determinants of credit rating of cooperative fish farmers

Journal of Agriculture and Food Environment Volume 7(2): 45-57, 2020 Achoja, 2020

45 JAFE 7(2): 45-57, 2020

Original Research Article

Determinants of Credit Rating of Cooperative Fish Farmers:

Evidence from Delta State, Nigeria

ACHOJA, Felix Odemero (Ph.D)

Department of Agricultural Economics and Extension

Faculty of Agriculture, Delta State University

Asaba Campus, Asaba, Nigeria

Orcid ID No: 0000-0002-9705-4923

[email protected]; [email protected]; +2348032726201

Received 23rd March 2020, Accepted 6th April, 2020; Corrected 11th June, 2020

Abstract The determining factors of credit rating of fish farmers’ cooperative societies were investigated in Delta State, Nigeria. Random selection of 180 respondents was achieved with the use of a multi–stage sampling method. Primary data were elicited using a structured questionnaire. Collected data were subjected to analysis with the use of descriptive and inferential statistics. Result shows that fish farmers’ credit rating was 56%. Furthermore, income of fish farmers and the revenue base of cooperative societies were important predictors of credit rating in cooperative societies. Majority of the surveyed fish farmers’ cooperative societies possessed excellent capacity to repay loans to financial institutions, sustained profile of revenue growth, capacity to generate annual surplus, high investment in capital assets and good credit rating reports from creditors. The paper recommended that credit institutions should extend credit to fish farmer cooperators on the basis of their good credit worthiness status. The current study has deepened our knowledge on the methodology for determining credit rating of cooperative fish farmers. It has implications for credit financing of fish farming and the development of the fisheries sub-sector in Nigeria.

Keywords: Credit rating, determining factors, income, cooperative fish farmers

Introduction

Throughout the world, cooperative societies are organizations formed with a view to achieving

common economic goals that may be more difficult to achieve with individual efforts (Adu, 2014).

Typically, members aggregate resources with which they assist themselves, usually in the form of

credit in cash or in kind, for the mutual benefit of their members with very favourable interest rates.

In the agricultural sector in Nigeria, agricultural credit provided by agricultural cooperatives

significantly contributes to development of the agricultural sector (Rahji, 2000; Ehigiamusoe, 2005;

Copyright © 2020 by The Faculty of Agriculture, Delta State University, Abraka, Nigeria

This is an Open Access publication distributed under the terms of the Creative Commons Attribution 4.0 International License

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46 JAFE 7(2): 45-57, 2020

Oladeebo and Oladeebo, 2008).

Agricultural cooperative society is societies are often grouped or classified as service cooperatives

and production cooperatives (Milovanovic and Smutka, 2018), and have been an important tool

for rural development.

Fish farmers, like other farmers in the agricultural sector, require external financing (e.g

cooperative loans) for the development of the fisheries sub-sector in Nigeria. However, in spite of

their relevance in national economic development, and the fact that sufficient credit awarded to

the poor in rural societies is key to agricultural developmental programs (Feder et al., 1985), the

majority of rural fish farmers are considered credit unworthy by most formal financial institutions,

and are therefore denied access to credit services. This is because the award of credit by the formal

financial institutions, and even cooperatives, to these farmers are often accompanied by associated

problems of default in repayment of loans.

The crisis of cooperative credit default risk, and the growing debate around effective cooperative

management models have prompted renewed research attention in the analysis of credit rating of

cooperators for the advancement of cooperative organizations in today’s globalized economy

(Parker et al., 2014).

The poor management performance of fish farmers’ cooperatives called for the current study with

the promise to reduce default rate of fish farmers’ cooperative societies, and improve their

performance. Financial institutions and governments that channel credit to fish farmers’

cooperative societies as credit intermediaries often evaluate their credit ratings and capacity to

repay loans before engaging in loan transactions. Cooperative societies assume dual status of

lending institutions, and institutional borrowers. Consequently, as institutional lenders, they need

to factor credit ratings of their clients into their management model. As institutional borrowers,

other lending institutions would require their credit ratings.

