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Researchjournali’s Journal of Agriculture
Vol. 3 | No. 5 May | 2016
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Obuo, Peter Obuo
World Bank Assisted Commercial Agriculture
Development Project, Cross River State Coordinating
Office, ADP Premises, IBB Way, Calabar, Nigeria
Inyang, Nyambi Ndifon
World Bank Assisted Commercial Agriculture
Development Project, Cross River State
Coordinating Office, ADP Premises, Ibb Way,
Calabar, Nigeria
Arigor, Arigor John
Department Of Agricultural Economics, University
Of Calabar, Nigeria
Ijogu, Bassey Jimmy
Department Of Agricultural Economics, University
Of Calabar , Nigeria
Determinants Of Loan
Recovery By Informal
Credit Sources: A Case
Study Of Ubam Groups
In Cross River State,
Nigeria
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ABSTRACT
This study was carried out to determine the characteristics influencing Loan Recovery by Informal Credit
Sources using Ubam groups as case study. Data was collected from 120 Ubam groups in the study area
through Purposive, proportionate and multi-stage random sampling techniques and analysed using descriptive
statistics and probit regression model. Probit regression result showed that average sizes of loan, disbursement
lag, age of the group, interest charge, and percentage of loans recovered previous year are positive and
significant in explaining the recovery of loans disbursed by the groups. Therefore, the group can play
intermediation role in rural financing.
Keywords: Ubam groups, Informal credit, Characteristics, Probit
* Ubam groups are informal credit associations made up of mostly farmers and traders in the study area
1. INTRODUCTION
The informal sector has been a good alternative source of credit for most rural and some urban people in need
of loans. These have existed side by side with the formal financial institutions and supplied most of the credit
demand of borrowers in rural areas. Though the supply of credit from the formal financial institution has
expanded, still less than 10% of rural households have access to adequate and affordable financial services
(Agom, 2001; Zhixiong, 2004; Okpukpara, 2009). According to Okpukpara (2009), rural farmers face severe
constraints when they seek credit from formal financial institutions, as their services are often not available to
the poor because of the restriction that loans be backed up by collateral.
In addition, more influential groups use their influence to derive more benefits from the credit programmes.
Consequently, majority of the credit worthy small and medium scale farmers in rural areas turn to other
traditional financial institutions or markets (Isijola and Ajetomobi, 2000). According to Isijola and Ajetomobi
(2000) informal credit sources play a significant role in eliminating the societal differences. Thus, the
possibility of expanding social polarization is reduced by the informal credit institution in meeting the capital
requirement of rural dwellers (Morduch, 2000). Truly, there are several informal sources of credit in both
rural and urban sectors being considered as more credible sources to both rural farmers and small scale
entrepreneurs, and these accounts for more than 80% capital accessibility by borrowers in rural areas
(Olomola, 2010). These informal institutions do not seem to be meeting the credit needs of small scale
farmers/rural dwellers who seek credit as evidence still abounds that farmers are still in dire need of adequate
capital (Oni, 1999). There is a perception that informal institutions do not have enough loanable funds to meet
the credit demand of applicants as well as the ability to sustain the credit delivery functions. Nevertheless,
emphasis is still being placed on the informal group approach in extending credit to the low income
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producers, with the expectation that the approach will mitigate the problems of inability of vast number of
small scale farmers especially in rural areas to access credit. These raise two broad questions as to whether the
existing informal credit groups (ICGs) hold some potential in terms of extending credit to farmers and to what
extent does this potential depend on the dynamics of the groups. According to Aryeetey and Udry (1997)
important lessons can be learned from the success of informal financial institutions. The success is attributed
to a high degree of flexibility and creativity employed in their operations. They further elucidate their
characterization mainly by short-term and small loans, increasing discipline in terms of savings, judgment of
borrower credit worthiness, and information about the borrower. Service is based on flexible arrangements to
adjust to changing economic circumstances, and reducing transaction costs to the borrowers who respond by
maintaining discipline in order to sustain their access to credit. The result is a dependable working
relationship between the lender and the borrower. Soyibo (1994) study also showed that, most services of
informal finance were client oriented, thus reducing the transaction costs of customers and making their
services attractive despite the explicitly high interest rates. Informal lenders are also able to design their
contracts to meet the individual dimensions, requirements and tastes of the borrowers. In the informal
financial markets, loans and deposits are often tied, enabling individuals to increase their access to credits by
improving their deposits performance (Aryeetey, 1996).
