1

DETERMINANTS OF CREDIT RATIONING AMONG

SMALL SCALE FARMERS IN OGUN STATE NIGERIA

BY

TAIWO OLABODE AJIBADE

PG/M.Sc/07/42502

DEPARTMENT OF AGRICULTURAL ECONOMICS

UNIVERSITY OF NIGERIA, NSUKKA

JULY, 2011.

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TITLE PAGE

DETERMINANTS OF CREDIT RATIONING AMONG

SMALL SCALE FARMERS IN OGUN STTATE NIGERIA

A THESIS SUBMITTED TO THE DEPARTMENT OF AGRICULTURAL ECONOMICS

UNIVERSITY OF NIGERIA, NSUKKA IN PARTIAL FULFILMENT OF

THE REQUIREMENTS FOR THE AWARD OF MASTER OF

SCIENCE IN AGRICULTURAL ECONOMICS

BY

TAIWO OLABODE AJIBADE

REG NO: PG/M. Sc/07/42502

JULY, 2011.

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CERTIFICATION

Taiwo, Olabode A. a postgraduate student of Department of Agricultural

Economics with registration number PG/M.Sc/07/42502 has satisfactorily completed the

requirement for the course and research work for the award of Master of Science (M.Sc)

in Agricultural Economics. This research work has been approved for the Department of

Agricultural Economics, University of Nigeria, Nsukka.

_____________________ ______________________ DR. (MRS) A.I. ACHIKE PROF. NOBLE J. NWEZE

Supervisor Head of Department

____________________ PROF. K.P. BAYERI

Dean of Faculty

_______________________ External Examiner

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DEDICATION

This research work is dedicated

to

Holy Trinity (God the Father, God the Son and God the Holy Spirit)

and

Madam Sophia Arinola Taiwo

(of blessed memory, rest in the Lord’s bossom)

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ACKNOWLEDGEMENT

I wish to express my gratitude to all who in one way has contributed immensely

towards the successful completion of this work. My special appreciation goes to all

lecturers in the department. I am also grateful to my amiable supervisor Dr (Mrs) A.I.

Achike for her perseverance and useful suggestion to the success of this work. Mum,

thanks so much for your motherly role.

My appreciation also goes to my HOD, Prof. Noble J. Nweze (my great

counselor), Prof. E.C. Okorji (you are a father), Prof C.J. Arene (a mentor, keep the fire

burning), Dr B.C Okpukpara ( stimulator of growing generation) , Dr A.A.A Enete (very

friendly and jovial), Dr. F. U. Agbo and other lecturers that taught and refined me for the

future challenge especially in the field of Agricultural Finance and Project Analysis. My

immense greeting to non academic staffs of this department; Sister Blessing (you are

wonderful-thanks so much), Mrs Romaine, and others are remembered in a special way.

I wish to express my sincere gratitude appreciation to all my classmates and

friends: Ifejirika Chika (you are very special), Ekpa Daniel (Uncle Dee), Mallam

Mammun (caring a lot), Talabi Felix (a nice friend), Uche Okpara, Rasaq Kazeem,

Badejo Christopher (thanks), Awodeji Kolawole, Malik Balogun, Segun Ogundimu

(Engr) and others (you are all appreciated) and also my dearest cousin; Odugbemi Seun

(God will reward you).

Finally, I appreciate my parents, Chief and Mrs. Dele Taiwo (indeed, you are the

best parents and you shall eat the fruit of your labour) for their financial support, prayer

and encouragement during the course of this programme and my siblings; Dominic,

Julius, Cornelius and Ann (we shall all put our names in golden book with God’s help).

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TABLE OF CONTENT

Title Page ------------------------------------------------------------------------------------------ i

Certification --------------------------------------------------------------------------------------- ii

Dedication ----------------------------------------------------------------------------------------- iii

Acknowledgement ------------------------------------------------------------------------------- iv

Table of Contents --------------------------------------------------------------------------------- v

List of Table --------------------------------------------------------------------------------------- viii

Abstract--------------------------------------------------------------------------------------------- ix

CHAPTER ONE: INTRODUCTION

1.1 Background to the Study -------------------------------------------------------------------- 1

1.2 Problem Statement --------------------------------------------------------------------------- 3

1.3 Objectives of the Study ---------------------------------------------------------------------- 5

1.4 Research Hypotheses ------------------------------------------------------------------------ 6

1.5 Justification of the Study -------------------------------------------------------------------- 6

CHAPTER TWO: LITERATURE REVIEW

2.1 Concept of Credit----------------------------------------------------------------------------- 8

2.2 Sources of Credit ----------------------------------------------------------------------------- 10

2.2.1 Informal Sources --------------------------------------------------------------------------- 10

2.2.2Formal Sources ------------------------------------------------------------------------------ 11

2.3 Operationalization of Financial Institutions ---------------------------------------------- 13

2.4 Factors affecting Demand of Agricultural Credit ---------------------------------------- 14

2.4.1 Socio-economic Characteristics of Farmers-------------------------------------------- 14

2.4.2 Interest Rate --------------------------------------------------------------------------------- 15

2.4.3 Risk ------------------------------------------------------------------------------------------ 15

2.4.4 Location / Transaction Cost -------------------------------------------------------------- 16

2.5 Factors affecting Supply of Agricultural Credit ----------------------------------------- 16

2.5.1 Wealth/ Level of Income ------------------------------------------------------------------ 17

2.5.2 Collateral ----------------------------------------------------------------------------------- 17

2.5.3 Information --------------------------------------------------------------------------------- 18

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2.5.4 Social Status -------------------------------------------------------------------------------- 19

2.6 Credit Constraint, Access to Credit and Participation in Credit Market ------------- 19

2.7 Effects of Credit Rationings ---------------------------------------------------------------- 20

2.7.1 Labour --------------------------------------------------------------------------------------- 20

2.7.2 Adoption of Technology ------------------------------------------------------------------ 20

2.7.3 Farmers’ Output ---------------------------------------------------------------------------- 21

2.8 Identification of Credit Constraint --------------------------------------------------------- 21

2.9 Theoretical Framework ---------------------------------------------------------------------- 22

2.10 Analytical Framework --------------------------------------------------------------------- 25

2.10.1 Multinomial Model --------------------------------------------------------------------- 25

2.10.2 Probit Model ------------------------------------------------------------------------------ 24

CHAPTER THREE: METHODOLOGY

3.1 Study Area------------------------------------------------------------------------------------- 27

3.2 Sampling Procedure -------------------------------------------------------------------------- 29

3.3 Data Collection ------------------------------------------------------------------------------- 29

3.4 Data Analysis --------------------------------------------------------------------------------- 30

3.5 Model Specification -------------------------------------------------------------------------- 30

3.5.1Probit Model -------------------------------------------------------------------------------- 30

3.5.2 Gross Margin Analysis -------------------------------------------------------------------- 31

3.5.3 Likert Rating Scale ------------------------------------------------------------------------ 31

3.5.4 Differences of Means Test ---------------------------------------------------------------- 32

CHAPTER FOUR: RESULTS AND DISCUSSIONS

4.1 Socio-Economic Characteristics of Farmers --------------------------------------------- 33

4.1.1 Age ------------------------------------------------------------------------------------------- 33

4.1.2 Sex ------------------------------------------------------------------------------------------- 34

4.1.3 Level of Education ------------------------------------------------------------------------- 34

4.1.4Marital Status -------------------------------------------------------------------------------- 35

4.1.5 Farming Experience ----------------------------------------------------------------------- 36

4.2.1Sources of Credit---------------------------------------------------------------------------- 37

4.2.2 Sources of Credit in terms of Patronage ------------------------------------------------ 37

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4.2.3 Comparison of Sources of Credit and Interest Rate Charged ------------------------ 38

4.3.1 Relative Importance of Different Sources of Credit ---------------------------------- 39

4.4.1 Amount of Credit Source ----------------------------------------------------------------- 41

4.4.2 Amount of Credit Received -------------------------------------------------------------- 41

4.4.3 Differences of Mean Test ----------------------------------------------------------------- 42

4.5 Factors that Determine Farm Credit Rationing among Farmers ----------------------- 43

4.6 The influence of Credit Rationing on Farmers’ Returns. ------------------------------- 44

CHAPTER FIVE: SUMMARY, RECOMMENDATIONS AND CONCLUSION

5.1Summary --------------------------------------------------------------------------------------- 46

5.2 Recommendations ---------------------------------------------------------------------------- 47

5.3Conclusion ------------------------------------------------------------------------------------- 48

References ----------------------------------------------------------------------------------------- 49

Appendix ------------------------------------------------------------------------------------------ 56

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LIST OF TABLES

PAGE

Table 4.1: Distribution of Respondents According to Age --------------------------------- 33

Table 4.2: Distribution of Respondents According to Sex ---------------------------------- 34

Table 4.3: Distribution of Respondents According to the Level of

Education Attained ----------------------------------------------------------------- 35

Table 4.4: Distribution of Respondents According to Martial Status -------------------- 35

Table 4.5: Distribution of Respondent by Farming Experiences -------------------------- 36

Table 4.6: Distribution of Farmers by Source of Credit ------------------------------------ 37

Table 4.7: Distribution of Farmers according to Source of

Credit in term of Patronage --------------------------------------------------------- 38

Table 4.8: Comparison of sources of Credit and Interest rate paid ----------------------- 39

Table 4.9: Relative Importance of different Sources of Credit ----------------------------- 40

Table 4.10: Distribution of Respondents According to the Amount of

Credit Sourced ---------------------------------------------------------------------- 41

Table 4.11: Distribution of Farmers According to the Amount of Credit Received ---- 42

Table 4.12: Differences of Mean test Result on Credit Sourced and

Received by Farmers --------------------------------------------------------------- 42

Table 4.13: Factors that determine the Farm Credit rationing among the farmers ----- 43

Table 4.14: The influences of Credit Rationing on Farmers Returns --------------------- 45

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ABSTRACT

Determinants of credit rationing among small scale famers in Ogun State, Nigeria was

carried out. The study was aimed at; describe socio-economic characteristics of farmers;

identify sources of credit available to them; relative importance of different sources of

credit; determine amount of credit sourced and received; identify factors that constrained

farm credit and determine the influence of credit rationing on farmers’ returns. A

multistage simple random sampling technique was used in the selection of the

respondents. Primary data were generated through the use of structured questionnaire.

Descriptive statistics, probit model, gross margin analysis and student t-test were

employed in data analysis. The study showed that majority of (73%) farmers in the study

area fell below 45years which is active age for farming activities. Educational attainments

of the respondents were mostly primary and secondary education. About 77% of the

farmers were married while majority of the farmers (89%) had more than 5years farming

experience. It was also observed that majority of the respondents (78%) sourced their

credit from informal source. It was indicated that majority of the farmers patronized

money lenders, followed by relatives/friends and rotating association/cooperative societies

with the least patronage of microfinance banks and commercial banks. Interest rate

charged by money lenders, relatives/friends, rotating/cooperative societies, microfinance

bank and Ogun State Microcredit Agency were 20-25%, less than 10%, 15-20%, 5-7%

and 10% respectively. More so, probit model indicated that age, education, farm worth,

dependency ratio are significant in determine the level of credit rationing among farmers

while interest rate and location had no significant on their credit rationing. About 75%

farmers’ sourced credit of less than and equal to N40, 000 while 77% of them received

less than and equal to N30,000. The student t-test further shown that there was statistically

significant difference between credit sourced and received by respondents. The gross

margin analysis of different amount of credit received showed that the gross margin of

N10,000, N20,000, N30,000, N40,000, N50,000 and greater than N50,000 were

N25,733, N38,900 N44,185, N59,975, N67,905 and N94,900 respectively.

