i

AMAGWU, IBEAWUCHI FRANCIS

PG/Ph.D/10/54559

EFFECTIVENESS OF MICROFINANCE SOURCES ON THE PROFITABILITY OF ENTERPRISE CLUSTERS IN

SOUTH EAST, NIGERIA

ENVIRONMENTAL STUDIES

INSTITUTE FOR DEVELOPMENT STUDIES (IDS)

Paul Okeke

Digitally Signed by: Content manager’s

Name

DN : CN = Webmaster’s name

O= University of Nigeria, Nsukka

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EFFECTIVENESS OF MICROFINANCE SOURCES ON THE PROFITABILITY OF ENTERPRISE CLUSTERS IN

SOUTH EAST, NIGERIA

BY

AMAGWU, IBEAWUCHI FRANCIS PG/Ph.D/10/54559

INSTITUTE FOR DEVELOPMENT STUDIES (IDS), UNIVERSITY OF NIGERIA,

ENUGU CAMPUS, ENUGU, NIGERIA

JULY 2015

iii

TITLE PAGE

EFFECTIVENESS OF MICROFINANCE SOURCES ON THE PROFIT ABILITY OF ENTERPRISE CLUSTERS IN SOUTH EAST, NIGERIA

BY

AMAGWU, IBEAWUCHI FRANCIS PG/Ph.D/10/54559

BEING A THESIS SUBMITTED IN PARTIAL FULFILMENT OF T HE REQUIREMENTS FOR THE AWARD OF PhD IN DEVELOPMENT ST UDIES

INSTITUTE FOR DEVELOPMENT STUDIES (IDS), UNIVERSITY OF NIGERIA,

ENUGU CAMPUS, ENUGU, NIGERIA

JULY 2015

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CERTIFICATION

Amagwu, Ibeawuchi Francis; a postgraduate student of the Institute for Development Studies

has satisfactorily completed the requirements for the award of Doctor of Philosophy (Ph.D.)

Degree in Development Studies. The work embodied in this thesis is original and has not

been submitted in part or full for any Diploma, or degree of this or any other university.

..................................................

Amagwu, Ibeawuchi Francis

PG/Ph.D/10/54559

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APPROVAL

This Dissertation “Effectiveness of Microfinance Sources on the Profitability of

Enterprise Clusters in South East, Nigeria” has been read and approved as having met the

requirements of the Institute for Development Studies (IDS), University of Nigeria for the

award of the Degree of Doctor of Philosophy (Ph.D.) in Development Studies.

……………………………………………… ………………………………… Professor U.J.F. Ewurum Date (Supervisor)

…………………………………………………. ………………………………… Professor Osita Ogbu Date (Director)

…………………………………………………. ………………………………… Prof (Mrs.) Mercy A. Anyiwe Date (External Examiner)

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DEDICATION

This research is dedicated to God Almighty from whom all the resources needed for the

research are obtained.

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ACKNOWLEDGEMENTS As I write this acknowledgement with tears and smiles, the joy in my heart is overwhelming

and flows like a river. To God be the glory, great things He has done for making me complete

this work. I appreciate the inspiration, guidance, good health and journey mercies He granted

me which made this work see ‘the light of the day’. I am deeply grateful to my supervisor,

Prof. U.J.F. Ewurum, for his fatherly role in the course of writing this dissertation. He

patiently listened to me even when I argued against some of his suggestions and he constantly

provided new insights and directions that eventually made me knowledgeable on the subject

matter.

To the director of Institute for Development Studies (IDS), Professor Osita Ogbu, I say ‘a big

thank you’ for your guidance and unquantifiable support for the Ph.D programme in IDS and

this work in particular. Your appointment as the director of the institute gave us hope and

further rekindled my interest to pursue the Ph.D degree. To Prof. J.U.J Onwumere whose

interest in the work brought us closer, I appreciate your encouragement. I am indebted to you

for all your care and support especially giving me an unrestricted access to you even at very

odd hours.

I also appreciate the inputs of Dr Uzochukwu Amakom, Dr Joseph Uduji, Dr Chukwuma

Agu, Dr (Lolo) Ogakwu, Dr Eneh, and Dr Bonifcae Umoh, other academic and non-academic

staff of the IDS and Mr. Yuni Denis Nfor, for their encouragement during the programme. To

Late Professor Ikechukwu Nwosu, former director of the Institute, I also remember your

word of encouragement which was instrumental to my pursuing a Doctorate Degree in

Development Studies. Mr. Enyinna Aham Ubani whose business center at UNEC became my

second residence, I appreciate you and your staff particularly Miss Jennifer Edeh, for all your

efforts in supporting this work. To my coursemates at the IDS Ph.D class, your individual and

collective contributions to the success of this work are highly appreciated. I also appreciate

the support and understanding of my colleagues at the Central Bank of Nigeria, particularly

Mohammed Musa of Development Finance office, Enugu. My brothers and sisters, in-laws,

other relatives and friends (too many to mention here), who expressed concerns especially

when the programme appeared abandoned; I appreciate your patience and understanding.

Finally to my dear wife, Dr Chinedum Christabel Amagwu and our Lovely children

Onyedikachi and keechiakolam, I owe you a lot for your patience, understanding and support

throughout the period. You are, indeed, a unique gift to me and I thank God for that. May the

good Lord bless you all!

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TABLE OF CONTENTS

Certification … … … … … … … … … … i

Approval … … … … … … … … … … ii

Dedication … … … … … … … … … … iii

Acknowledgments … … … … … … … … … … iv

Abstract … … … … … … … … … … v

List of Tables … … … … … … … … … … vi

List of Figures… … … … … … … … … … vii

List of Plates … … … … … … … … … … viii

CHAPTER ONE: INTRODUCTION

1.1 Background to the Study … … … … … … … 1

1.2 Statement of the problems … … … … … … … 5

1.3 Objective of the Study … … … … … … … 7

1.4 Research Questions … … … … … … … … 8

1.5 Research Hypotheses … … … … … … … … 8

1.6 Significance of the Study … … … … … … … 8

1.7 Scope of the Study … … … … … … … … 9

1.8 Limitation and structures of the study … … … … … … 9

1.9 Background Information on Development of SME ... … … … 11

1.10 Operational Definition of Terms … … … … … … 12

CHAPTER TWO: REVIEW OF RELATED LITERATURE

2.1 Introduction … … … … … … … … 15

2.2 Conceptual Literature … … … … … … … … 15

2.3 Theoretical Literature … … … … … … … … 21

2.4 Empirical Literature … … … … … … … … 53

2.5 Summary of the Review of Related Literature.… … … … … 60

CHAPTER THREE: RESEARCH METHODOLOGY

3.1 Introduction … … … … … … … … 64

3.2 Design of the Study … … … … … … … … 64

3.3 Data and Sources … … … … … … … … 64

3.4 Population of the Study … … … … … … … 55

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3.5 Tools for Data Collection … … … … … … … 55

3.6 Determination of Sample size … … … … … … … 57

3.7 Validity of the Research Instrument … … … … … … 68

3.8 Reliability of the research … … … … … … … 68

3.9 Method of Data Analysis … … … … … … … 68

3.10 Theoretical Framework … … … … … … … 69

3.11 Models Specification, Methods of Data Analysis and Results Evaluation … 70

3.12 Assessment of Level of Support of Microfinance Providers for the Sustenance of Profitability of Enterprise Clutters in South-East, Nigeria (Objective 4) 78

CHAPTER FOUR: DATA PRESENTATIONS AND ANALYSIS

4.2 Introduction … … … … … … … … … 79 4.2 General Enterprise Characteristics and Perceptions … … … … 79 4.3 Microfinance Sources and Enterprise Profitability/Objectives 1&2 (Models 1, 2a & 2b) 83 4.4: Determinants of the choice of the microfinance Source by Enterprise clusters in South East of Nigeria (Objective 3 and Model3) 88 4.5 Assessing the level of support of Micro Finance providers for the Sustenance of profitability of Enterprise - Objective Four… … … 92 4.6 Test Of Hypotheses … … … … … … … … 84 4.7 Discussion of Findings … … … … … … … 98

CHAPTER FIVE: SUMMARY OF FINDINGS, CONCLUSION AND

RECOMMENDATIONS

5.1 Summary of Findings … … … … … … … … 105 5.2 Policy Implication of Findings … … … … … … … 106 5.3 Policy Recommendation … … … … … … … … 107 5.4 Contribution of this study to Knowledge on the Subject matter … … … 108 5.5 Suggestion for Further Research … … … … … … … 110 5.6 Conclusion … … … … … … … … … … … 110

References … … … … … … … … … … … 112

Appendix … … … … … … … … … … … 120

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LIST OF TABLES

Table 2.1: Cross-Country approaches to defining SMEs 17

Table 2.2: Different classification of MSMEs in Nigeria 19

Table 3.1: Estimated Number of Enterprises across selected clusters in South-East 65

Table 3.2: Sample Size of Enterprises across Clusters in South-East Nigeria 67

Table 3.3: Model 1: Microfinancing source and profitability 73

Table 4.1: Summary of Enterprises Characteristics 79

Table 4.2: Perceived Challenges faced by enterprises in accessing micro finance 82

Table 4.3: Effect of microfinance sources on the profitability of enterprise clusters 83

Table 4.4: Determinants of the choice of micro financial sources for enterprises 88

Table 4.5: Assessing the level of support of Microfinance providers for the sustenance

of profitability of Enterprise (Objective 4) 94

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LIST OF FIGURES

Figure 2.1 Simple Illustration of the Value Chain 45

Figure 4.1: Perception of determinants of choice of microfinance choice 92

Figure 4.2: Extent of microfinance support perceived from formal and informal sources 93

Figure 4.3: Perceived extent to which funds have expanded business 95

1

LIST OF ACRONYMS Acronyms Meaning ADB African Development Bank ADP Agricultural Development Programme AU African Union CAC Corporate Affairs Commission CBN Central Bank of Nigeria

CEO Chief Executive Officer CTS Credit Tracking System DEMs Dual Economy Models DFID Department for International Development ECs Enterprise Clusters FGD Focus Group Discussion FGDs Focus Group Discussions GDP Gross Domestic Product GSM Global Systems Mobile Network ICs Industrial Clusters ICT Information and Communication Technology IGR Internally Generated Revenue

IUCN International Union for the Conservation of Nature IYMC International Year of Micro Credit LDCs Less Developed Countries LGA Local Government Area M&E Monitoring and Evaluation MAN Manufacturers Association of Nigeria MDGs Millennium Development Goals MFBs Micro Finance Banks MFIs Micro-Finance Institutions MoU Memorandum of Understanding MSEs Micro and Small Enterprises MSMEs Micro Small and Medium Enterprises MSSE Micro and Small Scale Enterprises NASME National Association of Small and Medium Enterprises NASME National Association of Small and Medium Enterprises NASSI National Association of Small Scale Industrialists NEPAD New Partnership for African Development NGOs Non-Governmental Organizations OLS Ordinary Least Square OPS Organized Private Sector PEP Poverty Eradication Programme R&D Research and Development RBRDAPs River Basin and Rural Development Authorities' Projects

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RMB Renminbi (Chinese Currency) ROA Return on Assets ROI Return on Investment SDM Sustainable Development Model SHGC Self Help Group Contribution SMEDAN Small Medium Enterprises Development Agency of Nigeria SMEEIS Small and Medium Enterprises Equity Investment Scale SMEs Small Medium Enterprises SMIs Small and Medium Industries SSEs Small Scale Enterprises UN United Nations UNDP United Nations Development Programme UNICEF United Nations Children’s Fund UNIDO United Nations Industrial Development Organization USA United States of America VECM Vector Error Correction Model WBPs World Bank Projects WCED World Commission on Environment and Development

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ABSTRACT

Micro and Small Enterprises (MSEs) are currently regarded as the backbone of every economy and have been globally regarded as engines of growth, vehicles for job creation, drivers of production and income generation as well as veritable tools for poverty reduction and wealth creation. The source of microfinance is equally important because at the centre of every enterprise objective is profitability and growth that can trigger its achievement of the expected roles. MSEs in Nigeria have not played these roles effectively due to the challenges of access to finance, infrastructural deficit and vocational skills deficiency. The main thrust of this thesis, therefore, is to evaluate the effectiveness of microfinance sources on the profitability of MSEs in South East, Nigeria as well as understanding the determinants of the choice of microfinance sources and the level of support that MSEs get from funds providers. The study employed multi-stage sampling technique in identifying clusters from three cities (Onitsha, Aba and Nnewi) of the South East, Nigeria and generated relevant data through instruments such as questionnaire, personal interviews and Focused Group Discussions (FGDs). A total sample of 540 enterprises out of 1994 enterprises were selected across different clusters comprising enterprises under production, trade and services in the three cities. Using multiple regression technique and logit regression, the study found that both formal and informal microfinance sources impacted significantly on the profitability of MSEs in South East, Nigeria. The study further found interest rate, repayment period, amount or volume of capital and proximity to enterprises as the major determinants of the choice of microfinance source used by MSEs in South East, Nigeria. Also, the respondents revealed that why most of them patronized informal source of microfinance is because of the quick response as well as the relationship with the provider (social capital). The study concluded that microfinance providers should be located closer to MSEs’ location for quicker response to their financing needs to the extent of taking advantage of social capital existing within the clusters as a possible cushion for the physical collaterals and documentations often requested for loan approvals. The study recommends that microfinance policy framework and interventions should encourage providers to locate closer to the enterprise clusters with the appropriate regulatory guarantee for operators.

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CHAPTER ONE INTRODUCTION

1.1 Background of the Study

The impact of manufacturing industry in every economy cannot be overemphasized as

it goes a long way to enhance production, create jobs, reduce imports, increase

exports and hence increase National revenue and income. In Nigeria, the growth

pattern has been quite sluggish over the last decades. This fact is connected to the

high increase in the level of poverty, which is further exacerbated by the pandemic

problem of low productivity (Sulaiman, 2005). Nigeria as a nation is blessed with

both human and material resources, but Maduagwu (2000) posits that poverty in the

midst of abundance is a popular paradox characterizing the Nigerian economy.

According to the Central Bank of Nigeria (CBN) (2006), foreign exchange inflow and

outflow through the Central Bank of Nigeria amounted to United States (US) $3.25

billion and US $ 1.16 billion respectively resulting to net inflow of US $2.09 billion.

Despite this huge amount of foreign reserves, Nigerian citizens suffer from

widespread poverty.

Micro enterprises have been referred to as the arm of the industry that could be

used to reach out to relatively low scale investors and develop the home industries.

The roles of micro enterprises cannot be overemphasized in economic development,

accordingly, Chibundu (2006), states “it is encouraging to note that research findings

and empirical evidences show that significant poverty reduction is possible and has

occurred in many countries where micro enterprises are encouraged”. They stimulate

private consumption, ownership and entrepreneurial ability; generate employment,

help diversify economic activities and make significant contribution to export and

domestic trade while utilizing local raw materials.

Micro and Small Enterprises (MSEs) are globally acknowledged as a

potentially critical economic sector. They contribute about 30 per cent of global Gross

Domestic Product (GDP) and account for about 58 per cent of global working

population (Kushnir, Mirmulstein, and Ramalho, 2010). They are numerically

dominant, providing the majority of employment and are the prime sources of new

jobs. They play a critical role as safety net for the bulk of the population in

developing economies including Nigeria. In addition, they provide amenable avenue

for creating new jobs in the economy.

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In Nigeria, the Corporate Affairs Commission (CAC) estimates that about 90% of all

Nigerian businesses in 2007 employed less than 200 persons. From the cluster

development programme in Eastern Nigeria, that is, administrative and infrastructure

costs’ survey of the manufacturing sector (Abia and Anambra States), prepared by

Skoup and Company Ltd for the International Finance Corporation and the World

Bank, February 2003, Nigeria envisions MSEs sector that can deliver maximum

benefits of employment generation, wealth creation, poverty reduction and sustainable

economic growth. Towards realizing this goal, the Nigeria’s Vision 20:2020

advocates measures to enhance the ability of MSEs to compete effectively in local,

regional and global markets, through increased productivity, greater technological

efficiency and reduced cost of doing business. In this context, growth and

competitiveness of MSEs are, therefore, the key objects of the national policy on

MSEs. In the same vein, the national policy seeks to enhance MSEs’ contribution to

GDP and employment and realize its potentials as a principal determinant of the

prospects for the growth and sustainability of Nigeria’s non-oil economy.

One of the major achievements towards MSEs development in Nigeria is the

institutionalization of a policy regime that is stable, supportive and consistent with

national economic reform agenda – the Vision 20:2020, New Partnership for Africa

Development (NEPAD) of the African Union (AU) – as well as being geared towards

realising the United Nations’ Millennium Development Goals (MDGs). For the above

to be achieved, there is the need to remember that we live in a globalizing and

increasingly interdependent world. For developing countries like Nigeria, dependence

on rich nations remains a stark fact of economic life. At the same time, the developed

world, which once prided itself on its apparent economic self-sufficiency, has come to

realize that in an age of dramatically increased capital flows, diminishing natural and

mineral resources, global environmental threats, accelerated international migration,

bourgeoning world trade in manufactured products and services, and new forms of

geopolitical tensions, it is becoming even more economically dependent on the

developing world.

The same applies to industries. They will need to relate with one another at the

national, regional and international levels in achieving the specific objectives and

broad goals of trade, economic growth and development; hence, the popular industrial

and labour maxim - “Industrial Relations for Industrial Growth and Development”.