2. CONCEPTUAL DEFINITION OF UBAM
Ubam is a term meaning ‘put together’ popularly used and practiced amongst the seven tribes of the
descendants of Agba in northern part of Cross River State and some areas in Benue State. These seven tribes
have a common dialect known as Bete, grouped as Bekwarra amongst the 3 major languages in Cross River
State. The seven tribes cut across Local Government Areas such as Obanliku, Bekwarra, Obudu, Yala and
some other L.G.As in Tiv tribe of Benue state. Field interview with the operators indicated that Ubam group
is as old as the origin of these tribes and is believed to be part of the culture of the tribe used in grouping
themselves and raising funds for their immediate family problems and farming activities. This culminates in
their festivals such as the new yam festivals celebrated on specific dates by each of the tribes but mostly
between August and December of every year.
3. METHODOLOGY
The research was conducted in Cross River State, which is one of the 36 states in Nigeria located at the south-
south geo - political region of the country. The state lies within latitude 40 28I and 60 55I and longitude 80 00I.
The state consists of 18 Local Government Areas and three Agricultural Development Programme (ADP)
zones which correspond with the three senatorial districts of the State. The Southern agricultural zone has two
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types of vegetation; the mangrove swamp and rainforest, and a coastal marine habitat Primary occupation of
the zone is fishing, farming, lumbering and mining. Major crops cultivated are oil palm, rubber, cassava,
maize and vegetables The central agricultural zone has rainforests vegetation with cocoa, oil palm, plantain,
maize, cassava, yams, rice etc, as the major crops cultivated in the region The northern agricultural zone
prevalent vegetation is derived savannah and farming is the major occupation of the people. The main crops
cultivated in the zone include, maize, yam, groundnut, beans, bambara nuts, pepper, cassava and rice.
Purposive, proportionate and multi-stage random sampling techniques were employed for the study. The first
stage involved dividing the state into 3 agricultural zones, the northern agricultural zone was purposively
selected for the study where the Ubam groups are existing. In the second stage, three Local Governments
Areas (LGAs) were randomly picked from the 5 LGAs in the zone. Third stage, 120 Ubam groups were
proportionately selected from the list of Ubam associations existing in the 3 LGAs, the sampling distribution
is presented in table 1 below. Fourth stage, the secretary of the groups was purposively selected to provide
information about the groups. Primary data used for this study was sourced using structured questionnaires
and interview with the ubam groups’ executives in the field and analysed using descriptive statistics and
probit regression model techniques.
4. MODEL SPECIFICATION
The method of analysis relied upon the Probit regression model, and in particular, the binary Probit regression
model (normits) for individual or ungrouped data. This method is justified because of the fact that the
explained (or predicted variable) has two outcomes, namely ‘recovery’ and ‘non-recovery’ of loans granted.