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CHAPTER ONE

INTRODUCTION

1.1 Background to the Study

The dynamic role of small scale farmers as engine of industrial growth and

development of Nigeria’s economy cannot be overlooked. The agricultural sector plays

an important role in Nigeria’s economy contributing about 40% of the Gross Domestic

Product (GDP) (Olomola, 2006) and employing 65% of the adult labour force (Adedipe,

Okuneye and Ayinde, 2004). Agricultural financing is one of the most important sectors

to develop rural dwellers especially farmers in developing countries. In fact, farm credit

has always been an important factor in improving agricultural productivity and

strengthening the rural economy.

Credit is one of the components of financial services considered fundamental in

all production circuits, networks-material and service products (Dicken, 2007). Credit has

a crucial role for elimination of farmers’ financial constraints to invest in farm activities,

increasing productivity and improving technologies (Kohansal, Ghorbani, and Mansoori,

2008). In addition, Khalid (2003) contended that limited availability of credit services has

undermined rural micro-enterprises activities due to lack of capital for investment and has

prevented farmers from adopting improved farming practices because of their inability to

purchase the necessary inputs required for the production. The provision of credit also

has increasingly been regarded as an important tool for raising incomes of rural

population, mainly mobilizing resources to more productive uses (Atieno, 2001).

Duca and Rosenthal (1993) argued that a farm household is credit constrained

only when it would like to borrow more than lenders allow or if preferred demand for

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credit exceeds the amount lenders are willing to supply. On the other hand, credit may be

readily available to farmers but due to some factors such as risk and transaction cost, they

are said to be demand side constrained (Boucher and Guirkinger, 2005). Credit being

fundamental to the operation of all production, circuits and networks can have direct and

indirect effects such as procurement of farm inputs and adoption of technologies

(Hussein, 2007). Rweyemamu, Kimaro and Urassa, (2003) pointed that without credit,

million of cash-starved small holders who dominate the rural landscape are unable to

adopt most productivity technologies and low return, subsistence-oriented production

practices therefore continue to underpin most rural livelihood strategies. Credit

constraints have a number of serious consequences for production and consumption in the

short run and for asset accumulation, poverty reduction, and the evolution of well-being

in the long run. Since the provision of credit is an important tool; one question that arises

is the extent to which credit can be offered to the rural farmers to facilitate their taking

advantage of the developing entrepreneurial activities. Therefore, an economy cannot

achieve the highest possible growth rate unless resources (capital, labour and so forth) are

used in the activities where they are most productive (Wohl, 2004).

Agricultural lending involves giving credit (in cash and kind) to small scale

farmers for the purpose of farming (Oladeebo, 2008, Abbot and Makeham, 1979). In

Nigeria, agricultural credit has long been identified as major input in the development of

the agricultural sector. Credit or loanable fund is considered to be more than just another

resource like labour, land, equipment and raw materials, hence credit is viewed from its

ability to energize or motivate other factors of production. It can make the latent potential

of underused capacity function. In such situation, credit acts as a catalyst that activates

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the engine of growth, enables it to mobilize its inherent potentials and to advance in the

planned or expected direction (Oladeebo, 2008).

The role of any government is to strive to become self-reliant in food production.

In pursuance of this, effective credit schemes are to be put in place to increase the access

of small scale farmers to credit facilities so that food and cash production would be

increased.

1.2 Problem Statement

Historically, agriculture has been the backbone of many economies and still holds

a special place, both economically as well as culturally, relative to other sectors of the

economy (Chloupkova and Bjornskov, 2001). Available evidence indicates that

agriculture in Nigeria and many developing countries is challenged by credit. Ajakaye

(1985) pointed out that farmers in discharging their responsibilities, they face a lot of

problems that include inadequate or restricted access to capital and limited access to

credit facilities. Moreso, Rahji and Fakayode (2009) believed that agricultural credit

access has particular salience in the context of agricultural and rural development in

Nigeria as it has serious implication on farmers operation. Credit constraint has plagued

poor farmers and rural dwellers for many years and was thought to be a critical part of a

package of inputs needed to boost agricultural production (Adams, 1995). Majority of

smallholder farmers lack access to formal credit and this has continued to be a constraint

limiting smallholders’ ability to adopt agricultural technologies and increase productivity

(Mohamed and Temu, 2008).

One of the reasons for the decline in the contribution of agriculture to the

economy is lack of a stable national credit policy and paucity of credit institutions which

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assist farmers (Rahji, 2000). In spite of the importance of loan in agricultural production,

its acquisition and repayment are fraught with a number of problems especially in the

small holder farm (Awoke, 2004). Institutional problems such as the lending conditions

which limit access of investors to credit facilities have not been adequately addressed

(Vargese, 2005). This is supported as part of the determinants of credit constraint by

Hussain, Sial, Akram and Hussain (2008) who noted that a large majority of needy,

willing and able to borrow farmers generally cannot avail agricultural credit because of

procedural and bureaucratic lending process that favoured and thus skewed toward

influential farmers in the rural sector. The formal financial sources have sufficient

loanable funds at their disposal but tedious bureaucratic procedures affect credit

accessibility. On the other hand, farmers resort to the indigenous system of resource

mobilization, which are in most cases insufficient in meeting the farmers’ needs (Nweze,

1995).

Educational level, farming experience including experience in borrowing and

dependency ratio also determined famers’ credit constraints (Ali and Flinn, 1989; Bravo-

Ureta and Pinherio, 1993; Parikh et al, 1995; Coelli and Battese,1996). Besley (1995)

noted that titled land may be offered as collateral and thus may loosen a binding supply-

side constraint and increase a households’ investment demand.

A large number of socio-economic factors all play a role influencing farmers’

ability to secure optimum credit market. Such factors include risk of loan default, age of

the farmers, location, and high interest rate charged by financial providers e.t.c.

According to Rweyamamu, Kimaro and Urassa (2003), high and increasing transaction

costs in credit procurement by farmers and disbursement hindered effectiveness of the

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credit demand. In addition, some borrowers cannot obtain the amount of credit they

desire at the prevailing interest rate, nor can they secure more credit by offering to pay a

high interest rate (Rahji and Fakayode, 2009). In such event, liquidity would become a

binding constraint on farmers’ operation. Therefore, the wedge between what credit

providers are willing and able to lend causes credit rationing. It has become difficult to

separate demand for credit from supply of credit. However, the effect of these factors

varies in time and space, depending on specific situations in the study countries or

localities, making it imperative to analyze those determinants and their effects in the

study area, Ogun state, where little or no record of this study has been found.

1.3 Objectives of the Study

The broad objective of this study is to examine the determinants of credit

rationing among small scale farmers in Ogun state. The specific objectives are to:

i. describe the socio-economic characteristics of farmers in the study area.

ii. identify different sources of credit available to farmers.

iii. examine the relative importance of different sources of credit available to the

farmers.

iv. determine the amount of credit sourced and received by the farmers.

v. identify and analyze the factors that constitute rationing of credit among the

farmers.

vi. determine the influence of credit rationing on farmers return.

vii. make recommendations based on the findings.

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1.4 Research Hypotheses

The following null hypotheses were tested:

1. Ho: Farmers are not significantly different in their sources of credit.

2. Ho: Farmers’ socio-economics characteristics have no significant influence on their

credit rationing.

3. Ho: There is no significant difference between amount of credit sourced and received

by farmers.

1.5 Justification of the Study

Governments in most developing countries use rural financial access as an

important instrument to support poverty reduction strategies and to promote economic

development (Changakham, 2008). In particular, agricultural credit has played a crucial

role in the process of agricultural development in the quest to be self reliant in food

production. In pursuance of this, credit policies and institutional designs have to be

assessed to improve small farmers’ credit facilities especially since lack of capital and the

absence of attractive investment opportunities are considered to be important reasons

behind inadequate economic development of many developing countries.

The study will help in terms of credit policy to both financial providers and

farmers to evolve structural and operational adjustment mechanisms for a better way of

channelling credit in terms of volume and time with better repayment rates. It would be

economically unattractive for farmers to receive loans that cannot meet their effective

demand for credit.

This study will provide information for the government as a whole to increase her

economic base since dependence on oil sector has brought the economy into shambles

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recently. Even though the agricultural sector contributes 40% of the Gross Domestic

Product (GDP), its overall performance has not generally satisfied expectation because,

Agricultural exports which accounted for nearly 62% total export in the early seventies,

fell to 10% during 1994-1999 and now it has dropped to 3.3% in 2003 (CBN,2003). In

2007, the value of total exports was $65.5 billion in which agricultural products (cocoa,

rubber, oil, nuts) accounted for 2.2% while the total value of imports was $29.5 billion in

which agricultural products accounted for 23.7% ( United States Department of State,

2009). More so, World Bank Report (2008) pointed out that Africa has great potential to

provide more food not only for itself but also for the world. Therefore, this study will

help farmers boost their production which will finally bring an increase in economic base.

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CHAPTER TWO

Literature Review

Relevant literatures are reviewed under the following sub-headings:

� Concept of Credit

� Sources of Credit

� Operationalization of Financial Institutions

� Factors affecting Demand of Agricultural Credit

� Factors affecting Supply of Agricultural Credit

� Credit constraint, Access to credit and Participation in credit market

� Effects of Credit Rationing

� Identification of Credit Rationing

� Theoretical Framework

� Analytical Framework

2.1 Concept of Credit

The word credit is derived from Latin word credo which means “to believe or to

trust” (Smith, 2007) which is explained as the sale of goods and services and money

claims in the present in return for a promise to pay in the future. The promise usually

based on the confidence and on the belief that the debtor whether a person, a business

firm or a government unit will be able and willing to pay on demand or at some future

time (Blurtit, 2008; Gregg, 2008).

According to Adegeye and Ditto (1985), agricultural credit as the process of

obtaining control over the use of money, goods and services in the present in exchange

for a promise to repay at a future date. Farm credit has always been an important factor in

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improving agricultural productivity and strengthening the rural economy in every country

(Kamakar, 1999). In fact, facilitation of access to credit has an important role for

elimination of farmers’ financial constraints to invest in farm activities, increasing

productivity and improving technologies (Kohansal et al, 2008). The provision of credit

has increasingly been regarded as an important tool for raising the income of rural

populations, mainly by mobilizing resources to more productive uses.

In the recent past, there has been an increased tendency to fund credit

programmes in the developing countries aimed at small scale enterprises. Despite

emphasis on increasing the availability of credit to small and microenterprises (SMEs),

access to credit by such enterprises remains one of the major constraints they faced

(Atieno, 2001). Improving the availability of credit facilities to this sector is one of the

incentives that have been proposed for stimulating its growth and realization of its

potential contribution to the economy.

Most policy and research interests regarding rural credit markets revolve around

the perception that poor rural households in developing countries lack adequate access to

credit, which is believed to have significant negative consequences on various aggregate

and household-level outcomes, including technology adoption, agricultural productivity,

food security, nutrition, health, and overall household welfare (Aliou et al 2008). The

majority of small scale farmers are not regarded as credit-worthy by the formal sector

financial institutions, and are forced to borrow from the money-lenders in the informal

credit market and this has continued to be a constraint limiting smallholders’ ability to

adopt agricultural technologies, on farm-labour supply and increase productivity.

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Important lessons from past rural credit programs point to the need to redesign or

improve delivery mechanism thus, open access of small-scale farmers to credit. In

financing the purchasing of inputs, the farm household must either dip into savings or

obtain credit. Hence, access to credit can significantly increase the ability of poor

household with no or little saving to acquire needed agricultural inputs encouraging

labour saving technologies and raising labour productivity, a crucial for development

especially in many African countries (Delgado, 1995; Zeller et al 1997).