6

Isolation and barriers have never worked to develop prosperity. According to Amobi

(2006), they have been the key obstacles preventing MSEs to boost their

competitiveness. To the United Nations Industrial Development Organisation

(UNIDO) (2006), “Firms or enterprises that have come together as a group (forming a

cluster) and which are located in close proximity have proved to be capable of rapid

economic growth, sustainable leadership in export markets, significant employment

generation and preservation of high-value added jobs”. Equally, studies from both

developed and developing countries have shown that MSEs cluster development

provides for economic development, poverty reduction and social equity (UNIDO,

2006).

The potentially networking gains of clustered firms or enterprises have led to

the view that clusters offer a specific path of regional, industrial and economic

development, as well as the possibilities of technical innovation and growth. Clusters

are also considered particularly relevant to developing countries since they motivate

significant policy initiatives within industrial development strategies. This has

fostered a growing academic literature on clusters (Markusen, 1996; Scott, 1998;

Malmberg, 1996 and 1997; Nadvi and Schmitz, 1999; Todaro and Smith, 2009).

From available literature, it is agreed that providing a microfinance framework

targeted at these clusters will create a more sustainable model to cushion the fears of

conventional banking institutions who would rather not lend to some individuals.

This would then cultivate high confidence level by the emerging microfinance

institutions that are now expected to grant micro credits to such target markets on

enterprise clusters.

Over the years, the Nigerian government has embarked on series of policies

and institutional reforms aimed at enhancing the flow of finance from the banking

system to Small and Medium Industries (SMIs) as well as those involved in the petty-

business (micro) activities at the informal level. The much talk on the need for

government, financial institutions, corporate organizations and government agencies

to support the establishment and development of the small enterprises subsector has

its merits and demerits. Although, it is not an indication that small business operators

should fold their arms and wait for the almighty handout from these agencies, either

in the form of loans or grants, getting such support could go a long way to

7

transforming the small business landscape in a number of ways and also help to

strengthen the economy of the nation.

According to Amagwu (2006), the focus of microfinance has been on the poor

in the society and the rural populace who are believed to be the most vulnerable. He

opines that, making micro finance available to this group of people would not only

guarantee that they are in a sustainable employment but also contribute to the

economic wellbeing of the nation. In line with this argument, existing community

banks were mandated to upgrade to microfinance banks. They had to raise the

minimum share capital or shareholders’ funds of one unit bank from N5 million to

N20 million with effect from September, 2006. The minimum capital of N20 million,

according to Godwin (2007), was to be deposited with the bank’s formal application

before it can be issued a unit bank operating licence. New investors into this area were

encouraged to do so. Individuals, co-operative societies, corporate organizations,

groups, investors are free to go into this area of investment.

Every year, the government at federal, state and even local and development

centres through budgetary allocations, policies and pronouncements express strong

interest and appreciation of the crucial role of this sub-sector of the economy and

hence, made policies for energizing same. Even local and international donor agencies

have been inundated with requests from non-governmental agencies and organized

private sector associations for grants and other forms of assistance to the sector.

With the above interventions, it is necessary to ascertain whether there have

been some achievements (positive or negative) among these MSEs in the South East

Nigeria, following the various pronouncements by the governments. Among the group

of people in South-Eastern Nigeria are the artisans, petty-traders, subsistence farmers,

fishermen, traders, local textile producers, intra-city transporters, cobblers etc. These

people in the South East, Nigeria region are found within the industrial clusters at

Nnewi, Onitsha, Aba and other rural but emerging locations in the region.

Interestingly, these clusters have the advantage of proximity to several industrial raw

materials which makes it possible to produce associated semi-finished or finished

goods cheaply. Thus, this study is expected to find how effective microfinance from

both formal and informal sources affect the profitability of these micro and small

enterprises.

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1.2 Statement of the Problem

The performance of Micro and Small Enterprises (MSEs) in Nigeria, particularly in

the South East has been affected by so many problems like poor infrastructure (that is,

inadequate power supply, bad roads, and poor transportation system), financial access,

poor corporate governance, insecurity and the hostile legal framework. At the core of

these problems is that of access to finance due to the fact that the people are mostly

informal operators. Hence, the conventional commercial banks and other formal

financial arrangements shy away from extending credit facilities to the sector.

Consequently, majority of the operators resort to informal sources like family and

friends, Isusu, cooperative societies, trust fund model and informal saving groups.

Unfortunately, these sources have limitations in ensuring effective contribution of

micro enterprises to economic growth and sustainable development.

MSEs in Nigeria have not performed optimally and, hence, have not played

the expected vital and vibrant role in the economic growth and development of

Nigeria, particularly in the South East region (CBN 2008). This situation has been of

great concern to both government and the Organized Private Sector (OPS) at various

levels considering the fact that over 70% of the Nigerian population are found in this

category.

Despite the apparent significance associated with these enterprises and the

numerous policy initiatives introduced by government in the past decade to accelerate

the growth and survival of small businesses, the performance has been disappointing.

A study conducted over thirty years on micro enterprises in the Eastern Region of

Nigeria found out that half of the MSEs in Nigeria do not survive beyond a tenth of a

century. The alarming rate of business failure gives the Nigerian economy cause for

concern and has made unemployment reach an embarrassing level. This loss of

employment opportunity has led to frustration, insecurity and uncertainty about the

future due to low performance of the existing micro enterprises in Nigeria hence, the

prevalence of chronic poverty.

According to the Manufacturers Association of Nigeria (MAN), more than

100,000 jobs have been lost between 2001 and 2007 due to continuous closure of

small businesses. Small businesses in Nigeria at present experience a lot of problems

and hardship. These bottlenecks include serious undercapitalization with difficulty in

gaining access to bank credits and other financial markets, corruption and very high

9

bureaucratic costs and government seeming lack of interest in small businesses. All

these have great damaging effect on the economy. Furthermore, inconsistencies in

government policies, natural disasters, and global economic downturn combined to

ensure the dwindling growth of micro enterprises in Nigeria. These dwindling

performances have necessitated this study which is geared towards assessing the

extent to which micro enterprises help in poverty reduction despite dismal

performance in Nigeria.

Of greater concern to all stakeholders is the fact that despite the acclaimed

strong focus on this critical segment of the economic foundation by policy makers, the

sub-sector has fallen short of expectations in terms of profitability and thus

employment generation. The situation becomes more scaring when compared with

other developing economies with similar profile in human and material resources like

Nigeria. It has been shown in the literature that there is a high correlation between the

degree of poverty, hunger, unemployment, economic well-being of the citizens of

nations and the effectiveness of the MSEs in the economic activities of the nation. If

Nigeria were to record a significant success towards attaining the Millennium

Development Goals (MDGs) for 2015, it would be important to vigorously pursue the

development of the micro and small scale enterprises sub-sector of the economy.

Attainment of the MDGs by 2015 may indeed be a mirage unless the micro and small

scale enterprises participate actively and effectively in the economic life of our nation.

Micro enterprises have been described as an engine of economic

empowerment and growth. MSEs are not just job creators but creators of wealth in the

society. While it has been argued that a small business can only make a minor

contribution to the economy as a result of its size, many micro enterprises can make

substantial contributions collectively. For example, according to data from the

European Observatory (CBN 2008), SMEs employing up to 250 people accounted for

68 million jobs in the European Union in 1995. Again, available data from some

African countries shows that in 2003, small enterprises in Kenya employed 3.2

million people, accounting for 18% of the national GDP. In Nigeria, according to

Manufacturers Association of Nigeria (MAN), small enterprises are the backbone of

the economy; they account for 95% of formal manufacturing activities and 70% of

Industrial jobs.

10

Though lack of capacity, inadequate coordination and synergy, poor

networking, isolation, lack of detailed articulation of stakeholders roles in the sector

operations and policy shortfalls have been identified as major problems of the sector,

at the center of it all is lack of access to formal credits. According to CBN (2008), less

than 5% of total credits to the private sector were allocated to micro and small scale

enterprises. It is therefore evident that MSEs do not have adequate access to formal

credit facilities and this situation had restricted the sector to informal financing

through traditional credit supports like Isusu, trade credits, cooperative societies,

market associations, Non-Governmental Organizations (NGOs), government grants

and interventions, etc.

The inadequacies in these forms of credit facilities like reliability volume,

training, standards, spread and repayments have limited the performance of such

enterprises and hence, their poor contributions to the economic growth and

development of the industrial clusters in the South East and the nation as a whole.

The introduction of micro finance banks by CBN in 2005, associated

microfinance institutions, microfinance institutions and development finance

institutions have not bridged this gap of inequality in credit accessibility in Nigeria

after close to 10 years of their operations. It becomes imperative to evaluate the

effectiveness of both the formal and informal sources of microfinance to MSEs,

especially in the South East of Nigeria. It is therefore believed that understanding

these micro credit problems and providing practical solutions for them would be the

right step towards making micro and small scale enterprises contribute effectively

towards growth and development of the industrial cluster in South East, Nigeria and

the nation as a whole, like their counterparts in other countries.

1.3 Objectives of the Study

The major objective of the study is to evaluate the effectiveness of the microfinance

sources on the profitability of Micro and Small Enterprise (MSE) clusters in South

East, Nigeria.

The sub-objectives are:

(i) To assess the impact of formal microfinance sources on the

profitability of enterprise clusters in South East of Nigeria,

(ii) To ascertain the impact of informal microfinance sources on the

profitability of enterprise clusters in South East of Nigeria,

11

(iii) To examine the determinants of the choice of the microfinance source

by enterprise clusters in south East, Nigeria,

(iv) To assess the level of support of microfinance providers for the

sustenance of profitability of enterprise clusters in South East, Nigeria.

1.4 Research Questions

The following are the research questions:

1) To what extent do the formal microfinance sources affect the

profitability of enterprise clusters in South East of Nigeria?

2) To what extent do the informal microfinance sources affect the

profitability of enterprise clusters in South East of Nigeria?

3) What are the determinants of the choice of the microfinance sources by

enterprise clusters in South East, Nigeria?

4) How much is the level of support of microfinance providers for the

sustenance profitability of enterprise clusters in South East, Nigeria?

1.5 Research Hypotheses

The following research hypotheses are presented in their null forms.

1. There is no significant impact of the formal microfinance sources on the

profitability of enterprise clusters in South East of Nigeria,

2. There is no significant impact of the informal microfinance sources on the

profitability of enterprise clusters in South East of Nigeria,

3. There exist no significant determinants (i.e. amount, interest, extent of

protocols including collateral availability, relationship with the provider) of

the choice of microfinance sources by enterprise clusters in South East,

Nigeria,

4. There is no high involvement of the microfinance providers for the sustenance

of profitability of enterprise clusters in South East Nigeria.

1.6 Significance of the Study

The significance of this study cannot be overemphasized. It is so significant in the

sense that;

• It will help to expose the various micro financing strategies employed in the

development of industrial clusters in Nigeria, with particular interest in the

South-Eastern part of the country. In addition to the above, it will help in

12

examining the strengths and/or weaknesses and relevance of these micro credit

strategies to the development of the industrial clusters in Nigeria, particularly,

in the South East.

• Since the microfinance supports for the development of industrial clusters in

Nigeria, with particular interest in the South East cannot be

effectively/efficiently carried out without active participation of stakeholders,

the study will therefore help to ascertain the contributions of various

stakeholders, and their levels of commitment in terms of relationship and

willingness in ensuring that the goals and objectives of the micro credit

supports for the development of industrial clusters in Nigeria, particularly in

the South East are actualized. Informal micro financing sources are (a) Esusu

(b) Self Help Group Contribution (SHGC) (c) TFM – Trust Fund Model, (d)

family and friends, (e) Non-Governmental Organizations (NGOs) (f) others

• The study will bring to the knowledge of the major stakeholders in the

development of industrial clusters and MSEs in Nigeria, i.e. the government,

the microfinance banks, the micro-business operators themselves, the national

and international donor/aid agencies etc., the efficacy of establishing and

developing industrial clusters in the country, the kind of impact (negative or

positive) the industrial clusters development would make on the economy,

which eventually will enable them formulate favourable and positive policies

and implement fully the developed strategies that would help the micro credit

scheme, aimed at eradicating poverty achieve its goals and objectives.

• Finally, this study will help to add to the already existing literature, especially

in the developing counties, which Nigeria is part of, and this will surely serve

as a reference material for scholars who may want to embark on further

studies on this subject matter or those related to it.

1.7 Scope of the Study

This study focuses mainly on the impact of microfinance sources on the profitability

of Micro and Small Enterprises (MSEs) in Nigeria with particular interest in the

South-East, Nigeria using enterprise clusters at Onitsha, Aba, and Nnewi. Profitability

as a key objective of every business is measured by return on investment. Micro

enterprises would be the target group based on the objectives and the information

13

required from the questionnaires. The study covers the period between 2013 and

2014.

1.8 Limitations and Structure of the Study

Like all other studies, this study witnessed its own circumstantial difficulties. The

main fact that the study employed primary survey analysis that warranted fieldwork

and questionnaires introduced all the challenges that go with it. First of all, the timing

of the fieldwork vis-à-vis the study programme was a major concern as the rainy

season could be a major hindrance to the field survey. There was, therefore, the need

to situate the field survey in a dry and friendly season in order to ease the distribution

and collection of the questionnaires.

The fieldwork itself had issues like every other field which include; reliability

of the information given, reliability of the enumerators amongst others. This was

however lessened with the degree of supervision and monitoring of the survey.

Nevertheless, this study faced peculiar issues due to the nature and occupation of the

respondents. The respondents (business men) had no time to respond to the questions

and the few that were able to respond, were still not very patient and needed a lot of

persuasion. The respondents were also very skeptical about the use of the data. Some

of them feared that the fieldworkers were actually tax officials who were sent as spies.

The respondents were equally very nonchalant about the documents as they opined

that the government had done little or nothing in the past and that that was just

another paper framework.

Also, the fieldwork was very expensive. It employed fieldworkers who went to the

three clusters at Aba, Nnewi and Onitsha and were supposed to cover the three sectors

of production that include; production, trade and services. The fieldworkers had to be

motivated to go to the clusters and spend some days. Also, the supervision required

moving to the clusters to monitor groundwork and equally implied increasing cost of

the field survey. The Focus Group Discussion (FGD) which was intended to collect

responses from questionnaires and interviews was difficult to obtain from traders as

most were busy with trading transactions and had little time to sit for discussions.

In terms of research structure, the first chapter contains the introduction,

problem statement, study objectives, research questions and hypotheses; as well as the

14

study scope, limitations, MSEs background and operational definitions as used in the

study. The second chapter reviewed not just the conceptual literature but theoretical

and empirical literature. It also summarized all reviewed literature and identified

potential gaps and how they were covered in the current study. Chapter three

presented the theoretical framework and the study design including study area, study

population, sample size, study models, estimation procedures and hypotheses testing

techniques. Chapter four presented all the analyses and study results as well as

findings, decisions on hypotheses tested and discussion of findings while the final

chapter (five) summarized the findings, identified policy implications and based on

that made recommendations. It also contains areas and issues for further research,

contributions to knowledge and conclusions.

1.9 Background Information on Development of MSEs

Microfinance institutions were created in Nigeria by the Central Bank in 2005.

However, before the emergence of formal microfinance institutions, informal

microfinance activities flourished all over the country. Informal microfinance is

provided by traditional groups that work together for the mutual benefits of their

members. These groups provide savings and credit services to their members. The

informal microfinance arrangements operate under different names: Esusu, among the

Yoruba of Western Nigeria, Utuu, for the Igbo in the South East and Adashi, in the

North for the Hausa (CBN, 2003). The key features of these informal schemes are

savings and credit components, informality of operations and higher interest rates in

relation to the formal banking sector.

The informal associations that operate traditional microfinance in various

forms are found in all the rural communities in Nigeria (Otu, Ramlal, Wilkinson, Hall,

and Hecky, 2011). They also operate in the urban centers. However, the size of

activities covered under the scheme has not been determined. The non-traditional,

formalized Microfinance Institutions (MFIs) are operating side by side with the

informal services. The financial services provided by the MFIs in Nigeria include:

savings, credit and insurance facilities. The major formal microfinance suppliers

include the Commercial Banks and Microfinance as well as the Development Finance

Institutions. However, microfinance suppliers exist so as to provide low and

accommodative rates of interest because of the existence of inequitable distribution of

wealth and income and to reach out to the poor. From the appraisal of existing

15

microfinance-oriented institutions in Nigeria, the following facts have become

evident: weak institutional capacity, weak capital base, existence of a huge un-served

market, economic empowerment of the poor, employment generation and poverty

reduction, the need for increased savings opportunity, the interest of local and

international communities in micro-financing and utilization of the small and medium

enterprises equity investment (SMEEIS) fund.

SMEEIS, however, is said to have failed due to the fact that, it required a

partnership of ownership between the micro enterprises and the microfinance

operators as a means of involving the banks fully into developing these enterprises.

This effort failed partly because the entrepreneurs had a jealous and, of course,

protective ownership attitude of their enterprises and so did not accept to get in terms

with the banks. On the other hand, several bureaucratic engagements that are involved

in becoming co-owners such as the Memorandum of Understanding (MoU) scared the

banks from actively getting involved. This led to the creation of the Microfinance

Development Fund in 2013 by CBN. This project was launched with a seed capital of

₦220 billion, having 80% devoted to micro enterprises and 20% to small and medium

size enterprises. The Microfinance Development Fund that is now operational has the

advantage of asserting specific amounts for interest rates, sectorial loan quotas, and

sex ratios. Unlike the SMEEIS that compelled both banks and entrepreneurs to be co-

owners, the Microfinance Development Fund allows banks to operate from a distance

yet ensures that the modalities are moderate.

1.10 Operational Definition of Terms

Micro Enterprise: It is a firm whose total cost including working capital and

excluding cost of land is not more than ten million naira (N10,000,000) and/or with a

labour size of not more than ten (10) full-time workers and/or an annual turnover of

less than two million naira (N2,000.000) only.

