LITERRATURE REVIEW OF INFORMAL CREDIT DEMAND AND SUPPLY AMONG FOOD CROP FARMERS

2.0 Literature Review
Agricultural credit has been variously defined by authors. According to Nwaru (2004): agricultural credit is the present and temporary transfer of purchasing power from a person who owns it to a person who wants it, allowing the later the opportunity to command another person's capital for agricultural purposes but with confidence in his willingness and ability to repay at a specified future date. It is the monetization of promises and exchanging of cash in the present for a promise to repay in future with or without interest. Without the willingness and ability to repay, the promise to repay at a future date would be futile. Credit can be in cash or in kind; however, our concern here is those in cash. This control over the use of money, goods and services of another person (Adegeye & Dittoh, 1985) termed credit is at a price usually regarded as the interest rate (Ellis, 1992), which is required to be paid together with the amount borrowed at a specified future period.


Credit is an instrument whose effectiveness depends on the economic and financial policies that go with it (Nwaru, 2004). If well applied, credit should increase the size of farm operations, introduce innovations in farming, encourage capital formation, improve marketing efficiency and enhance farmers' consumption (Nwagbo, 1989; Nwaru, 2004). However, Udoh (2005) reported that the demand for credit tends to be a derived demand, which indicates the borrowers will demand for credit based on the need for it and the satisfaction to be derived. The demand and supply of credit is influenced by several factors such as personal attributes of the individual, area specific attributes and credit source attributes (Udoh, 2005). These attributes influence individuals differently irrespective of their gender such that what might determine the demand for credit by a particular female farmer might be different from what determines credit demand by another farmer. For instance, in studying informal lenders and formal credit groups in Madagascar, Zeller (1994) indicated that informal lenders and group members obtain information about the wealth, indebtedness and income potential of loan applicants and hence ration loan demands an indepth view of total household wealth and leverage of the household. In line with this, Nwaru (2004) examined rural credit markets and resource use in arable crop production in Imo State, Nigeria, using multiple regression analysis by the two stage least squares. 

The result revealed that credit demand was significantly influenced by interest rate, educational level of farmer, amount borrowed previously, farm size and gross savings, while gross income of lender, total cost of lending, source of loan (whether formal or informal), worth of loan application and previous loan repayment significantly influenced credit supply. The total amount of money a borrower would have asked for given a favorable borrowing and investment conditions will also depend on the age of the farmer, farm size, educational level of the farm household head, distance of technical services (km), household size, socio-economic associations such as age grade, co-operative societies and farmer and women associations (Ewuola & Williams, 1995). Considering the rate of interest and profitability as one of borrowing and investment conditions, a farmer would borrow funds when the expected rate of return from the project is greater than the cost of the borrowed funds. According to Sylvanus (2003), expected income (eYt), output gap (Y-t), the real rate of interest (Rt) and the expected inflation rate (ePt) explained the real demand for credit (Ldt). The demand for credit is expected to depend negatively on the real rate of interest as bank business customers may be expected to delay investment when interest rates are rising. The inflation rate is used here as a proxy for general macro-economic conditions and is negatively related to demand for credit. In addition, expected decline in inflation may also affect credit demand by increasing the real cost of debt payment. Furthermore, the study revealed that income is expected also to affect credit demand positively. The inclusion of output gap (Y1) as an explanatory variable is intended to gauge the impact of the deviations of the output from its long-term trend. 

Using a probit model to study the determinants of demand for credit, Dutta & Magabich (2004) reported that individual characteristics, household characteristics, repayment ability variables that reflect the individual's ability to secure a loan and other factors affecting the individuals' decision such as having social events and responsibilities, religious beliefs, application cost, availability of lender in local areas and availability of a mediator affected farmers' demand for credit. The result of that study revealed that male individuals were less likely to apply for credit than female individuals. This may reflect the male's ability for self-financing or the ability to access other credit markets or lack of demand for micro credit. Being single and having a tendency for being financially independent from family, being the head of household, having enough knowledge about sources of credit, availability of microfinance providers and having effective loans schemes were reported to increase the probability of applying for loans. 