2.2 Sources of Credit

Access to financial services by smallholders is normally seen as one of the

constraints limiting their benefits from credit facilities. Financial services are in form of

borrowing, lending and other services. Based on the extent of control by the government

have been broadly classified into formal and informal sources. In considering the

dichotomy between formal and informal segments of the credit, it is important to note

that rural credit markets in Africa are mainly fragmented with the various segments

serving borrowers with different characteristics (Nissanke and Aryetey, 1995). The

crucial role of credit in agricultural production and development can also be appraised

from the perspective of quantity of problems emanating from lack of it. In modern

farming business in Nigeria, provision of agricultural credit has become an important

factor in order to increase productivity (Adebayo and Adeola, 2008).

2.2.1 Informal Sources

Aryeeteey et al (1997) defined informal finance as referring to all transactions,

loans and deposits occurring outside the regulation of a central monetary authority. The

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informal financial sector plays great roles in the allocation and mobilization of credit and

services to the rural sector of Nigerian economy at a comparatively lower costs and risk

than formal financial institutions (Soyibo, 1996). The informal sources include relatives,

friends, credit and saving associations, moneylenders, e.t.c. The informal sector has a

dense and effective information network at the grassroots level for close supervision and

monitoring of borrower activity particularly their cash flow- whether they are members of

an informal association or not (Germidis et al, 1991). This contributes to the efficient

mobilization of savings and ensures high loan repayment rates. The informal financial

services have certain advantages such as: good knowledge of the local economy; good

outreach to clients; transaction costs; very little or no bureaucracy and paperwork e.t.c

(IFAD, 2000).

2.2.2 Formal Sources

Formal financial sources are those who operate within the legal, fiscal, regulatory

and prudential framework of the monetary and financial authorities (Onah, 1994).

Therefore, formal sources are those established by law and which can be influenced by

government policies. Examples of formal financial institutions include commercial

banks, microfinance banks, NGOs e.t.c. The structure of the formal credit sector severely

constrains its ability to respond effectively to the requirements of rural development,

particularly in meeting the credit needs of small farmers. Perhaps the important constraint

is the lack of information about borrower characteristics and actions, which critically

limits the ability of banks to guarantee repayment. Consequently, loans (when

forthcoming) require substantial collateral. Small farmers are simply not in a position to

provide collateral, at least in a form acceptable to a formal financial intermediary. The

12

formal institution sources of farm credit in Nigeria include: Agricultural Credit Guarantee

Scheme (ACGS), Nigeria Agricultural and Cooperative Bank (NACB), the Commercial

and Merchant Banks, the Cooperative Institutions and the Agricultural Development

Programmes (ADP) which give credit more in kind rather in cash ( Oni, Oladele and

Oyewole, 2005). Other institutional arrangements were National Directorate of

Employment (NDE), Nigerian Agricultural Insurance Cooperation (NAIC), defunct

Peoples Bank of Nigeria (PBN), Community Banks (CBs) and Family Economic

Advancement Programme (FEAP). In year 2000, the Federal government merged NACB,

PBN, and FEAP to form the Nigerian Agricultural Cooperative and Rural Development

Bank (NACRDB) to enhance provision of finance to agricultural sector. The bank is

currently striving to meet the challenges of financing agricultural enterprises via the use

of cooperatives. Government in both developed and developing countries attempt to

overcome perceived inadequate supply of credit in form of subsidized credit programs,

credit quotas, and targeted loan policies at below market rates of interest setting up to

guarantee fund and stimulating institutional innovations in the financial system (Floro

and Ray, 1997). In Nigeria, this available at 16-18% interest rate to individuals and 15-

16% percent to cooperatives or cooperate farms when the ruling market interest rates for

the other sectors of the economy range between 30 and 40% ( Rahji and Fakayode,

2009). It is hardly surprising that the benefits of such programs and policies are

concentrated on a small number of borrowers, mainly large farmer who can provide

collateral (or at least inspire formal sources confidence through their reputation.

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2.3 Operationalization of Financial Institutions

The formal and informal financial systems co-exist and operate side by side with

one another (Kessler, Marigue and Ullmo, 1985). The reality of operations of the two

forms of market, however, is more complex and each segment of the financial market

provides credit services that differ from each other with respect to important factors. The

demand for agricultural credit is increasing parallel to meeting the requirements of

increasing agricultural productivity and improving the livelihood of farmers. In context of

financial sector, dualism exists where both the formal and informal financial providers

exist side-by-side (Mpuga, 2004) with the latter controlling a big proportion of the market

in rural area. Formal financial institutions are sources of more loan funds for informal

institutions. Most of the farmers’ sourced loans from informal institutions since most

services of informal finance are client oriented, thus reducing the transaction costs for

customers and making their services attractive. The regulatory framework of financial

institution dictates demand and supply of agricultural credit.

Most formal lenders constituted by banks and access to them is restricted to a

small proportion of the population who can meet their stringent requirement, which

include minimum balances for account opening, onerous collateral requirements for

loans, and long and costly bureaucratic processes. The development and commercial

banks view the small scale and micro-entrepreneurs as risk borrowers and extending

loans to them is to cut down their profitability in the transactions and to incur

irrecoverable losses to the banks (Levisky,1993). This can be seen as an indication of

general inadequacy of the formal credit institutions in meeting the existing credit

demand. The criterion of creditworthiness, delays in loan processing and disbursement,

14

has limited the amount of credit available to smallholders and efficiency with which the

available funds are used (Atieno, 1994).

2.4 Factors affecting Demand of Agricultural Credit

The demand for credit for productive investments usually comes from those

farmers who are less risk fear and enable them overcome liquidity constraints, making it

possible to undertake investment that can boost production and income. The factors

affecting the demand for financial services can be categorized into two: the

individual/household characteristics and attributes of financial institutions (Mpuga,

2004). Among the individual/household characteristics, we have the level of income, sex,

age, education and whether one has obtained credit before or not. Among the attributes of

the financial institution that may affect an individual’s/household’s decision to demand

financial services, from that source are the interest rate, and distance from the provider

(Mpuga, 2004). In addition, farm expenditure, the amount borrowed from alternative

sources; savings may also be determinants of credit demand.

2.4.1 Socio-economic Characteristics of Farmers

Farmers’ characteristics are expected to have important implications for demand

of credit which include age, gender education and marital status. Following the life-cycle

hypothesis, the young and energetic individuals, with an ambition to earn higher incomes,

are expected to be more active in term of savings/dissaving in order to accumulate

wealth. Mpuga (2004) pointed out that the young may tend to invest in off-farm

activities, which require large capital outlays, while old and retired, will tend to invest in

farm activities. In addition, Zeller (1997) has found age to positively affect the decision

15

to demand for credit. Because educated are likely to have higher incomes and savings and

more likely to have assets that act as collateral and more likely to be engaged in business

and other economic activities. Farmers who are married are more likely to be stable and

financial institutions are likely to view them as more reliable (Mpuga,2004).

2.4.2 Interest Rate

The demand for credit (loanable funds and kinds) is driven by the rate of interest.

Interest is the amount paid by a borrower to a lender in exchange for the use of the

lender’s money for a certain period of time. Farmers borrowing decisions are influenced

by the interest rate charged for the money they borrow to finance their expenditure.

Loanable funds is downward sloping indicating that demand for loan increases as the rate

of interest falls, and decreases with rising rate of interest. Interest rates have significant

impact on the agricultural industry by affecting the cost of borrowing money, affecting

investment decisions and affecting values of farmland (Alberta Agriculture and Rural

Development, 2005). More so, when credit is rationed, some borrowers cannot obtain the

amount of credit they desire at the prevailing interest rate, nor can they secure more credit

by offering to pay a higher interest rate. In such circumstances, liquidity can become a

binding constraint on many farmers’ operation.

2.4.3 Risk

Rural economies are marked by high risk and limited mitigation techniques. Rural

farmers who contract a financial obligation are subject to systematic variability that may

result in involuntary default. According to Wenner and Proenza (1999), the typical

sources of risk include:

16

(i) Yield: supply variability that can stem from unfavourable production conditions

(weather, pests, disease, and equipment failure) that reduces yields below expected

and thus income.

(ii) Price: variability in input prices that increases costs of production or forces a

change in choice of production technique resulting in less profit and / or less yield

and variability in supply arising from variability in price expectations of output.

2.4.4 Location / Transaction Cost

Most formal financial institutions are mainly urban based, thereby adding to

burden of transport cost of the predominantly rural population wishes to use bank

facilities. The cost of transportation in the formal financial institutions is often

prohibitively high (Howard et al 1999). As a result of constrained access to formal credit,

the poor rely almost exclusively on the informal financial sector which is insufficient in

meeting their needs. More so, transaction cost also constrained farmers. This is all non-

interest financial costs related to approving, processing, disbursing, and complying with

financial contract play a significant role in financial intermediaries. Large scale farmers

who demand huge loan and maintain large saving balances are less sensitive to

transaction cost compared to low income farmers.

2.5 Factors affecting Supply of Agricultural Credit

In principle, it is possible for credit supply to be constrained by the available

demand for credit. This may particularly occur in a situation where the cost of credit,

whether in the form of interest rates or other aspects, is perceived to be onerous. The

major determinants of credit supply that could be identified may include interest rate

17

charged, the level of saving of respondents, the amount of loan demanded and the

proportion of previous loan factors repaid. Other determinants include, net wealth,

income, socio-economic factors etc. Understanding the factors that determine

creditworthiness of the borrowers help the lenders to reduce risk of default and improve

profitability and financial sustainability (Okurut et al 2004).

2.5.1 Wealth/ Level of Income

Net wealth is a good measurement of the borrowers’ repayment ability. Farmers’

wealth may have impact on the credit supply. The higher the net wealth is, the higher the

probability of obtaining a loan. (Chen Chen and Chivakul, 2008). If household wealth is

observable to lenders, it may be taken as a positive signal of capacity to repay because

high current wealth is likely to be positively correlated with the household’s ability and

quality of their investment projects (Boucher et al, 2005). At the household level, the

level of income is an important factor that would determine the demand for financial

services. At low level of income, the household has limited resources to save and less

demand for credit than at higher level income.

2.5.2 Collateral

A critical barrier to credit access is the frequent inability of small an informal

borrowers to securitize loans with collateral (Field and Torero, 2004). There is need to

examine the relationship between the probability of being credit constrained and the

possession of a land-title-collateral. One frequently cited contributing factor is the fact

that in much of the developing world a large percentage of both rural and urban property

is untitled (Holden, 1997). The land titled may be offered as collateral and thus may

18

loosen a binding supply-side constraint. Although land is an advantageous form of

collateral due to the fact that it cannot be removed and it is noted that many borrowers

face barriers securing transactions with land simply because ownerships are not formally

documented. It is believed that with higher income, farmers are able to save more and to

acquire more assets, which can be used as collateral to acquire more loans. Reliance on

collateral by banks often, however, excludes many otherwise creditworthy small scale

borrowers in many African countries where land titles are not well documented or readily

transferable (Steel, Aryeetey and Nissanke, 1997).

2.5.3 Information

Information is a major factor in credit market. A crucial impediment to the

efficient functioning of the financial system is asymmetric information (Mishkin, 1991).

A lender’s willingness to lend may hinge on the information about the borrower. It is

obvious that the analysis of information problem has general relevance for rural financial

market in developing countries. The absence of perfect information may explain why

lenders choose not to serve some individuals (Timothy, 1994). The implication is that

lenders may reduce the amount that they decide to lend; resulting in too little investment

in the economy. Information imperfections are important in explaining the segmentation

of credit markets. Information flow is typically efficient over relatively close distances

and within social groups, as found in the informal setting. This is one of the merits the

informal sector has over the formal financial sector.

19

2.5.4 Social Status

The reputation of the potential borrower is another important yardstick that

influences the information lender’s credit rationing behaviour (Siamwalla et al., 1990).

For instance, loan in the information sector are mainly character loans (i.e not backed by

any collateral security). For this reason, information lenders invest both financial

resources and time to gather information about potential borrowers from people knows to

them.