Small Enterprises: It is an enterprise whose total cost including working capital but

excluding cost of land is between ten million naira (N10,000,000) and one hundred

million naira (N100,000,000) and/or a workforce between eleven (11) and forty nine

(49) full-time staff and/or with annual turnover of not more than ten million naira

(N10,000,000) in a year.

Medium Enterprises: It describes a company with total cost including working

capital but excluding cost of land of more than one hundred million naira

16

(N100,000,000) but less than three hundred million naira (N300,000,000) and/or a

staff strength of between fifty-one (51) and two hundred (200) full-time workers

and/or with an annual turnover of not more than twenty million naira (N20,000.000).

Industrial Clusters: It refers to geographical proximate group of interrelated

enterprises and associated institutions in a particular business environment linked by

commonalities and complementarities. Clusters are considered to increase the

productivity with which companies can compete, nationally and globally.

Micro Credit: This means making financial services available to the poor, low income

earners and Small Scale Enterprises (SSEs). The United Nations (UN) declared 2005

International Year of Micro Credit (IYMC).

Microfinance Institution: This is an institution that extends small loans or

microfinance to applicants who typically belong to the lowest strata of society. Loans

are extended to borrowers to allow them to initiate a business, repair their homes and

improve the general living conditions of their families and the community.

Cluster Strategy: It is an economic development strategy that provides a coordinated

and efficient way to promote economic growth. By making a cluster approach a key

part of a state economic development strategy, state agencies are more likely to

coordinate their efforts, avoid duplication of services, and develop a more

comprehensive approach to economic development.

Poverty Alleviation: Poverty alleviation (or reduction) describes strategies to

ameliorate poverty. It is any process which seeks to reduce the level of poverty in a

community, or amongst a group of people or countries. Poverty alleviation

programmes may be aimed at economic or non-economic poverty.

Economic Development: It refers to the sustained increase in the economic standard

of living of a country's population, improving the quality of human life through

increasing per capita income, reducing poverty and enhancing individual economic

opportunities by developing technology, making more productive and efficient use of

physical capital, and increasing human capital.

Social Development: This refers to the improvement in qualities of life and human

well-being by organizing human governance and affairs to accomplish such tasks as

the alleviation of poverty, the reduction of income disparities, the elimination of

violence, the guaranteed right to clean water and health services, the increased respect

17

for nonhuman creatures and their ecosystems, and the structuring of a just legal

system and system of representation.

Profitability: This term is used to describe the gain or compensation to an

entrepreneur or a firm for engaging in economic activities. It is usually defined by

returns on investment or assets. Profitability is derived from gross earnings either

after tax or before it.

Private Sector-led Growth: This is the private sector engagement as the main driver

of economic and social progress, with businesses, not governments, providing the

bulk of the investment, innovation, employment and income, which can bring about

the growth and productivity increases.

18

CHAPTER TWO REVIEW OF RELATED LITERATURE

2.1 Introduction

This chapter reviews conceptual, theoretical and empirical literature on issues around

microfinance and micro financing, cluster and clustering and an analysis of

microfinance in the Nigeria context. Details of the issues are organized under the

conceptual framework, theoretical and empirical literature and Nigerian situational

analysis.

2.2 Conceptual Literature

Micro financing is the provision of financial services to poor and low income

households without access to formal financial institutions (Conroy, 2003).

Microfinance is also described as banking for the poor. Microfinance programmes

provide loans, savings and other financial services to low income earners and poor

people for use in small businesses. Originally based on traditional forms of

community financing (a cross between finance and development assistance),

microfinance is found all over the world, especially Africa, Latin America and Asia.

The microfinance movement began in earnest in the early 1980s (Anyanwu, 2004).

According to Khan (2007), Micro Finance Institutions (MFIs) cover a variety of

activities like qard-hasan, financing housing, meeting basic needs, and promoting and

financing small entrepreneurs. All these aspects can be covered in a comprehensive

integrated programme tagged ‘micro-financing’ as practiced in Bangladesh and

Bolivia over the last 20 years.

Microfinance institutions are essentially needed to serve the poor city

dwellers, residents in slums or squatter settlements in appalling conditions. They lack

access to basic services such as education and health care, consequently, they lack

basic skills for employment. Many of them are women who are poorly trained, and

play dual roles of provider and caregiver. These poor people are more exposed to the

threats of contaminations, bad sanitation, and disease than the rest of the population

(Ornorodion, 2007).

Micro enterprises constitute the most dynamic and heterogeneous sector in

Nigeria (CBN 2006). Between 1990 and 1995, an average of 84 out of 100 new jobs

was generated by micro enterprises. The GDP contribution to the economic sector is

19

harder to estimate due to its scale and widespread informality. Measurements of micro

enterprises’ participation in GDP range from less than 10% to 50%, depending on the

economy and method of estimation. There are two views of the micro enterprise

sector with different policy implications. The first one considers workers in the micro

enterprises sector as either underemployed or surplus labour (Chari, 2000). These

workers are not employed in the formal sector due to their low skills (unemployment

view).

The second view focuses on the fact that some workers choose this sector for

its flexibility and earning opportunities (micro-entrepreneur view). While the

existence of high levels of poverty in the sector is strongly suggested by the first view,

poverty is not necessarily a permanent micro enterprise condition according to the

second view. Since late 1970s, the African Development Bank (ADB) has adopted the

micro entrepreneur view, which posits that micro enterprises development can be an

effective mechanism for poverty reduction through market-driven and productive

activities. Policies oriented towards supporting and promoting micro enterprises have

three major fronts: micro finance, change in the regulatory framework, and business

development services. There are also other policy interventions that have a positive

impact on micro enterprise development such as, the provision of productive

infrastructure and child care programmes for female workers.

2.2.1 Defining Micro, Small and Medium Enterprises (MSMEs) in Nigeria

Micro, Small and Medium Enterprises (MSMEs) is a core necessity in developing

well-targeted policies, legislation, programmes and services. It is vital to define

MSMEs in order to build a consistent and reliable database and identify object for

evaluating impact of policies and developing appropriate responses. The challenge

lies in the fact that MSME is not an absolute concept, but relative to large business

and industrial sectors, both of which are also relative to the size and nature of the

domestic economy. Criteria for defining the scale of a business vary from country to

country, depending on country’s circumstances. Some countries define MSMEs by

total assets, others by employment rate, turnover, or paid-up capital; while some

countries by sectors and several others use more than one indicator. In developing a

national policy, it is proper to state if there should be one exclusive definition, how

complex that definition should be and whether the definition should vary to some

degree according to specific policy, legislative and programme objectives.

20

Experiences of other countries are mixed. Some countries, such as the United

States legislate on SME definitions. But, in many countries, the central statistical

authorities or lead policy agency may use one or more criteria, while separate

programme departments pre-determine different criteria in order to achieve

organization-specific programme objectives. International approaches in defining

SMEs can be deciphered from the examples as follows (Table 2.1).

Table 2.1: Cross-Country approaches to defining SMEs Country Classification approaches and criteria

Australia Australian Bureau of Statistics defines small businesses as those employing fewer

than 20 persons. The Australian Tax Office uses a definition of average annual

turnover of less than $1million and net assets of less than $3million. By contrast,

the Export Insurance scheme targets small businesses with annual turnover not

exceeding $10million

China China is introducing new criteria for enterprise classification based mainly on

sales revenue and total assets. Small enterprises have sales revenue and total assets

not more than RMB 50million, medium enterprises have between RMB 50-500

million

India Small scale industry has different meanings for different agencies. The Planning

Commission, National Sample Survey Organisation, Central Excise Department

have varying definitions relating to annual turnover, fixed assets and/or investment

ceilings.

South

Africa

The National Business Act of 1996 identifies four different categories – micro,

very small, small and medium – differentiated by sector, and then by number of

full-time employees, the value of annual turnover and total gross asset value

(excluding fixed property). While turnover and gross asset criteria vary

significantly across sector, there is a great deal of commonality with respect to the

employment criteria. Employment criteria for the manufacturing, construction and

utility sectors are: micro – 5, very small – 20, small – 50, medium -200. But, in the

service industries, these employment criteria are: micro – 5, very small – 10, small

– 50, medium – 100.

United

Kingdom

There is no single definition for small businesses. But, the Small Business Service

defines businesses according to number of employees: micro, 0-9; small

21

Country Classification approaches and criteria

businesses, 0-49; medium sized businesses, 50-249; and large businesses, 250+

employees. It is, however, noted that size is relevant to sector, a firm of a given

size could be small in relation to one sector where the market is large and there are

many competitors; whereas a firm of similar proportions could be considered large

in another sector with fewer players and generally smaller firms within it. It is also

recognized that it may be more appropriate to define size by the number of

employees in some sectors but more appropriate to use turnover in others. Across

government, it is most usual to measure size according to numbers of full-time

employees.

United

States

The Small Business Act requires that the definition of a small business be varied

by industrial sector to reflect essential differences between the sectors. The

fundamental definition used by the Small Business Agency is the numerical “size

standard” which is almost always stated in terms of either number of employees or

average annual receipts. In developing the standards, the SBA considers economic

characteristics of particular industry, average firm size, start-up costs and entry

barriers and distribution of firms by size.

Canada For statistical purposes, small businesses are defined as those with less than 100

employees in manufacturing and less than 50 employees in the service sectors.

Source: International Experiences in Governmental Policies and Processes for SMEs: A Comparative Analysis. Prepared for the International Development Research Centre by Growth Connections, December 17, 200: p 37

In Nigeria, the current classification is based on number of employees and

assets (excluding land and buildings). But, defining MSMEs based on multiple

criteria could be problematic, since it could lead to non-mutually exclusive categories.

This is because of potential enterprise asymmetry across criteria, that is, incidence of

outliers due to sectoral biases. For instance, some enterprises, depending on the levels

of capital-intensity vis-à-vis labour-intensity may not match the dual employment-

cum-assets-based criterion. Some enterprises may have very low size of labour

employment but high capital outlay, e.g. Information and Communication Technology

(ICT) enterprises. A contradistinction is enterprises that have very high labour

employment but very low capital outlay, e.g. low technology manufacturing. Besides,

22

inflationary trend may erode the assets-based definition and make it necessary to

revise the definition periodically.

Some of the current classifications in Nigeria are shown in Table 2.2, as follows. Table 2.2: Different classification of MSMEs in Nigeria

Agency Employment based classification

Assets based (excluding real estates)

Micro Small Medium Micro Small Medium IFC <10 10-50 50-100 -------- <N2.5million --------- CBN SMIEIS study <10 10-50 51-300 -------- ---------- --------- WB RPED <20 20-49 50-99 -------- --------- --------- UNIDO NMES ≤5 5≤20 21≤75 FMI <10 11-35 36-100 ≤1million 1<N40million N40-150million NBS <10 10-19 Sourced from several reports Based on the cross-country experiences, the Nigerian National Policy of MSMEs

adopted definition based on the number of employees in the business. This is because

of many factors:

• In practice, the number of employees is the most common standard used in

national SME policies worldwide.

• Experience shows that the number of employees is the most unifying criterion

for classifying enterprises, others such as turnover or assets base tend to be

more asymmetric across sectors.

• Governments worldwide adopt the size of employees. Most governments

usually measure size according to numbers of full-time employees.

• The criterion – number of employees – is the most amenable to

synchronization across the various government agencies – National Bureau of

Statistics, Federal Ministry of Industry, etc, and across sectors of the economy

• Number of employees is already the most popular criterion adopted by various

government agencies and development stakeholders in the country

However, the use of number of employees does not preclude specialized agencies

such as export promotion, loan guarantee and tax authorities from targeting

enterprises for their respective organizational programmes based on turnover or other

criteria. Also, the definition can be varied by sectors of the economy. For example,

the benchmark number of employees for small enterprises may be lower for service

23

sectors due to higher capital intensity. Similar situation of varying benchmark for

enterprise size exists in United States, Canada, South Africa and United Kingdom.

2.2.2 Sector Classification of MSMEs

Another dimension of MSMEs characterization is the classification by sector and/or

sub-sector. MSMEs are classified according to several schemes.

2.2.3 Generic Categorization

MSMEs can be classified based on traditional sectors, for example, primary

(agriculture, mining), secondary (manufacturing) and tertiary (commerce, finance and

personal services). Furthermore, manufacturing MSMEs are often classified into

categories which include: Chemicals and Paints; Food and Beverages; Metal; Non-

metal; Paper, Printing and Publishing; Pharmaceuticals; Plastic, Textile and Leather

and Wood (The World Bank, 2003).

Another classification of the MSMEs manufacturing sector includes Food

Processing; Textiles and Garments; Wood (including furniture and paper processing);

and Metal, Machinery and Chemicals (The UNIDO 2001 & 2004).

2.2.4 Technological Intensity Criterion

MSMEs are often classified according to levels of technological intensity of the

products. Based on this criterion, five classes can be distinguished: primary products;

resource-based manufactures; low-technology manufactures; medium-technology

manufactures and high-technology manufactures. Primary products include minerals,

agricultural and forest products exported in unprocessed state. Resource-based

manufactures include processed foods, simple wood products, dyes, leather and

organic chemicals. Low-technology manufactures are characterised by well-diffused

technologies largely embodied in capita products, low Research and Development

(R&D) and skill requirements, and low economies of scale. They include textiles,

garments, footwear, other leather products, toys, furniture and glassware. Medium-

technology manufactures are heavy industries like automobiles, industrial chemicals

and standard electric and electronic products. They have relatively high capital

requirements, complex technologies, with moderate levels of R&D but advanced

engineering and design skills and large scale production. High-technology

manufactures are complex electrical and electronic products, aerospace products,

24

precision instruments, fine chemicals and pharmaceuticals. They call for advanced

manufacturing capabilities, large R&D investments, advanced technology

infrastructure and close interaction between firms, universities and research

institutions (Ministry of Finance, 2004).

2.2.5 National Bureau of Statistics Classification

Recently, the National Bureau of Statistics divides business firms into 14 categories,

namely, agriculture, hunting and forestry; fishing; mining and quarrying;

manufacturing; electricity, gas and water; building and construction; wholesale and

retail trade; hotels and restaurants; transport, storage and communication; financial

intermediation; real estate, renting and business activities; public administration and

defence; health and social work and other community, social and personnel. This

existing classification is further extended to the national policy.

2.3 Theoretical Literature

Developing and less developed economies currently strive to boost micro enterprises

and small enterprises especially those that exist within a cluster. This struggle to boost

these micro enterprises and small enterprises are in line with the current global

concern and paradigm shift for sustainable development. Three appropriate theories or

models that underpin this move are the growth pole, the dual economy and the

sustainable development models. It is noteworthy that this current study is not entirely

focused on microfinance effect on enterprises growth and profitability per se but on

the microfinance sources for these Micro and Small Enterprises (MSEs), and their

effects on profitability, in other words, it is a study on capital structure. It deals with

the importance of financing choice to MSE’s profitability. Financing choice raises

particularly important research and policy questions for enterprises. Micro and Small

enterprises (MSEs) promotes micro and small scale investments that may end up

generating sufficient revenues from otherwise unrealized market activities while

yielding a return on the investment.

Financing choice involves a tradeoff between risk and return to

maximize shareholder wealth (Berger and Bonaccorsi di Patti, 2006). The objective of

an optimal financing choice for any enterprise is therefore, to have a mix of debt,

preferred stock, and common equity that will maximize shareholders wealth. For

example, changes in financial leverage affect enterprise value. A lower debt ratio can

25

enhance the rate of return on equity capital during good economic times. On the

contrary, a higher debt ratio increases the riskiness of the enterprise’s earnings stream.

One of the important financial decisions confronting an enterprise is the choice

between debt and equity. The seminal paper dealing with irrelevance of debt in capital

structure for determining enterprise value by Modigliani and Miller (1958) included a

number of assumptions — one of which was absence of corporate tax. Subsequently,

when Modigliani and Miller (1963) factored corporate tax in the model, it was found

that theoretically, the value of an enterprise should increase with debt because of

higher interest tax shield. But monotonic increase of debt for higher tax shield

increases bankruptcy cost especially when profitability of the enterprise is low and

fluctuating. This leads to ‘trade off’ theory of capital structure that postulates an

optimum debt level or target level, where the marginal increase of present value of tax

saving is just offset by the same amount of bankruptcy cost.

This section in addition to the growth pole, the dual economy and the

sustainable development models therefore, reviews trade off, agency cost hypothesis

and pecking order theories of capital structure and relates them to MSEs. Details of

these theories are reviewed below.

2.3.1 The Growth Pole Theory

The main tenet of Perroux's (1950) growth pole hypothesis is that a growth pole is 'a

place of passage of forces, which attracts men and objects to it and also repels

them'. It is a centre 'from where centrifugal and centripetal forces operate'.

Boudville (1966) had polarized a region which is characterised by the dominance

of a regional centre (growth space) to which all flows, such as, goods, services,

capital, ideas or political allegiance are predominantly directed. The regional

centre or growth centre links a heterogenous continuous area into inter-dependent

and inter-regional units. According to Lasuen (1969), the spatial investment-

strategy of growth centers purport to advance developmental efficiency and

equality goals, and have thus become the predominant investment policy strategy in

many countries, especially, the developing ones.

Basically, it is held that "growth does not appear everywhere at the same

time; it manifests itself in points or 'poles' of growth; with variable terminal effects

for the economy as a whole" (Perroux, 1950). In a specifically geographic sense, a

growth centre has been defined as '... an urban centre of economic activity, which can

26

achieve self-sustaining growth of the nation' (Mergos, Papadaskalopoulos,

Christofakis, Arseniado and Kalliri, 2004). Thus, initially, growth is held to be

concentrated at a matrix of favourable points, and subsequently the growth impulses

so generated are transmitted to the surrounding area - 'the growth space'. Hence,

according to Mergos et al (2004), '...the spatial incidence of economic growth is a

function of distance from a central city... The growth potential of an area situated

along an axis between two cities is a function of the density of interaction

between them…’ The growth potential of a region is thus held to be closely related

to the 'intensity of interaction’ between the growth centre and its surrounding regions.