Credit rating, as a variable, is one of the core cooperative management tools that drive the life

cycle and survival of cooperative societies (Cook, 1995). Credit rating reveals the financial

commitment of a potential borrower. It varies among cooperators. Limniosa et al. (2018)

investigated the level of member commitment, loyalty and strategic importance in cooperative and

mutual enterprises, and found that any credit patronage/benefit is linked to credit

rating/requirement of co-operators to alleviate some of the “generic” problems that are associated

with credit risks. The collapse of the once-strong co-operative ideology is often attributed to the

compromise of credit rating criteria (Okonkwo-Emegha, et al., 2017) and the lack of assessment

of members’ financial commitment (Fulton, 1999) before credits are awarded by cooperative

managers. Over its operational existence, the sustainability and resilience of a cooperative

organization depend on governance, organizational effectiveness, economic performance and

access to financial resources (Hendrikse and Bijman, 2002; Rebelo et al., 2002; Bijman and Van

Bekkum, 2005; Van Bekkum and Bijman, 2006; Plunkett et al., 2010). All these are influenced by


47 JAFE 7(2): 45-57, 2020

credit rating potentials of borrowers. Despite the relevance of credit rating, most works that

examined sustainability and resilience of a cooperative organization had primarily focused on the

interplay between-members’ patron and investor roles, without adherence to the theory of credit

rating criteria, and the tensions this can create if poorly managed (Nilsson, 2001).

Predictions of performance of cooperative societies are often tied to their adherence to general

principles, trust and worthy characters of cooperative members and managers. Credit rating

measures and mirrors a borrower’s worthy behaviour towards meeting debt obligations within a

given period (www.blgf.gov.ph, 1987).

Theory of cooperative organizations relates to cooperative institutions and other organizations that

practice the principles of cooperation such as consumers’, housing, producers’, and workers’

cooperatives, fish farmers’ cooperatives, credit unions and social entrepreneurship (JCOM, 2018).

Credit rating theory is a branch of the theory of cooperative organizations. Lending agencies often

express worry in assessing credit-worthy potentials (Mbanasor and Nto, 2008; Nwachukwu et al.,

2010). Also, there is inherent retrogressive procedure in disbursing credit to fish farmers by formal

lending institutions, which constitutes serious impediments to successful credit utilization (Arene,

1993).

Credit rating of cooperators determines the volume of credit mobilized and accessed in cooperative

societies.

The problem of access to cooperative credit by fish farmers has gained prominence in the context

of rural and agricultural development in Nigeria. Cooperative institutions often express worry

over losses arising from loan defaults. Cooperative management failure and success are closely

related to credit rating of members (JCOM, 2018). Cooperative capital capacity largely depends

on credit rating of members. Yet it is not often adopted in loan award decisions of managers of

fish farmers’ cooperative societies.

There is the need, therefore, to minimize credit default rate among farmer cooperators (Anderson,

1990). Application of the theory of credit rating of fish farmer cooperators is, as a consequence,

fundamental to the effective management of fish farmers’ cooperative societies.

A few studies deepend on the analysis of credit rating methodologies without necessarily being

incorporated into fish farmers’ cooperative management models (Errasti, 2015; Errasti et al.,

2016). Scholars need to examine various credit rating methodologies and their applicability. No

study has analyzed credit rating of fish farmers’ cooperative management model in Nigeria, and

the possibility of being replicated in other countries (Flecha and Ngai, 2014). Few literature on

credit rating of cooperative fish farmers (Ezeh, 2003) underlined important dimensions and

questions that pertain to its absence in the management of cooperative organizations.

Consequently, it is important to investigate deeply and more extensively, cooperative policies and


48 JAFE 7(2): 45-57, 2020

practices, especially as they relate to credit rating of fish farmers and implications for fish farmers’

cooperative management.

Cooperative financing has been recognized as an important driver of the fisheries sub-sector in

Nigeria (Gbigbi and Achoja, 2019). Credit worthiness of the fish farmer must be determined

through appropriate credit rating methodology. Access to cooperative loan depends on the

borrower’s credit rating. It is important to investigate factors that influence the credit rating of fish

farmers’ cooperative societies in Nigeria.