2.6 Credit Constraint, Access to Credit and Participation in Credit Market

There is a distinction between access to credit and being credit constrained

including participation in the micro-credit market. A household/farmer has access to

microcredit if it is able to borrow from the source (Diagne and Zeller, 2001). Thus a

household can have access but choose not to borrow i.e. does not participate in the credit

market while household is credit constrained if its demand for credit exceeds the supply

of credit (Simtowe and Phiri, 2007). Generally, credit accessibility is important for

improvement of quality and quantity of farm products so that it can increase farmer’s

income. Therefore, with limited access to credit, the budget balance becomes a constraint,

where expenditures have to remain less or equal to the sum of revenues during the period,

accumulated savings and credit availability. Hence, credit constraint limits the optimum

production or consumption choices.

Gilligan et al; (2005) classified household as credit constrained if they stated the

willingness to use more credit from any loan sources or if its members said that they

could not obtain credit. Simtowe and Phiri (2007), pointed out that the demand for credit

may exceed supply for credit for a number of reasons. First, the demand could exceed

20

supply due to quantity rationing. Quantity rationing occurs when a lender sets credit limit

lower than the household credit demand. Second, high transaction costs may also restrict

the supply of credit to the household. Thirdly, due to risk rationing also referred to

discouraged borrowers’ scenarios. Thus-holding constant productivity and risk

preferences-small farmers will be more likely to voluntarily withdraw from the credit

market due to transaction cost.

2.7 Effects of Credit Rationings

2.7.1 Labour

It encompasses the productive capacity of human physical and/or mental efforts

measured in terms of ability to do work or produce goods and services (Ahmed, 2000). In

a relative term, it describes the effort of human being used in the production process

(Adegeye and Dittoh, 1985). Labour is classified into family labour and non-family

(hired and exchange) labour. In rural economy, family labour is insufficient to increase

farm production. Lack of labour constraints the extent of work done in smallholder

agriculture because labour use is correlated with total output. Therefore, access to credit

is an important pillar to hire non-family labour to complement family labour in helping

small farmers increases their production since all farm household are not equally

endowed with family labour may wish to satisfy its labour demand externally, and to pay

for this, will demand credit ( Hussein,2007). Therefore, if the farm household is

constrained in the credit market, it may also be constrained in the labour market.

2.7.2 Adoption of Technology

Agricultural innovation brings about the change that is needed in the lives of the

farmers. Without credit, the role of adoption will be very minimal of agricultural credit.

21

Majority of smallholder farmers lack access to formal credit and this has continued to be

a constraint limiting smallholders’ ability to adopt agricultural technologies and increase

productivity (Mohammed, 2008). In addition, Lubwama (1997) also revealed that

adaptability of suitable implements for on-farm technology is affected by the availability

of finances.

2.7.3 Farmers’ Output

According to Hussein (2007), credit supply that is responsive to effective credit

demand of farm households would result in higher outputs, which would also increase

creditworthiness of the farmers. On the contrary, it would be economically unattractive

for farmers to receive a loan that cannot met their effective credit demand, as they will

remain credit constrained and cannot increase their efficiency. Therefore to boost

agricultural production and productivity, farmers have to be using improved agricultural

technologies. It is relatively expensive and small scale farmers cannot afford self finance.

It is argued that enhanced provision of rural credit would accelerate agricultural

production (Briquette, 1999). Consequently, improving credit user’s income and savings

and enhance investment and reinforce high incomes.

2.8 Identification of Credit Constraint

There are two (2) approaches for identifying credit constraints namely the indirect

methods and the direct methods. According to Gillgan et al (2005), indirect methods are

based on tests of theoretical model involving credit constraints while direct methods are

based on responses to qualitative questions about credit constraint status collected in

surveys. The indirect methods involve comparison of parameter estimates for specific

22

outcome across constrained and unconstrained groups (Simtowe and Phiri, 2007). The

direct method involves a direct elicitation in which respondents are asked questions on

their perception of constraints. Using survey questions that are usually qualitative in

nature respondents are to be asked about the current credit demand and supply in order to

identify households facing credit constraint. Methods for identifying credit constrained

households based on direct elicitation of credit status from survey questions about

restrictions on credit have several attractive features (Jappelli, 1990; Federa et al, 1990;

Pischike and Souleles, 1998). The necessary information can be gathered directly in

surveys, providing a simple, unambiguous method of identifying credit constraints Since

indirect method has generated a subject of discussion and it is inconclusive (Diagne et al

2008), direct elicitation would be used to capture most sources of credit constraint,

including self imposed rationing due to high default risk, which provides more

comprehensive measure of constraints than other method.

2.9 Theoretical Framework

Credit constraint is one of the problems faced by farmers in general but more

seriously by those in developing countries. A number of conceptual difficulties have been

identified in estimating credit market, especially in fragmented markets with imperfect

information. The theoretical credit rationing literature starting from as early as the 1950s

generally seeks to develop an economic rationale for the allocation of credit by some

means other than the price (interest rate).The most influential early theoretical studies are

Jafee and Russell (1976) and Stiglitz and Weiss (1981). Jafee and Russel (1976) were the

first to develop a model of rationing in an imperfect loan markets where borrowers have

more information about the likelihood of default than lenders. Their model suggests that

23

in competitive markets; single-contract equilibrium (i.e equilibrium with a single set of

term) will tend to occur at a point of rationing. Stiglitz and Weiss (1981) built on this

work to show why such equilibrium might occur. The interest rate a lender charges may

itself affect the riskiness of the pool of loans by either sorting potential borrowers (the

adverse selection effect) or affecting the action of borrowers (the incentives effect).

Through either of these effects, an interest rate that is set too high will inevitably cause

the riskiness of the applicant pool to increase. Thus, there is an optimal interest rate

which is often below the market-lending rate that maximizes the returns for lenders. Also,

because the interest rate influences the nature of the transactions. It can act as an indirect

screening device sorting out “good-risk” from “bad-risk” borrowers: those who are

willing to pay high interest rate may, on average be worse risks because they perceive

their probability of the model follows a contract-theoretic view of loan transaction.

Essentially, the lender chooses the credit limit and the amount he wants to be repaid; the

borrower then chooses the amount to be borrowed within this range and once the credit is

disbursed, whether and when to pay back. More precisely, the lender chooses the pair

(bmax, R1) where bmax is the amount he is willing to lend, and R1 is a repayment

function (0, bmax) specifying how much, when, and under what conditions he wants to

be repaid for any given loan size b (0, bmax). In other words, the lender offers the

contract (bmax, R1) to the borrower who accepts or rejects it by his choice. The contract

is accepted if b*is strictly possible, and rejected if b*= 0. The lender’s optimal choice of

the credit limit bmax, which is interpreted as the supply for credit, is a function of the

maximum he is able to lend, bmax. It is also a function of the lender’s subjective

assessment of the likelihood of default and of other borrower’s characteristics. Therefore,

24

borrowing constraint is stochastic, and depends on the prevailing state of nature.

However, lenders usually provide enough information about their loan policies

(eligibility criteria, types of project funded, collateral and down payment requirements,

e.t.c) to enable potential borrowers to have reasonably accurate expectations about their

bmax from sources of credit.

In most models where there is the possibility of loan default due to imperfect

contract enforcement, and upward sloping supply curve, it is assumed that lenders offer

borrowers a choice of points on the supply curve, to which they are restricted (Atieno,

2001). The credit demand function can only be identified from the borrower’s

participation decision; namely, the decision to borrow or not and from which sector to

borrow. According to Zeller (1994), this approach assumes that an individual decision is

only affected by internal factors. However, external factors also play an important role in

credit market. Bigsten et al (2000) estimate credit market participation and constraints

faced by firms by modelling the explicit demand for funds by firms and assessing the

decision rules by financial institutions to grant loans. Therefore the concepts of demand

and supply have significant bearing on the amount of credit a farmer might receive. It

becomes difficult to disentangle the demand shifters from the supply shifters (Khandker

and Farugee, 2001). The lender’s optimal choice of the credit limit which is interpreted as

the supply for credit is a function of the maximum he is able to lend. It is the lender’s

decision to further restrict the supply of credit below the amount he is actually able to

lend that causes credit rationing. From the analysis of Stiglitz and Weiss (1981), there is

a wedge between what a lender is willing and able to lend which results from exclusive

choice of the lender.

25

2.10 Analytical Framework

The framework of analysis adopted for any research depends on the available

information and the purpose of research. Eboh (1998) declared that more detailed and

higher level analysis will be required for case studies and sample surveys especially those

that deal with quantitative.

Descriptive statistics such as calculation of rates, means, frequency distribution

and percentages which may be adequate for some exploratory studies will be used to

describe socio-economic characteristics of the farmers, sources of their agricultural

credit, amount of credit applied for and received e.t.c . Apart from descriptive statistics,

the analytical tools to be used are organized under the following subheadings:

� Probit model

� Likert rating scale

� Gross margin analysis

� Difference of Mean Test

2.10.1 Probit Model

A probit model is a popular specification of a generalized linear model. Probit

models were introduced by Chester Bliss in 1935. Because the response is a series of

binomial results, the likelihood is often assumed to follow the binomial distribution. Let

Y be a binary outcome variable, and let X be a vector of regressor. The probit model

assumes that

Where Φ is the cumulative distribution function of the standard normal

distribution. The parameters β are typically to be estimated by maximum likelihood.

26

While easily motivated without it, the probit model can be generated by a simple

latent variable model. Suppose that

Y* = X1 β + ε,

Where ε/x~ N (0, 1), and suppose that Y is an indicator for whether the latent

variable

Where ε/x~ N (0,1), and suppose that Y is an indicator for whether the latent variable

Then it is easy to show that

For this study, dependent variables are binary (0,1), it will be modelled against

explanatory variables such socio-economic characteristics, interest rate, transaction cost,

collateral e.t.c.

27

CHAPTER THREE

METHODOLOGY

3.1 Study Area

Ogun-State is the study area. Ogun-State is located in the South West geopolitical

zone of Nigeria, and is one of the thirty-six states that constitute the Nigerian Federation.

The state came into existence 3rd February, 1976. Ogun-State has an estimated land area

of about 16,409.26 square kilometers and a population of 3,728,098 (NPC, 2006). It is

located between longitudes 3.0oE to 5.0oE and latitudes 6.2oN to 7.80N. It is bounded in

the West by the Benin Republic, in the South by Lagos-State and the Atlantic Ocean, in

the East by Ondo State and in the North by Oyo-State. The climate of Ogun state follows

a tropical pattern with raining season starting around March and ending in November

then followed by dry season. The mean annual rainfall varies from 128mm in the

southern parts of the state to 105mm in the northern areas. The average monthly

temperature in state ranges from 23oC and 32oC. The northern part of the state is mainly

of derived savannah vegetation, while the central and southern parts fall in the rain forest

belt and mangrove swamp respectively (Ogun State Agricultural Development

Programme Bulletin, 2000). Small scale farmers sought credit from different source

which include moneylenders, friends, rotating association, microfinance banks and more

importantly from Ogun State Agricultural Multipurpose Credit Scheme ( OSAMCA).

Ogun State is made up of twenty (20) Local Government Areas and these are

Obafemi Owode, Yewa North, Yewa South, Ewekoro, Ifo, Imeko-Afon, Ado-Odo Ota,

Remo-North, Sagamu, Ikenne, Odogbolu, Ogun-Waterside, Ijebu-North, Ijebu-East,

28

Ijebu-North East, Odeda, Abeokuta South, Abeokuta North, Ipokia and Ijebu-Ode local

government areas.

Ogun State is divided into four (4) agricultural zones namely:

Abeokuta zone comprises Abeokuta South, Abeokuta North, Ewekoro Ifo Odeda

and Ado-odo/Ota.