Unlike, the ordinary least squares (OLS) regression model, the probit model predicts the probability that a
group either ‘recover’ or ‘fails to recover’ a loan given the values assumed by the explanatory variable. This
is so because the explained variable is not continuous and in this case, dichotomous (that is, assumes the value
of ‘1’ for recovery and the value ‘0’ for non-recovery). Thus, given this situation, the assumptions of the OLS
regression model fail. The inverse normal cumulative distribution function (CDF) is written as,
Y = F-1(X) ---------- (1)
Where:
X = Sets of explanatory variables
Y = binary or dichotomy of two value 1 and 0
Y = 1 represents the probability that the group recovers her loan denoted by P and Y = 0, the probability that the group fails to
recover denoted as 1-P
Therefore the probit model is expressed as
Y = (Pi 1 Pi﴿ = ezi -------------------(2)
The standard normal commulative distribution function of equation (2) is expressed as
Y = (Pi 1 Pi﴿ = 1/ √2π ∫ᵞ-∞ ezi/2dzi ------- (3) (Gujarati and Porter, 2009)
Where:
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Zi = B0+B1X1+B2X2+---+BnXn
Pi = ezi/1+ ezi is the probability that the group recovers her loan
Therefore the probability that the group fails to recover her loan is
1 Pi = 1/1+ e-zi
For the purpose of estimation equation (3) can be written explicitly as
Y = B0+B1X1+B2X2+B3X3+B4X4+B5X5 + ui
Where;
Y is a binary response variable defined as Y=1 if the groups recover her loan given a threshold or critical Value(Y*) of above 75%
and Y = 0 if Y≤ Y*
X1 = Ave sizes of loans disbursed (‘000 naira)
X2 = Disbursement lag (days)
X3 = Age of the groups (years)
X4= Interest charge (%)
X5 = percentage of loans recovered previous year (percent)
B0 = intercept,
B1 … B5 = Parameter estimate
Ui = Stochastic error term
5. A PRIORI EXPECTATION OF THE VARIABLES
Y = Recovery of loans disbursed, a group will be assigned 1 if above 75% of the Loans are likely to be recovered and 0 if otherwise.
X1 = Ave sizes of loans disbursed measured in a thousand naira. It is expected to have positive relationship with the recovery of loans
granted by the group.
X2 = Disbursement lag measured in days. It is expected to have a negative relationship with the recovery of loans granted by the
group.
X3 = Age of the groups measured in years and is expected to have a positive relationship with the recovery of loans granted by the
group.
X4= Interest charge in percent. It is expected to have a negative relationship with the recovery of loans granted by the group.
X5 = percentage of loans recovered previous year in percent is expected to have a positive relationship with the recovery of loans
granted by the group.
6. RESULTS AND DISCUSSION
6.1. UBAM GROUPS CHARACTERISTICS
The descriptive statistics analysis of the Ubam groups’ characteristics is presented in Table 2. About 67 per
cent of the groups cited lending/savings as the most important objective of their formation while roughly 20
and 13 per cent indicated that savings and contribution was the most important aim of formation of their
groups respectively. This result clearly shows that all of the groups perform the role of financial
intermediation in the informal sector and also highlights the role of credit in the economies in which these
groups exist. This is similar to Aryeeteh and Udry (1994) findings on the objectives of rural credit groups in
Africa which include; groups whose major aim is to lend out money, groups that collect regular savings from
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members and relinquish same at specific periods, while others pulled themselves and contribute predetermine
amount at regular interval to a common pool and hand over to each member in turn.
The result also shows that about 57 per cent of the group members are mostly drawn from the same village or
locality irrespective of the individual members’ occupation or means of livelihood while the remaining 43 per
cent constituted their members base on commodity or social interest which suggests Ubam to be a commodity
based or social interest group as well as the people adopting a participatory group approach in achieving their
means of livelihood within the locality.
This result is similar to Aryeeteh (1996) assertion on group lending as a means of rural household
empowerment and poverty reduction.
According to the findings presented in the same table, 70.8 per cent of the groups have both male and female
as members, 19.2 per cent consist of females only, 10 per cent have only male as members suggesting that the
people are not gender biased since greater number of the groups have both male and female as members.
Also, 38.3 per cent of the groups meet every market day. Interview in the field shows that market day interval
in the study area is between 4 to 7 days, 47.5 per cent meet every Saturday or Sunday while 14.2 per cent
meet monthly. Another 37.5 per cent of the groups operate from one new yam festival to another new yam
festival, while 62.5 per cent operates from one Christmas to another Christmas. All the groups do not accept
savings from non-members as well as giving loans or contributions to non-members. About 39 per cent have
existed for 2 to 5 years while 60.8 per cent have existed for more than 5 years.
