IMPACT OF FINANCIAL SERVICE ASSOCIATIONS’ CREDIT SCHEME ON INCOME OF BENEFICIARY FARMERS IN KATSINA STATE NIGERIA

The study examined the impact of FSAs’ credit scheme on beneficiary farmer’s income in Katsina State, Nigeria. Primary data were collected from two categories of farmers; beneficiaries and non-beneficiaries of Financial Service Associations’ (FSAs) credit scheme with the aid of structured questionnaire and interview method. A multi-stage sampling procedure was adopted and slovin’s formula to obtained the sample size from the four Local Government Areas (LGAs): Bakori, Danja, Funtua and Malumfashi. The data were analyzed using Descriptive statistics such as percentage, frequency etc. and inferential statistics such as Propensity Score Matching (PSM) model. The results from PSM estimator revealed that FSAs’ credit scheme had a positive and statistically significant impact on the income of beneficiary farmers. The Radius matching (0.50) shows that an increase of N21, 158.360 and Kernel Matching shows an increase of N11, 977.580 on average was achieved, by beneficiary farmers who benefited from the scheme. The study revealed that there is a positive and significant impact of the loan scheme on the income of the beneficiaries in the study area at one percent level. There is a need to linking FSAs with formal financial institutions, the provision of adequate amount of loan and timely disbursement of funds to improve loan utilization by the beneficiary farmers. There is also the need to improve the administrative and management processes of the associations. The loan should be monitored by relevant bodies for effective utilization in agricultural production and other economic activities to avoid diversion of funds to consumption activities and miss-used of loan to low income generating


INTRODUCTION
The crucial role played by credit in improving productivity and performance of the agricultural sector has gained considerable attention in the literature and empirical studies Kareem et al., (2013), Awotide, Abdoulaye, Alene and Manyong, (2015), Idi et al., (2019) Ojo andAyanwale, (2019) Mathew et al., (2019) and Osabohien, Mordi and Ogundipe, (2020) among others. The Federal Government in recognition of the credit need of farmers established several institutions, interventions and programmes aimed at increasing income, reducing poverty and attracting smallholder farmers to agricultural practices across the value chain. Some of these agricultural programmes have been developed and executed by successive administrations in Nigeria from early 60s to date. According to the World Bank (2009); Badiru (2010) and Olowu (2011), the major credit institutions established by law and which can be influenced by government policies to provide farm credit for agricultural growth and development in Nigeria are Bank of Agriculture (BOA), Commercial Banks, international development agencies, state government owned-credit institutions, Microfinance Banks, Financial cooperatives, among others. While the major agricultural financing schemes were the Agricultural Credit Guarantee Scheme Fund (ACGSF) and the Agricultural Credit Support Scheme (ACSS). And recently, programmes like, Rural Finance Institution-Building programme (RUFIN) and Anchor Borrowers of CBN were also introduced among others. This means that farmers have to depend on various financial agencies for acquiring funds to expand their agricultural productions and investments. In this regard, International Fund for Agricultural Development-Financial Service Associations (IFAD-FSAs) provides such financial assistances to its respective members in form of credits or savings. It relies on members share capital as the main source of funds (International Funds for Agricultural Developments-Climate change Adaption and Agribusiness Support Program (IFAD-CASP), 2017). Financial Services Associations are a microfinance model invented and first developed by International Funds for Agricultural Development (IFAD) in 1997 whose approach is the provision of financial services through rural banking. The Financial Service Associations' (FSAs) credit was designed to help less-privileged and marginalized poor farmers to have access to credit to develop and finance farm production and other income-generating activities. International Funds for Agricultural Development (IFAD) contributed to financing projects and there is an increase in the number of financial institutions so as to change the slow pace of agricultural and rural development in the study area and country as whole. But in spite of all the concerted efforts being made, rural farmers still experience low income and decrease in production, difficulty in accessing loan and also increase in poverty remains to be a challenge in Nigeria. Despite all of these programmes and interventions majority of the rural small scale farmers are unable to either meet their day-to-day requirements from their own sources of income or invest in other productive enterprises for improving their living standard and economic conditions. Agriculture continues to be a fundamental instrument for sustainable development and poverty reduction; yet, financial constraints in the sector remain pervasive, agriculture remains costly and finances inequitably distributed, severely limiting small holders' ability to compete (Bee, 2007;World Bank, 2013). Furthermore, a lot of poor rural farmers in Nigeria live without adequate food, income, health facilities and shelter. Therefore, savings and borrowing small amount can make a significant difference in their lives. Farm credits are important means for improving farm capital investment without which there may be no progress in the agricultural sector to adequately fulfill its expected roles (Musa et al., 2010). This means that farmers have to depend on various financial agencies for acquiring funds to expand their agricultural productions and investments. In this regard, International Fund for Agricultural Development-Financial Service Associations (IFAD-FSAs) provides such financial assistances to its respective members in form of credits or savings. Therefore, considering role of credit as a crucial factor in changing the slow face of agriculture and rural development to attain sustainable development in the sector, it is necessary IFAD and FSAs to find ways to strengthen and link the FSAs with formal financial institutions that would expand credit delivery and its availability in which beneficiary farmers can access credit with less difficulty. . Furthermore, this study is also justifiable because it generate data that revealed the impact of FSAs' credit schemes that will enable policy makers to come up with appropriate interventions on credit. Finally, the study will also enable policy makers to design appropriate policies that bring about effective program participation through informed decisions on how to promote IFAD and FSAs practices within the study area and beyond. This research work greatly enable policy makers to identify constrains facing FSAs and also identify issues which may bring about creating policies aimed at reducing rate of loan default in the study area. Also, it clearly reveals the comprehensive knowledge about the outcome of FSAs services which includes the extent to which FSAs loans, saving mobilization has enabled growth of investments and income, self-employment and improved social services in the study area. The research examine the extent to which FSAs have contributed towards increasing farmers' income, their standard of living and further allow the IFAD to maximize coverage, strengthening FSAs. Therefore, assessing the impact of FSAs on the benefiting farmers can provide essential information to not only policy makers and agricultural financing agencies but also farmers and general public alike. Finally, the result is hoped contribute equally to the general body of knowledge and provide basis for future research studies, planning and development process.