Ilaro zone comprises Yewa North, Yewa South, Ipokia and Imeko-Afon

Ikenne zone comprises Sagamu, Ikenne, Remo-North and Obafemi-Owode

Ijebu-Ode zone comprises Ijebu-North, Ijebu East, Ijebu North-East, Ogun

Waterside, Odogbolu and Ijebu-Ode.

28

O O O O O O

V V V V V V

χ χ χ χ χ χ χ χ

Abeokuta Zone

Ilaro Zone

Ikenne Zone

Ijebuode Zone

Source: Ogun State Agricultural Development Programme Bulletin, 2000

Fig 1 Map of Ogun State showing the four (4) Extension Operational Zones Source: Ogun State Agricultural Development Programme Bulletin, 2000

29 3.2 Sampling Procedure

A multistage simple random sampling technique adopted for the study.

Firstly, two (2) Local Government Areas out were randomly selected of each of the four

(4) agricultural zones, making it 8 LGAs of the existing 20 LGAs.

Secondly, two (2) communities from each of the sampled Local Government Areas were

randomly selected, a total of sixteen (16) communities.

Lastly, ten (10) farmers affected by credit rationing from each of the sampled sixteen (16)

communities were randomly selected, to give a total of one hundred and sixty (160) respondents

for the study.

3.3 Data Collection

Data collected from both primary and secondary sources. Primary data collection was

done with structured questionnaire which was administered to the selected farmers with the help

of trained enumerators who are conversant with the localities. Data collected from farmers

include socio-economic characteristics, different sources of credit such as friends, relative,

saving association, commercial banks, including factors that constrain credit such as collateral,

farming experience, household size e.t.c. Data were also collected from financial providers and

incorporated into identified factors perceived by farmers to have broad knowledge of the factors

that behind the rationing of credit.

Secondary data were obtained from relevant published materials such as journals,

textbooks, magazines, research bulletin, project works and other relevant documents from State

Ministry of Agriculture and Ogun – State Agricultural Development Programme (OGADEP).

30 3.4 Data Analysis

Both descriptive and inferential statistics were used where appropriate for the analysis of

the data collected. Objective (i), (ii) and (iv) were realized using descriptive statistics like

average and percentages. Objective (iii) was realized using a mean weighted scale . A probit

model was employed to realize part of objective (v). Objective (vi) was realized using gross

margin analysis.

3.5 Model Specification

3.5.1 Probit Model

A probit model was employed to analyze the factors constraining credit among the

farmers. The probability is

Pr (C1=1) = iXiB

iXiB

e

e

+1;

Where C1= the probability of credit rationing among the farmers

The model is;

Pr (y=1)= enn XXXXX ++++++ ββββββ 443322110

=0βis the intercept

tscoefficienn =− ββ1 of independent variables

X1-Xn = factors that determine rationing of credit.

X1 = farm worth (N)

X2 = distance from credit source-location (kilometres)

X3 = interest rate (%)

X4 = farm size (hectare)

X5 = age of farmers (years)

31

X6 = sex (1 if male; 0, otherwise)

X7 = farming experience (years)

X8= marital status (1 if married, 0, otherwise)

X9=education attainment (years)

e = stochastic error term

3.5.2 Gross Margin Analysis

It was applied to determine the level of return from different size of credit received.

GR= TR - TVC

Where GR = Gross margin

TR = Total revenue

TVC = Total variable cost

3.5.3 Likert Rating Scale

4-point Likert rating scale was adopted. The 4-point scale were graded as Strongly agree

=4, Agree=3, Disagree=2 and Strongly disagree=1. Based on this grading, farmers’ perception

of performance of different sources of credit based on selected criteria (volume of credit, interest

rate, timeliness, collateral, location and social status) will be ranked using weighted mean (X).

The weighted mean score of respondents based on the 4-point Likert rating scale is;

∑=

=

+++=

4

1

4321 )4()3()2()1(

i

i

w

nn

n

nnnnm

n= the number of respondents to different grade point.

32 3.5.4 Differences of Means test

Z-test was employed for testing hypothesis 3. The formula for the Z-test is given as

Z =

r

r

S

r

n

Var

n

Vars

XsX

+

−−−

sX = mean of credit preferred

rX = mean of credit amount received

Vars = sample variance of credit preferred

Varr = sample variance of credit amount received

ns and nr = number of observations

degree of freedom (df) = ns + nr -2

33

CHAPTER FOUR

RESULTS AND DISCUSSIONS

4.1 Socio-Economic Characteristics of Farmers

The major socio-economic characteristics considered in this study include: age, sex, level

of formal education attained, marital status and farming experience.

4.1.1 Age

Age is the number of years a person has lived. Age to a large extent affects outputs from

the farm operations. It can also affect the marginal physical productivity of labour. The age of

the respondents studied varied as shown in Table 4.1

Table 4.1 Distribution of Respondents According to Age

Age Range Frequency Percentage %

(in years)

18-24 36 22

25-34 38 24

35-44 44 27

45-54 30 19

55-64 9 6

65 and above. 3 2

Total 160 100

Source: Field survey, 2010.

The result showed that the majority of the farmers were in the age range of 35-44years while the

least fell within age range of 65years and above.

34 4.1.2 Sex

Sex refers to the gender of the respondents either being a male or female. In relation to

farming activities, male farmers are more involved in on-farm production such cultivation,

planting, weeding and harvesting while female farmers dominate off-farm activities such as

processing and marketing.

Table 4.2 Distribution of Respondents According to Sex

Sex Frequency Percentage %

Male 97 61

Female 63 39

Total 160 100

Source: Field survey, 2010.

The farmers were predominantly male with average number of female farmers. Table 4.2

showed that the male farmers constituted 61% while remaining 39% were constituted by female

farmers. This agrees with the finding of Olaleye (2000) that small scale farming is carried out

mostly by males while females are involved in light farm operation such as processing and

marketing. Perhaps, this may be because both formal and informal credit sources are relatively

more favourable disposed to giving credit to male compared to female farmers.

4.1.3 Level of Education

Level of education plays a significant role in skill acquisition and enhances one’s ability

to understand and apply new ideas, technological innovations as well as his or her ability to plan

and take risk. An analysis of the level of formal education attained by the respondents is essential

because it helps to determine to what extent the finance providers used educational qualification

in giving out credit.

35 Table 4.3 Distribution of Respondents According to the Level of Education Attained.

Qualification Frequency Percentage %

No formal Education 20 13

Primary 46 29

Secondary 76 47

Post secondary 19 11

Total 160 100

Source: Field survey, 2010.

Table 4.3 indicated that out of 160 farmers, 13% had no formal education, 29% with

primary education qualification, 47% of them were with secondary qualification while the

remaining 11% possess post secondary qualification.

4.1.4 Marital Status

Marital status refers to a state of being single, married or divorced. Marital status is

believed to influence the ability of obtaining credit from different sources and married farmers

are advantageous in terms of farm labour supply compared to unmarried farmers.

Table 4.4 Distribution of Respondents According to Marital Status

Status Frequency Percentage %

Single 37 23

Married 123 77

Total 160 100

Source: Field survey, 2010.

Table 4.4 indicates that 23% and 77% of farmers were single and married respectively.

This marital status distribution showed that most of the farmers are married. Credit providers

36 might consider married farmers because it is believed that they are more responsible in financial

handlings compared to unmarried ones.

4.1.5 Farming Experience

Farming experience refers to years farmers have been involved in on-farm operation. It

varies from one farmer to another. Some farmers have been involved in the farming activities for

long period of time and the experienced acquired are expected to impact positively on farm

productivity and output.

Table 4.5 Distribution of Respondents by Farming Experience

Farming Experience Frequency Percentage %

(years)

< 5 17 11

6-10 66 41

11-15 41 26

> 15 36 22

Total 160 100

Source: Field survey, 2010.

The table 4.5 showed that 11% farmers have less than 5 years of farming experience,

41% were between 6-10years, and 26% were between 11-15years while the remaining 22% are

those farmers having above 15years experience in farming. From the table, it can be observed

that 89% of farmers have more than 5years of farming experience.

37 4.2.1 Sources of Credit

Source of credit refers to a place or a source of sum amount of money/wealth that will be

used to facilitate a business. This can be either through personal savings, money lenders, co-

operatives and banks.

Table 4.6 Distribution of Farmers by Sources of Credit

Source Frequency Percentage %

Informal 125 78

Formal 11 7

Both informal and formal 24 15

Total 160 100

Source: Field survey, 2010.

Table 4.6 indicated that 78% sourced their credit from informal sources, 7% borrowed

from formal sources while 15% borrowed from both informal and informal sources. This implies

that majority of the farmers received credit from informal source. This result is in line with

Mayanja (1995) and Mpuga (2004) that rural smallholder farmers rely on informal finance

providers as their major source of credit.

4.2.2 Sources of Credit in terms of Patronage

This shows the frequency levels of farmers seeking for credit from the various providers.

Farmers sourced credit from different providers, therefore, this ascertain the number of times

they patronize one source to another.

38 Table 4.7 Distribution of Farmers According to Source of Credit in term of Patronage

Source Frequency Percentage %

Relatives / Friends 45 28

Money lenders 110 69

Rotating saving association/

Coop. societies 30 19

Banks 7 4

OSAMCA 20 13

*OSAMCA –Ogun State Microcredit Agency

Source: Field survey, 2010

Table 4.7 indicated that majority of the farmers patronized money lenders, followed by

relatives/ friends. The cooperative societies were the next important source of credit while banks

were the least to be patronized. This wide variation in patronage might be due to many factors

among which are the low levels of literacy, lack of collateral and so on in which the credit

providers considered by allocating their resources in favour of those farmers that can meet their

stringent conditions leading to rationing of credit among the farmers especially formal sources.

4.2.3 Comparison of Sources of Credit and Interest Rate Charged

Interest paid by farmers on credit received differs based on sources of credit. Most of the

financial institutions based their interest rate according to their financial capacity and rate of

patronage.

39 Table 4.8 Comparison of Sources of Credit and Interest charged

Source Interest rate charged

Relatives / Friends <10

Money lenders 20-25

Rotating saving association/Coop. societies 15-20

Microfinance Banks 5-7

OSAMCA 10

Source: Field survey, 2010.

*OSAMCA –Ogun State Microcredit Agency

Table 4.8 showed that money lenders charged highest interest 20-25%, followed by

rotating association/cooperative with 15-20% interest rate. Also, Ogun State Microcredit Agency

and Microfinance bank charged 10% and 5-7% respectively. Information obtained from the field

indicated that microfinance banks demanded for guarantors who led to farmers borrowing mainly

from moneylenders, rotating associations and cooperatives and less from banks. This result is in

line with Pischike (1991), and Adeola and Adebayo (2008) who had earlier reported that

moneylenders generally received high interest on credit giving out to farmers.

4.3.1 Relative Importance of Different Sources of Credit

The importance of different sources of credit to farmers was analysed using likert scale

method. Here, farmers’ perception of performance of different sources of credit based on

selected criteria (volume of credit, interest rate, timeliness, collateral, location and social status)

were analysed using average weighted mean. The result is presented in table 4.9

40 Table 4.9 Relative Importance of Different Sources of Credit

Sources of credit Variables Moneylenders Friends Relatives Cooperatives Microfinance/ Commercial banks

Volume of credit 2.71 3.18 2.21 3.41 3.43

Interest rate 3.53 2.42 2.38 2.49 3.17

Timeliness 3.45 2.69 2.55 2.78 2.12

Collateral 2.63 2.08 1.85 2.07 2.89

Location 3.48 3.15 3.07 3.24 3.39

Social status 1.53 2.00 2.03 2.76 3.02

Source: Field survey, 2010

NOTE: Using weighted Mean (Mw), the mean ranges as follows.

a. 3 – 4 ………… Strongly Agree (SA) b. 2 – 3 ……………….. Agree (A)

c. 1 – 2 ………....Disagree (D) d. 0 – 1 …………. Strongly Disagree (SD).