The negative experiences faced by farmers and entrepreneurs in the formal financial market have brought about a renewed interest in the operations of the informal financial market and its place in the mobilisation and allocation of funds (Srinivas, 1991). Favorable comments on the workings of indigenous savings and credit groups as autonomous (self-help) institutions, have brought home the fact that the informal sector is made up of several other actors and modalities of financial intermediation, than those of money lenders, traders, and landlords (Bourma, 1979). Thus, while there can be little doubt of the formal sectors' superiority over the informal sector, when it comes to financing large scale economic development and projects of national and regional importance, the role and the strength of informal finance agents in small-scale economies and their subsequent importance to low income households should not be under-estimated (Srinivas, 1993). Formal credit institutions are bogged down in their functions by government regulatory controls, interest rate limits, loan ceilings, collateral requirements, high administrative and procedural costs, and subsidized discounts (Srinivas, 1991). These processes consequently reduce their share of the credit market leaving a huge gap in the demand and supply of credit (Hoff et al., 1994). This is where the informal credit markets move in with the advantages of unregulated money supply, easy accessibility, easy liquidity, low administrative and procedural costs, little or no collateral, and flexibility in interest rates and repayment schedules (Srinivas, 1993). Informal operations are therefore highly heterogeneous, with a wide variety of operations and services, including information lending and borrowing, using a wide variety of debt instruments (Germidis, 1990). Srinivas (1991) pointed out that the common elements which run through informal credit arrangements is their informality, adaptability and flexible options. This reduces their transaction cost and gives them comparative advantage and economic rationale over formal finance. Srinivas (1993) further indicated that the informality in informal finance is characterized by unregulated and non-subsidized finance, easy accessibility, loan availability in very small size and for short periods, low administrative and information costs, little or no collateral, flexible and variable interest rates, highly flexible transactions and repayments tailored to individual needs. However, as a result of the above characteristics, flexibility dominates informal credit operations and this enables them to reach borrowers beyond the profitable reach of the formal sector with lower transaction costs.

4.0 Results
4.1 Estimated Credit Demand Function
The estimated credit demand function was summarized and presented in Table 1. The F- ratio was statistically significant at 1 percent. This implies that the sample data fit the model and that the independent variables are important explanatory factors of the variations in credit demand. The R2 was 0.6910 indicating that about 69% of the variation in the amount of credit demanded by food crop farmers was explained by farm income, household size, profit, farm size, gender of the farmer, level of education, and interest amount payable. However, the coefficients for household size, farm size and gender of the farmer were not significant even at the 10% level. The co-efficient of farm income was statistically significant at 1% and, in conformity with a priori expectations, it was positively signed. Ceteris paribus, increase in farm income would lead to increased saving which could be re-invested leading to increased business activities and a concomitant increase in credit demand. Moreover, lenders would prefer to grant credit facilities to farmers whose income is high because they have higher chances of repaying the loan. This result reflects the pecking order theory which states that firms will first use internal equity financing, followed by external debt financing and finally external equity financing. This result is in agreement with Nto (2006) & Essein (2009) who reported a positive and significant relationship between credit demand and farm income. Nwaru et al. (2008) reported a positive relationship between credit demand and saving (used as a proxy to measure the influence of farm and nonfarm sources of income on credit demand).

Table 1. Estimated Credit Demand Function
Variable Intercept Farm Income Household Size Profit Farm Size Gender Education Interest R R
2 -2

Coefficient -53995.480 0.291 719.925 0.715 -2192.263 -0.958 6164.737 -7.584 0.6910 0.6690 32.654***

Standard Error 26231.800 0.109 3313.877 0.325 6333.153 0.617 1629.933 0.877

t-ratio -2.058** 2.670*** 0.217 2.200*** -0.346 -1.55 3.782*** -8.648***

F-ratio

Source: Computed from field survey data, 2008 ***, **, * = statistically significant at 1, 5, and 10 percent respectively.

The coefficient for farm profit was significant at the 5% level and positively signed. This conforms to a priori expectations and to Essein (2009). Increase in profit would lead to increase in amount of credit demanded. Nwosu (1998) noted that profitability is a criterion for granting loans by banks and other credit agencies to the farmers. This implies that a major tool for repositioning the rural credit markets would be to provide anappropriate rural socioeconomic enviroment that will yield the enabling environment for higher levels of farm business successes, incomes and profit. The level of education of the farmer was statistically significant at the 1% level and maintained the right a priori positive sign with credit demand. This result is in line with Nwaru (2004), who explained that educated farmers are more amenable to risk taking than non-educated ones because they are better equipped to access, evaluate and understand improved production techniques. This implies that as education is made more functional and accessible to farmers and other rural entrepreneurs, policies and programmes for sustainable farm credit provisioning should also be considered. Amount the farmer pays as interest on money borrowed was significant at the 10% level and had a negative sign. This is in agreement with a priori expectations and with the results from Desai & Mellor (1993), Eboh & Akpomedaye (1995), Nwaru (2004) and Essein (2009). Interest is the unit cost for taking credit. Ceteris paribus, as the price increases, credit demand decreases and vice versa.

4.2 Estimated Credit Supply Function
The estimated credit supply function was summarized and presented in Table 2. The F-ratio was statistically significant at 1%. This implies that the data fit the model and that the independent variables were important explanatory factors of the variations in credit supply. R2 has a value of 0.9750 which implies that 98% of the variation in the amount of credit supplied was explained by interest amount, experience in lending, leverage, liquidity, and availability of surety.