Indeed, it has been argued that 'the spatial structure of a region and the size and

spacing of its towns may be the crucial factors in explaining regional potential'

(Lasuen, 1969), hence, the popularity of the concept in developing nations, including

Nigeria.

A crucial aspect of the growth centre concept is the idea that growth generated

in the growth centres will spread to their hinterland. The spread mechanism may take

the form of stimulation of food production for urban industrial markets; increased

production of industrial raw materials for processing industries; employment

opportunities for any surplus rural labour following agricultural mechanism within the

growth-space; financial remittances to the rural areas by migrant workers; diffusion of

innovations into growth space; and subsidiary investments made by rich firms located

at the growth centre in surrounding regions (Lasuen, 1969).

It is also argued, however, that there is an opposing set of backwash effects including

‘the migration of the educated, the skilled, the professionals, and the technical

workers from the hinterland to the growth centre and consequent adverse changes on

the former’s skill mix; the diversion of savings that might have been used

productively in the hinterland; the displacement of any embryonic industries that

might exist in the hinterland, and the stronger relative pull of the growth centre on

new locators...’ (Otero, 1999). Thus, powerful backwash effects may, in fact, 'erode'

the economy of the surrounding regions rather than stimulate growth. There is a

constant interplay of spread and backwash effects with the net result that the

hinterland is either impoverished or enriched depending on the strength of spread

effects (Otero, 1999: 20).

27

The logical implications of this for policy making is that any growth-centre

strategy must devise mechanisms for stimulating strong spread effects, while at the

same time cushioning the effects backwash forces. In view of the role played by urban

growth centre in the spread of innovations, a growth-centre strategy may substantially

raise a region's capacity for the spread and adoption of innovations. However, it is

important to stress that preliminary research ought to be conducted so as to identify

the major constraints on the spread and adoption of innovations in the region

concerned, as mere creation of a set of growth centres in a region may not necessarily

remove all the obstacles to the acceptance of new ideas and technology. Isolated

growth centres, however powerful in generating growth, may not bring about

transformation, but will rather parasitize on the hinterland (Otero, 1999:20).

The main attraction of the growth centre concept and its variants as a planning

strategy is the recognition that market forces alone often create spatial inequalities in

economic development and there is often the need for a deliberate policy of

intervention to correct this trend. The strategy advocates the identification and

creation of growth centres of different orders in any space economy which will help

speed up and even out development across space. As a development planning

strategy, it has been used in many countries, especially the developing ones (Otero,

1999:21). According to Kudiabor (1974), the Ghanaian government used it and

designed a four-tier hierarchy of growth centres in the economy as a strategy of rural

development. This comprises: (a) rural development service centres, (b) growth

centres at the district level, (c) growth centres at the regional level and (d) growth

poles at national level.

Industrial/enterprise clusters are a typology of a growth pole. In Nigeria, the

Federal Government's Integrated Rural Development Approach comprises

Agricultural Development Projects or the World Bank Projects; the Farm Settlement

Schemes; the River Basin and Rural Development Approach; and the Local

Government Reform of 1976, were all deliberate interventionist planning policies

aimed at developing the rural areas based on the diffusion and growth centre models.

As a result of the 1976 Local Government Reform, for instance, 301 local government

headquarters emerged as new lower order growth centres that would facilitate a more

even and faster grassroots-oriented development. Similarly, the Agricultural

Development Programme (ADP) or the World Bank Projects (WBP) and lately, the

28

River Basin and Rural Development Authorities' Projects (RBRDAPs) were designed

to serve as growth centres from where ideas, techniques and innovations in agriculture

would diffuse to the wider hinterland.

In all, there has been much variation in the performance of these projects, but

many of them have enjoyed limited success (Kudiabor, 1974). This is because the

projects lacked an essential ingredient in rural development planning. The main

impact of the schemes has been 'implosive' rather than the ultimately desired

'explosive' growth (Otero, 1999). The growth centres have proved to be 'parasitic'

rather than being 'generative' because they were not tuned properly to the needs,

interests, aspirations and capabilities of the local people. Rather, the process has

further heightened inter-regional inequalities instead of levelling them up.

In the same vein, Kudiabor (1974) had seriously criticised the growth centre

strategy or the development 'from above' paradigm, which had its roots in neo-

classical economic theory. The logic of the growth centre strategy is that productive

investments should be concentrated in urban-industrial centres so as to take advantage

of the external economies, labour specialization and cumulative causation processes.

It is argued that from these dynamic growth centres, development would ‘trickle

down' or diffuse to the rest of the spatial economic system.

Questioning the idea, Kudiabor (1974), further pointed out that these spread

mechanisms are very slow in effecting substantial changes in the rural sector because

of the fundamental weaknesses of interaction in the communication field between the

rural and urban centres. Moreover, a matrix of 'backwash effects' usually reduces the

beneficial influence of any spread effects. The distinct failure in Nigeria of the spatial

policy of concentrating investments and power in a few places so as to gradually

transform the surrounding regions has led to a search for more effective strategies.

In line with objectives 1 and 2 of the study, it is imperative to energise

industrial clusters as “growth poles” or centres of industrial experiments for

interventions. Understanding the existing credit processes to the operators and their

limitations would inform and determine new and more effective strategies to be

adopted in making the industrial clusters “growth poles” in name and indeed. Since

micro-credits to such clusters positively affect the poor, women and Small Scale

Enterprises (SSEs), such a framework will make the SSEs contribute effectively

29

towards economic growth which will enhance the attainment of MDGs and ultimately

lead to sustainable development.

Michael Porter “Clusters” Theory of Competitive Advantage

An economic definition of a city is an area with relatively high population density that

contains a set of closely related activities. Firms often also prefer to be located where

they can learn from other firms doing similar work. Learning takes place in both

formal relationships such as joint ventures, and informal ones, such as from tips learnt

in evening social clubs or over lunch. These spillovers are also agglomeration

economies, part of the benefits of what Alfred Marshall called “industrial districts”

and they play a big role in Michael Porters “Clusters” theory of competitive

advantage (Todaro and Smith, 2009:608). Firms located in such industrial districts

also benefit from the opportunity to contract out work easily where an unusually large

order materializes. Thus, a firm of modest size does not have to turn down a big job

due to lack of capacity, an arrangement that provides “flexible specialization”.

Furthermore, firms may wish to operate in well-known districts for the marketing

advantages of locating where consumers know to get the best selection (Todaro and

Smith, 2009:328).

Another interesting sample of the new, post-neoclassical genre of international

trade modes is contained in Micheal E. Porters’ fundamental departure from the

standard, neoclassical factor endowment theory that posits a qualitative difference

between basic factors and advanced factors of production.

The study argues that standard trade theory applies only to basic factors like

undeveloped physical resources and unskilled labour. For the advanced factors which

are more specialized and include highly trained workers with specific skills and

knowledge, resources such as government and private research institutes, major

universities, leading industry associations and industrial clusters, standard theory does

not apply. Porter (1990: 675-676) concludes that:

The central task facing developing countries is to escape from the straitjacket of factor – driven national advantage...where natural resources cheap labour, locational factors and other basic factor advantage provide a fragile and often fleeting ability to export...and are vulnerable to exchange rate and factor cost swings. Many of these industries are also not growing; as the resource intensity of advanced economies falls and demand becomes more specialized...Creation of advanced factors is perhaps the first priority.

30

It is a well-known fact that some economies, like the four Asian Tigers (Taiwan,

South Korea, Singapore and Hung Kong), have succeeded in transforming their

economies through purposeful effort from unskilled labour to skilled labour and to

capital – intensive production. Other Asian countries, notably China and the MIT

countries of Malaysia, Indonesia and Thailand, are also following in their footsteps.

This is also true of the current four rapidly developing and large “BRIC” countries:

Brazil, Russia, India and China (World Economic Forum et al, 2007:7-8). However,

for the vast majority of poor nations including Nigeria, the possibility of trade itself

stimulating similar structural economic changes is more remote without the

application of judicious development policies, which include pragmatic economic

policies on industrial clusters.

Understanding the dynamics of industrial clusters in the South East of Nigeria

will provide the framework of interventions needed to upgrade the operations of firms

within the clusters, especially skill upgrade, firm expansion (vertical and horizontal),

network and establishment of industrial estates, parks and training institutes. The

essence is to increase the social capital and the external economies coming from such

centres. It also provides stakeholders with defined roles and responsibilities and

evaluation of such and how they impact on the activities of the operation in such

locations. This theory supports and amplifies our research objectives 3 – 4 which set

out to assess the parameters used in providing micro-credit support, the commitment

of stakeholders, willingness and commitment of these stakeholders to the sustenance

of MSEs in the south East.

The Classical and Neo-Classical Model

The proponents of the model (Boeke, 1953; Lewis, 1954; Ranis and Fei, 1961; and

Keynes, 1936) argue that the growth of an economy whether rural or urban, is a

function of capitalist investment and employment of labour. Because of the fact that

capital tends to flow into sectors characterised by high rates of return and high

marginal productivity of capital while labour similarly moves into a sector

characterized by high wage rates, the classical and neo-classical proposition stipulates

that the promotion of economic development in the rural areas should involve

measures which will raise the rate of return to capital investment and the earnings of

labour.

31

To a certain extent, the classical and neo-classical model has some relevance

to industrial cluster development and urbanization in a developing country, such as

Nigeria, where emigration of labour and capital from agriculture is usually attributed

to much lower return to these factors of production in rural areas than in urban

investments. Nevertheless, the model has a number of obvious limitations.

First, it ignores the importance of improved quality of labour as a factor in

economic development, especially, since it is well known that in both the advanced

and developing countries, agricultural and economic development are positively

related to the quality of the labour force. Second, the model ignores the role of

community service and infrastructures, which by generating external economies,

account for high rate of return to capital investment. Third, the model places

exaggerated emphasis on factor and input prices as determinant of investment and

growth, thereby ignoring the role of institutional and organizational arrangements.

Finally, the model fails to take into consideration the crucial role of technology,

which by shifting the production function upwards to reduce costs and increase the

rate of return to capital investment (Otero, 1999).

2.3.2 The Dual Economy Models

The dual economy models (classical, neo-classical and post-Lewisian), stipulate that

the typical less-developed country is characterised by the existence of two distinct

sectors, namely, the modern sector and the traditional (rural) subsistence sector. While

the modern sector is market oriented and uses considerable capital equipment and

technology, the subsistence sector produces for family consumption and it relies on

non-purchased inputs, such as, family labour and land for production. Unlike the

modern sector, the subsistence sector is also characterised by absence of savings and

capital formation.

Briefly, in the original Lewisian model (Lewis, 1954; Ranis and Few, 1961),

industry via capital accumulation provides the 'engine of growth'. The agricultural

sector is important but plays a supportive and passive role in the growth sense by

merely providing a pool of unlimited cheap unskilled labour for use by industry

(Todaro and Smith, 2009). Agriculture also supplies cheap food to the urban industrial

dwellers. From the foregoing, the development process in the classical Lewisian

scheme consists of the progressive enlargement of the capitalist industrial sector.

Thus, in a labour surplus economy, aggregate employment increases as capital

32

formation increases in the industrial sector. In such a scheme, the agricultural sector

continues to play a passive role provided it is a source of cheap unlimited labour. But

when cheap unlimited labour is exhausted, agriculture now imposes a limit to the

expansion of the capitalist industrial sector. The increase in wages causes profits to

decline and consequently, capital accumulation and employment will fall (Todaro and

Smith, 2009).

In neo-classical dualism (Jorgcnson, 1961; Dixit, 1969; and Zarembka, 1970),

the agricultural sector is no longer the passive supplier of food and unlimited cheap

labour. Rather, it plays a more active role since steady-state equilibrium in a dualistic

economy depends on the rate of agricultural output per man. Thus, enlargement of the

industrial sector is not at the expense of the agricultural sector, as in the basic

Lewisian theory, but depends on investment in, and hence, expansion of agriculture.

The post-Lewisian dual economy is characterized by the availability of an unlimited

cheap labour and unlimited cheap land. In such an economy, capital accumulation

plays the classic role of being the 'engine of growth', but for steady growth,

agriculture must be commercialised - a process which requires considerable

investment by government in the agricultural sector as in Nigeria (Otero, 1999).

Given the above characteristics of the two sectors, the formulators of the dual

economy models had no difficulty in prescribing it as the most appropriate

development strategy for developing economies. This approach consists of

concentrating resources in the dynamic, commercial modern sector and withdrawing

resources from the subsistence sector for this purpose. It was believed that this

strategy would ensure cumulative growth of incomes, employment and rapid

structural transformation of the underdeveloped economies. Indeed, Ranis and Feith;

(1961) were at pains to emphasize that as development advanced in the modern

sector, a time would come, when surplus labour would cease to exist in the

subsistence sector. At this point, government would undertake measures to raise

labour productivity on the subsistence sector in an effort to prevent inflationary prices

of farm products from putting a damper on the process of industrialization of the

urban areas.

Although the two-sector models are simple, roughly in conformity with the

historical experience of economic growth in the West, and highlight some basic

relationships in dualistic development, they have three assumptions, which are at

33

variance with the realities of migration and underdevelopment in most contemporary

Third World Countries (Todaro, and Smith, 2009).

First, the models implicitly assume that the rate of labour transfer and

employment creation in the urban (capitalist) sector is proportional to the rate of

capital accumulation; the higher the growth rates of the modern sector, the faster the

rate of new job creation. But what if surplus capitalist profits are reinvested in more

sophisticated labour-saving capital equipment rather than duplicating the existing

capital as it is implicitly assumed in the two-sector models?

The second assumption of the models, at variance with reality, is that 'surplus'

labour exists in rural areas while there is full employment in the urban areas. Most

contemporary research indicates that almost exactly the reverse is true in most Third

World countries (including Nigeria). There is substantial open unemployment in the

urban areas but little general surplus labour in rural locations (Todaro, 1977), because

of disguised unemployment in peasant farming. The third unreal assumption of the

models is the notion of the continued existence of constant real urban wages until the

point where the supply of rural surplus labour is exhausted. One of the most striking

features of urban labour markets and wage determination in developing countries has

been the tendency for these wages to rise substantially over time, both in absolute and

relative to the average rural incomes, even in the presence of rising levels of open

unemployment.

According to Otero (1999), as a guide to rural development, the models have

very serious shortcomings. First, the models do not give an accurate representation of

the structure and performance of a typical underdeveloped country. There are no

countries where the agricultural (subsistence) sector is characterized by absence of

saving, capital formation and growth. It is true that compared to the small and fast

growing industrial sector, the savings and capital formation are quite small but this is

not to say that there are no savings at all in the sector as the models portray (Gaile

(1978).

The dualistic models have a partial view of the relationship between the two

sectors. For example, by concentrating on capital accumulation in the industrial

sector, the models portray an incomplete interaction between agriculture and industry.

But a complete interaction between the two sectors has three components, namely; the

problem of capital accumulation, the problem of agricultural output, and the 'market

34

surplus' problem. Only the problem of capital accumulation was tackled by the

models, particularly, the Lewisian version (Otero, 1999). The models concentrated

emphasis on the industrial sector and this means, in essence, that the basic issue of

optimal resource allocation between agriculture and industry for maximum overall

growth remained unanalyzed.

Second, Mergos et al (2004) queried the suitability of the models when

applied to Africa in general, and Nigeria in particular. His basic objection is that the

dual-sector models' assumptions do no simply fit the facts; and for planning purposes,

any policies based on the theory derived from such assumptions, will produce

erroneous results. An example of such assumption is the key Lewisian notion of

unlimited cheap labour. Nicholas (1969) argued further that the growing urban

unemployment in Africa only superficially resembles unlimited labour supply; but in

essence, only voluntary unemployment is allowed in the Lewisian model. By contrast,

Nicholas (1969) view of the urban unemployment situation in Nigeria, presumably,

strengthened by the Callaway School Leaver unemployment hypothesis is more of the

Keynesian involuntary type (Boudville, 1966). The Callaway School Leaver problem

seemingly weakened the link between the industrial labour force and the agricultural

sector because the surplus labour is not endogenous to the industrial sector.

Third, the authors of these models have a narrow conception of development

as a process of concentrating resources on already developed urban areas. As the

experience of most developing countries shows, such a strategy does not lead to

development because the resulting neglect of the rural areas where the majority of the

population dwells creates a situation where food and raw material shortages, low

income and inflation of food prices, adversely affect both demand and cost structure.

This, therefore, impede the process of industrial development. In addition, the

concentration of efforts in the dynamic urban sector, in line with the prescriptions of

the dualistic models, causes a gap in the earnings of urban and rural resources and

contributes to the outflow (migration) of capital and labour resource industries from

the rural to urban areas. The effect of this is massive unemployment in urban areas,

tremendous demand for urban social services, and the provision of scarce funds from

productive investments to the provision of costly urban social services (Gaile (1978).

Fourth, the application of the surplus labour theory in Helleiner’s Land-

surplus model used to explain the secular expansion of export crop production in

35

Nigerian during the first half of the century (Helleiner, 1960). One of Helleiner’s

objections to the surplus labour theory is that, for a class of tropical countries typified

by Nigeria, agriculture is historically the ‘prime mover of economic growth’ through

expansion of agricultural exports. This is an apparent contrast to the Lewisian theory

in which the growth process is pivoted on the expansion of the capitalist industrial

sector through capital accumulation.