Further results revealed that meeting days are arranged to target when income is earned so that part of the
income could be used to meet their meeting obligations (either savings or loans repayment).The cycle of
operation seems to be one year which revealed that the groups dissolve their year of operation by sharing all
proceeds either in kind or cash, disburse savings to members who did not borrow and agree on how to treat
loan defaulters with most of the groups confiscating certain quantities of their harvest or properties that is
worth the amount of default. The results also show that, the groups do not extend their obligations (savings,
lending or contributions) to non-members which emphasized cooperation among the people, intra-group risk
pooling as well as intermediation. Thus it enhances the mitigation of credit accessibility problems and
repayment delinquency. This is similar to findings made by Olomola (2000), Haugen (2005) and Adegbite et
al (2009); that group lending enhances risk pooling and sharing and reduces rationalization in the credit
market.
The findings also show that, most of the groups have existed for more than 5 years. Field interview with the
operators disclosed that, some of the groups have a written constitution and a Legal Adviser which is an
indication of dexterity in their operations, effective social cohesion and as a going concern. Membership of
the 120 groups under study ranged between 18 to 210 members with 114 members on the average as
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presented in Table 3. Moreover, total savings or “hands of contribution” of the groups ranged from N217, 000
to N3, 127,000 per year or cycle of operation.
Results presented in Table 4 show that, farmers category has the highest number (58) representing 30.9 per
cent while processors category has the least of 4 representing 3.5 per cent. Further, traders constituted 15.8
per cent, civil servant 18.4 per cent, while others such as craftsmen, tradesmen and pensioners, make up 11.4
per cent, which supports the facts that, farming is their primary occupation and most of the produce is
marketed unprocessed which could be the reason why processors are few.
Thus farming is the predominant means of livelihood and source of revenue in the area. Agom (2001) study
also identified keeping of livestocks, crop farming and processing as the prevalent occupation in the study
area. The age category of less than 20 years and, above 60 years have the least average members while the
age category of 21-60 years has the highest number of members (Table 4) suggesting that most of the
members are within the productive age which guarantee their risk pooling and repayment of loans. This is
also similar with the findings in study conducted by Mkpado et al (2010) that group membership is
constituted mostly within the age bracket of 40 – 49 years which guarantee intra – group risk pooling.
6.2. CREDIT DELIVERY MECHANISMS
Result of the credit delivery mechanisms of the 80 groups that operate both savings and lending is presented
in Table 5. The amount generated for loans per cycle or year range from N210, 000 to N3, 825,000. Loanable
funds are mostly generated from savings and levies. Average number of 89 members requested for loans, of
which an average of 71 members’ applications were granted. Total amounts of loans disbursed ranged
between N162, 000 to N2, 675,000. In addition, disbursement lagged between 1-4 weeks while loans
repayment duration was 2-10 months in an operation year. The interest charged by the groups ranged from 5
to 30 per cent, with an average of 18 per cent per year. Application fees for loans were between N200 to
N2,500 for the 80 groups under study. Loans recovered by the groups were between N145,000 to N2,135,000.
While an average of 15 members defaulted. Sizes of loans disbursed to applicants ranged between N8, 000 to
N35, 000.
Further results revealed that membership of the group is open and often large which may be proned to
imperfect information about members, screening and contract enforcement. However, the average
membership of the group is 116 which could be the reason for fragmentation or existence of several clusters
(Sub - group) in line with the theories of informal credit market. The average interest of 18 per cent charge by
the groups is fairly high compared to the 9% bench mark lending rates to agriculture by commercial banks as
proposed by Central Bank of Nigeria. The administrative cost is probably at 1 per cent of loan sizes coupled
with the prompt disbursement of loans, suggesting the reason why small scale farmers and poor households
still prefer sourcing for loans from the group. Furthermore, the average sizes of loan seem to be small
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compared to the amount required to fund their agribusinesses which may be a factor why farmers in the
locality are still in need of credit. Interview in the field with the operators also revealed that acquired loans are
not only used in production but consumption expenses such as burial, paying of children school fees,
marriages, hospital bills, off season expenses and so on.