Empirical information that connects and combines credit rating of fish farmers (members) with

cooperative societies in a single study is lacking, especially in Nigeria before now. This important

information is needed to further deepen our understanding of the extension of the theory of credit

rating and application to fish farmers’ cooperative organizations and their management. This study

was aimed at filling this information gap.

Theories create 5 platforms (analysis, clarity, understanding, prediction of behaviors and

application to reality). As it stands, analysis, clarity, understanding, predictors and applications of

credit rating potentials of cooperators are not yet known. These could be useful in management of

cooperatives and mutual enterprises.

Such important information is missing from the cooperative management model in Delta State,

Nigeria. There is need therefore to evaluate credit rating potentials of cooperators, the cooperative

societies and their predictors. Empirical findings of this research can influence the credit award

decisions of financial institutions.

The major purpose of this research was to investigate the determining factors of credit rating of

fish farmers’ cooperative societies in Nigeria. The specific objectives were to:

i. examine credit rating potentials of fish farmers’ cooperators;

ii. evaluate credit rating of fish farmers’ cooperative societies;

iii. identify important determining factors of credit rating of fish farmers; and

iv. assess loan repayment rate of farmer cooperative members in Delta State, Nigeria.

Materials and Methods

The study employed a survey research design. As a result, the methodology of survey research was

observed. Delta State, Nigeria, was chosen for the study because the State has many fish farmers’

cooperative societies with organizational and management challenges. The researchers are also

familiar with the terrain and languages of the people of Delta State, Nigeria. Both ordinary and

executive members of fish farmers’ cooperative societies were the subjects of the study. A multi-

stage sampling method was adopted in the selection of locations, cooperatives and cooperators.


49 JAFE 7(2): 45-57, 2020

Firstly, a list of all the registered fish farmers’ cooperatives in the Ministry of Commerce and

Industry, Delta State was obtained, and used as the sample frame. Stage two, involve the random

selection of one 3 Local Government Area (LGA) from each of the three agricultural zones in

Delta State: Aniocha-South, Isoko-North, and Ethiope-West LGAs. In stage three, five registered

fish farmers’ cooperative societies were randomly selected from each of the three selected Local

Government Areas. This gave a total of 15 fish farmers’ cooperative societies that were chosen for

the study. In stage four, 10 cooperative members were randomly selected from each of the 15 fish

farmers’ cooperative societies that were selected. In total, 150 fish farmers were chosen and

studied. Their financial records including loan transaction data were studied. In addition, Focused

Group Discussions (FGD) were held with 30 Executive members of the 15 selected fish farmers’

co-operative societies comprising of the President and General Secretary of each cooperative

society. Thus a total of 180 respondents participated in the survey.

The study employed primary data which were generated through a structured questionnaire. The

questionnaire was considered appropriate for obtaining primary data from individual members

while focused group discussion technique was considered useful for eliciting information about

the cooperative societies. The respondents’ response rate was 100%.

The questionnaire was designed to capture the specific objectives of the study. The information

contained in the questionnaire included some variables that relate to credit rating of individual

members and credit rating of cooperative societies. Data were collected from farm cooperators

through questionnaire administration by the researchers. Interview schedule was conducted for

respondents that could not read or write in English language. The essence was to obtain

comprehensive information on credit rating of borrowers. In this regard, both the individual

members and cooperative societies were treated as borrowers. In some cases, the cooperative

society assumed the status of a primary borrower while the members were secondary borrowers.

Tab1e 1: Credit rating scale

Credit rating indicators of fish farmer cooperator Credit rating

scores

Credit

rating

Excellent capacity to pay due debts. 81 – 100% very high

Has strong capacity to pay due debts. 71 – 80% high

Has adequate capacity to meet its debt service 61 – 70% good

Needs to be reminded severally before debt service

obligations are made.

51 – 60% average

Ability for timely debt recovery is doubtful 41 – 50% below average

Capacity is inadequate to meet debt payment 31 – 40% weak

Does not have the capacity to pay due debts 30% and below negligible

Adapted and modified from www.blgf.gov.ph 1987. Note: Single value of mid-point of credit scores

were used for computation. A borrower with a credit score of above the threshold of 51% is considered credit worthy.