Fifth, the dual economy models assign a very restricted role to agriculture. In

the opinion of their formulators, the role of agriculture is to serve the ends of

industrialization via the provision of cheap food, cheap raw materials and the release

of labour and other resources. It is not realized that a strategy of cheap food, cheap

raw materials and cheap labour has adverse effects on rural purchasing power and can

seriously determine the capacity of agriculture to play the very limited role prescribed

for it.

Sixth, the models generally mislead policy makers in the underdeveloped countries by

exaggerating the capacity of urban industries for cumulative growth (Gaile, 1978).

This emphasis rests on assumptions regarding entrepreneurial ability of urban

industrialists, the capacity of urban industrialists for savings and investments of

profits, and the availability of worthwhile and profitable investment projects in the

urban areas of the underdeveloped countries. However, the development experience

of most less-developed countries bears testimony to (i) the scarcity of real

entrepreneurial talents in these countries, (ii) the inability of most urban industries to

make substantial profits despite their monopoly of the domestic markets, (iii) the very

small value added in a number of manufacturing industries, (iv) the tendency for most

of the profits to be sent away as dividends to foreign shareholders, and (v) the failure

of industries to train a sizeable number of local skilled people and generate

employment (Gaile, 1978).

Finally, when one takes into account the labour-saving bias of most urban

technological transfer, the widespread non-existence of rural 'surplus' labour, and the

tendency for urban wages to rise rapidly even where substantial urban open

unemployment exists, the dual economy models can be seen to offer limited analytical

and policy guidance for the Third World unemployment and migration problems

(Todaro, 1977). Kofi (1974) observed that the rural sector was neglected because

everyone was pre-occupied with wage employment in keeping with the Lewisian

36

model. According to Kofi (1974), 'Lewis Dual Theories' have failed in helping to

absorb the unemployed. Nevertheless, the models do have some redeeming analytical

value in that they do, at least, emphasize two major elements of the employment

problem - 'the structural and economic' differences between the urban and rural

sectors, and the central importance of the process of labour transfer which links them

together (Olatunbosun, 1975).

Even though Nigerian's rural development policies and programmes right from

the colonial period to date lacked clearly stated theoretical orientation, the strategies

have largely been in line with the Lewisian prescriptions. The recognition of the

existence of differences between the urban and rural areas and the perception of the

need to bridge the gap between the two sectors, all indicate a tacit acceptance of the

basic tenets of the dual sector models. The concentration of development projects in

the urban centres, which has been the case in the country in accordance with dual

economy models, has resulted in the rapid expansion of the modern industrial (urban)

sector. But this expansion has not been enough to absorb the unemployed rural

migrants. Rather, the situation encouraged rural-urban migration causing spiralling

demand for limited urban employment and created a situation of urban unemployment

or underemployment. Even though the greater proportion of those who migrated from

the rural areas into the urban areas remain jobless and do not usually share in the

transfer of resources from the rural sector, they often prefer to remain in the cities

because urban misery is better than rural woes (Olatunbosun, 1975).

These arguments as interesting and useful as they appear to be, do not offer

any deliberate contradiction that the basic element of dual economy model (classical,

neo-classical and post-Lewisian) is in tandem with the realisation of objectives 3 and

4 of the study, which take root in the sustenance of MSEs. Since we have identified

effective provision of microfinance to Enterprise Clusters (ECs) as a framework for

industrial growth, this model is supporting sustainable development of the dualistic

rural and urban areas which involves economic empowerment, income and

employment generation, wealth creation, poverty reduction, gender empowerment,

and effectiveness of Micro and Small Scale Enterprises (MSSEs) in the industrial

process and economic growth of the country.

37

2.3.3 Sustainable Development Model

The term “sustainable development” came into the public arena in 1980 when the

International Union for the Conservation of Nature (IUCN) and Natural Resources

presented the World Conservation Strategy (IUCN, 1980). It aimed at achieving

sustainable development through the conservation of living resources. However, the

focus was rather limited, primarily addressing ecological sustainability, as opposed to

linking sustainability to wider social and economic issues (Baker, 2006).

It was not until 1987 when the World Commission on Environment and

Development (WCED) published its report, titled Our Common Future, that the links

between the social, economic and ecological dimensions of development were

explicitly addressed. The WCED was chaired by Gro Harlem Brundtland, the then

Norwegian Prime Minister, who stated that Our Common Future is sometimes known

as the Brundtland Report (Baker, 2006).

According to the classical definition given by the United Nations World

Commission on Environment and Development in 1987, development is sustainable if

it meets the needs of the present without compromising the ability of future

generations to meet their own needs”. (Todaro and Smith, 2009). The term

sustainability reflects the need for careful balance between economic growth and

environmental preservation. Implicit in the statement above is the fact that future

growth and overall quality of life are critically dependent on the quality of the

environment. The natural resource base of a country and the quality of its air, water,

and land represent a common heritage for all generations. To destroy that endowment

indiscriminately in the pursuit of short-term economic goals penalizes both present

and especially, future generations. It is therefore advisable that development policy-

makers incorporate some form of environmental accounting into their decisions

(Todaro and Smith, 2009).

Sustainable development could probably be otherwise called “equitable and

balanced”, meaning that in order for development to continue indefinitely, it should

balance the interests of different groups of people within the same generation and

among generations, and do so simultaneously in three major interrelated areas which

are economic, social and environmental. It emphasizes the creation of sustainable

improvements in the quality of life of all people through increases in real income per

38

capita, improvements in education, health and general quality of life and improvement

in quality of natural environmental resources.

Njoku (2009) submitted that the Brundtland paradigm of sustainable

development integrates economic, social and environmental issues in development for

both intra-generational and inter-generational interests, needs and equity. In the final

analysis, sustainable development is about long-term conditions for humanity’s multi-

dimensional well beings. For example, the famous Rio Declaration adopted by the

United Nations Conference on Environment and Development in 1992 (also called the

Earth Summit, held in Rio de Janeiro, Brazil), puts it thus: “Human beings are at the

centre of concern for sustainable development. They are entitled to a healthy and

productive life in harmony with nature”. (Soubbotina, 2004).

Unarguably, the most critical problem of sustainable development in each

country and in extension, globally is eradicating extreme poverty. This is because

poverty is not only an evil in itself but also hinders other goals of development from

clean environment to personal freedom. Another closely related global problem is

establishing and preserving peace in all regions and countries. War, as well as

poverty, is inherently destructive to all economic as well as social and environmental

goals of development (Soubbotina, 2004).

The developed western countries of the North are promoting poverty and wars

in the South and developing countries of Africa and Asia. This is through the unequal

exchange in trade relations, capitalist exploitation, neo-colonialism, debt peonage,

unhealthy relations with local compradors who pander to western interests, suck their

countries dry and store their loot in western banks, sale of arms and instigating natural

resource conflicts, tokenism aid, improper representation and voicelessness in

international fora and institutions including the United Nations. When we recall the

popular Walter Rodney’s submission in How Europe Underdeveloped Africa, it

becomes obvious that the western world developed and are still developing at the

expense of the Less Developed Countries (LDCs) of Africa, Asia and Latin America.

The nugget of “equity and balance” in sustainable development is also

contestable in the issue of the environment. Global warming, ozone layer depletion

and climate change are principally caused by the industrial activities of the West.

And global efforts to check this in form of Kyoto Protocol is being made difficult by

39

some western countries, notably the United States of America (USA) who have

refused to sign and ratify the document.

Prompting sustainable development without consideration of the needs of the

female half of the world’s population is an empty gesture (Dobson, 1996 in Baker,

2006). At the minimum level, it breaches the principles of inter-generational and

intra-generational equity. This means that account has to be taken on how

environmental degradation affects men and women differently. This arises from the

different societal tasks men and women perform in terms of their different roles in

relation to reproduction and their differences in access to and distribution of power.

Equitable participation of women in environmental decision making is also a

minimum requirement for the promotion of sustainable development (Baker, 2006).

2.3.4 Trade-off Theory of Capital Structure

The Trade-off theory of capital structure refers to the idea that an enterprise chooses

how much debt finance and how much equity finance to use by balancing the costs

and benefits. Trade-off theory of capital structure basically entails offsetting the costs

of debt against the benefits of debt. The Trade-off theory of capital structure discusses

the various corporate finance choices that a corporation experiences. The theory is an

important one while studying the Financial Economics concepts. It states that the

companies or enterprises are generally financed by both equities and debts.

Trade-off theory of capital structure primarily deals with the two concepts -

cost of financial distress and agency costs. An important purpose of the trade-off

theory of capital structure is to explain the fact that corporations usually are financed

partly with debt and partly with equity. It states that there is an advantage to finance

with debt, tax benefits of debt; and there is a cost of financing with debt, financial

distress including bankruptcy costs of debt and non-bankruptcy costs (e.g. staff

leaving, suppliers demanding disadvantageous payment terms, bondholder/

stockholder infighting, etc.).

The marginal benefit of further increases in debt declines as debt increases while the

marginal cost increases, so that an enterprise that is optimizing its overall value will

focus on this trade-off when choosing how much debt and equity to use for financing.

Modigliani and Miller in 1963 introduced the tax benefit of debt. Later work led to an

optimal capital structure which is given by the Trade-off theory. According to

40

Modigliani and Miller (1963), the attractiveness of debt decreases with the personal

tax on the interest income. An enterprise experiences financial distress when the

enterprise is unable to cope with the debt holders' obligations. If the enterprise

continues to fail in making payments to the debt holders, the enterprise can even be

insolvent. The first element of Trade-off theory of capital structure considered as the

cost of debt is usually the financial distress costs or bankruptcy costs of debt. It is

important to note that this includes the direct and indirect bankruptcy costs.

Trade-off theory of capital structure can also include the agency costs from

Agency theory as a cost of debt to explain why companies do not have 100% debt as

expected from Modigliani and Miller (1963). 95% of empirical papers in this area of

study look at the conflict between managers and shareholders. Others look at conflicts

between debt holders and shareholders. Both are equally important to explain how the

Agency theory is related to the Trade-off theory of capital structure.

The direct cost of financial distress refers to the cost of insolvency of a

company. Once the proceedings of insolvency starts, the assets of the enterprises may

be needed to be sold at distress price, which is generally much lower than the current

values of the assets. A huge amount of administrative and legal costs are also

associated with the insolvency. Even if the company is not insolvent, the financial

distress of the company may include a number of indirect costs like cost of

employees, cost of customers, cost of suppliers, cost of investors, cost of managers

and cost of shareholders.

The enterprises may often experience a dispute of interests among the

management of the enterprise, debt holders and shareholders. These disputes

generally give birth to agency problems that in turn give rise to the agency costs. The

agency costs may affect the capital structure of an enterprise. There may be two types

of conflicts - shareholders-managers conflict and shareholders-debt-holders conflict.

The introduction of a dynamic Trade-off theory of capital structure makes the

predictions of this theory a lot more accurate and reflective of that in practice.

41

2.3.5 Pecking Order Theory (Pecking Order Model)

The theory is an approach to defining the capital structure of a company as well as

how the business goes about the process of making financial decisions. Pecking Order

Theory was first suggested by Donaldson in 1961 and it was modified and developed

by Nicola Majluf and Stewart C. Myers in 1984. The theory seeks to explain how

companies prioritize their financing sources. The general idea is that companies will

tend to take the course of least resistance, obtaining finance from sources that are

readily available, and then steadily moving on to sources that may be more difficult to

utilize.

While the specifics of the Pecking Order Theory are somewhat involved, the

general idea can be explained by using the example of a local business entity. When it

comes to financing the operation, the business is likely to make use of its internal

resources first, such as using funds in a savings or other interest bearing accounts to

manage operational costs or to order more stocks or raw materials for use in the

operation. When this first line of financing is exhausted or not available for some

reasons, the business will then turn to lenders or investors as a means of generating

the funds needed to keep the company going. When no other options are available, the

business may choose to make use of the equity found in any assets held by the

business.

It postulates that the cost of financing increases with asymmetric information.

Financing comes from three sources, internal funds, debt and new equity. Companies

prioritize their sources of financing; first preferring internal financing, and then debt,

lastly raising equity as a “last resort”. Hence, internal financing is used first; when

that is depleted, then debt is issued; and when it is no longer sensible to issue any

more debt, equity is issued. This theory maintains that businesses adhere to a

hierarchy of financing sources and prefer internal financing when available, and debt

is preferred over equity if external financing is required (equity would mean issuing

shares which meant 'bringing external ownership' into the company). Thus, the form

of debt an enterprise chooses can act as a signal of its need for external finance.

The Pecking Order Theory is popularized by Myers and Majluf (1984) when

they argue that equity is a less preferred means to raise capital because when

managers (who are assumed to know better about true condition of the enterprise than

investors) issue new equity, investors believe that managers think that the enterprise is

42

overvalued and managers are taking advantage of this over-valuation. As a result,

investors will place a lower value to the new equity issuance.

Pecking order theory starts with asymmetric information as managers know more

about their enterprises prospects, risks and value than outside investors. Asymmetric

information affects the choice between internal and external financing and between

the issue of debt and equity. Therefore, there exists a pecking order for the financing

of new projects.

Asymmetric information favours the issue of debt over equity as the issue of

debt signals the board’s confidence that an investment is profitable and that the

current stock price is undervalued (where stock price is over-valued, the issue of

equity would be favoured). The issue of equity would signal a lack of confidence in

the board and that the board feels the share price is over-valued. An issue of equity

would therefore lead to a drop in share price. This does not, however, apply to high-

tech industries where the issue of equity is preferable due to the high cost of debt

issue as assets are intangible.

Tests of the Pecking Order Theory have not been able to show that it is of

first-order importance in determining an enterprise's capital structure. However,

several authors have found that there are instances where it is a good approximation

of reality. On the one hand, Fama and French (2002) and Myers and Shyam-Sunder

(1999) found that some features of the data are better explained by the Pecking Order

than by the Trade-off theory. Goyal and Frank showed, among other things, why

Pecking Order theory fails where it should hold, namely, for small enterprises where

information asymmetry is presumably an important problem.

The Pecking Order Theory explains the inverse relationship between

profitability and debt ratios as shown below.

Enterprises prefer internal financing.

1. They adapt their target dividend payout ratios to their investment

opportunities, while trying to avoid sudden changes in dividends.

2. Sticky dividend policies plus unpredictable fluctuations in profits and

investment opportunities mean that internally generated cash flow is

sometimes more than capital expenditures, and at other times less. If it is

more, the enterprise pays off the debt or invests in marketable securities. If it

43

is less, the enterprise first draws down its cash balance or sells its marketable

securities rather than reduce dividends.

3. If external financing is required, enterprises issue the safest security first. That

is, they start with debt, then possibly hybrid securities such as convertible

bonds, then perhaps equity as a last resort. In addition, issue costs are least for

internal funds, low for debt and highest for equity. There is also the negative

signaling to the stock market associated with issuing equity and positive

signaling associated with debt.

In comparing the two theories, the main difference between them is the potential

benefit from debt in a capital structure. This benefit comes from tax of the interest

payments. Since the Modigliani and Milner’s capital-structure irrelevance theory

assumes no taxation, this benefit is not recognized, unlike the trade-off theory of

leverage where taxation system and its benefit are recognized.

2.3.6 Agency Theory

Agency theory explains the processes in determining capital structure to consider the

costs associated with the difference in interest between the owner and the

management company. Jensen and Meckling (1976) suggested the presence of two

potential conflicts in agency theory (also known as the agency problem), namely the

conflict between shareholders and creditors and a conflict between shareholders and

the management. The reduction in the cost of conflict in the agency problem is called

the agency cost. According to the Eng and Mak (2003), potential agency problem will

be even greater if managers are not involved in the company's share ownership so that

managers use policy on their own outside of the policy for the purposes of private

shareholders. If managers participate in the ownership of the shares of the company,

managers are much more concerned about the interests of shareholders because

managers also want the dividend distribution as shareholders.

Agency Costs

According to Jensen and Meckling (1976), agency conflicts lead to agency costs

which manifest in three types:

44

a. Monitoring Cost: cost that is spent by the principal to monitor the activity and

behavior of management, such as paying auditor to audit the financial report,

and insurance to protect company’s assets.

b. Bonding Cost: cost that is spent by the company to give an insurance to the

shareholders to make sure that managers would not do anything that will harm

the company

c. Residual Loss: cost that is spent by the principal to influence manager’s

decision to increase the shareholder value

Agency cost happen when agent’s interest and principal’s interest are not aligned,

because managers assumed to be self-interested and this will reduce the shareholder

value (Jensen and Meckling, 1976).

2.3.7 Micro Enterprises in Developing Economy

Microenterprises are defined as unorganized, privately owned manufacturing/service

enterprise whose work force ranges between two and ten employers (Adebayo et

al:2001).Therefore, micro enterprises provide income and employment for significant

proportion of workers in rural and urban areas by producing basic goods and services

for rapid growing population. Adelaja (2005) declared they account for more than

60% of all regional enterprises and up to 50% of paid employment. Micro enterprises

development is seen as a broad-based growth expected to improve the well-being of

poor men and women by providing significant income and employment generating

opportunities, and by encouraging indigenous investment. Consequently, there is an

increasing policy focus on the need to strengthen entrepreneurship and the

contribution of micro enterprises to attain economic growth with equity, as well as in

addressing gender and poverty reduction issues (Billsborrow: 1994: Kpakol: 2005;

Akinboyo: 2007). Internationally, according to Eyiuche (2005), micro enterprises

development can be found to contribute to any or all of following objectives:

• Promoting national and regional economic development goals.

• Promoting empowerment, particularly in creating new jobs.

• Alleviating poverty and assisting those who are disadvantaged.

• Facilitating the transition to a market economy.

• Promoting equity and addressing uneven development.

• Promoting democracy and the development of civil society.