In conformity with a priori expectations, liquidity was significant at 1% and positively signed, implying that as the liquidity of the lender increases, supply of credit increases. That is, informal lenders will respond to higher level of liquidity by adjusting upwards their credit supply. This result is in line with Tra et al. (2004) & Essein (2009) who indicated that informal lenders readily disburse credit to prospective borrowers based on the level of their liquidity. Table 2. Estimated Credit Supply Function
Variable Intercept Liquidity Leverage Surety Gender Experience in lending Interest R2 R
-2

Coefficient -944527.64 5.290 -1694.016 1140.484 1.978 413.168 40.625 0.9750 0.9710 242.473***

Standard error 23226.94 1.283 1529.736 9871.654 0.413 207.475 2.619

t-value -4.070*** 4.123*** -1.017 0.115 4.789*** 1.991* 15.116***

F-ratio

Source: Computed from field survey data, 2008 ***, **, * = statistical significant at 1, 5, and 10 percent

The coefficient of gender is significant at the 1% level and positively signed. This is in line with a priori expectations. Informal credit suppliers will usually disburse credit depending on the gender of the farmer. Since this is a dummy variable defined as unity for males and zero for females, it implies that male lenders supplied more credit facilities than their female counterparts. This result might be explained by the observation from authors like the World Bank (2005) & Johnson (2006) which indicated that the female gender at individual, household, and wider community and national context are affected by financial, economic, cultural, political and legal obstacles. This requires appropriate economic policies to deal with these obstacles as they pertain to the female lenders while at the same time strengthening the economic activities of the male lenders. The coefficient for experience in lending was significant at 10% and has a positive relationship with credit supply. This agrees with a priori expectations and the reports from Nwaru et al. (2004) & Essein (2009). The number of years a lender has been involved in lending may give an indication of the practical knowledge he has gained on how to overcome the problems associated with lending at minimal costs. Such practical knowledge would help him to handle loan applicants better; critically sorting them for honesty and genuineness. Nwaru et al. (2004) observed that this would lead to a reduction in the risk of his loan portfolio and an increase in the supply for credit. Therefore, experience in lending should be considered as a critical factor in modeling the development of informal rural markets. In the credit market, interest is paid by the borrower to encourage the creditor to forgo his potential command over current output and future investment possibilities (Nwachukwu, 1994) and to cover the cost he incurred in administering and possibly supervising the loan (Nwaru, 2004). Therefore, interest is the price of money lent. The coefficient for interest amount was positively signed and statistically significant at 1%. The implication of this result is that as the rate of interest increases, credit amount supplied will equally increase ceteris paribus. This result is consistent with a priori expectations and Nwaru (2004), who reported that interest receivable played a significant and positive role in determining the volume of credit supplied.

5.0 Discussion and Conclusions
This study was designed to examine the determinants of credit demand and supply in the informal credit markets by food crop farmers in Akwa Ibom State of Nigeria. Primary data were collected from the chosen sample of 120 food crop farmers using a structured questionnaire. The analyses of data using simultaneous equations by the two stage least squares model to estimate credit demand and supply functions revealed that interest amount significantly influenced both functions at 1% and signed according to a priori expectations. Other significant determinants of credit demand include farm income, profit and education, household size and farm size. Credit supply was significantly influenced by liquidity and experience in lending. Leverage and surety had no significant influence on credit supply though they were appropriately signed. It could be concluded from this study that informal credit suppliers consider several factors before supplying credit to rural farmers. In line with the findings of this study, it is recommended that steps for reducing the high interest rates charged by informal credit suppliers should be taken. Education was an important factor influencing credit demand and use. Designing appropriate educational packages for farmers, both formal and informal such as evening schools and adult educational programmes, will be good. Government and financial institutions should ensure that credit facilities meant for farming were used for farming by putting in place measures to check abuse. This is because it has been observed that farmers sometimes borrow money in clear understanding that it is meant for farming and then divert the borrowed funds to some other uses. Finally, it should be noted that, extending credit alone is not a sufficient condition to reduce poverty and improve productivity and income. Therefore, additional intervention that goes hand in hand with micro financing should be implemented. That is, securing an appropriate operational environment for informal credit operators and markets for their products as well as providing appropriate educational services, training and skill development on how to manage effective and efficient business are needed amongst the operators of rural credit markets.

6.0 References

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  • Journal of Rural and Community Development
  • Determinants of Informal Credit Demand and Supply among Food Crop Farmers in Akwa Ibom State, Nigeria
  • Department of Agricultural Economics, Michael Okpara University of Agriculture Umudike, Nigeria
  • Federal College of Education Owerri, Nigeria
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