6.3. MEMBERSHIP, NUMBER OF LOAN REQUESTED, GRANTED AND DEFAULTED
The groups have average membership of 116, out of which average number of 89 representing 76.7 per cent
requested for loans while an average of 71 members loan were granted representing 79.8 percent of loans
requested, an average of 15 members defaulted representing 21 per cent of loans granted as presented in Table
6. Comparing the average number of loans requested to membership and number of loans granted suggests
that, greater number of the people are in need of credit facilities, and the group holds the potential of
extending credit to a vast majority and has a special way of determining credit worthiness of applicants by
tieing savings to loans such that greater proportion have access to loans compared to the numbers being
rationed out in the formal credit institutions as revealed in studies conducted by Aryeeteh (1996), Atieno
(2001), Magbagbeola (1999) and Olomola (2010).
6.4. AMOUNT OF FUNDS GENERATED, DISBURSED AND RECOVERED
The 80 groups operating savings and lending generated an average amount of N2, 017,000 for loans out of
which N1, 419,000 representing 70.4 per cent of the average money generated by the groups was disbursed
out as loans. An average amount of N1, 140,000 representing 80.3 per cent of the money disbursed was
recovered by the groups (Table 7). The result shows that amount disbursed for loans was 70.4 per cent of the
group money generated from savings and other charges for loans suggesting that not all the members that
save may have requested for loans which confirms the intermediation role played by informal credit sources
revealed by Isijola and Ajetombi (2000) and Aryeeteh and Udry (1994) in their studies. Also, 80.3 percent of
the loans were recovered which implies that the conditions such as tieing loan amount to savings used in
releasing loans to members as well as contract enforcement may have been effective in loan recovery.
Table 1: Sampling distribution of the groups
L.G.A
Number of groups listed
Sample size
Obudu
116
50
Obanliku
93
41
Bekwarra
72
29
Total
281
120
Table 2: Ubam groups characteristics
S/N
CHARACTERISTICS
FREQUENCY
N=120
PERCENT
100
1.
Objective of formation
Savings
Contributions
25
15
20.8
12.5
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Savings & Lending
80
66.7
2.
Basis of membership
Same family
Same village or locality
Same Church
Work in the same place
Farming the same crop
Others
12
68
0
12
18
10
10
56.7
0
10
15
8.3
3.
Gender composition
Males only
Females only
Males and females
12
23
85
10
19.2
70.8
4.
Meeting days
Market days
Saturday or Sunday
Monthly
46
57
17
38.3
47.5
14.2
5.
Cycle of Operation
New yam Festival – New Yam Festival
Christmas – Christmas
45
75
37.5
62.5
6.
Savings from non-members
Yes
No
0
120
0
100
7.
Loans to non- members
Yes
No
0
120
0
100
8.
Years of Existence
2 – 5 years
Above 5 years
47
73
39.2
60.8
Source: Field Survey, 2013
Table 3: Membership and total savings mobilized
S/N
Items
Range
Average
1
Membership
18 – 210
114
2.
Total Savings Mobilized per
year (N1,000)
217 – 3127
1669
Source: Field survey, 2013
Table 4: Occupation and age of members
S/N
Items
Ave. number
Ave. Percent
1.
Occupation of members
Farmers
Traders
Civil Servant
Processors
Others
58
18
21
4
13
30.9
15.8
18.4
3.5
11.4
2.