The result in table 4.9 showed that farmers strongly agreed that commercial/microfinance

banks will meet their credit needs with the highest weighted mean score of 3.43 while relatives

had lowest weighted mean score of 2.21. Farmers strongly agreed that moneylenders charged

highest interest rate with highest weighted mean score of 3.53 while relatives scored least

weighted mean of 2.38. Farmers strongly agreed that giving out credit by moneylenders

correspond best relatively with timeliness in term of planting and post planting operation such

land preparation, planting, weeding, fertilizer et.c with highest weighted mean score of 3.45

while commercial bank had the lowest weighted mean score of 2.12. This might be due to

tedious procedures and criteria in obtaining credit. Farmers had more access to moneylenders

due to location’s proximity with highest mean score of 3.48.

41 4.4.1 Amount of Credit Sourced

This is the amount of credit that the lenders (both formal and informal sources) are

willing to give out to farmers after being applied for credit. It should be noted that in credit

rationing, there is always a wedge between credit applied for and received.

Table 4.10 Distribution of Respondents According to the Amount of Credit Sourced

Amount Frequency Percentage %

(N)

10,000 – 20,000 19 12

21,000 – 30,000 50 31

31,000 – 40,000 52 32

41,000 – 50,000 19 12

>50,000 20 13

Total 160 100

Source: Field survey, 2010.

The results in table 4.10 showed that 32% and 31% of farmers sourced credit of N 31,

000 – N 40, 000 and N21, 000 – N 30, 000 respectively while 12% of farmers’ sourced credit of

N10, 000 – N 20, 000 and N41, 000– N 50, 000. This implied that majority of farmers (75%)

sourced credit less than and equal N 40, 000.

4.4.2 Amount of Credit Received

This refers to the amount received by the farmers after passing through criteria set up by

the credit providers.

42 Table 4.11 Distribution of Farmers According to the Amount of Credit Received

Amount Frequency Percentage %

(N)

10,000 – 20,000 74 46

21,000 – 30,000 50 31

31,000 – 40,000 21 13

41,000 – 50,000 13 9

>50,000 2 1

Total 160 100

Source: Field survey, 2010.

The results in table 4.11 indicated that 46% and 31% of farmers received credit of N10,

000 – N 20, 000 and N21, 000 – N 30, 000 respectively. More so 13% of farmers received credit

of N31, 000 – N 40, 000 while 10% received more than N40, 000. The implication is that

farmers received far below what they sought for which might be due a series of factors such as

age, farming experience, farm worth e.t.c. that were considered by different sources of credit.

4.5.3 Difference of Mean Test

It was used to ascertain if there is statistically difference between credit sourced and

received by farmers.

Table 4.12 Difference of Mean Test Result on Credit Sourced and Received by Farmers

Credit Sourced Credit Received

Mean (N) (N) 46,963 (N) 28,063

Standard Deviation 17,280 13,246.43

T-value: 5.39 E-24

Degree of Freedom (df) : 159

Source: Field survey, 2010

43 From the table, the means of credit sourced and received were (N) 46,963 and (N)

28,063 respectively. The t- test showed statistically significant difference between credit sourced

and received by farmers. The t-calculated value is less than one ( t-cal<1) indicated that farmers

received less than credit they sourced for.

4.5 Factors that Determine Farm Credit Rationing among Farmers

Credit providers considered a lot of factors before giving out credit to farmers. All these

factors were based on the principle of credit default to avoid the risk of losing their credit to

defaulters. As such, important variables obtained from the sources of credit were incorporated

into other factors perceived by the farmers. A probit model was used to analyze the variables in

relation to rationing of credit among the farmers.

Table 4.13 Factors that determine Farm Credit Rationing among Farmers

Variables Co-efficient Dw Probabilities

Sex - 0.703 2.247 0.134 Age 0.658 6.635 0.010 * Education 0.684 3.966 0.046** Marital status -0.563 1.302 0.254 Farming experience 0.124 0.165 0.685 Interest rate -0.177 0.265 0.607 Farm worth 0.001 5.760 0.016** Dependency ratio 0.367 1.382 0.012** Constant 3.866 0.029

* = 1% level of significance ** = 5% level of significance Dw= Durbin watson Source: Field survey, 2010.

44 Variables

Age: Its co-efficient is 0.658 while its wald statistics is 6.635 which is significant at one percent

probability level. Its means that age has a relationship with the rationing of credit among

farmers i.e age increases the probability of accessing more credit.

Education: Its co-efficient is 0.684 while its wald statistics is 3.966 which is significant at five

percent probability level. This implies that there is a high probability of more educated farmers

will have access to larger credit due to acquired knowledge in meaningful way. Musebe et al

(1993) supported that as the farmers get more formal education, the probability of obtaining

more credit increases.

Farm worth: Its co-efficient is 0.001while its wald statistics is 5.760 which is significant at five

percent probability level. Its means that farm worth is one of the major factors contributed to

rationing of credit among the farmers i.e as farm worth increases, the probability of obtaining

more increases.

Dependency ratio: Its co-efficient is 0.376 while its wald statistics is 0.012 which is significant

at five percent level of probability. It implies that increase in number of dependants contributed

negatively to more credit supply by the finance providers. This is because credit might be

diverted to consumption, schools and other purposes other than faming.

From the analysis the socio-economic characteristics such as age education farm works

were very significant in obtaining more credit from finance providers.

4.6 The influence of Credit Rationing on Farmers’ Returns.

Credit is an important factor in farmers’ investment. Acquisition of more credit will

increase farm returns which in turn increases the profitability. Therefore, the more farmers have

access to credit, the higher returns.

45 Table 4.14: The Influence of Credit Rationing on Farmers’ Returns

Gross margin analysis was used to determine the influence of credit rationing on their returns.

Amount Labour Planting Weeding Fertilizer Others TVC Income Gross margin

Received materials application

≤10,000 6,000 1,500 12,000 - 19,500 45,233

25,733

20,000 9,000 2,100 15,000 26,100 65,000 38,900

30,000 10,535 4,414 17,700 3,750 36,400 80,585 44,185

40,000 13,250 5,392 21,300 4,000 2,656 46,600 106,575 59,975

50,000 18,205 6,595 23,500 7,000 2,000 57,300 125,205 67,905

>50,000 23,400 9,000 26,000 10,000 1,500 68,400 163,300 94,900

NB: All values in Naira

Source: Field survey, 2010.

Table 4.14 showed different levels of gross margin in relation to amount of credit

received and personal contribution. From the table, it was observed that revenue less total

variable cost (TVC) resulted in different gross margin (GMs). It was shown that farmers who

received less than and equal N10,000 had gross margin of N25,733, credit of N20,000 and

N30,000 equal to gross margin of N38,900 and N44,185 respectively. Further more, credit of

N40,000 and N50,000 with gross margin of N59,975 and N67,905 while credit greater than

N50,000 with highest gross margin of N94,900. This suggests that the more credit receives, the

more the returns i.e. farmers with higher credit accessibility will have higher return on credit

invested.

46

CHAPTER FIVE

SUMMARY, RECOMMENDATIONS AND CONCLUSION

5.1 Summary

The study investigated determinants of credit rationing among small scale farmers in

Ogun State. The data required for the study were obtained from primary and secondary sources.

The primary sources involved the use of structured questionnaires to solicit information about

socio-economic characteristics of farmers, sources of credit, relative importance of different

sources of credit, factors that constrained farmers and the influence of rationing on farmers’

returns. The secondary sources of data included gazetted documents from Ogun State

Agricultural Development Project (OGADEP), journals, periodicals and magazines.

Multi-stage simple random sampling techniques were employed to select the Local

Government Areas, communities and respondents. Altogether, one hundred and sixty

respondents were selected. Statistical and econometric tools such as percentages, frequencies,

means, multinomial logit model, probit model and gross margin analysis were used. T-test was

used to ascertain the significant difference between credit sourced and received.

The socio-economic characteristics of the farmers showed that the majority (73%) of the

farmers in the study area fell below 45years. This implied that most of the farmers in the study

area are within the active age bracket. Levels of educational attainment were mostly primary and

secondary education. About 77% of the farmers were married. Also, majority of the farmers

(89%) had more than 5years farming experience.

The results further showed that majority of the respondents (78%) sourced their credit

from informal source. This implies that there is high accessibility and wider coverage of the

informal sources. Majority of the farmers patronized money lenders while very few of them

patronised microfinance banks. More so, money lenders charged highest interest rate (20-25%)

47 while microfinance bank accounted for 5%. Though there was high patronage of money lenders

than others in spite of relative high interest rate.

A probit model indicated that age, education, farm worth, dependency ratio were

significant in determine the level of credit rationing while interest rate, location had no

significant impact on their credit rationing. The gross margin analysis of amount of credit

received has shown that the gross margin of N10,000, N20,000, N 30,000, N40,000, N50,000,

and greater than N50,000 loan successfully received were N 25,733, N 38,900, N 44,185, N

59,975, N 67,905 and N 94,900 respectively. This implies that the more farmers invested in

farming activities, the more the net return on investments.

5.2 Recommendations

From the findings of the research work, the following recommendations are made:

(a) The bureaucratic procedures by credit providers’ most importantly formal sources should

be flexible enough to accommodate small scale farmers by having access to more credit.

(b) Income generated by farmers who have access to credit should be used by credit providers

to assess the performance of farmers who utilize resources in a right way for provision of

more credit.

(c) Most of the farmers sourced credit from informal source; Central Bank of Nigeria (CBN)

should compel formal source especially commercial banks to complement effort of

informal source with closed supervision.

(d) Adequate information on interest rate payable to various sources of credit should be made

available to small holder farmers since presently rate of interest of formal sources

commensurate with interest rate charged by informal finance providers.

48 5.3 Conclusion

Results of socio-economics characteristics show that majority of the farmers are in

active age bracket which is instrumental to ability of obtaining more credit. The informal finance

providers are the backbone of small scale farmers compared to formal financial institutions due

to wider coverage, accessibility and timely acquisition. More importantly, credit sourced by

farmers are not adequately met by lenders due to some factors such as farm worth, farming

experience, age, e.t.c. The gross margin analysis indicated that farming activities were worth

being invested in. This is based on the results that higher investments yield higher returns.

49

REFERENCES

Alberta Agriculture and Rural Development (2005). How interest rates affect agricultural markets: Retrieved on 28th of march, 2009 http://www1.agric.gov.ab.ca/$department/deptdocs.nsf/all/sis10122

Adams, D. W. (1995). Difference in uses of Rural Financial Market in Taiwan and the

Philippines. World Development 21, no 4. 555-63 Adebayo, O. and G. Adeola, (2008). Sources and Uses of Agricultural Credit by Small scale

farmers in Surulere Local Government area of Oyo state. Retrieved on 15th of April,2009 from http://www.krepublishers.com/.pdf

Adedipe, N.O., P Okuneye and I. Ayinde (2004). “The relevance of Local and Indigenous Knowledge of Nigerian Agriculture” Being a paper presented at the International Conference on Bridging Scales and Epistemologies; Linking Local Knowledge with Global Science in Multi-Scale Assessment; March16-19 2004, Alexandra, Egypt.

Adegeye, A. and J. Ditto (1985): Essentials of Agricultural Economics. Impact Publishers Economics Nigeria, Limited, Ibadan.

Ahmed (2000). Principles of Environmental Economics. Proutledge, London and New York.

Ajakaye, M. (1985). “Financing Agricultural Programme in Nigeria. The Role of the Nigerian Agricultural and Cooperative Bank Limited”. In Aja Okorie and Martin O. Ijere (Eds). Reading in Agricultural Finance: Nigeria, Longman pp59-65.