METHODOLOGY Study Area
Bakori and Danja Local Government Areas are the study area. Bakori LGA is geographically between latitude 11° 33′ 20.12″ North and longitude 7° 25′ 27.08″ East. It has a land area of about 679 km 2 (262 sq. mi.). It has a projected population of 1,006,183 with 3% as the growth rate in the year 2021. Danja LGA is geographically located between latitude 12° 2′ 54″ North and longitude 8° 44′ 50″ East covering a land area of about 501 km 2 (193 sq. mi.) with projected population for 2021 of about 1, 97,142 people at 3% growth rate. Both areas are characterized by dry and wet seasons with an average rainfall of about 900 to 1000mm and minimum and maximum temperatures of 19 0 C and 37 0 C, respectively.

Sampling Procedure, Sample Size and Data Collection
A multistage sampling procedure was adopted for this study. The first stage involved purposive selection of Bakori and Danja LGAs, selected based on the fact that they are among the LGAs participating in IFAD programme and presence of FSAs in their village communities (VAs) out of which three Village Areas are purposively selected. And then two nonparticipating LGAs are randomly selected to serve as control group. Thereafter, the slovin's formula was used to get the sample size from of the sampling frame, from the list of Beneficiary and non-beneficiary farmers obtained from the respective IFAD-FSAs offices and KTARDA offices in the study area. Resulting in a total number of 510 respondents for the entire study as illustrated in Table 1. Primary data were collected with structured questionnaires administered on both beneficiaries and non-beneficiaries of FSAs with the help of trained enumerators. Source: IFAD, FSAs and KTARDA, September, 2019

