A household is constrained if it lacks access to credit or cannot borrow as much as it wants. Credit constraints can affect farmers investment behavior (Eswaran and Kotwal, 1990a, b; Rosenzweig and Wolphin, 1993; Fafchamps and Pender 1997). Social capital has been reported to improve access to credit. Social capital, which refers to the ability of people to derive benefits by virtue of their membership in associations (Portes, 1998). Such benefits include having access to services and other resources.
Feder et al. (1990) used cross-sectional household level data from a study in Northeast China to measure productivity differences between farmers who are credit constrained and those who are not. The study reported that a significant proportion of credits were actually utilized for consumption so that the productivity effect is smaller than expected. Kochar (1997) also measured the effect of credit on productivity by examining the impact of credit constraints on land leasing decisions and found that credit does not play as important a role as ownership of irrigated land in India. Narayan (2001) in a study on role of credit in determining land-leasing decision of farmers in India observed that contrary to the assertion of Feder et al. (1990) and Kochar (1997), credit does constrain agricultural production and can have a positive impact on productivity. The study further revealed that credit had a positive effect on land-leasing for farmers with smaller holdings of land. Debela (2001) found out, in a study of impact of credit on soil conservation and farm productivity in Nepal, that providing credit for investment and production at affordable terms had great positive impact on both soil conservation investment and productivity, especially for farmers classified as credit constrained. Furthermore, Benu (2001) discovered that an increase in agricultural credit as well as a reduction in the cost of production had a positive impact to the tune of 60% on agricultural productivity in Indonesia.
Croppenstedt et al. (2003) in a study on technology adoption in Ethiopia
found that large-scale supply of credit, transportation, health and education
investment program had 55% positive impact on smallholder productivity and household
welfare. However, in as much as production credit can enable farmers increase
their agricultural production and enjoy a worthwhile, increase in their net
income; Miller (1977) believed that with the increase in farmers net income,
loan default is likely to be reduced.
In Nigeria, defaults on loans have been found to be a key limiting factor to credit access and studies carried out show that loan delinquency plague small-farmers credit programmes in Nigeria. According to Adejobi (1999) high default rate characterized the Agricultural Credit Corporation of Oyo State credit scheme. Also, Balogun and Alimi (1988) revealed that high default (in the region of 55-90%) crippled the agricultural credit program in Lagos State. Olomola (1990) equally reported that a sizeable portion of the purchasing power of available loan volume was lost to the ravages of loan default among small-scale farmers in Ondo State. With this background, constraint to credit access among cocoa farmers need to be studied in relation to social capital as a means of assessing the effect that strengthened network among individuals, households and communities would have on credit access constraints.
MATERIALS AND METHODS
Primary data were collected from 150 randomly selected CFHs from the two cocoa producing Agricultural Development Project (ADP) zones (Osogbo and Ife/Ijesa) with the aid of well structured questionnaire using multistage sampling procedure. In the first stage, six Local Government Areas (LGAs) were selected proportionate to the number of LGAs in each zone. In the second stage, five villages were randomly selected from the chosen LGAs using the ADP list of villages. In the last stage, 150 households were randomly selected proportionate to the number of households in the villages selected. Data collected include socio-economic, social capital and credit characteristics. Analysis was done using descriptive statistics, social capital indices and censored Tobit regression model.
Tobit regression model on credit constraint of cocoa farmers: The model follows the general form of OLS and is stated as Eq. 1 and 2:
||Vector of unknown parameters
||Constraints to credit access by ith cocoa farmer (%)
||Vector of explanatory variables
||Random error term
RESULTS AND DISCUSSION
Households socio-economic characteristics: The households included in the sample have varying socio-economic characteristics.
Table 1 presents, socioeconomic characteristics of cocoa
farming households in the study area. Most cocoa farming households are headed
by males with 91.33% of the total respondents interviewed, 8.67% are females.
This means that in Osun State, most cocoa farming households are headed by males.
About 38.67% of the cocoa farming household heads are in the age bracket of
51-60 years while, 4.67% above 70 years.
||Households socio-economic variables
|Source: Field survey
||Ranking of identified constraints to credit access by cocoa
|Source: Field survey
This is an indication that the future of cocoa production in the producing
zones is in danger because most of the producers are getting old and have passed
their productive and active stage of life. Majority (45.33%) of the household
heads have no formal education, while those with the primary education are only
30.67% of the total, 16.67% have the secondary education, while 7.33% have tertiary
education. This is an indication that most cocoa farming household heads did
not attend school from the onset and have been primarily engaged in cocoa farming
from their childhood, this indicates more expertise in cocoa production practices.
About 83% of the respondents are primarily engaged in cocoa farming while,
17% are involved in cocoa farming as a secondary occupation. This is also, an
indication that most of the respondents have been involved from their childhood
since they did not really have advanced educational training. About 49% of the
cocoa farming household heads have 5-8 persons in their households. This is
also indication that the cocoa farming households may spend less on hired labor
rather they will enjoy family labor. Majority 35.33% have between 1-2 ha farm
size while, 22% have above 4 ha of farmland. This means most cocoa farms are
small and fragmented and this may be due to the fact that most cocoa farming
households cannot maintain large plantations due to the constraint of credit
facilities they have. About 35% of the cocoa farms are between 11-20 years of
age while, 8% are between 41-50 years of establishment. This has an implication
that there are prospects for increase in cocoa production output in the state
because there are younger plantations, which will give better yields than the
aging and moribund plantations. Thirty two percent of the household heads have
21-30 years of cocoa farming experience while, 7.33% have above 40 years experience.