45

Okonjo-Iweala (2005) holds the view that the critical development issues in

Nigeria today revolves around the need to design and implement policies and

strategies for an efficient, competitive and diversified industrial system which creates

employment, generates wealth and thus, eradicates poverty. To achieve these goals, a

strong entrepreneurial base is an essential driver of growth and prosperity in a modern

economy. It is therefore, the vision of government to entrench a culture that values

and supports entrepreneurship such that everybody with talents, potentials, drives and

courage to succeed in business should be given the opportunity irrespective of their

gender. This is because micro enterprise empowers the populace and provides greater

possibilities for the use of available local raw material for vertical and horizontal

linkages (Okonkwo: 1996). The development of micro enterprise would greatly

improve the economy and the welfare of Nigerians by minimizing the incidence of

marginalization and improvement of greater part of the society. New business brings

new or improved products and services to consumers, thereby increasing competition,

and challenges to existing business hence, improving their performance. It, therefore,

becomes imperative that micro enterprises are the answer to the poverty-bedeviled

economies.

2.3.8 Micro and Small Enterprises/Industrial Clusters

Literature abounds on the progress made on academic and policy research on

industrial clusters, in particular the ways in which clustering enhances

competitiveness and promotes growth. There is an implicit assumption that such

growth translates into rising levels of employment and incomes, with improving

conditions and standards for labour engaged in clustered SMEs. Yet, for the most part,

such issues are rarely explored. In particular, relationships between clustered firms

and workers are insufficiently analyzed.

Industrial clusters can make a potential important contribution to this agenda.

Not only do they enhance the ability of small firms to compete in global markets but

also promote sustainable employment and incomes and thus, better the situation for

the working poor. This assumption is grounded in the notion that SMEs account for a

significant proportion of manufacturing employment in developing countries, and that

they are predominant in labour intensive sectors with a propensity of employment of

the working poor. Clusters, as a distinct form of industrial organization, allow SMEs

to overcome constraints on their size, and offer possibilities of collective action in the

46

face of common problems. Such benefits are brought into sharper perspective by the

process of globalization which, while offering new opportunities for developing

country enterprises and workers, inter alia, raises the vulnerability of small firms, and

those who work in them, to external shocks. Clusters are also relevant in that they

offer potentially important benefits of developing social capital and social protection

through local trust-based relations. Such forms of social assets can be of significant

advantage to firms and to labour. At the same time, it is important to recognize the

heterogeneity between clustered firms, and amongst labour within clusters, and to

recognize that the gains from clustering can be unevenly distributed.

Determinants of the Cluster Growth Path

The growth path of enterprise clusters is determined by five major factors:

• Size of the market and nature products.

• The stock of economies of scale and scope.

• The rate of upgrading.

• The nature of the supporting institutions.

• The form of collective efficiency. Size of the Market and Nature of Products

The size of market is reflected on the number of participants and its

geographical spread. Market size is an important factor for cluster development

because the scale of product demand determines the growth rate of the producer. At

this end, the impacts of clustering in Africa are limited because of low demand for

their products caused by low income. Relative growth in African clusters is due to

positive changes in money income, because small enterprises can gain from such

changes without increasing their own efficiency (Pederson, 1997: 26). Such growth is

related to what Humphery and Schmitz (2000) and also Kaplinsky and Readman

(2001) described as "immiserising growth" because increase in money income does

not mean increase in real income. Policy measures are required to induce the

increase on real income in Africa. McCormick (1998) further argued that interregional

restrictions on movement of goods and people, and underdeveloped distribution systems

undermine market development in Africa.

The quality of the products offered to final consumer plays an important role to

the growth of any SME cluster. The traditional quality standard of any product is

47

measured by the value of the products' characteristics. This implies that the

durability, the reliability, and the conformity to specification and safety standards of the

products are considered as important criteria. In recent times the issue has also shifted

not only to ensure the quality of end-product, but also of being able to verify the quality

control process that has been used, and the quality values that have been installed at each

and every stage of production (Nadvi, 1999; Kaplinsky and Readman, 2001).

Product quality is a function of process and functional upgrading. Improved

efficiency implies that the product quality has been raised through better application of

factors and management on one side, and that the firms can provide documented,

verifiable and acceptable quality assurance for their buyers on the other. Some

entrepreneurs visited in Enugu still do not engage in intensive learning processes in

production engineering and investments. Furthermore, organisational deficiencies

observed in the firms are due to what Schmitz (1982) and Hansohm (1992) described as

limitation in advanced planning in African production system. This leads to unsystematic

production processes and poor product quality standards. The costs and benefit

analyses are ignored, therefore, resulting in a high cost of production and waste of

scarce resources.

The Stock of Economies of Scale and Scope

The ability to penetrate larger market is important for SME cluster dynamics. Reaching

larger market will not only induce proportionate increase in productivity but also lead to

increasing returns. Increasing returns are the by-product of economies of scale. Such

returns arise when an increase in all production inputs results to a more than

proportionate increase in output (Schmitz, 1997). Stocks of economies of scale that

induce increasing returns are measured by the ability of the firms to reach more markets

and to acquire more capital goods, effective managerial skills and opportunity to enjoy

division of labour in the production. African small enterprises are found to be

unorganised in production activities. Low investment on capital goods and lack of

division of labour in production make the enterprises to remain weak (Hansohm, 1992;

McCormick, 1998).

Economies of scope on the other hand arise due to the efficiency of the firm to

engage in more than one activity successively. There are three kinds of economies of

scope namely, concurrent scale economies; coordinative scale economies; and technical

know-how and working skills sharing economy (McCormick, 1998). Concurrent scale

48

economies are the economies gained by enterprises in diversifying their products with

the least possible output costs (e.g. fabricating bakery ovens/palm kernel nut crackers).

This aspect of an economy is commonly found in Nigerian machine makers (Oyeyinka,

2001) but generally still very weak in Africa. Coordinative scale economy implies that

the enterprise have the organisational ability to integrate factors of production in an

effective production system. Sharing of technical know-how and skilled workers are

benefits gained by small firms clustering in developing countries because individual

firms cannot alone afford the costs of high technical skilled workers or invest in capital

goods (Brautigam, 1997).

The Rate of Upgrading

Control over the market only cannot sustain the profitability of SME clusters in the long

run. Profitability of the firms in the cluster can be sustained firstly, through the nature of

internal process that encourage learning. Secondly, by acquiring specific comparative

advantage or competence, this is important in maintaining the firms' competitiveness.

The third factor is the path chosen by the firms, because changes in any firm are path-

dependent (Kaplinsky and Readman, 2001). Thus, through upgrading, these three factors

are rapidly identified in order to meet the needs of markets quicker than the rivals.

Upgrading is important for firms. The processes are systemic in nature and are achieved

effectively when firms are linked together. It is important to understand the concept of

value chain in order to get true picture of upgrading. The relationship between value

chain and upgrading are based on identifying not only the key problems in entire

production organisation but also the methods through which upgrading can occur. An Up-

grading can occur at each stage of the chain as shown below in Figure 2.1:

Figure 2.1 Simple Illustration of the Value Chain Source: Kaplinsky, R. and Readman, J. (2001), Integrating SMEs in Global Value Chains, Towards Partnership for Development, Vienna: UNIDO.

Value chain is the process which is required to bring a product or service from the

conception, through the intermediary phases of production, then delivery to the final

consumers and finally disposed off after use (Kaplinsky, 2000). Upgrading can be

Design

Production

Marketing

Consumption

Recycling

49

done in different chains namely, process upgrading, product upgrading, functional

upgrading and chain upgrading. Process upgrading occurs when SMEs can improve

effectively by organising the internal working process of entire production system

better than their local rivals. Product upgrading entails ability to introduce new

products or improve old products faster than the local rivals. Functional upgrading

takes place in firms where the productivity is increased by improving the quality of the

management skills and the organisation of the labour factor. Chain upgrading in turn

means moving to the new value chains. An example is moving from household

products to industrial input products (cf. Kaplinsky, 2000; Humphery and Schmitz;

2000 Kaplinsky and Readman, 2001). Kaplinsky and Readman (2001) further warned

that rate of upgrading should not be more than the competitors; otherwise, it will lead to

immiserising growth. This is an important case particularly for African clusters,

because the market in the region is underdeveloped and competition is very low.

The policy that aimed at improving quality standard for example, should first

consider how to stimulate market competition in the region. SMEs development in the

region can be effective only when the market is developed and growth can be

sustained in the long run when the capability to upgrade is developed.

[

The Nature of Institutions Supporting the Cluster

It is now generally accepted in economic discussions that institutions are important

not only in industrialisation process but also to development in general. Clustering

can foster economic exchange quickly. Therefore, it is important that a third party

is available in order to support the transactions. Furthermore, the quality of the

service rendered by the third party should be dynamic and in line with the growth-path of

the cluster. To understand the importance of institution in cluster development, it is

important to make brief overview on the role of social capital in economic development.

Social capital is defined as a capital jointly owned by parties in relationship and

is not divisible. None of the parties has exclusive right of ownership of the capital. It is

the final arbiter of competitive relations, because it generates positive interactions

within a firm, among groups of firm, within an industrial district in order to reduce

transaction costs and propagate growth. It is a critical variable and has influence on the

mobilisation of other factors of production such as financial capital and labour, crucial in

producing public goods (Burt, 1992; Putnam, 1993). The voluntary and spontaneous

50

cooperation existing within a given community depends on stock of inherited social

capital. It can be referred to as "moral resources", which are resources that increase in

value when they are frequently used in transactions, but depreciate when they are not

applied. This is called social capital of trust (Hirschman, 1985). Trust manifests as a

result of the existing cooperation among a set of actors in order to maximise their

current desires (Sabel, 1992). The more it is displayed in a relationship, the greater a

mutual confidence is developed. Social capital is then classified in two forms –

collective and specific social capital.

Collective social capital exists when the cooperative norm is embedded in

the production of common goods of various kinds by group of firms or a community. The

costs and benefits of deflecting or cooperating are determined by internal and external

sanctions existing in the community (Putnam, 1993). Furthermore, it is defined as the

mutual cooperation that sustains the survival of economic relations, repeated market

transactions and inter-firm transactions in a community or in an industrial cluster

(Gambetta, 1988; Barr, 2001). Such capital is open for all members in the community. It is

a by-product of common values that allow participants to obtain gains from

transactions. The capital possesses considerable value such as trust, which has impact

on reducing the need for various forms of monitoring. Monitoring the actors’

behaviours and transactions usually implies not only considerable direct costs, but has

also the negative effects in generating distrust in business community (Dasgupta, 1988;

Dei Ottati, 1994). Social capital can play an important role in integrating the economic

policy makers in finding solutions to economic problems in any region and can also

induce effective socio-economic problem solving (Putnam, 1993; Brown and Ashman,

1996; Gsanger, 2001). Conflicts between heads of institutions and heads of corporate

organisations have led to negative economic growth in Enugu state region. For example,

the vegetable oil factory, Nachi in Enugu, was closed because of management problems

on the one side and conflict among the shareholders on the other. Thus, it renders

(US$ 40 million) project to be useless as well as making roughly 500 employees to be

jobless.

Specific social capital is based on personal reputation necessary to sustain repeated

transactions. The effect of reputation is recorded in the economic exchange of self-

enforcement agreements that are not backed by explicit contracts. The desire to continue

successful business exchanges induces the partners to avoid conducts which might

51

interfere with attending such objective. This implies that the reactions of the other

parties are integrated in general business reputation (Raub and Weesie, 1990). Personal

reputation is very effective when the economic activities are linked because information

about past performances of an actor can be easily accessed. Both collective and specific

social capital can then be substituted by institutional trust when the society become

more heterogeneous and the volume of business transactions increase and become more

complex. Zucker (1986) and North (1990) acknowledged the importance of institution as

the third party that can maintain the rules of the game in transactions by enforcing

contract agreements and prevent opportunistic behaviours. In Nigeria, such institutions

exist, but are very weak, and are, thus, undermining private sector development.

Fafchamps (1996) observed that enforcement of commercial contracts is problematic in

Ghana due to institutional problem and unstable economic conditions under which the

firms operate.

The Nature of Collective Efficiency Existing in the Cluster

Recent literatures in developed countries have explained how the concept of collective

efficiency is being used to the success of exporting cluster concept in developing

countries (Schmitz, 1995; Nadvi, 1996; Rabellotti, 1997; Humphrey and Schmitz, 1998

McCormick, 1998; etc). Such efficiencies are likely when enterprises operate in a close

proximity. Collective efficiency is defined as the competitive advantages derived by

small firms from local external economies and the joint action. There are two sets of

benefits believed to arise from clustering of producers. First are the efficiency gains, in

other words, external economies that firms can reap simply by being located near each

other (Marshall, 1890; Mishan, 1971; Nadvi, 1996; McCormick, 1998). The second is

the gains for firms acting together to achieve some desired ends (Nadvi, 1996; Schmitz,

1997; McCormick, 1998).

External economies are not new in economic discussions. Marshall made a lead

way without definition by pointing out the importance of localisation of industries and the

significance of concentration of specialised industries in a particular locality. According

to Marshall, concentration of small firms of similar character can help to improve

efficiency and competitiveness (Schmitz, 1995: 535). The question is what are the

gains associated with externality? Krugman (1991) analysed the gains associated with

agglomeration of industries in geographical location. Such gains include pooling of

52

labour, quick access to intermediate inputs and technological spillovers. In this context,

clustering facilitates easy supply of inputs, pooling of specialised skills workers and

rapid diffusion of know-how and ideas (Schmitz, 1995). Further investigations show

that externalities alone cannot sustain a cluster growth-path because such gains were

incidental by-product of some other firms' legitimate activities. Nadvi (1996) made a

distinction between external economies and joint action. He used the term active

collective efficiency to emphases on the importance of deliberate actions needed to

improve performance.

Joint actions are some deliberate and strategic actions that do not oppose

market competition but needed to sustain the growth of the cluster. The actions are

deliberately and jointly pursued in order to strengthen the growth, to overcome impeding

constraints or to induce a positive turning point of the cluster. Some empirical results

have shown that joint action plays an important role in SME upgrading (Kaplinsky, 2000;

Kaplinsky and Readman, 2001); in product quality standard to meet export condition

(Nadvi, 1996); in transaction costs reduction (Brautigam, 1997). The question is how

effective is collective efficiency in African clusters? As Pedersen (1997) stressed, the

gains from collective efficiency can be achieved through growing market. Expansion

requires overall collective efficiency in absolute terms through specialisation and

vertical integration. In this context, the growth of the African clusters tends to be

limited because of the partial monopoly they enjoy in the local market without increasing

their efficiency. Furthermore, collective efficiency in African clusters cannot be effective

due to the quality of human capital.

Clustering sets into motion, a range of potential benefits that can directly

affect the poor, as waged workers, home workers, own-account workers as well as

small entrepreneurs. This can be through externality gains, joint action, and local

social capital.

1. External Economies: Agglomeration benefits may not only raise efficiency but

they may also make it possible for smaller firms to access markets through a division

of labour. Economies of scale and scope can allow individual small firms to survive

by specializing in specific tasks within the production process and by accessing

specialist skills and services and inputs from within the cluster. Similarly, external

economies that arise from agglomeration can result in a significant lowering of costs

in accessing inputs, labour and information. Again, this can help small firms to

53

survive and grow in ways that would be infeasible if they operate in isolation.

Knowledge spill-overs found in clusters may also make it feasible for small firms to

acquire new know-how, new products and new production techniques that could not

be obtained through markets. Clustering can thus enhance the individual capacities of

small firms to access markets, and acquire skills, knowledge, credit and information.

2. Joint Action: Clustering can also promote collective capacity. In addition to

the direct economic benefits that passively accrue to small firms by virtue of their

location within the cluster, there are significant gains from active local collaboration

that clustering can set into motion. Local cooperation, both between individual firms

and through cluster institutions can strengthen the ability of clustered actors to

compete in markets, by sharing costs and by engaging in joint tasks such as shared

marketing and distribution. Moreover, such forms of joint actions can help clustered

firms confront external threats and challenges. These external challenges are

pronounced as local clusters engage in global markets. Globalization, namely, the

increasingly rapid flows of capital, goods, peoples, and ideas across borders, can help

bring local actors into global markets and enhance their income earning opportunities.

Globalization can also potentially increase the vulnerability of local actors to sudden

changes in global demand, in trading rules and in financial stability. Thus, with

globalization, there is also greater instability and vulnerability. Clusters can help

SMEs reduce their exposure to exogenous shocks and risks. Local institutions such as

business associations and collective service centres can help clustered firms acquire

the skills and the technical abilities to reduce their vulnerability to the exigencies of

globalization, thereby enhancing the well-being of workers and producers.

3. Social Capital: Local initiatives and local collaboration are themselves often

strengthened by local social capital. Clusters tend to have a strong presence of social

capital, which can take the form of shared norms and/or common identities. This can,

potentially, help reduce vulnerability, help flows of knowledge within the cluster,

provide the basis to strengthen local institutions, and help firms upgrade. There is the

need to consider how social capital works to do this, and in particular how it may

mitigate against poverty. Social capital can also serve to raise local competition as

much as it helps local cooperation. Divisions within communities can reduce local

cooperation and serve to worsen poverty impacts. Also, there is the need to note the

54

different ways in which social capital works for different types of firms (large versus

small) and workers (men versus women, or high versus low castes). Finally, it is

important to recall that social capital is not static. Its forms and operations can change

over time. In particular, it is affected by economic change (and growth) within the

cluster. Clusters can set into motion processes that improve the ability of small firms

to improve market access through externality gains and joint action. This can raise

incomes for those who work in clusters, raise their assets and capabilities and have a

significant impact on lowering levels of poverty and social deprivation. Joint action,

often cemented through social capital, can improve local networks and support

mechanisms that help reduce future risks and vulnerability to shocks.