Analytical Techniques
Descriptive statistics such as frequency, percentage, mean, standard deviation and also inferential statistics were used such as PSM. PSM was used to examine the impact of FSAs credit scheme it utilization on beneficiary farmers' income in Katsina State. The estimated propensity scores are used to estimate the Average Treatment Effect on the Treated (ATT) which was the parameter of interest. The logit empirical model is specified as Where: = probability of falling into treatment or control group Given by: = ∝ + 1 1 + 2 2 + 3 3 + 4 4 … … … … … … . + … … … … . The study used more than one matching algorithms to ensure robustness of the impact's result. Each matching algorithm to estimate the outcome variable was repeated 200 times with bootstrapping in order to reduce the sample errors and reduces bias. Thus, in this study we present the extent of bias between beneficiaries and non-beneficiaries, number of matched control followed by the estimation of the propensity score and finally the impact's results.   Table 3 presents the difference between the income of beneficiaries and the non-beneficiaries based on the mean values of their selected socio-economic characteristics and the extent of standard bias reduction. Four matching algorithms were considered as the maintained hypothesis and then seven statistics: the number of control matched, ATT, Standardized Bias reduction (SPB), standard error before and after bootstrapping and t-value were retained for the evaluation of the impact of FSAs' credit scheme between the treated and the control respondents after matching based on the matching algorithms in the study. For impact estimation to be robust as much as possible, four of the matching algorithms were used in this study as shown in Table 3 Each matching algorithm to estimate the outcome variable (dependent variable) was repeated 50 times with bootstrapping in order to reduce the sample errors and reduces bias. Four of the matching algorithms were used to estimate the impact of FSAs credit on beneficiaries' income. These are; nearness neighbor matching, kernel matching, radius matching and stratified matching. The result of ATT for matching algorithms in Table  3 implies the average gain from the loan to a beneficiary farmer randomly drawn from the beneficiary farmers rather than to any member of the population. For impact estimation to be robust as much as possible, four of the matching algorithms were used in this study as shown in Table 3 Each matching algorithm to estimate the outcome variable (dependent variable) was repeated 50 times with bootstrapping in order to reduce the sample errors and reduces bias. The result of the radius (0.50) matching showed that N21, 158.360 was the difference in income after comparing between beneficiary and non-beneficiary farmers. This implies an additional of N21, 158.360. The results concur with the findings of Idi et al. (2019) who confirmed that micro-finance credit increases famers' output which consequently increases their income level. The result showed that nearness neighbor matching had difference of N10, 225.410 between the beneficiaries and non-beneficiaries. Implying that benefiting respondents have N10, 225.410 more income compared to non-benefiting respondents. This result is in line with the findings of Hussain (2012) who reported that credit beneficiaries had increased income compared to non-beneficiaries. On the other hand Kernel matching indicated a difference of N11, 977.580 after comparing beneficiaries with nonbeneficiaries respondents. The result implies that beneficiaries have N11, 977.580 increased in their income than non-beneficiaries. This result is in line with findings of Dadson et al. (2012) who reported that credit beneficiaries had increased income compared to non-beneficiaries. Similarly, Table 3 shows that, the result from stratified matching algorithm presented a difference of N10, 532.189 after matching beneficiary with non-beneficiary respondents. This connotes that FSAs' credit scheme had significant impact on the income of beneficiary farmers. The result agrees with the findings of Zahran et al. (2016) who reported positive credit impact on credit beneficiaries' income compared to non-beneficiaries. Source: Field Survey (2019), P ‫|‪z‬|>‬ = 0.000 *** = 1% level Significance, S. E= standard error and ATT= average treatment on the treated.

Test of Hypothesis
It was hypothesized that FSAs' credit scheme have no significant impact on beneficiary farmers' income. The result in Table  3 presented above depicted that the credit has positive impact on beneficiary farmers' income. As indicated by radius (0.50) matching with an increase of N21, 158.36 and kernel matching with an increase of N11, 977.580 for beneficiaries' income compared to non-beneficiaries. Therefore, the null hypothesis is rejected and alternative hypothesis is accepted. Table 4 present the major constraints faced by beneficiaries who associated with loan procurement in FSAs' credit scheme were the insufficient amount loan; this problem accounted for about 41.80%. This could be attributed the low capital base of the FSAs. As reported in microfinance letter, (2008) that one of the problem facing micro credit institutions in Nigeria include low capital base of the institutions which affect the quantity of credit to be disbursed. This would affect the income earnings and productivity of the loan beneficiaries. While, delay in disbursement of funds, poor leadership and duration of loan were too short. These problems accounted for 23.77%, 13.52% and 10.66% respectively. This implies that some beneficiaries find it difficult to obtain loan from the associations to finance their economic activities in the study area. As observed by Olaitan (2006) that among the problems militating against micro-loan users include delay in disbursement, reluctance on the side of credit official to grant loans among others.

CONCLUSION
The study revealed that there is statistically significant difference between beneficiaries and non-beneficiaries of FSAs' credit scheme microcredit with respect to socioeconomic variables It was observed that micro-credit had a positive impact on beneficiary farmers' income in the study area. Hence; there is a need to strengthen and link the FSAs with formal financial institutions for the provision of adequate amount of loan which would improve the productivity and income earnings of the farmers. The loan should be monitored by credit officers and relevant financial institutions for effective utilization loan to avoid diversion to consumption activities or low income generating activities.

RECOMMENDATIONS
Based on the findings, the following recommendations are made: i. Since, FSAs' credit scheme had impacted positively on the income level of beneficiary farmers, it is recommended that, the IFAD should strengthen and link the FSAs with Commercial bank and other formal financial institutions in order to expand and replicate the project in IFAD benefiting and other non-benefitting LGAs of the state in order to improve per capital income of farmers within Katsina state. ii.
Insufficient amount of loan and untimely disbursement of funds by FSAs credit management were found to be the major factors constraining credit acquisition from FSAs' credit scheme in the study area. It is therefore recommended that, the disbursement of funds should be centrally coordinated, with a fixed time for credit application and credit disbursement by all the associations.