Age
Less than 20
21 – 40
41 – 60
Above 60
4
40
51
19
3.5
35.1
44.7
16.7
Source: Field Survey, 2013
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Table 5: Credit delivery mechanisms
S/N
Items
Range
Ave.
1.
Membership
21 – 210
116
2.
Total Amount generated for loans per year (N’000)
210 – 3825
2017
3.
Total number of loan applicants per cycle of operation
16 – 146
89
4.
Applications granted
11 – 120
71
5.
Total amount of loans disbursed (N’000)
162 – 2675
1419
6.
Disbursement lag (weeks)
1 – 4
3
7.
Loans repayment duration (months)
2 – 10
6
8.
Interest charge (Percent)
5 – 30
18
9.
Administrative cost of loans in naira
200 – 2500
1350
10.
Total amount of loans recovered (N’000)
145 – 2135
1140
11.
Number of loan default
1 – 25
15
12.
Average sizes of loans to beneficiaries (N’000)
8 – 35
Source: Field survey, 2013
Table 6: Average membership, number of loan requested, granted and defaulted
S/N
Items
Ave. number
Ave. Percent
Average number of members
116
Average number of loan request
Average number granted
Average number of defaults
89
71
15
76.7
79.8
21
Source: Field survey, 2013
Table 7. Amount of funds generated, disbursed and recovered
S/N
Items
Ave. number
(n’1000)
Ave.
Percent
Average Amount generated for loans
Average amount disbursed
Average amount recovered
2017
1419
1140
70.4
80.3
Source: Field survey, 2013
6.5. DETERMINANTS OF LOAN RECOVERY BY UBAM GROUPS
The result of the probit analysis of the determinants that influence the recovery of loans disbursed by the
groups is presented in Table 8. Chi Square diagnostic statistic (Pearson goodness - of - fit test) was reported.
Its estimated value of 1726.928, is significant at 1 percent level indicating that the independent variables
included in the model significantly predicted the outcome of the dependent variable in the probit regression.
The estimation indicated that the coefficients of the five explanatory variables included in the Probit model to
predict the probability that a loan would be recovered or not recovered were positive and significant (P ≤
0.01) level in predicting loan recovery of the groups in the study area.
Particularly, the probit estimate of the coefficient of the average size of loans disbursed was positive and is
significant at 1 per cent level of probability in predicting recovery of loans disbursed. Its value of 0.119
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means that, with other variables held constant, if average size of loans disbursed increases by one unit, on
average, the estimated Probit would increase by 0.119 units indicating a direct relationship between the two.
This means that as the average size of loans increase by one unit, the probability of recovery increases by
0.119 percent. In terms of odds ratio, we have 1.126 for the average size of loans disbursed. This suggests that
when the average size of loans disbursed increases by 0.119 units, the groups are more than 1.126 times likely
to recover the loans.
Disbursement lag is positive and is significant at 1% level of probability. The coefficient estimated is 0.502.
This means that if disbursement lag increase by one unit with other variables held constant estimated probit
increase by 0.502 unit indicating a direct relationship between the two. This means that as disbursement lag
increase by one unit the probability of recovery increases by 0.502 percent. In terms of odd ratio the value is
1.652 suggesting that as disbursement lag increases by 0.502 unit, the groups are more than 1.652 times likely
to recover the loans.
Age of the group is positive and is also significant at 1percent level of probabiliy. The estimated coefficient of
0.173 means that, with other variables held constant, if the age of the groups increase by one unit, the
estimated probit increases by 0.173 units indicating a direct relationship between the two. This means that as
the age of the group increases by one unit, the probability of the group recovery of loans disbursed increases
by 0.173 percent. In terms of odds ratio, we have 1.188 for the age of the group. This suggests that when the
age of the group increases by 0.173 units, the groups are more than 1.652 times likely to recover the loans.