Clusters are dynamic. They evolve as a consequence of local and external

linkages. A key process of change within clusters comes about through local

upgrading. This results in enhancing human capital, improving technological

capacities for firms and enhancing capabilities for workers and small producers. There

has been substantial recent discussion on upgrading in clusters (UNIDO, 2002;

Humphrey and Schmitz, 2003) - which raises the competitiveness of firms, improves

their ability to appropriate a larger share of value added, and advances their position

within global value chains through distinct forms of upgrading -product, process and

function. The significance of cluster upgrading for poverty cannot be overemphasized.

Raising human capital improves productivity and leads to rising incomes and wages

as well as sustaining employment growth. Moreover, it is only through a systematic

pattern of upgrading, often aided through national innovation and learning systems

that clusters are able to compete in global markets on the basis of the high road to

growth.

This requires a stronger explanation of why the high road to growth (as

opposed to increasing competition on wage costs) might have a more positive impact

on poverty reduction in the medium to long-term. But upgrading not only relies on

local and external linkages, it also has consequences for such linkages. That is to say,

the process of upgrading is often determined by the nature of governance of ties

within the cluster as well as ties between cluster actors and external players within the

value chains in which clusters are inserted. Global lead firms can exercise significant

power in determining the actions of local firms, and thus the autonomy of clustered

firms to engage in tasks that enhance their technical and resource capacities.

55

Moreover, external ties can over time erode local linkages and weaken cluster

governance. This implies that clusters have to be seen in the context of dynamic

trajectories – where certain types of producers and workers gain and others lose. For

example, as firms upgrade does the demand for new skills affect all workers

uniformly, or do some categories of workers (say women) become marginalized by

not being provided the requisite training and skills?

A strong nexus exists amongst cities, urbanization, industrial clusters and

development. To a large extent, cities ‘over-formed’ because they provide cost

advantage to producers and consumers through what are called agglomeration

economies (Todaro and Smith, 2009).

Agglomeration economies come in two forms namely, urbanization and

localization economies. Urbanization economies are effects associated with the

general growth of a concentrated geographic region. Localization economies are

effects captured by particular sectors of the economy such as finance or automobiles,

as they grow within an area. Localization economies often take the form of backward

and forward linkages. When transportation costs are significant, users of the inputs of

an industry may benefit from a nearby location to save on these costs. This benefit is a

type of forward linkage. In addition, firms of the same or related industries may

benefit from being located in the same city, so they can all draw from a large pool of

workers with the specific skills used in that sector or specialized infrastructure. This is

a type of backward linkage. Workers with specialized skills appropriate to the

industry prefer to be located there as well as that they can readily find a new job or be

in a position to take advantage of better opportunity (Todaro and Smith, 2009).

It may not matter so much where such industrial clusters are located as they

somehow got an early start in a place, perhaps because of a historical accident. For

example, in the United States, many innovative computer firms located in Silicon

Valley, Califonia, simply because other firms are located there. Analogously,

suppliers of shoe firms are located in the Sinos Valley in Southern Brazil and in

Guadalajara in Mexico, because so many shoe firms located in those regions. Some of

the benefits are gained by the fact of location in what is called “passive collective

efficiency”. But other benefits must be achieved through collective action, such as

developing training facilities or lobbying government for needed infrastructure as an

56

industry rather than as individual firms. This is “active collective efficiency” (Todaro

and Smith, 2009).

Again, not all of the efficiency advantages of an industrial district are realized

through passive location. Others are actively created by joint investments and

promotional activities of a firm in the district. One such factor determining the

dynamism of a district is the ability of its firms to find a mechanism for such

collective action. While the government can provide financial and other important

services to facilitate cluster development, social capital is also critical, especially

group trust and a shared history of successful collection action, which requires time to

develop (Todaro and Smith, 2009).

2.4 Empirical Literature

Over the years, academics and evaluators have conducted many studies on the impact

of microfinance, especially microcredit. Yet, the average effect is still unknown

because nearly all studies to date share similar shortcoming. For instance, when

studying complex social systems such as families and communities, it is extremely

hard to use a correlation to prove causation. If affluence and microcredit go hand-in-

hand, does that mean that the better-off borrow more or that borrowing makes people

better-off?

Mano, Iddrisu, Yoshino and Sonobe (2011) examined how micro and small

enterprises in Sub-Saharan Africa become more productive and the impacts of

experimental basic managerial training. The performance of MSE clusters is

especially low in Sub-Saharan Africa. While existing studies often attribute the poor

performance to factors outside firms, problems within firms are seldom scrutinized. In

fact, entrepreneurs in these clusters are unfamiliar with standard business practices.

Based on a randomized experiment in Ghana, the study demonstrated that basic-level

management training improves business practices and performance.

Loca and Kola (2013) analyzed how and to what extent microfinance services

in Albania have affected the entrepreneurial activity, and how these entrepreneurial

companies can benefit by using it. In order to do so, the study focused on different

aspects of microfinance impact to firms credited by MFIs in Albania. The

methodology combined the application of both quantitative and qualitative tools

including questionnaire on different indicators addressed to beneficiaries. Qualitative

information was collected through Focus Group interviews and Semi-structured

57

interviews to understand the situations that people faced how they used and perceived

microfinance, especially enterprises served by microfinance sector in Albania. The

results indicated a strong relationship between being MFIs clients and the change in

enterprise profits over the last 12 months. The study concluded that lending practices

have a positive effect on entrepreneurial activities in increasing employee salaries, in

job creation or generating employment, in increasing profit margin of enterprises

served as shown by the cases and models analyzed. This is consistent with objectives

1 and 2 of this study.

Furthermore, Kessey (2014) examined micro credit and the promotion of

small and medium enterprises in informal sector of Ghana. The study showed that

among SME entrepreneurs who repay credit on monthly basis there is a default rate of

2. 8 per cent while those who repay annually have default rate of 6.5 per cent. The

study recommended that it would be necessary for Micro Finance Institutions to

extend to other products such as business advisory products and social products to

SMEs, to raise their productivity and improve upon their performance. This was based

on an observation that only Micro Credit would not take SMEs out of poverty in

developing countries. This is in line with objective 4 of this study.

Wanambisi and Bwisa (2013) investigated the effects of Micro Finance

Institutions lending on micro and small enterprises performance within Kitale

Municipality. This study adopted a descriptive survey research with the use of

frequency tables, pie charts and figures. The association between microfinance

lending and MSE performance variables was established through Chi square and

correlation tests at 95% significance level. A multivariate logistic regression was used

for significant bivariate variables at 95% significance level. The study discovered that

the amount of loans was significantly and positively related with performance of

MSEs in Kitale Municipality. The study therefore recommended that Micro Finance

Institutions should reduce the period required for MSEs to participate in training and

group formation to facilitate speedy access to MFI loans. It also recommended that

the amount of loan given by MFIs to MSEs should be increased to enable the MSEs

grow to medium scale enterprises. This is consistent with objectives 1 to 4 of this

study.

58

Ali, Peerlings and Zhang (2012) examined clustering as an Organizational

Response to Capital Market inefficiency for Micro-enterprises in Ethiopia. The study

showed that industrial clusters, through specialization and division of labor can ease

the financial constraints of micro enterprises, even in the absence of a well-

functioning capital market. By using data from microenterprises of the handloom

sector in four regions of Ethiopia, the study found that clustering lowers capital entry

barrier by reducing the initial investment required to start a business. This effect is

found to be significantly larger for microenterprises investing in districts of high

capital market inefficiency, indicating the importance of clustering as an

organizational response to a credit constrained environment. The findings highlighted

the importance of cluster-based industrial activities as an alternative method of

propagating industrialization when local conditions do not allow easy access to credit.

Ocholah (2013) determined the effect of micro financing on the performance

of women owned enterprises in Kisumu City, Kenya. Specifically, the study

determined the effect of microfinance on productivity, profitability and growth and

expansion of women owned enterprises. The study population comprised 3000

registered women businesses, out of which a sample of 341 was drawn. Clustering,

simple random and purposive sampling approaches were applied. Questionnaire and

interview were used to obtain primary data. Quantitative data was analysed by use of

both the descriptive and inferential statistics. The results indicated that micro

financing in sufficient quantities would have greater effect on profitability,

productivity and growth and expansion of women owned enterprises. The study is

significant in reformulating women business credit policies, for improving credit

services to entrepreneurs.

Mersland & Strøm’s (2007) study focused on the performance and corporate

governance works. The study employed panel data analysis and regression analysis to

find the impact of board characteristics, ownership type, competition and regulation

on financial measures like ROA, yield, and outreach, to name a few. It found that the

presence of female Chief Executive Officer (CEO) has huge impact on the size of

loans provided.

Thapa (2007) worked on comparison between cross-continental MFIs in terms

of financial sustenance. The paper also supported that MFIs increasing their

59

accessibility to poor cannot be self-sufficient as far as the factor of sustainability is

concerned.

Similarly, Hartungi (2007) studied the various factors involved in the success

of MFIs in Indonesia. The major activities identified were dynamic adaption of MFI

with the local conditions, and the usage of the technology (information technology as

specific) in the outreach to the people. The study underlined that the active

involvement of the MFI employees and increase in transparency helped in better

functioning of MFIs in Indonesia. It concluded that prior intimation of the incentives

to the client and employees provided better efficiencies for the MFIs. This is

consistent with objective four of this study. Also, the role of MFIs as evident in the

above empirical result is similar to the objective three of this particular study. Thus, a

basis upon which this study can examine the extent to which control and evaluative

measures employed by the micro finance providers in ensuring the sustenance of

MSEs is established.

Kim & Kim (2008) in their research paper used descriptive analysis as the first

step to understand the characteristics of the ranges, variance etc. The second stage

consisted of factor analysis to attain the important factors which could estimate the

maximum variance. The final stage consisted of regression analysis to understand the

relationship between the dependent and independent variables in South Korea. This

format helped the authors in understanding the characteristics of the process involved

in a better manner.

Moreover, Petridou & Glaveli (2008) in their study found that the

implementation of the microfinance results in the improvements of the lives of the

society who utilizes the scheme. The study found that the earning capabilities of the

women folk in the rural sector also increased through the implementation of

microfinance. It further found that literacy, or the knowledge of scheme was affected

as microfinance increases, and that the women could understand the economics of the

present dynamic world. The study further concluded that the increase in income and

consumption from the lower section of society resulted in the improvement of the

society in general.

In a paper by Rodman (2009), it is discovered that there was no changes in

household income, spending, or diet between 1–2 years after microfinance has been

obtained. It found that borrowers did appear to cut back on some types of spending,

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including paid helpers, health insurance, and home improvements, perhaps because of

belt-tightening at the beginning of new, loan-enabled investments. In the same vein,

in India, there was no impact on total income, spending, health, or school enrollment

rates after some 15–18 months after credit was offered. However, it was found that

microcredit boosted profits for families that already had a business, more land or more

working-age or literate women. In Kenya the study used diaries rather than a one-off

follow-up survey to track subjects’ financial transactions. Despite the small sample of

122 people that were randomly offered an account, and only 67 took it, the study

found significant impacts on the recipients (Remeyi and Quinoess 2000). Rodman

(2009) adopted similar research methodology of random sampling with descriptive

and inferential statistical analysis to arrive at the same findings.

In their own study, Crombrugghe et al. (2008) used the regression analysis to

understand the relation between the financial self-sustenance and operational self-

sustenance in India. The independent variables considered in the paper were yield,

cost per customer, age etc. The paper found that there is no need for increasing the

size or monitoring costs of loans in order to meet the financing costs. Their findings

informed objectives 4 of this study, which seeks to ascertain the level, nature and

extent of relationship and willingness of stakeholders in micro-financing provision

and the sustenance as well as performance of MSEs clusters.

In the Nigerian context, not so many works have examined the effect of micro

finance of enterprise clusters as a strategy for enterprise performance. However, some

related works including Osotimehin et al., (2012) examined the challenges and

prospects of micro and small scale enterprises development in Nigeria. Most business

enterprises in Nigeria by classification are grouped under micro and small scale

enterprises. This study was conducted in Lagos State, South Western Nigeria with the

use of questionnaire and interview to gather relevant data that was statistically

analyzed. The study opined that, the phenomenal growth of small and medium

enterprise in Nigeria is mainly due to the people’s quest to be self-employed and not

because it is easy to establish or manage. Financial constraints and lack of managerial

skill hamper the efficient performance of micro and small scale enterprises in Nigeria.

It recommended that government and other non-governmental organization should

regularly organize seminars for potential and actual small and medium enterprise

operators on how to plan, organize, direct and control their businesses. It also

61

recommended that micro, small and medium enterprises operators’ should device

effective marketing strategies and good management customers relations at all times.

Babajide (2011) investigated the effects of micro-financing on micro and

small enterprises (MSEs) in South West Nigeria. The study used survey data of 443

micro enterprises and validated the reliability of the instruments with Cronbach alpha.

The results suggested that, micro financing enhances the survival of micro and small

enterprises but the enhancement was not sufficient for growth and expansion of such

micro and small enterprises. Also the study showed that micro-financing is not

financially effective and practiced in Nigeria as many Micro Finance Banks (MFBs)

grant more individual loans than group-based loans, thereby increasing their running

cost and putting their portfolio at a risk.

Also, Irobi (2008) compared the performance of loans granted to small and

medium enterprises by banks with that of micro-credit institutions in Nigeria, using

Ondo State as a case study. The study employed descriptive statistics method to

analyse the data collected through primary source. The paper revealed that the

average repayment rate of 92.93% for banks was higher than the 34.06% for the

micro-credit schemes. That is, the banks performed at much higher levels than

microcredit schemes. Based on the findings, it was recommended that there should be

stern efforts by the credit institutions in screening of loan applications, monitoring of

approved loans and enforcing loan contract. Thus, government should provide the

basic infrastructural facilities, which unnecessarily increase the cost of doing business

in the country.

Furthermore, Suberu et al. (2011) assessed the impact of microfinance

institutions on small scale enterprises in Nigeria. The Simple random technique was

employed in selecting the small scale enterprises used in the research. The findings

revealed that, a significant number of the small scale enterprises benefited from the

microfinance institution loan even though only few of them were suitable to secure

the required amount needed. Interestingly, it was also found that the microfinance

institutions have grown phenomenally in the last ten years. Majority of the small scale

enterprises acknowledged positive contributions of microfinance institutions loan

towards promoting their market excellence and overall economic company

competitive advantage. Rather than tax incentives and financial supports, the study

recommended that the government should provide adequate infrastructural facilities

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such as electricity, good road network, and training institutions to support small scale

enterprises in Nigeria.

Akinbola et al., (2013) examined the extent to which micro financing has

contributed to entrepreneurial development and found out the extent to which

marketing techniques have been employed for effective and efficient delivery of their

services. The study employed questionnaire to collect data from bank customers. The

study was limited to the customers of ten micro finance banks located in Ojo Local

Government Area (LGA) of Lagos State, Nigeria. The result suggested that micro

finance banks have been able to contribute significantly to the entrepreneurial

development in Nigeria. The descriptive statistics showed that some marketing

techniques have not been fully employed by micro finance banks. This is consistent

with the major objective of this study.

Olowe et al. (2013) investigated the impact of microfinance on SMEs growth

in Nigeria. The study was restricted to Ibadan metropolis and used a total of 82 SME

operators that constituted the sample size. Pearson correlation coefficient and multiple

regression analysis were used to analyze the data. The results from this study showed

that financial services obtained from MFBs have positive significant impact on MSEs

growth in Nigeria. The results also revealed that duration of loan has positive impact

on SMEs growth but not statistically significant. It also showed that high interest rate,

collateral security and frequency of loan repayment could cripple the expansion of

SMEs in Nigeria. The paper recommended that MFBs should lighten the condition for

borrowing and increase the duration of their customers’ loan and also spread the

repayment over a long period of time.

On the other hand, some authors have challenged the positive effects of

microfinance on poverty reduction and alleviation. For instance, Adam (2007)

observed that microfinance institutions in Nigeria have grown phenomenally, driven

largely by expanding informal sector activities and the reluctance of commercial

banks to fund emerging microenterprises. But, the number of beneficiaries of

microfinance institutions is an insignificant proportion of the people in need of

microfinance services. It has been estimated that formal microfinance institutions only

service less than one million clients in a country where over 70% of the country’s

population live below the poverty line (Dahiru and Zubair, 2008). The results also

63

suggested that micro-financing is unsuccessful at reaching the group most prone to

destitution, the vulnerable poor.

Essien et al (2013) came closest to this study by examining both formal and

informal credit sources and the role of social capital. The study examined credit and

receipt and enterprise performance by small scale agro-based enterprise in the Niger

Delta region of Nigeria. It used a multi-stage sampling technique in selecting 264

agro-based enterprises and 96 agro-based that accessed informal and formal credit

received by the enterprise. The results revealed that gender, age, and social capital are

significant determinants of informal credit while gender, education, age, size and

collateral are significant determinants of formal credit.

The findings from the above empirical literature are in line with the focus of

this study and major objectives, thereby providing enough empirical evidence with

which to compare the findings of this current study.

2.5 Summary of the Review of Related Literature

A review of theoretical literature have shown that the growth pole theory holds the

views that growth does not appear everywhere at the same time; it manifests itself

in points or 'poles' of growth; with variable terminal effects for the economy as a

whole. This supports the importance of starting from somewhere like a cluster of

enterprises since the theory is also of the opinion that growth generated in the growth

centres will spread to their hinterland. The spread mechanism may take the form of

stimulation of food production for urban industrial markets; increased production of

industrial raw materials for processing industries; employment opportunities for any

surplus rural labour following agricultural mechanism within the growth-space;

financial remittances to the rural areas by migrant workers; diffusion of innovations

into growth space'; and subsidiary investments made by rich firms located at the

growth centre in surrounding region.