Ali, M and J. Flinn, (1989). Profit efficiency among basmati rice producers in Pakistan Punjab. American Journal of Agricultural Economics 71, 303-310

Aryeetey, E., Hettige, M. Nissanke and W. Steel. (1997). Financial market integration and reforms in Ghana, Malawi, Nigeria and Tanzania. World Bank Economic Review, 11(2):195-218.

Atieno, R. (2001). Formal and Informal Institution Leading Policies and Access to Credit by Small-Scale enterprise in Kenya: An Empirical Assessment.

Atieno, R. (1994). “Institutional credit lending policies and the efficiency of resource use among small scale farmers in Kenya”.

Awoke, M.U. (2004). Factors Affecting Loan Acquisition and Repayment Patterns of Smallholders farmers in the North-East of Delta state Nigeria. Journal of Sustainable Tropical Agricultural Research, 9:61-64

Battese, G., S. Malik and M. Gill. (1996). An investigation of technical inefficiencies of production in four districts of Pakistan , Journal of Agricultural Economic, 47, 37-49

Bell and Diagne (1997). Rationing Spillover and Interlinking in Credit Markets: The Case of Rural Punjab: Oxford Economic Papers.

50 Besley, T. and S. Coate. (1995). Group lending, repayment incentives and social collateral.

Journal of Development Economics, 46, 1-18

Bigsten, and M. Floro (2000). “Credit Constraint in manufacturing enterprises in Africa”. Paper presented at the Conference on Opportunities in Africa: Micro-evidence from firms and households, 9-10 April, University of Oxford.

Boucher S and C. Guirkinger (2005). Direct elicitation of credit constraints: Conceptual and practical issues with an empirical application to Peruvian agriculture.

Bravo-Ureta, B and A. Pinheiro (1993). Efficiency analysis of developing country agriculture: A review of the frontier function literature. Agricultural Resource Economics Review 22, 88-101

Briquette (1999). Better Practices in Agricultural Lending. FAO Publication.

Brunton P (1999). Financing small scale and rural manufacturing enterprises. Caribbean Development Bank Barbados.

CBN (2003). Annual Report and Statement of Accounts of Central Bank of Nigeria as at 31st December. Pp60-155.

Changakham, P. (2001). Rural Financial Access and its Causal Effects on Rural Agricultural Development in the Lao PDR. Retrieved on 13th of January,2009 from http://www.igss.ynu.ac.jp/library/collection/thesis/2006/153.pdf

Chlapkwa, J. and C. Bjorrskov (2001). Reaping the Gains form Trade: Constraints and Opportunities of Agricultural Credit Markets. Retrieved on 13th of January, 2009from http://www.foi.life.ku.dk/Publikationer/~/media/

Chandavarkar. A. (1985). “The Non-Institutional Financial Sectors in Developing countries: Macroeconomic implications for Savings Policies. “ Saving and Development”.

Chenchen K and M. ChivaKul (2008). What Drives Household Borrowing and Credit Constraints? Evidence from Bosnk and Herzegovina.

Delgado, C. (1995). Africa’s changing agricultural development strategies past and present practices as a guide to the future 2020 vision food, Agriculture, and the Environment Discussion paper 3, Washington, D.C International Food Policy Research Institute.

Diagne, A., A. Zeller and M. Sharma. (2008). Empirical Measurements of Households Access to Credit and Credit Constraints in Developing Countries: Methodological Issues and Evidence. Retrieved on 13th of January, 2009 from http://www.ifpri.org/divs/fcnd/dp/papers/fcnbr90.pdf

Dicken, P. (2007). Global Shift: Mapping the Changing Contours of the World Economy. 5th

Edition, SAGE Publications Inc. London, California, New Delhi.

51 Eboh, E. (1998). “Social and Economic Research: Principles and Methods.” Lagos: Academic

Publications and development Resource Ltd.

FAO(2008). Comprehensive Africa Agriculture Development Programmes. Retrieved on 13th of January,2008 from http://www.fao.org/docrep/005/y6831e/y681e-09.htm

Feder, G., L Lau., J. Lin and X. Luo (1990). The Relationship Between Credit and Productivity in Chinese Agriculture: A Microeconomic Model of Disequilibrium. American Journal of

Agricultural Economics 72 (5): 1151-1157.

Field, E and M., Torero (2004). Do Property Titles Credit Access Among the Urban Poor? Evidence from a Nationwide Titling Program. Retrieved on January, 5th, 2010 from http://www.rwj.harvard.edu/papers/field/Field%20Do%20Property%20Titles%20Increas

Floro, M. And D. Ray (1997). Vertical Link Between Formal and Informal Financial Institutions. Review of Development Economics 1 (1), 34-56.

Germidis, D., D. Kessler and R. Meghir (1991). Financial Systems and Development: What Role for the Formal and Informal Financial Sectors? Paris : OECD Development Centre.

Gilligan, D S Harrower and A Quisumbing (2005). How Accurate Are Reports of Credit Constraints’ Reconciling Theory with Respondents Claims in Burkidon, Philippines- IFPRI Washington DC.

Gloy, B., M. Gunderson and E. Ladue. (2005). The costs and returns of agricultural delivery. American Journal of Agricultural Economics, 87, 703-716.

Gregg, S. (2008). The Credo of Credit-The Acton Institute. Retrieved on 10th may 2009 from http://www.acton.org/commentary/475

Holden, P. (1997). Collateral without consequence. Some causes and effects of financial underdevelopment in Latin American. The Financier 491): 12-21.

Howard, J., N. Harford and A. Sey.(1999). Improving Financial Services for Renewable Natural Resource Development (RNRD) in Ghana. Establishing Policy Guidelines for the Informal Financial Sector”. Final report, submitted to Policy Research Programme NRRAD, DFID.

Hussain, Z., M. Sial, W. Akram and I. Hussain (2008). Agricultural credit constraints and Borrowing behaviour of farmers in rural Punjab. European Journal of Scientific

Research.

Hussein H. (2007). Influence of Credit Constraints on Production Efficiency: The Case of Farm Households in South Eastern Ethiopia.

IFAD (2000). Rural Financial Services Project. Pre-appraisal Mission Working Paper: "Re-Packing the Rural Finance Sub-sector in Ghana Poverty Gender and Rural Informal Sector Perspectives" and Working Paper: "Strategy for Mainstreaming Gender with a

52

Specific Focus on Northern Ghana." Retieved on 5th of May, 2009 from http://www.ifad.org/gender/learning/sector/finance/41.htm.

Jappelli, T (1990). Who is credit Constrained in U.S. Economy? Quarterly Journal of Economics

105(1): 219-234.

Jappellis, T., J. Pischike and N. Souleles (1998). Testing for Liquidity Constraint in Euler Equations with Complementary Data Sources. Review of Economics and statistics 80(2):251-262.

Karmakar, K (1999). Rural Credit and Self-Help Groups: Micro-Finance needs and concepts in

Indian.

Kessler, D., A Marigue and P. Ullmo. (1985). “Ways and Means to Reduce Financial Dualism in Developing Countries” Working paper, OECD Development Centre.

Khalid, M. (2003) Access to Formal and Quasi-Formal Credit by Smallholder Farmers and Artisanal Fisheries.

Khandker, S. And R. Faruguee (2001). The Impact of Farm Credit in Pakistan, World Bank Technical Paper no. 258, Washington, DC.

Kohansal, M., M. Ghorbani and H. Mansoori (2008). Effect of Credit Accessibility of farmers on Agricultural Investment and Investigation of Policy Options in Khorasan-Razavi Province. Retrieved on 13th of January, 2009 from http://www.scialert.net/pdfs/jas/2008.

Levisky, J. (1993). Innovations in Financing small and micro-entrepreneurship and Management. International Labour Office.

Lubwama (1997). Socio-economic and gender issues affecting the adoption of conservation tillage practices.

Mayanja V. (1995). “The role of the informal financial sector in providing financial services to small scale farmers: A case study of Mawokota county in Mpigi district,” unpublished M.A dissertation, Institute of Economics, Makerere University.

Mishkin, F. (1991). “Asymmetric Information and Financial Crises. An historical Perspective” In R.G Hubbard, (eds) Financial Markets and Financial crises, Chicago: University of Chicago Press.

Mohamed, K. and A. Temu (2008). Access to credit and its Impact on Agricultural Technologies adoption: A case of Zazibar.

Mpuga, P (2004). Demand for credit in Rural Uganda: Who cares for the peasants? A paper presented at the conference on Growth, Poverty Reduction and Human Development in Africa Centre for the Study of African Economies.

Musebe, R., Oluoch. W. Kosura and C. Wangia (1993). An Analysis of Agricultural Credit Market in Vihiga Division of Kakamega District, Kenya, East Africa. Agriculture and Forestry Journal, 58(3):4.

53 NPC (2006). Nigeria Population Commission. Retrieved on 28th of August, 2009 from

http://www.populationgov.ng.

Nissanke M and E. Aryeetey. (1995). Financial integration and development in Sub-saharan Africa”. Report prepared for the African Technology Department, AFTPS, Washington, D.C. The World Bank.

Nweze N. (1995). The Role of Informal Finance in Rural Areas of Nigeria: Concepts, Processes and Prospects in: Eboh; E.C, C.U. Okoye and D. Ayichi (Eds) Rural Development in Nigeria. Authocentury Publishing Company. Enugu. Pp.192-199.

OGADEP (2000). Ogun State Agricultural Development Programme Bulletin.

Okurut, N., Schoombee and Berg, S. (2004). Credit Demand and Credit Rationing in informal financial sector in Uganda.

Oladeebo, J. and Oladeebo E. (2008). Determinants of Loan Repayment Among Smallholder Farmers in Ogbomoso Agricultural zone of Oyo-state, Nigeria.

Olaleye, R. (2000). Effectiveness of Development Intervention for Economic Empowerment on Rural Women in Ondo, Nigeria. An unpublished Ph.D dissertation submitted to the Department of Agricultural Extension and Rural Development, University of Ibadan, Ibadan.

Olomola, A. (2006). ‘Competition Issues in the Agricultural Sector in Nigeria”.Invited Paper Presented at the Regional Conference of 7Ups Project- Capacity Building on Competition Policy in Selected Countries of Eastern and Southern African. Organized by the Aha Ethiopian Consumer Protection Association and Consumer Unity and Trust Society (CUTS), Held in Hilton Hotel, Addis Ababa, Ethiopia. March27-28, 2006.

Omabola, Ade S; and Akande, S.O. (1999) Agricultural Financial Issues in Nigeria. Nigeria Institute of Social and Economic Research (NISER), Ibadan.

Oni, O., Oladele, O. and Oyewole, I. (2005). Analysis of Factors Influencing Loan default

among poultry farmers in Ogun state, Nigeria. Retrieved on 13th of April,2009 from http://www.agr.hr/jcea/issues/jcea6-4/pdf/jcea64-29.pdf

Parikh, A., F. Ali. and M. Shah, (1995). Measurement of economic efficiency in Pakistani Agriculture. American Journal of Agricultural Economic, 77, 675-685.

Rahji, M.A.Y. (2000). An Analysis of the Determinants of Agricultural Credit Approval/Loans size By Commercial Banks in South Western Nigeria. Nigerian Agricultural Development studies, 1(1): 16-25.

Rahji, M and S, Fakoyede (2009). A Multinomial Logit Analysis of Agricultural Credit Rationing by Commercial Banks in Nigeria.

54 Rural Policy Research Institute (RUPRI)(1997). “ The Adequacy of Rural Financial Market:

Rural Development Impacts of Seven Key Policy Issues”. A Background White Paper RUPRI Rural Financial Task Force pp 97-98.