This means more expertise in cocoa production because they have been involved
in cocoa farming from their childhood. Eighty four percent belong to one form
of association (s). Eighty nine percent have forms of savings in either Ajo
groups, cooperatives or the bank, while 10.66% have no form of savings at all.
Only 18.67% have acceptable collateral in terms of cocoa farms, houses/landed
properties, regular salary from an employer while, 81.33% have no acceptable
physical collateral. This is an indication that most cocoa farming households
do not have acceptable physical collaterals for accessing credit facilities.
Thirty percent source their credit from cooperatives, 29% source from community/social
association, 23% source from farmers association and only 18% source from the
bank. The cooperative society is the most important association to the cocoa
farming households. This is an indication that most cocoa farming households
belong to one association or the other and also get such credit benefits from
them this result corroborates the assertion of Portes (1998) who said social
capital stands for ability of people to secure benefits by virtue of their membership
in social networks or other social structures.
Ranking of constraints to credit access by cocoa farming households in Osun State, Nigeria in order of magnitude: The identified constraints to credit access among cocoa farming households range from 1-12:
||Fear of high interest rate
||Lack acceptable physical collaterals
||Lack of access to finance house
||Long time lag between application and disbursement of credit
||Inadequacy of credit (not getting as much as demanded)
||None availability of credit facility (money)
||Bureaucratic bottlenecks and processing
||Problems of both time lag and inadequacy of credit
||Problems of both high interest rate and time lag of disbursement
||None application for credit
According to the result shown in the Table 2, 36.67%
of the respondents indicated not having any constraints/problems in accessing
||Determinants of social capital and credit access constraint
for Osun State
|Figures in parenthesis are t-values significant at ***1, **2
and *10%, Dependent variable, percentage of credit constraint. Number of
observations 150. Source: Computed from field data
It is believed that the 36.67% are among those that source their credit from
the cooperatives, friends/relatives and community associations. These groups
of people know and have all that their group requires of them to access credit,
most especially their membership. The ranking showed long time lag between application
and disbursement of credit facility (17.33%) as the major constraint of cocoa
farming households getting access to credit then followed by the problem of
acceptable collaterals (8.67%) and thirdly by the problem of inadequacy or credit
rationing to the farmers (8.00%). The least of the constraints faced by cocoa
farming household as stated is the problem of access to finance house (0.67%).
Pooled data: Table 3 shows the result of the Tobit analysis for Osun State. The result shows that Chi square is 33.09 with pseudo r2 = 0.1364; hence, the Chi square is statistically significant (p<0.05). This indicates that the model has a good fit to the data.
Gender had a positive significant coefficient of 195.8212 (p<0.05) meaning
that the more male-headed cocoa farming households increases, the credit access
constraints reduces by 195.82%. This implies that gender is positively related
to credit access in the zone. This observation therefore, means that there is
bias towards male headed households and that the few female headed households
are marginalized when credit facility is being given out to cocoa farming households.
Educational status had a negative coefficient of -6.7604 (p<0.05), which implies that a unit increase in the educational status of the CFH head, results in a reduction of 6.76% in the constraint faced in credit access by CFH. This may be due to beliefs that the more educated the household head, the more focused they will be towards achieving set target for which, demand for credit is made.
Years of experience had a negative coefficient of -4.6108 (p<0.05), which implies that a unit increase in the CFHs experience in cocoa production will reduce the constraint to credit access faced by the households by 4.61%. This is because it is believed that the more experienced the household head, the more proficient they will be in cocoa production and the judicious use of the credit being demanded.
The request for collaterals as a basic requirement for credit access had a positive significant coefficient of 83.0325(p<0.05) implying that the higher the demand for collateral when cocoa farming household are applying for credit facility, results in a 83.03% increase in the constraints to credit access This observation can be explained because the demand for collateral on its own is a barrier to credit access or even application for loans among cocoa farming households. Request for collateral is positively related to credit access constraint.
Presence of savings had a negative coefficient of -173.6247 (p<0.05), which implies that the presence of savings will reduce credit access constraint of CFH by 173.63%. This observation is true because savings is a form of security when demanding for credit facility. Presence of savings is negatively related to credit access constraint.
Decision making index has a negative coefficient of -2.7041 (p<0.05), which means that increased active participation in decision making by the members of the CFH in associations will reduce their constraints to credit access by 2.70%. This is because increased participation in decision making by the CFH is seen as a form of commitment in association, which can help reduce their constraint to credit access in Osun state.
This study found that the female-headed cocoa farming households are marginalized in accessing credit that is, they are more constrained than their male counterparts when accessing credit.
This study also concludes that social capital can go a long way to reduce the
credit access constraints faced by cocoa farming households if they improve
on their memberships in associations, savings and decision making index in their
Also, the government should launch campaign that attract the youth into cocoa production because this group of farmers will be more receptive to adoption of technologies that can improve their productivity and output.
The study also recommends that cocoa farmers should improve their level of education coupled with the years of cocoa farming experience to their credit; they will be less constrained to access funds for their cocoa production activities.