50 JAFE 7(2): 45-57, 2020

Descriptive statistics such as frequencies, means and percentages were used to describe the socio-

economic characteristics of the farmer cooperators. Thrift savings and amount borrowed have

often been used to ascertain the credit rating of farmer cooperators. Accordingly, percentage credit

rating score of the cooperators was determined and regressed against the associated factors. Hence

important factors that influenced credit rating of farmer cooperators were investigated using a

regression model.

Credit rating calibrated on a scale of 100% (Table 1) was used to measure the credit rating of

farmer cooperators.

Determination of loan repayment performance.

Loan repayment rate was measured as the amount repaid, expressed as a percentage of the amount

borrowed, as stated by Achoja (2011).

Loan Repayment rate = Amount repaid X 100

Amount borrowed 1 % (1)

The multiple regression model specification

The multiple regression model was fitted to estimate the determinants of the credit rating of fish

farmer cooperators.

The model was specified explicitly as:

CRT = β0 + β1TEC + β2FFI + β3LD + β4NFI + β5LRH + β6TL + β7GND + ℮i (2)

The symbols of variables in the model are described in Table 1.

Table 2: Description of symbols of variable in model

Symbol

Description

Measurement

Expected

signs

CRT Fish farmer’s credit rating Credit rating score (%)

TEC Fish farming Technology Earthen pond=3; Concrete pond=2; Others=1 +ve

FFI Fish Farm Income Yearly Income from fish farms (₦) +ve

LRD Loan Repayment Duration Months -ve

NFI Non-farm income Yearly Income from other sources (₦) +ve

LRH Loan repayment history 1, if positive, 0, otherwise +ve

OTL Outstanding loan Naira (₦) -ve

GND Gender 1, if male, 0, otherwise +ve

β1 –β7 Coefficient of parameter estimate

β0 Intercept

℮i Stochastic error term


51 JAFE 7(2): 45-57, 2020

Results and Discussion

Credit rating of fish farmer cooperators

The financial report of annual general meeting shows the thrift saving and amount of loan

borrowed by individual members. Those whose thrift savings fell below 1/3 of the amount of credit

borrowed were not considered credit worthy. To be credit worthy requires enough thrift savings.

The result showed that majority of farmer cooperators had a mean credit rating potential of 56%

and others with the mean of 44% were not credit worthy (Table 3).

Table 3: Percentage distribution of credit rating of farmer cooperators in the sample

Class Frequency Percentage (%)

Credit worthy potential 84 56

Non-credit worthy potential 66 44

Total 150 100 Source: field survey, 2018.

Determining factors of credit rating of fish farmers

Multiple regression model fitted in this study measured the determining factors of credit ratings of

fish farmer cooperators. The result of income and other variables as predictors of credit rating of

fish farmers is presented in the Table 4.

Table 4: Result of multiple regression analysis of predictors of credit rating potentials of fish farmers

Variables† coefficient Standard error Z P>|Z|

Constant 1.620 0.884 1.83 0.067

TEC 1.387 0.655 2.12** 0.034

LRD -1.007 0.485 -2.47** 0.041

FFI 3.098 0.971 3.19* 0.001

NFI 2.315 0.964 2.40** 0.016

OTL -0.073 0.028 -1.97** 0.048

LRH 0.911 0.431 2.11** 0.034

GND 3.089 0.971 3.18* 0.001 * = 1% significant level; ** = 5% significant level; R2 = 67.7%;

† Acronyms explained in Table 2.

The linear function was the best model on the basis of number of significant variables, and the R2

value of 67.7%.

The results of the statistical significance of the individual explanatory variables in the model in

Table 5 are discussed as follows:

Fish farming technology: The finding shows that fish farming technology adopted by fish farmers

entered the model with a positive sign in line with a priori expectations. The choice of technology


52 JAFE 7(2): 45-57, 2020

types (earthen pond, concrete pond, tarpaulin pond and plastic tank) had a positive and significant

1.387 (2.12**) relationship with the credit rating of the fish farmers (p< 0.05). Better technology

type has the capacity to boost fish output and income of fish farmers.

Fish farmers’ income: The coefficient of fish farmers’ income was positive and significant

(3.098) (3.19*) at 1%. This result is in conformity with a priori expectations that higher income

of fish farmer cooperators enhance their credit rating. The finding implies that the higher the fish

farm income, the more the credit rating of the fish farmer, all other things being equal. A high fish

farm income will translate to high capacity, and tendency for loan repayment of the fish farmer. A

fish farmer with high level of income will have enough financial resources to cater for his family,

and still be able to meet loan obligations of the cooperative society. This result corroborates

Ugbomeh et al. (2008) who reported that income is an important determinant of loan repayment

and credit worthiness of cooperators.

Loan repayment history: The finding shows that loan repayment history of fish farmer entered

the model with a positive sign in line with a priori expectations. This implies that a positive

repayment history had a positive and significant 0.911 (2.11**) effect on the credit ratings of fish

farmers (p<0.05). A positive loan repayment history can be an important predictor that can boost

the confidence of the lender, and a better credit rating of the fish farmer.

Outstanding loan: The finding shows that outstanding loans of fish farmers entered the model

with a negative sign in line with a priori expectations (-0.073 (-1.97**)). This result implies that

the higher the outstanding loan, the lower the credit rating of the fish farmer. Where there are

outstanding loans, extending more loans could further increase the debt burden of the fish farmer,

who, as a result, may not be able to service loan obligations satisfactorily.

Loan repayment duration: The result shows that there was a negative and significant

relationship, -1.007 (-2.47**), between loan repayment duration and credit rating of fish farmer

cooperators. The longer the loan repayment duration, the less the possibility of being credit-

worthy. A fish farmer cooperator that delays loan repayment beyond the statutory period is

showing a sign of loan default. Consequently, such a farmer will have low credit rating and low

probability of being qualified to borrow.

Non-farm income: The result in Table 5 shows that non-farm income, entered the model with a

positive and significant sign (2.315 (2.40**)). It is the extra income that is not earned from fish

farming activity. It is an important predictor of a cooperator’s contributions to thrift, and hence

credit rating potential. A fish farmer who diversified into other livelihood options is capable of

generating substantially more financial resources than the farmer who earns income solely from

fish farming.

Gender: The coefficient of this variable is positive, 3.089 (3.18*), in line with a priori

expectations that gender of farmer cooperators will significantly determine their credit rating. This


53 JAFE 7(2): 45-57, 2020

implies that being a male fish farmer will increase the likelihood of high credit rating. Male fish

farmers are more credit worthy. This finding could be attributed to the fact that male fish farmers

are able to accumulate more thrift savings that can serve as collateral.

Loan repayment rate of fish farmer cooperators: The finding shows that loan repayment rate

of farmer cooperators was 85.72% with a default rate of 14.28%. This result implies a high loan

repayment performance among the surveyed farmer cooperators in Nigeria.

There are sufficient findings that can be discussed on credit rating potentials of both fish farmers

and their cooperative societies in Nigeria.

Firstly, credit rating potentials vary from member to member and from time to time. However,

majority (56%) of fish farmer cooperators were credit worthy, while 44% were not. The

cooperative societies in the study area must have adhered to the Bye-laws of their society by

ensuring that farmer cooperators must have 1/3 of the amount applied for, as thrift savings, before

awarding loans. Thrift savings are a boost to fish farmers’ credit rating, and the financial standing

of the cooperative society. The higher the credit rating potentials of a farmer, the more the amount

credit he can access. The more the volume of credit accessed by members, the more the harvest of

interests paid on loans. Credit rating potentials of farmer cooperators are pointers to the growth of

cooperative societies in the future. It will also enhance the sustainability of the credit mobilization

cycle. When there is stability in income, farmer cooperators contribute higher subscriptions in their

thrift savings. Also, farmer cooperators are credit worthy when there is additional income outside

their farm income. This finding is in line with the earlier report of Nwachukwu et al. (2010) that

non-farm income contributes to high productivity and credit rating potentials.