Rweyemamu, D., P. Kimaro and O. Urassa (2003). Assessing Micro-Finance Services in Agricultural Sector Development. A case study of Semi-formal financial Institutions in Tanzania. http://www.ifpri.org/2020/nw/nwpapers/tanzbr200308.pdf

Sabiiti, R. (2008). The Developing World’s Predicament in the face of worsening Food Security. Retrieved on 28th march, 2009 from http://www.africanexecutive.com/modules/magazine/articles.php?article=3705&magazine=199.

Sianwalla, A., C. Pinthong, N. Poapongsakorn, P. Satsanguan, P. Nettayarak, W. Mingmanneenakin and Y. Tubpun (1990). The Thai rural credit system: Public subisidies, private information and segmented markets. World Bank Economic Review 4(3):271-295.

Simtowe, F. and A. Phiri (2007). To what Extent Are Credit Constraints Responsible for the Non-Separable Behaviour at Household level? Evidence from Tobacco Growing Household in Rural Malawi.

Smith, A. (2007). “In a financial crisis, the Public trusts no one”. Retrieved on 15th of May, 2009 from www.independent.co.uk/opinion.

Soyibo, A. (1996). Financial Linkage and Development in Sub-Saharan Africa: A study of Informal Finance in Nigeria”. Report of study prepared for the overseas development institutes, London.

Stiglitz, J. And A.Weiss (1981). Credit rationing in markets with imperfect information. American Economic Review 71 (3) 393-410

Timothy, J and S. Coata (1994). “ Group Lending Repayment Incentives and Social Collateral” RDPS Discussion Paper. 152. Princeton University.

Vargese, A. (2005). Bank- money lender Linkage as an alternative to bank competition in rural credit markets; Oxford Economic papers, vol. 59, No. 2, Retreived on 13th of January from http://www.ifpri.org/divs/fcnd/dp/papers/fcnbr90.pdf

Von-Pischike, J. (1991). A critical survey of approaches to the role of credit in small-holder development. Discussion paper Institute for Development Studies, University of Nairobi.

Wenner, M and F. Proenza (1999). Rural Finance in Latin American and the Caribbean: Challenge and Opportunities. Prepared for the Panel on ‘’ Strategies for Microfinance in Rural Area’’ Second Inter American Forum on Microenterprises Buenos Aires Argentina.

55 Wohl, S. (2004). Testing for Credit constraint in Entrepreneurship Retrieved on

13thofJanuary,2009fromhttp://economics.uchicago.edu/download/ssw_applications_20050207.pdf.

Zeller, M. (1994). A Study of Informal Lenders and Formal Credit Groups in Madagascar,’’ World Bank Development 22:12 pp1895-1907.

Zeller et al (1997). Rural Finance for Food Security for the poor; Implications for Research and Policy. Food Policy Review No.4 Washington, D.C. International Food Policy Research Institute

56

APPENDIX

Department of Agricultural Economics University of Nigeria Nsukka Februrary, 2010.

Dear Respondents,

I’m a student of the above mentioned university carrying out research project on:

Determinants of Credit Rationing Among Small Scale Farmers in Ogun State, Nigeria.

The research is in partial fulfillment of the requirements for the award of Master of

Science in Agricultural Economics. The responses to the questionnaire will purely be for

academic purposes and will be treated in confidence.

Thanks.

Yours faithfully

Taiwo Olabode Ajibade

57

QUESTIONNAIRE

Please, tick [ ] against the box you consider the most appropriate answer in the space provided.

SECTION A

(Socio-economic characteristics)

1. Sex: male [ ] female [ ]

2. Age: 18-24 [ ] 25-34 [ ] 35-44 [ ] 45-54 [ ] 55-64 [ ] 65 & above [ ]

3. Educational qualification: Primary Leaving Certificate [ ] SSCE [ ] Diploma [ ]

NCE [ ] HND/B.sc [ ] Others specify………………..

4. Marital status: single [ ] Married [ ] Divorced [ ]

5. Farming experience: 0-5years [ ] 6-10years [ ] 11-15years [ ] above 15years [ ]

SECTION B

Sources of Credit/Volume of Credit Approved

6. Have you ever sourced for credit? (a) Yes (b) No

7. What was the source(s) of your credit?(a) Informal source (b) Formal source (c) Both

informal and formal sources

8. If both (option c), which one did you patronize most? (a) Informal source (b) Formal

source.

9. Kindly tick source(s) of your credit ( ) Friends ( ) Relatives ( ) Moneylenders

Microfinance bank ( ) Commercial banks Others specify………………….

58

10. Did period of getting credit in informal source(s) concede with planting season than the

formal sources? (a) Yes (b) No

11. How much did you apply for? (a) less than N10,000 (b) N10,000- N20,000 (c) N21,000-

N30,000 (d) N31,000-N40,000 (e) N41,000-N50,000 (f) others specify……………

12. Would you have wanted a larger credit? (a) Yes (b) No

13. How much did you receive after loan application (a) less than N10,000 (b) N10,000-

N20,000 (c) N21,000-N30,000 (d) N31,000- N40,000 (e) N41,000-N50,000 (f) others

specify…………..

14. What was the interest charged? (a) less than 5% (b) 5-10% (c) 11-20% (c) 21-30% (d)

31-40% (e) above 40% (f) Other specify………………..

15. What did your farm worth before applied for credit? (N)……….

16. What is the present worth of your farm after credit utilization? N)..................

SECTION C

Rating of Sources of Credit in term of Preference

4-Strongly agree, 3-Agree, 2-Disagree and 1-Strongly disagree

Informal sources Formal sources

SOURCES OF CREDIT Moneylenders Friends Relatives Saving

association

Microfinance/

Commercial

banks

17. Volume of the

59

money met my demand.

18. Interest rate is

bearable.

19. Timeliness i.e credit

acquisition concedes

with planting/operation

season.

20. Much collateral is

not required.

21. Location (it is easily

accessible).

22. Social status is not

considered.

SECTION D

Factors that determine credit rationing as perceived by the farmers

Kindly provide appropriate answer for the following questions.

23. Was sex of the farmers considered in credit acquisition?

(a) Yes (b) No

24. Which sex obtains more credit? (a) Male (b) Female

25. Why is it so? (a) Male farmers divert all or parts of credit from farming (b) Female farmers

are more judicious in using resources in farming activities. (c) Others

(specify)…………………….

26. Did finance providers consider age as a criterion for borrowing?

60

(a) Yes (b) No

27. What age range did finance providers consider most appropriate?

(a) 21-30years (b) 31-40years (c) 41-50years (d)51-60years (e) 60 above

28. How long have you been in farming? (a) Less than 5years (b)5-10years (c) 10-15yeras

(d)15years above

29. Did farming experience count in credit acquisition?

(a) Yes (b) No

30. Did farm size increase chance to have access to credit?

(a) Yes (b) No

31. Did your previous loan repayment ability count in credit accessibility?

(a) Yes (b) No

32. Did high interest rate charged constrain you from obtaining optimum credit?

(a) Yes (b) No

33. Did high dependent ratio considered by finance providers for credit acquisition?

(a) Yes (b) No

34. Did location serve as an hindrance to required credit?

(a) Yes (b) No

35. Did collateral or lack of it deprive you in obtaining credit?

(a) Yes (b) No

36. Did farm worth determine the extent the credit will be given?

(a) Yes (b) No

37. Did the risk of failure in paying back credit discourage you from obtaining credit?

(a) Yes (b) No

61 38. Was wealth or level of income considered by lenders as an attribute to repay credit?

(a) Yes (b) No

39. What did you use as collateral? (a) Land (b) residential property (c) farm asset (d)

Others………………

SECTION E

(The influence of credit rationing on farmers’ returns)

Resource/Input type Cost per unit (N) Total cost(N)

Family

labour(Mandays/hour)

Hired

Labour(Mandays/hour)

Planting materials

Fertilizer( if applicable)

Weeding

Herbicides

Labour (Harvesting)

Processing (if applicable)

Others specify……………….

Revenue/ Return on Crops (output) Produced

Crop type (output) (N)

Cassava

Maize

Others (specify)

62 (Assessment of other related effects of credit rationing on farmers)

Did credit rationing

affect:

Strongly agree Agree Disagree Strongly

Disagree

Farm Labour

Adoption of

improved technology

Farms’

expansion/investment

What are your recommendations to eradicate/ minimizing credit rationing to a bearable

level?

………………………………………………………………………………………………………

………………………………………………………………………………………………………

………………………………………………………………………………………………………

………………………………………………………………………………………………………

………………………………………………………………………………………………………

………………………………………………………………………………………………………

………………………………………………………………………………………………………

………………………………………………………………

63

QUESTIONAIRE 2

Department of Agricultural Economics University of Nigeria, Nsukka. February, 2010.

Dear Respondents,

I’m a student of the above mentioned university carrying out research project on:

Determinants of Credit Rationing Among Small Scale Farmers in Ogun State, Nigeria.

The research is in partial fulfillment of the award of Master of Science in Agricultural

Economics. The responses to the questionnaire will purely be for academic purposes and will be

treated in confidence.

Thanks.

Yours faithfully,

Taiwo Olabode Ajibade

64

QUESTIONNAIRE

Please, tick [ ] against the box you consider the most appropriate answer in the space provided.

17. Name/title of Organization……………………………..

18. Type of organization/source (a) Informal source ( b) Formal source

19. How long have you been lending credit/ loan to farmers? (a) less than 5years (b) 5-

10years (C) 11-15years (D) 16-20years (E) more than 20years

20. Did you always meet demand for credit? (a) Yes (b) No

21. If no, did you consider farming as risky venture? (a) Yes (b) No

22. Why is it so?......................................................

23. Did you consider age in loan approval? (a) Yes (b) No

24. Which age did you prefer to lend loan? (a) less than 25years (b) 25-35years (c) 36-

45years (d) 46-55years (e) 56-65years (f) 65years above.

25. Did maturity is criterion for careful handling and potential repayment of loan? (a) Yes (b)

No

26. Did sex play important role in loan rationing? (a) Yes (b) No

27. Why is it so? (a) Most male farmers do divert loan from farming activities (b) Females

are conscious in using credit for farming operations. (c) Other

specify……………………..

65

28. Did farming experience count in loan approval? (a) Strongly agree (b) Agree (c) Disagree

(d) Strongly Disagree

29. How many years in farming activities did you consider? (a) Less than 5years (b)5-

10years (c) 10-15yeras (d) 15years

30. Why it so? Farming experience avoids diversion of credit and mucous use of loan. (a)

Strongly agree (b) Agree (c) Disagree (d) Strongly disagree.

31. Did farm size increase farmers to have access to credit? (a) Strongly agree (b) Agree (c)

Disagree (d) Strongly disagree.

32. Did you consider previous loan repayment? (a) Strongly agree (b) Agree (c) Disagree (d)

Strongly disagree.

33. What was the interest charged? (a) less than 5% (b) 5-10% (c) 11-20% (c) 21-30% (d)

31-40% (e) above 40%

34. Did farmers pass through bureaucratic procedure when applied for credit?

(a) Strongly agree (B) Agree (C) Disagree (D) Strongly Disagree

35. Did collateral or lack of it deprive them in obtaining credit? (a) Strongly agree (B) Agree

(C) Disagree (D) Strongly Disagree

36. What did you prefer to be used as collateral? (A) land (B) residential property (C) farm

asset (D) Others………………

21. Did wealth or level of income is considered as a positive signal to grant credit?

(A) Strongly agree (B) Agree (C) Disagree (D) Strongly Disagree

66

22. What other factors did you consider in determine credit rationing among farmers?

What are your recommendations to eradicate/ minimizing credit rationing to a bearable

level?

………………………………………………………………………………………………………

………………………………………………………………………………………………………

………………………………………………………………………………………………………

………………………………………………………………………………………………………

………………………………………………………………………………………………………

………………………………………………………………………………………………………

………………………………………………………………………………………………………

….