Interest charge by the groups is positive and is significant at 1percent level. The coefficient estimated is
0.210. This means that if interest charge increase by one unit on average with other variables held constant
estimated probit increases by 0.210 units indicating a direct relation between the two. This means that as
Interest charge by the groups increase by one unit the probability of recovery increases by 0.210 percent. In
terms of odd ratio the value is 1.234. Suggesting that as Interest charge increases by 0.210 units, the groups
are more than 1.234 times likely to recover the loans. Percentage of loans recovered previous year by the
groups is positive and is significant at 1 per cent level. The coefficient estimated is 0.873 means that if
Percentage of loans recovered previous year increases by one unit with other variables held constant estimated
probit increase by 0.873 unit indicating a direct relation between the two. This means that as Percentage of
loans recovered previous year increase by one unit the probability of recovery increases by 0.873 per cent. In
terms of odd ratio the value is 2.394. Implying that, as Percentage of loans recovered previous year increase
by 0.873 unit, the groups are more than 2.394 times likely to recover the loans.
On a priori expectation, two of the variables, disbursement lag and interest charge are positive and do not
satisfied the expectation. The inability of interest charge to satisfy the a priori expectation may be due to the
fact that, interest rate charge by the groups in the area is fixed and is tied to loan balance each time the group
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meets and when remittance is expected to be made by borrowers. So the higher the interest the faster the
farmers clear off their balances to avert accumulation of debt which in turn increases the loan recovery of the
groups. In terms of disbursement lag, its implies that, there is timeliness between when a borrower requested
for loan and when it is granted such that the purpose of the loan is not defeated which increases the chances of
recovery of the loan. Average size of loans, age of the group, and percentage recovery of previous year, are
positive and conforms to a priori expectation. Implying that, large sizes of loan increase borrowers’ solvency.
In the case of age of the group, the longer the existence of the group the more the experience gained in
recovery of loans, while percentage recovery of previous year guide against granting loans to defaulter, who
may decrease the recovery.
Table 8. Probit regression results (dependent variable: Loan recovery)
Independent Variables
Estimate
Odds ratio
Standard
error
Z
Significance
X1
0.119
1.126
0.037
3.174
0.002***
X2
0.502
1.652
0.047
10.780
0.000***
X3
0.173
1.188
0.031
5.515
0.000***
X4
0.210
1.234
0.036
5.843
0.000***
X5
0.873
2.394
0.204
4.273
0.000***
Intercept 0
-6.327
0.961
-6.583
0.000***
1
-6.398
0.985
-6.494
0.000***
Diagnostic Statistics
Pearson Goodness of
Fit Test
1726.928***
LR statistic(5df)
11.0705
No. of iterations
19
Source: Field Survey, 2013
Note: *** (significant at 1Percent level)
7. RECOMMENDATIONS
Based on the findings of this study the following recommendations are made:
Farmers in dire need of loans should be sensitized to organize themselves into cluster groups relative to
their objectives or interest to increase their credit worthiness and accessibility.
Ubam groups play financial intermediary role by mobilizing savings from all members and allowing
those who need credit to borrow from this savings, therefore financial institutions, government and
NGOs can synergized with these groups in extending credit facilities to farmers in the area.
Economic activities to boost farmers’ liquidity should be promoted in the area to increase members’
savings since loan size is tied to savings and size of loans influences loan recovery by the groups
significantly.
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The groups recover more than 80 percent of their loans in each cycle of operations. Lessons can be
drawn from their loan recovery strategies to reduce the rate of loan defaults in the informal sector.
8. CONCLUSION
It is worthy to note that Ubam groups perform a very significant role in the provision of credit to finance
farming and other value chain activities in the study area. Evidence from the study showed that Ubam group
is a popular credit association and is fragmented in clusters in the area. Also, the group extends credit to more
than 70 percent of its applicants and as well perfected in loan recovery strategies that enable them to recover
about 81 percent of their loans plus the loan costs. Credit Institutions can therefore synergized with these
groups in a bid to increase capital availability and accessibility for rural dwellers which can grow the agrarian
economy rapidly.
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