., The nature and determinants of rural loan repayment performance innigeria: The case of FADU s microcredit programme. NISER monograph series NO 3, (NISER), Ibadan, pp: 57. Oni, T.K., Bank credit facilities for smallholder farmers: Implications for food security in Nigeria. In Fabiyi, Y. L. and E. O. Idowu (eds.) Poverty alleviation and food security in Nigeria, Ibadan, NAAE, pp: UNCDF, Micfinance: Nigeria country report, United Nation Capital Development Fund, Women s World Banking, Nigeria - Community development and microfinance roundtable, Zeller, M., M. Sharma, A.U. Ahmed and S. Rashid, Group-based financial institutions for the rural poor in Bangladesh: An institutional-and household-level analysis, Washington, D. C., IFPRI, pp: