ABSTRACT
This study assessed smallholder farmers’ credit rationing, creditworthiness and distribution by microfinance banks in South-east Nigeria. Specifically, the study described the socioeconomic characteristics of the smallholder farmers; ascertained the loan approval rates of the farmers and estimated the determinants of loan approval rates. The study also estimated the determinants of credit worthiness of the farmers; ascertained the extent of credit rationing among smallholder farmers by microfinance banks (MFBs) and its determinants. Multi-stage sampling techniques were adopted for the selection of 360 small holder farmers who applied for microfinance credit and another 360 smallholder farmers who were discouraged from applying for microfinance banks credit in Imo State and Enugu State of Nigeria. Data collected were analysed using descriptive statistics, multiple regression, credit scoring model for farmers, discriminant analysis, multinomial logit model and the gini coefficient as derived from the Lorenz curve. The result showed that there was little difference in the mean ages of male and female smallholder farmers (36 years and 34 years) for male and female farmers respectively. Fifty per cent (50 %) of males and 50.4 % of female had between secondary and tertiary education, while about 14.4% of males and 16.7% of females had no formal education. Differences also existed in the mean loan approval rate for male and female smallholder farmers (34.3% while 48.9%). Farming experience, age of farmers, relationship with bank, years spent in school, proximity to MFB, household size, farm size, and annual income significantly influenced loan approval rates of male farmers, while farming experience, marital status, years of schooling, proximity to microfinance banks, household size, interest amount, and annual income significantly influenced loan approval rates of female smallholder farmers. Furthermore ,the coefficients of farm size, age of the respondents, annual farm income, educational level, farming experience, proximity to bank, and off-farm income are the significant factors that influenced male smallholder farmers’ discouragement from loan applications in the study area, while coefficients of farm size, age of the respondents, household size, annual income, education level, proximity to bank, and accessibility to account officer are the statistically significant factors that influenced female smallholder farmers who are discouraged from MFBs loan applications. Thirty-three per cent (33.3%) of males and 45.5% of females were credit worthy. Off-farm income, farm size, and loans with other banks positively influenced full rejection of loan application of males while, farming experience, educational level, annual income and banking relationships negatively influenced full rejection of loan application by male farmers. Farm size, loan duration, and default history positively influenced full rejection of loan application by female farmers, while farming experience, educational level and banking relationship negatively influenced it. The study recommended that microfinance banks should be gender neutral when assessing smallholder farmers’ loan applications and also in the disbursement of loans provided that the basic requirements are met.
TABLE OF CONTENTS
Title
Page ii
Declaration
Page ii
Dedication iv
Certification v
Acknowledgements vi
Table
of Contents vii
List
of Tables xii
List
of Figures xiv
Abstract xv
CHAPTER 1: INTRODUCTION
1.1
Background Information 1
1.2
Problem Statement 5
1.3
Objective of the Study 9
1.4
Hypotheses 10
1.5
Justification of the Study 11
CHAPTER 2: LITERATURE REVIEW
2.1
Conceptual Literature 14
2.1.1
Agricultural credit 14
2.1.2 Smallholder
farmers 16
2.1.3
Creditworthiness 18
2.1.4
Credit rationing 22
2.1.5
Information symmetry 24
2.1.6 Credit scoring 24
2.1.7 The concept of gender 27
2.1.8 Micro financing. 30
2.2 Theoretical Literature 33
2.2.1 Loanable funds theory 33
2.2.2
Asymmetric information theory 34
2.2.3 Pecking
order theory 34
2.2.4 The
theoretical foundation of credit rationing 35
2.3 Empirical Literature 36
2.3.1 Empirical literature on bank credit rationing
behavior 36
2.3.2 Determinants of creditworthiness. 38
2.4 Analytical Literature 40
2.4.1 Loan
approval rate model for smallholder farmers 40
2.4.2 The
ordinary least squares model. 40
2.4.3 The
loan discouragement indices model (LDI) and censored Tobit regression model for analyzing borrowers’ discouragement 41
2.4.4
Discriminant analysis model 42
2.4.5
Multinomial logistic model for analysing smallholder farmers credit
rationing 45
2.4.6 Gini
coefficient model for measuring inequality in credit disbursement 47
CHAPTER 3: METHODOLOGY
3.1
Study Area 49
3.2
Sampling Technique 50
3.3 Method
of Data Collection 52
3.4 Data
Analysis 53
CHAPTER 4: RESULTS AND DISCUSSION
4.1 Socioeconomic Characteristics of Respondents
68
4.1.1 Socioeconomic characteristics of the
smallholder farmers by gender (Loan Applicants) 68
4.1.2 Number
of MFBs account officers and credit scoring criteria of MFBs 73
4.2 Loan
Approval Rate of Smallholder Farmers, and Their Determinants, Along Gender lines 75
4.2.1
Loan approval and denial rate of
smallholder farmers along gender line 75
4.2.2
Determinants of loan approval rate of
male smallholder farmers 77
4.2.3
Determinants of loan approval rate of
female smallholder farmers 79
4.2.4
Determinants of loan approval rate for
smallholder farmers along gender lines (Pooled) 82
4.3 Loan
Discouragement Indices and Factors Influencing Smallholder Farmers Discouragement from Loan Applications 86
4.3.1 Loan
discouragement indices for smallholder farmers along gender lines 87
4.3.2 Factors
Influencing Smallholder Farmers’ Discouragement from Loan Applications, by Gender 88
4.3.2.1 Factors
influencing male smallholder farmers’ discouragement from loan applications 88
4.3.2.2 Factors
influencing female smallholder farmers’ discouragement from loan Applications 92
4.3.2.3 Factors
influencing smallholder farmers who are discouraged from loan applications (pooled) 97
4.4
Creditworthiness of Smallholder
Farmers and their Determinants, by Gender 101
4.4.1
Creditworthiness of smallholder farmers
by gender using the credit
scoring model 101
4.4.2
Comparison of the creditworthiness of
smallholder farmers by gender
102
4.4.3
Credit risk class as derived from the
credit scores of smallholder farmers by gender 103
4.4.4
Discriminant analysis of the determinants
of the creditworthiness of smallholder farmers by gender 104
4.4.4.1 Diagnostic tests 104
4.4.4.1.1
Box's test of equality of covariance matrices 104
4.4.4.1.2
Eigen values of the canonical discriminant functions 105
4.4.4.1.3 Wilk's Lambda Ratio of unexplained total
variance of discriminant scores 107
4.4.4.1.4
Canonical discriminant function coefficients 108
4.4.4.1.5
Classification function coefficient of the variables mostly effective in
the discriminant Function 110
4.4.4.1.6 Average group discrimination function values 112
4.4.4.2
Discriminant analysis classification success results of farmers’
creditworthiness 113
4.5 Extent of Credit Rationing of Smallholder
Farmers by Microfinance banks and the determinants along gender lines 116
4.5.1 Extent of
credit rationing of smallholder farmers by microfinance banks along gender
lines 116
4.5.2
Determinants of Credit Rationing of
Smallholder Farmers by Microfinance Banks along
Gender Line 117
4.5.2.1 Determinants
of credit rationing of smallholder male farmers by microfinance banks 117
4.5.2.2 Determinants of credit rationing of
smallholder female farmers 122
4.5.2.3 Determinants of credit rationing of
smallholder farmers (pooled) 127
4.6 Amount of Credit Disbursed and the
Level of Inequality in the disbursement of Loans to smallholder farmers
along gender lines 131
4.6.1
Amount of credit
disbursed to smallholder farmers, along gender line 132
4.6.2 Level of inequality in the disbursement of
loans to smallholder farmers, along gender Line 133
4.7 A
Priori Expectation Testing of Significant Variables 137
4.7.1
Hypothesis one 137
4.7.2
Hypothesis two 138
4.7.3
Hypothesis three 140
4.7.4
Hypothesis four 141
4.7.5 Hypothesis five 143
CHAPTER 5: SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Summary of Findings 144
5.2
Conclusion 147
5.3
Policy Recommendations 148
5.4
Contributions to Knowledge 150
References 151
Appendices 163
LIST OF TABLES
3.1: Credit
Scoring Model for Smallholder Farmers (CSMSF). 59
3.2: Credit
risk class derived from the credit scores. 61
4.1: Socioeconomic
characteristics of the smallholder farmers by gender 68
4.2: Number
of MFBs account officers and their credit scoring criteria 69
4.3: Loan
approval rate of smallholder farmers along gender line 73
4.4: Regression
result on the determinants of loan approval rate for male
smallholder Farmers 77
4.5: Regression
result on the determinants loan approval rate for female smallholder
farmers in the study area 80
4.6:
Regression results on the determinants of loan approval rate for
smallholder
farmers pooled) in the study area. 82
4.7: Loan
discouragement indices of smallholder farmers along gender lines
87
4.8: Censored
Tobit regression result of the factors influencing male smallholder
farmers’ discouragement from loan applications 89
4.9: Censored
Tobit regression result of the factors influencing female smallholder
farmers’ discouragement 93
4.10: Censored
Tobit regression results of the factors influencing smallholder
farmers who are discouraged from loan applications in the study area
(pooled) 99
4.11: Distribution of the creditworthiness of
smallholder farmers by gender 101
4.12: Test of
significance difference in the creditworthiness (using credit score) of
Smallholder farmers by gender 102
4.13: Credit risk class as derived from the credit
scores. 103
4.14: Box's
test of equality of covariance matrices 104
4.15: Eigen values of the canonical discriminant
functions 105
4.16: Wilk's
Lambda ratio of unexplained total variance of discriminant scores 107
4.17: Canonical
discriminant function coefficients for the male, female
and pooled loan Applicants 108
4.18: Classification
function coefficients of the variables mostly effective in the
discriminant Function 111
4.19: Average
group discrimination function values 113
4.20: Discriminant
analysis classification success results 114
4.21: Distribution
of the respondents by the extent of credit rationing by
Microfinance Banks 116
4.22: Estimated
output of multinomial logit model for determinants of credit
rationing of smallholder male farmers by microfinance banks 118
4.23: Marginal
effects and quasi-elasticity estimates 121
4.24: Estimated
output of multinomial logit model for determinants of credit
rationing of smallholder female farmers by microfinance banks 123
4.25: Marginal
effects and quasi-elasticity estimates 126
4.26: Determinants
of credit rationing of smallholder farmers by MFBs
banks in the study area. 128
4.27: Marginal
effects and quasi-elasticity estimates
130
4.28: Distribution
of the amount of credit disbursed to smallholder farmers by
gender in the study area 132
4.29: Summary
result of the Gini coefficients for the level of inequality in
MFBs
loan allocations to smallholder farmers along gender line 134
LIST OF FIGURES
1.
Graphical representation of the Gini coefficient 48
2: The
Credit Scoring Process 58
3: Lorenz curve for MFBS loan allocations to
smallholder farmers 67
4:
Lorenz curve for MFBS loan allocations to smallholder farmers by
gender 136
CHAPTER 1
INTRODUCTION
1.1 BACKGROUND
INFORMATION
The agricultural sector is relevant in
Nigeria's quest to achieve a number of the targets of the Sustainable
Development Goals (SDGs) unfortunately, food
production in this region has not kept the pace with the developing populace in
decades and it is susceptible to
further decrease. To exacerbate the circumstance, it is
anticipated that harvests yield in the locale will probably decrease to 50
percent by 2030 (Intergovernmental Panel on Climate Change, 2001). For the
anticipatable future, prosperity of the rustic populace in Nigeria will be
attached to Agriculture.
In
this way, encouraging rural development can offer a
definite pathway out of destitution. Expansion into business farming is
significant for making development sustainable, to diffuse its advantages to
rustic zones, and to fence against the stuns from a solitary asset reliance on
oil (NBS, 2010).
In
Nigeria, credit to agriculture is perceived as a
fundamental device for advancing rural improvement particularly among
provincial poor farmers that establish a significant level of the
cultivating populace (Nwaru, 2004; Mejeha and Ifenkwe, 2007; Nwaru, 2011).
Microfinance Banks (MFBs) give credits to low pay people (Samareen and Farheen,
2012). Like each advance, they should be repaid.Thus, MFBs must assess their
client’s financial activities as well as the risks of their operations due to
the fact that lending is risky, but at the same time profitable. Interest and
fees on the loans are sources of profits to Microfinance banks. Most banks will
not want to grant credit to farmers who are not able to repay the loans.
Credit
to Agriculture has shown a high level of unpredictability throughout the years.
It declined from N67.74 billion in the year 2004 to N49.39 billion of in 2006, increased
to N149.57 billion in 2007, declined again to N106.35 billion in 2008 and was
N135.7 billion in 2009 (CBN, 2011). This pattern gives certain components of
uncertainty.The microfinance banks have not been playing out any better as an
insignificant percentage of their lending goes to the agrarian segment. For
example, average percentage of advances to agribusiness by the microfinance
banks for the period 2004 - 2008 was 4.4 percent of the total loans given out
by the microfinance banks (CBN, 2010). Thus, just a set number of smallholder
farmers are in a situation to meet their budgetary necessities.
The
agricultural section received 2.1 percent of the total credit disbursed to the
economy in spite of its normal commitment of 42.2 percent to the GDP (CBN,
2010). As at December 2011 there were 24 commercial banks with 5,789 branches
and 816 microfinance banks bringing the total number of banks to 6,605(Sanusi, 2012).The
proportion of bank officesto the populace was 24,224 branches per population,
demonstrating a significant level of financial exclusion, this is additionally
validated by the 2010 Enhancing Financial Innovation and Access (EFInA) study,
which saw that 46.3 percent of Nigeria's populace is still monetarily
prohibited when compared with South Africa, Kenya, Botswana with 26.0 percent,
32.7 percent and 33.0 percent, respectively. Farmers’ capacity to procure
credit will also vary extensively, based on the perceived riskiness of the loan(Ofonyelu
2013).
Younger
farmers may represent a greater risk, both because of the lack of a significant
credit history, and because younger farmers tend to have substantially higher
failure rates than more progressively developed farmers.
In
the Agricultural credit market, before a farmer accesses
credit the individual in question must be screened. The screening procedure is
known as the creditworthiness assessment otherwise known as creditreliability
evaluation. As indicated by Ofonyelu (2013), financial soundness appraisals
are made for various reasons. Firstly, it is essential
for the reduction of bad debts and non-performing loan occurrence in banks.
Furthermore, acomprehension of creditworthiness is significant in view of the
uncertainties. Inappropriate appraisal of borrowers'
attributes places the bank in a hindered situation to sufficiently protect
itself in case of default outcomes as it ascertains the risks and default
probabilities of prospective borrowers, which is a standard which must be pursued
before the credit is approved. Credit ratings can be utilized to decide
whether to grant credit and, to decide the value that ought to be charged for
that credit.
Credit
rationing occurrence in the theory of agricultural finance is considered as a
fixed disequilibrium on the finance market apparent by the extent of price
adjustment. Generally the theory defines credit rationing as a situation in which
the demand for loans exceeds the supply of these loans at the loan rate quoted
by banks. Models of credit rationing will in general support why banks are
probably going to set the interest rate beneath the market-clearing rate and in
this way limit the distribution of credit as opposed to expanding interest
rates in accordance with the increasing demand for credit particularly in
credit boomeras.
Homestead
family units make decisions on choices running from when to plant, whether to
adopt innovation as well as which innovation to embrace, whether to contract
additional labour for furrowing when, how and whom to sell the farm produce to,
and also whether or not to participate in non-farm economic activities, among
others. One other significant choice that farm families are confronted with is
whether to utilize farm credit.
Customarily,
credit constraints are said to stem from asymmetric and imperfect information (Stiglitz
and Weiss, 1981). Imperfect markets make banks and different lenders to gather farmers’
data for the purpose of evaluating their creditworthiness. In any case, there
exists another side of information asymmetry with respect to the farmers who review
the probability of successfully applying for credit but can't know a priori if
the application will be approved.
Credit
markets are in no way, shape or form gender neutral (African Development Bank,
2015; FAO, 2011; Baden, 2006). Men and women both contribute to agricultural
productivity but, their access to these farming resources are relatively
different (Deere and Doss 2006; FAO, 2010). Credit transactions depend actively
on the connection between loan officials and borrowers. In a situation where
the bank officials and borrowers share gender identity, this could improve
proficiency through a superior comprehension of the customers' specific
conditions. For example, female loan officers may better appreciate the ability
of female farmers in terms of completing their farming activities and/or
repaying the debt. Conversely, a gender bias can also generate unfair pricing.
Widening
inequality between men and women in Nigeria is a characterizing challenge
within recent memory. In advanced economies, the gap between the rich and poor
is at its most significant level in decades. Disparity patterns have been
progressively blended in emerging markets and developing countries (EMDCs),
with certain countries encountering declining inequality, however unavoidable
disparities in access to training, health services, and credit remain. Not surprisingly then, the extent of
inequality, its drivers, and what to do about it have become some of the most
hotly debated issues by policymakers and researchers alike.
1.2
PROBLEM STATEMENT
Financial
institutions in Nigeria are known for the long-lasting process of a loan
application and it may take several weeks or months to complete the formalities
in full in some cases the loan applications may be approved but it makes no
sense when an application takes so long to be approved especially if the
project to be executed is time sensitive. A farmer’s loan application that gets
denied is one of the reasons why agricultural transactions fail. When a
farmer’s loan application is denied it’s in most cases the fault of the farmer
or the lender. The reality is that there can be issues with the bank appraisal.
This is not very healthy for the Nigerian agricultural sector especially when
farmers need credit for immediate agricultural projects and the loans are
denied or approved much later. It is very important to investigate the various
factors which influence the rate of credit approval so as to reduce losses in
the agricultural sector. This study tends to bridge the gap by providing
information about the drivers of smallholder loan approval rates along gender
lines in South East Nigeria due to the fact that an efficient utilization of
agricultural credit is necessary to enhance the agricultural sector's
productivity and hence the national economy (Yasir et al., 2012).
Secondly,
most research on agricultural finance has either avoided discouraged borrowers
from the examinations or joined them with inappropriate groups like denied
borrowers. In any case, ongoing proof shows the presence of significant differences
between discouraged and denied farmers which calls for separation of these two
groups when carrying out an analysis of credit availability (Cole, 2010).
However, in conditions of imperfect information amongst potential smallholder
borrowers, some do not apply for bank loans even if they need capital this is
because they think their applications will be rejected. Such borrowers are
called 'Discouraged Borrowers'. However, to examine the borrower-lender loan
dynamics in the Nigerian agricultural credit market in its fullest sense
requires the inclusion of those potential smallholder borrowers who might want
a loan for their businesses but choose to not formally apply because they are
sure they will be refused by the bank. This study tends to fill up research gap
by involving smallholder farmers who have not been featured in previous
research as being discouraged borrowers and also by ascertaining their
determinants.
In
some parts of Nigeria, the gender and ethnic background of the farmers adds
information (by acting as a proxy for additional unobserved risk factors), and
the lender uses this information in the loan granting or rate setting process,
thereby engaging in “statistical” discrimination. Given observed differences in
access to credit by male farmers compared with female farmers, (Abosede, and
Aminat 2011) it is important to determine if banks employ discriminatory
lending practices when evaluating loan creditworthiness assessments for male
and female smallholder farmers. A major challenge facing the banking industry,
is how to determine bad loan applicants, because continuous disbursement of
credit to non-creditworthy customers may cause serious problems in the future
by increasing the loss in bank capital, lower bank revenues and bankruptcy (CBN, 2014).
Nigerian
banking system is faced with problems of classifying farmers into creditworthy
and non-creditworthy groups(CBN, 2014). There is limited research of the
determinants of farmers’ credit assessments in Nigeria especially along gender
lines. This has led to discouragement of credit institutions in extending
credit facilities to farmers and also led to decreased agricultural
productivity and also increased poverty in the country.
In
Nigeria, there are some cases where the customers either through personal
contacts or through friends know loan officers that might influence their
evaluation capabilities. There are cases where
customers need a loan urgently but they might
not be worthy of a loan because their application does not meet the required
criteria. Their interaction with loan officers can enhance their other
qualifications and they will be able to get the loan at the expense of the
qualified applicants who have genuine need for the loan. That means the human
touch in the service industry has a special flavor that might enhance its
growth in some cases. After some experience, these officers develop their own
experiential knowledge or intuition to judge the worthiness of a loan decision.
Given the nonappearance of objectivity, such judgment is one-sided and will in
general restrain the development of the agricultural sector. Credit scores
outline farmers credit history, and are utilized by different banks to assess
farmers' creditworthiness, there is limited research work done in Nigeria, on the
utilization of credit scoring strategies in farmers creditworthiness
assessments. Hence this study aims to bridge this gap.
Farmers are constrained by credit severely,
when it is rationed (Rui&Xi., 2010). Rationing of credit causes a
significant loss in income levels and consumption expenditure of rural farmers
(Li et al., 2013). More so, inadequate research on the factors influencing
credit rationing especially along gender lines has been a problem as credit
rationing of farmers affects farmers' productivity due to the fact that the
necessary credit needed for the expansion and modernization of their faming
activities is hampered.
A
significant issue that remains a puzzle is trying to overcome this difficulty
is determining the extent and nature of this credit rationing across the
country. If credit is rationed more for female applicants than it is for male
their male counterparts then credit rationing is a form of discrimination.
The
microfinance banks were founded because of the apparent inadequacies in the
existing financing schemes for the poor people and small businesses (CBN,
2007).They were authorized to start activities in 2007 and existing community
banks and NGO microfinance institutions that met the conditions spelt out by
CBN for licensing were permitted to transmute into microfinance banks, however,
since its inception in 2007 limited research has been carried out in Southeast
Nigeria, as a whole to investigate the borrowers considerations in the loan
allocations, especially to smallholder farmers.
The
scarceness of studies regarding the equity considerations in the distributing
of loans to finance agricultural production occasioned the need for an
empirical study in southeast Nigeria. In Nigeria there is limited information
on research carried on the level of inequality in credit disbursement gap
between the male farmers and female farmers. Despite the paucity of information
on the amount of credit allocated to farmers, it's difficult to ascertain the
number of farmers that got the highest fraction of credit allocation. Not
surprisingly then, the extent of inequality in credit allocation, has become
some of the most hotly debated issues by policymakers and researchers (IMF,
2015).
In
summary, there are a number of studies that are conducted at a global level to
examine creditworthiness, credit rationing and gender issues in credit markets,
but most of the studies were made with reference to borrowers in the
non-agricultural sectors in developed countries like Italy, Spain, Greece,
Europe and USA among others. This means they do not explain the issues for
emerging market particularly in Nigeria. There is also limited information on
credit worthiness and credit rationing analysis along gender lines. In
addition, most creditworthiness analysis carried out in Nigeria did not involve
the use of credit scoring approach. Viewed against this backdrop, there is also
the need to examine the operation of microfinance banks in terms of
agricultural credit approval and rejection. This study therefore seeks to bridge
the research gap by determining the determinants of credit rationing decisions
of microfinance banks along gender lines, in south east Nigeria in the absence
of asset based collateral requirement. The assessment of creditworthiness is a
very important factor of the credit granting processes for microfinance
institutions due to the fact that they do not demand for asset-based
collaterals from borrowers.
1.3 OBJECTIVE OF THE STUDY
The
broad objective of this study was to perform a gender-based assessment of
smallholder farmers’ credit rationing and worthiness distribution by microfinance
banks in South-east Nigeria.
The
specific objectives were to:
- describe
the socioeconomic characteristics of the smallholder farmers by gender;
- ascertain
the loan approval rate of smallholder farmers, and their determinants, by
gender;
- to ascertain the level of loan
discouragement and the factors influencing male and female smallholder
farmers who are discouraged from loan applications;
- derive
and compare creditworthiness of smallholder farmers by gender and their
determinants;
- ascertain
the extent of credit rationing of smallholder farmers by microfinance
banks and the determinants by gender;
- examine
the amount of credit disbursed and the level of inequality in the
disbursement of loans to smallholder farmers by gender.
1.5
HYPOTHESES
The following
hypotheses that guided this study were tested:
- Loan
approval rate for smallholder farmers along gender lines is positively and
significantly influenced by farming experience, relationship with the
banks, marital status, years spent schooling, proximity to MFB, interest
amount, farm size, and annual income but negatively and significantly
influenced by age of the farmers and household size.
2. Discouragement from borrowing among
smallholder farmers is significantly influenced by age of farmers, farming
experience, farm size, educational level, number of banks in the location,
household size, annual farm income, proximity to bank, accessibility to account
officer, off-farm income and membership of cooperative society.
- There is no
significant difference in the creditworthiness of smallholder male and
female farmers.
- Credit rationing of
smallholder farmers by microfinance banks along gender lines is
significantly determined by age of respondents, household size, farming
experience, annual farm income, off-farm income, farm size, educational
level, proximity to bank, loan duration, banking relationship, default
history and loan in other banks.
- There is equality in
credit distribution among smallholder farmers in southeast Nigeria.
1.5 JUSTIFICATION OF THE STUDY
The
findings of this study will have the following significance:
The
outcome of the socioeconomic characteristics will be of immense benefit to the
microfinance banks and all other financial institutions that extend credit
facilities to farmers. It will also be of benefit to researchers in the area of
finance, the federal and state governments and finally the national bureau of
statistics.
Secondly,
one of the critical problems faced by most MFBs is poor loan repayment. This
problem has negatively affected agricultural producers who need to obtain
capital for their operations (Njoku and Obasi, 2001). This will be beneficial
to researchers in the area of finance, and the Federal Government. The
methodologies and findings will also be useful to policy makers and credit institutions.
The study findings also aim at improving the existing literature that was
developed by previous studies and also be of benefit to the farmers themselves.
Thirdly
the study will shed additional light on the puzzling question of why farmers do
not apply for bank financing even when they are in critical need of additional
credit. Most research on financial constraints has either excluded discouraged
borrowers from the analyses or combined them with inappropriate groups such as
denied borrowers. However, recent evidence shows the existence of significant
differences between discouraged and denied borrowers and hence calls for
separation of these two groups when carrying out analyses of credit
availability (Cole, 2010).
Furthermore,
the findings and methodologies of this study will provide insights into
significant variables that should be considered when evaluating farmers’ loan
applications and also provide a better understanding of the factors that
predict default risk of agricultural credit and therefore help improve access
to credit by farmers. In addition, this study will enable government agencies
to identify problems faced by farmers in their bid to access credit facilities
and this can help them come up with interventions that will help bridge the gap
between what is and what ought to be. Information from this study will also
help the government to recognize, facilitate and support the development and
use of suitable credit assessment methods in both agricultural and commercial
banks
Credit
rationing has been viewed as the main reason for capital market imperfections
caused by adverse selection and moral hazard problems. Thus, the study
contributes to the existing literature on discrimination and credit rationing
by showing whether women are rationed more than their male counterparts and
also in ascertaining the reasons for low or high loan application rates. A
section of this research ascertains the extent and degree of credit rationing
across the microfinance banks in south east Nigeria. This is necessary because
of the likelihood of the credit-rationing problem differing across the globe
and therefore there is a need not only to view the problem holistically but
also to consider it based on the agricultural sector so that it can be resolved
effectively. The methodologies and findings will be useful to policy makers,
researchers, the government and most importantly the credit institutions.
Inequality
and poor credit distribution exist in the Nigerian agricultural credit market
especially along gender lines. Regardless of one’s socioeconomic class, there
are systematic gender differences in material well-being, although the degree
of inequality varies across countries and over time. The findings will also be
useful to MFBs with regard to the formulation of a more equitable policy of
loans disbursement. Furthermore, the
study uses the Lorenz Curves and Gini-coefficients as techniques of determining
inequalities in loan allocation. As such, the apparent inequalities are exposed
making it possible to seek for remedies.
In
addition, Central Bank of Nigeria (CBN)
has unveiled plans to establish a National Credit Scoring System to enhance
easy screening of loan applicants (CBN,
2014; Nwonyeet. al., 2015). It is
important to have a clear idea of the role of credit scoring in the general
Agricultural credit context. Credit scores summarize consumers’ credit history,
and are used by various lenders and financial institutions to evaluate
consumers’ creditworthiness, there is no research work in done in Nigeria, on
the use of credit scoring techniques in farmers creditworthiness
assessments. Hence this study aims to
bridge this gap.
The
study will focus on microfinance credit and small holder farmers, in view of
the fact that the solution to the financial exclusion of the rural farmers lies
more in the microfinance system. The study will provide a pioneering
contribution by extending the empirical analysis of credit rationing beyond
credit constraints to include beneficiaries who are partially satisfied, fully satisfied
and totally rejected. It will also make a contribution by modeling these three
types of rationing using data that will
be obtained from the South-East States of Nigeria rather than employing data
from only one state or local government.
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