Secondly, up to 80% of the cooperative societies were credit worthy (Table 3). Credit worthy

cooperative societies possess features such as strong and excellent capacity for timely loan

repayment, sustained profile of revenue growth, capacity to generate appreciable amount of annual

surplus, high investment in capital assets, adequate capacity to meet its debt service commitments

to creditors (financial institutions), and good credit rating reports from their creditors. These

features are classified as best, high and good credit qualities (www.blgf.gov.ph, 1987). This result

implies that credit qualities are the indicators of credit rating of a cooperative society.

Income of farmer is a predictor of credit rating potential. Higher income can translate to higher

thrift savings and high potential for credit rating. This supports the earlier findings of Ezeh (2003),

Mbanasor and Nto (2008), and Nwachukwu et al. (2010).

The result on gender, as a positive predictor of credit rating potential of cooperators, shows that

gender affects the financial and economic conditions of farming households. Any gender of

household’s heads may be a credit worthy farmer in the cooperative society, provided they have

satisfied stipulated criteria. There is crystal clear evidence that more women are currently engaged

in agricultural cooperative activities, with an honest and positive attitude to pay loans.


54 JAFE 7(2): 45-57, 2020

The result on loan repayment rate of 85.72% reported among the surveyed farmer cooperators is

comparable to the earlier finding of Achoja (2011) who reported a loan payment performance of

80.02% in Delta State. The present study has revealed that there is an improvement in loan

repayment rate among farmer cooperators in Delta State from 80.02% to 85.72%. This finding

could be attributed to the level of farm and non-farm income of cooperators, positive disposition

of cooperators to the payment of loans, and strategies adopted by cooperative managers in loan

recovery drive. The result implies that loan repayment index was a significant predictor of

available loanable financial resources for further disbursement to deserving farmer cooperators. It

measured the sustainability of loan schemes of farmer cooperative societies.

Conclusion

The current study has provided useful information that is needed to deepen the extensive spread

of analysis, clarity, understanding of the theory of credit rating and its application to fish farmers’

co-operative organizations and management in Delta State, Nigeria. The credit rating of the farmer

cooperators can be predicted with farm income, non-farm income and loan duration. Gender-based

cooperative grouping will not encourage credit rating of fish farmers’ cooperative organizations,

provided farm income, loan duration and amount borrowed by the fish farmers are encouraging.

As more fish farmer cooperators become credit worthy, more loans would be circulated, accessed

and repaid, thereby making fish farmers’ cooperative societies sustainable.

In this regard, information on credit rating is important for sustainable management of fish

farmers’ cooperative societies in Delta State, Nigeria. Fish farmers’ cooperative management

principle that is built on credit rating of members can greatly enhance acceptance and integration

of the practices by members, stakeholders and financial institutions

It was recommended in this paper that cooperative organization and management team must not

compromise credit rating criterion. Also, policy directed at cooperative development by relevant

organizations should be anchored on credit rating potentials of members and cooperative societies.

Managers that are seeking to revive failing cooperative societies should adopt credit rating policy

in the Bye-laws. It should form part of the guiding principles of cooperative societies. It should be

practiced with all sense of strictness to reduce the rate of credit default. Cooperative managers

must fairly serve twin masters: business practice and social movement without compromising

credit rating of borrowers.

This study has extended the frontier of existing literature on credit rating analysis and its

implications for fish farmers’ cooperative management in Nigeria. The paper recommended that

credit institutions should extend credit to fish farmer cooperators on basis of their good credit

worthiness status. The current study has deepened our knowledge on the methodology for

determining credit rating of fish farmers’ cooperative societies.


55 JAFE 7(2): 45-57, 2020

An important area for further research lies in assessing the relationship between credit rating of

cooperators and performance indicators of cooperative organizations, as well as and how credit

rating can form part of the core management practices that can improve the performance of

cooperative organizations.

Acknowledgement

The author acknowledges all the authors whose works, were incisively consulted in the process of

developing this paper.

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