The Dual Economy Models (DEMs) stipulate that the typical less-developed

country is characterised by the existence of two distinct sectors, namely, the modern

sector and the traditional (rural) subsistence sector. These models have been criticised

by the fact that they have a partial view of the relationship between the two sectors

and their suitability when applied to Africa in general, and Nigeria in particular is still

doubtful.

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Sustainable Development Model (SDM) believes that in order for

development to continue indefinitely, it should balance the interests of different

groups of people, within the same generation and among generations, and do so

simultaneously in three major interrelated areas namely, economic, social and

environmental. The models have been applauded by most scholars for the fact that

sustainable development integrates economic, social and environmental issues in

development for both intra-generational and inter-generational interests, needs and

equity. These theories therefore, apply to this study as it related to the objectives of

the study in terms of the poles of growth, production function, characterization of

sectors, and affinity to develop as the study examines micro finance performance and

enterprise performance of clusters.

Trade-off theory raised an important issues relating to experience in terms of

dispute of interests among the management of an enterprise, debt holders and

shareholders. These disputes (shareholders-managers conflict and shareholders-debt-

holders conflict) generally give birth to agency problems that in turn give rise to the

agency costs. The agency costs may therefore affect the capital structure of any MSE.

The introduction of a dynamic Trade-off theory of capital structure makes the

predictions of the Trade-off theory a lot more accurate and reflective of that in

practice.

Perking Order model postulates that the cost of financing increases with

asymmetric information which may come from three sources (internal funds, debt and

new equity). The theory suggests that enterprises prioritize their sources of financing,

first preferring internal financing, and then debt, lastly raising equity as a “last resort”.

This has implications based on the ambition of such enterprise by first using internal

financing; when it is depleted, then debt is issued; and when it is no longer sensible to

issue any more debt, equity is issued.

Agency theory, on other hand, explains that in determining capital structure,

there is the need to consider the costs associated with the difference in interest

between the owner and the enterprise management which raises the issue of agency

costs. In summary, the theory argued that agency cost happens when agent’s interest

and principal’s interest are not aligned, because managers assumed to be self-

interested and this will reduce the shareholder value.

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In the empirical angle, earlier studies about micro-financing have evaluated

whether micro-credit programmes in Nigeria reach the relatively poor and vulnerable

in their operations. Recent studies for Nigeria and other developing countries have

shown evidence of positive impact as it relates to first six out of seven Millennium

Development Goals (MDGs). Studies by Adam, (2007); Irobi, (2008); Wrigth, (2000);

Zaman, (2000); as well as McCulloch and Baulch, (2000) do subscribe to the fact that

microfinance is becoming an effective and powerful tool for income improvement of

the economically active.

At the international scene, there exist related empirical works at country level

and across regions. At the regional level, there exist studies like those of Remenyi and

Quinones (2000) in Asia and Pacific regions, as well as Mano et al., (2011) in Sub-

Saharan region. Country specific studies include; Barnes and Erica (1999), Ellul

(2005), Wang (2010), Wellalage (2012), Cull et al. (2007), Mersland & Strøm’s

(2007), Thapa (2007), Hartungi (2007), Crombrugghe et al. (2008), Kim & Kim

(2008), Petridou & Glaveli (2008), Rodman (2009), Loca and Kola (2013), Zheng

(2013), Sanvicente and Bortoluzzo (2013), Kessey (2014), Wanambisi and Bwisa

(2013), Ali et al. (2012), and Ocholah (2013) who carried out such studies in

Zimbabwe, Indonesia, India, South Korea, Philippines, Kenya, Albania, Ghana, and

Ethiopia.

In Nigeria, related empirical studies include Osotimehin et al., (2012) who

examined the challenges and prospects of micro and small scale enterprises

development in Nigeria, Babajide (2011) investigated the effects of micro-financing

on micro and small enterprises (MSEs) in South West Nigeria. Irobi (2008) compared

the performance of loans granted to small and medium enterprises by banks with that

of micro-credit institutions in Ondo State. And Suberu et al. (2011) assessed the

impact of microfinance institutions on small scale enterprises in Nigeria. Also,

Akinbola et al., (2013) examined the extent to which micro financing has contributed

to entrepreneurial development in Ojo LGA of Lagos State. And, Olowe et al. (2013)

investigated the impact of microfinance on SMEs growth in Ibadan. These studies

were all necessary to the Nigerian society as they looked at micro financing and

enterprise performance from several aspects, and in different regions.

However, this study departs from existing studies as it is targeted at

performance using profits based on the microfinance source. It is not a study that look

66

at how microfinance has affected enterprises in clusters but rather examines how

different microfinance sources (formal, informal and both) have affected the

performance of micro and small enterprises operating in a cluster. Also the study

explores the relationship in the different sectors of economic activities (clusters),

which include production, trade and services. A look at micro enterprise operating in

a cluster will reveal the reality of the spread mechanism. These mechanisms which

may take the form of stimulation of food production for urban industrial markets,

increased production of industrial raw materials for processing industries,

employment opportunities for any surplus rural labour following agricultural

mechanism within the growth-space and diffusion of innovations into growth space.

In addition, the study concentrates on three cities in two states of the South East,

namely Aba, Nnewi and Onitsha. The study equally highlights the social capital that

stands out as a critical determinant to the choice of business in most communities,

especially that Nnewi is known for spare parts and Aba for textiles. Therefore, the

study departs from the economic perspective that most studies are built on to include

psychological and social benefits that contribute to existing knowledge, and stimulate

the debate on the subject.

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CHAPTER THREE

THEORETICAL FRAMEWORK AND RESEARCH METHODOLOGY

3.1 Introduction

This chapter focused on the theoretical framework guiding the study as well as

research methods, procedures and strategies adopted to realize the objectives of this

study. This included adequate description of research design, data collection

instrument, methods of data collection and analysis.

3.2 Design of the Study

The research design for this work was essentially survey research method with the use

of structured questionnaire administered to selected enterprise clusters in three cities

of South East Nigeria viz. Onitsha, Nnewi and Aba. The instrument used

(questionnaire) consisted of four sections namely, Demographics; Finance and Credit;

Enterprise Association; and Entrepreneurs’ Perception. The questionnaire was

structured in line with the objectives of the study. The 5-point likert scale were

analysed while the average for each question was approximated to the nearest whole

number.

The generated data was tabulated for statistical and econometric analyses to

obtain results and test appropriate hypotheses. The Focus Group Discussions (FGDs)

were employed to confirm and revalidate the information analysed from the

administered questionnaires.

3.3 Data and Sources

The source of data for this study was mainly from primary sources generated through

questionnaire, FGD and personal interviews. The contents of the questionnaire were

structured from available literature with specific inputs from the Small and Medium

Enterprises (SME) center at Enugu and other micro finance institutions visited in the

course of this work. The questionnaire is presented in detail in the annex.

3.4 Population of the Study

Covering all MSEs in South East Nigeria would be cumbersome. Therefore, the

survey only covered selected industrial/enterprises clusters operating at Nnewi,

Onitsha and Aba of South-East geopolitical zone of Nigeria. The choice of the three

cities was based on factors such as geographical proximity and prevalence of cluster

of microenterprises.

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A three stage cluster sampling was adopted in this study. A sample of primary

unit was selected from different clusters, existing in South-East and that gave rise to

the selected three cities. The second stage was the selection of sample of secondary

units which were chosen from each selected primary units. This stage resulted in the

choice of the following: A.M.E Shoe Makers Cluster for production, Omenma

Traders Cluster for trade and Global Systems Mobile Network (GSM) and Allied

Components Cluster, Aba Central for services, Nnewi Technology Incubation centre

for production, Nnewi Automobile spare parts cluster for trade, and GSM and Allied

Components Cluster in Nnewi, as well as Tinkers Dealers Cluster for production,

Building Materials Cluster for trade; and GSM and Allied Components Cluster in

Onitsha. Finally, a sample of tertiary unit was selected from each selected secondary

unit (nine selected clusters) from the three cities with the help of a sample frame from

the National Association of Small and Medium Enterprises (NASME). The lists of all

the clusters in the three cities are presented in Annex IV.

It is noteworthy that with a three stage sampling, covering a large city may be

impossible. Therefore, such city can be sub-divided into administrative units. The

total number of enterprises located within the city can be determined by first,

selecting a sample of administrative units, then, choosing a sample location within the

selected administrative units and finally, interviewing/administering a sample of

firms/enterprises at the selected location. Details of estimated number of enterprises

across the nine clusters are presented in the Table 3.1 below:

Table 3.1: Estimated Number of Enterprises across selected clusters in South-East

S/No Town/City/State Estimated Number of enterprises 1 Aba 658

2 Nnewi 456

3 Onitsha 880

Total 1994

Source: Enterprise Directory, National Association NASME 2011

3.5 Instruments for Data Collection

The study employed both questionnaires and personnel interviews:

Questionnaire: The questionnaire was designed and distributed to the respondents.

The questions were structured to enable the research obtain data in respect to the

stated objectives of the study.

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Personal Interview: A guided structured interview was used. The questions were

designed to obtain data on certain specific issues which may not be easily available to

the general public.

Focus Group Discussion (FGD): This is a research method that involves

understanding attitudes and behaviours of the audience. It aimed at ascertaining

audience disposition towards a given issue. The study, therefore, engaged about 15-20

people per each of the selected area or city, cutting across the different sectors of the

study (production, Trade and Services). The essence of a FGD is to elicit qualitative

information from a homogenous group (Nwodu, 2006:132). The study, therefore,

employed the focus group discussion to confirm and revalidate the information got

from the questionnaires that were administered.

3.6 Determination of Sample Size

To calculate the optimum sample size, the study applied the formula used for

determination of sample size for a simple random sampling calculated at 95%

confidence level and 5% standard error.

The formula is:

n =

Where: n = Sample Size

p = Proportion of Positive Response

q = Proportion of Negative Response

ME = Error Margin

Z = Critical Z-value

N = Population size

Calculating an error margin of 2.5% to ensure a larger sample size and solving for “n”

n =

n = 540.46

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With this outcome, the researcher used 540 as the total sample size.

Using the formula for calculating the proportionate sample size for clusters, the

sample size for the various clusters was determined. The formula is;

nh = Nh x n N

where;

Nn = cluster sample size

Nh = cluster population size

N = total population size

n = population sample size

Based on the population, the various clusters were calculated as:

Aba

naba = 658 x 540 1994

naba = 178.2 ≈ 178

Nnewi

nnnewi = 456 x 540 1994

nnnewi = 123.5 ≈ 124

Onitsha

nonitsha = 880 x 540 1994

nonitsha = 238.3 ≈ 238 The proportionate sample size for the various clusters are presented in Table 3.2

below.

Table 3.2: Sample Size of Enterprises across Clusters in South-East Nigeria S/No Town/City/State Estimated Number of

enterprises Sample Size

1 Aba 658 178 2 Nnewi 456 124 3 Onitsha 880 238 Total 1994 540 Source: Author’s calculation

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3.7 Validity of the Research Instrument Validity refers to the degree to which an instrument measures what it is supposed to

be measuring (Akuezuilo, 2007, Udo, 2004, Osuala, 2005). This was achieved by

sending the prepared research instrument to experts for vetting in terms of relevance

to the subject matter, coverage of the content areas, appropriateness of language usage

and clarity of purpose.

3.8 Reliability of the Research Instrument

Reliability of the research instrument goes hand in hand with its validity. A research

instrument is said to be reliable to the degree that it measures accurately and

consistently, yielding comparable results when administered in a number of times

(Akuezuile, 2007, Osuala, 2005; Udo, 2004). This was achieved through test-retest

measure of the research instrument administered to 10 entrepreneurs of the clusters.

The values obtained from the surveys were computed using Spearman’s Rank

Correlation coefficient (Spearman’s rho). The Spearman’s Rank Correlation

Coefficient formula is:

p =

where p = spearman’s rank correlation coefficient

d = difference in rank xi and rank yi

n = sample size

With a correlation coefficient above 0.7, the instruments were considered to be

reliable. Validity and reliability are characteristics of good measuring research

instruments aimed at achieving research objectives and answering appropriately

research questions. For the current study, the correlation coefficient was 0.7515 which

confirms the reliability of the instrument utilized for the study (See Annex IV).

3.9 Pilot Survey

The study conducted a pilot survey where 70 of the research questionnaires were

administered randomly to the Management and Members of Staff of the Ministry of

Commerce and Industry; Small and Medium Enterprises Development Agency of

Nigeria (SMEDAN); National Association of Small and Medium Enterprises

(NASME), and selected Microfinance Banks and enterprises in clusters in Nnewi,

Onitsha and Aba.

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Out of the 70 respondents, 60 questionnaires were correctly completed and

retrieved. The pilot survey was considered as positive response and successful. The

remaining 10, represented the ones that were rejected. They were regarded as negative

response. The percentage of responses and non-responses therefore were 86% and

14% respectively.

3.10 Theoretical Framework

This study used the theoretical framework of Mayoux (1999). It interlinks

Microfinance to Micro and Small Enterprises (MSEs) empowerment (profitability).

Mayoux (1999) identified and linked the three contrasting ‘paradigms’ which are:

financial self-sustainability paradigm; poverty alleviation paradigm; as well as the

feminist empowerment Paradigm. According to Mayoux (2000), ‘profitability’ is a

multidimensional and interlinked process of change in power relations which can

operate in different spheres of life (economic, social, political, and so on) and at

different levels like individual, enterprise, cluster, and so on’. For some authors like

Cheston and Kuhn (2002) profitability is ‘a process of change by which individual,

MSEs, or group of MSEs (cluster) with little or no power, gain the power and ability

to make choices that affect their lives and businesses’. They also pointed out three key

elements of profitability which are ‘change, choice and power’. In relation to that,

Kabeer (1999) saw MSEs profitability as ‘the process by which those who have been

denied the ability to make strategic life choices acquire such ability’. With this regard,

the study pointed out three interrelated dimensions to measure profitability as

resources, socio-economic factors and achievement. According to the study,

‘resources’ include access to capital from different sources and future claims to both

material and social resources which serve to enhance the ability to exercise choice.

The second dimension of ‘socio-economic factors refers to characteristics that

help MSEs operating within a group (cluster) to define one’s goal and act upon them.

It includes the process that affects decision making, negotiation or ‘Power within’.

And then as a result of both resources and socio-economic factors, there is a

dimension of ‘achievement’ which refers to what Kabeer quoting Sen (1985b) called

the potential that people have for living the lives they want, of achieving valued ways

of ‘being and doing’(Kabeer 1999).

Again, Afshar (1998) in defining ‘profitability or empowerment’ supported Kabeer’s

notion of socio-economic factors stating that MSEs empowerment or profitability is

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something that cannot be done to or for enterprises but has to begin from them -

‘power within’.

For that reason, it has been found by development practitioners that building

MSEs capacity (economically, socially, and politically) is one of the prior activities

on agenda. Then, increasing MSEs especially those operating in a cluster access to

financial services through Microfinance programme has been found as one of the

tools which can lead to wellbeing improvement as well as profitability or

empowerment. But, as Swain and Wallentin (2009) mentioned, not all activities that

lead to an increase in the well-being of an MSEs are necessarily empowering in

themselves. From the same researchers’ point of view, ‘empowering activities’ are

those activities that reflect the changes that MSEs have effectively made to improve

the quality of their lives by resisting the traditions and norms that reinforce inequality.

MSEs operating in clusters are characterized by a different production

function to that of single MSE or other entities. MSEs operating in clusters are

diverse in terms of industrial organization and hence, it is plausible that there are

additional factors that impacted MSEs operating in clusters profitability in addition to

enterprise level specifics such as the source of capital. An empirical approach built on

the above theoretical predictions relevant to MSEs is useful in identifying the impact

of various funding instruments that predict profitability. Literature on MSEs devotes

considerable attention to Trade-off and Pecking Order theories of capital structure and

choices of source of capital.

3.11 Models Specification, Methods of Data Analyses and Results Evaluation Modelling for Microfinance Sources on Profitability (Objectives 1 and 2)

The argument as presented in the theoretical framework in 3.10 above informs the

present study’s focus on impact of different sources of funding on the outcome or

empowerment ― profitability. The study therefore, estimates the following basic

regression:

1 1

N Jn j

ic n ic j ic icn j

X Xα β β ε= −

Π = + + +∑ ∑ …………….………………………………

….3.1 Where outcome is the measure of profitability icΠ of MSE i located in

clusterc , with i =1-N and c =1-3 (Aba, Nnewi and Onitsha);

α is the regression constant;

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nicX stands for micro finance source(s) variables and amount received;

jicX represents other enterprise level characteristics; and

ic i t icε ν γ µ= + + is the disturbance term with tγ as the unobservable time effect, iν is

the unobservable complete set of enterprise-specific effect and icµ is the idiosyncratic

error. sβ are the coefficients to be estimated. Due to the significant differences that

exist in the clusters, the study tested for potential cluster effects and the econometric

model is therefore expanded as follows:

11 1

N Jn j

ic n ic j ic c icn j

ic i t ic

X X Dα β β δ ε

ε ν γ µ

−− −

Π = + + + +

= + +

∑ ∑…………..……………………………….

………….3.2 In the above model, D denotes the cluster-specific dummy variables (locations of the

cluster e.g. Aba, Nnewi and Onitsha). ic i t icε ν γ µ= + + is the disturbance term with tγ

as the unobservable time effect, iν is the unobservable complete set of enterprise-

specific effect and icµ is the idiosyncratic error. Thus, apart from observed

heterogeneity ( nicX and j

icX ), the model also accounts for MSEs-specific unobserved

heterogeneity and random idiosyncratic errors. The study acknowledges the

possibility of an alternative model, where funding may be assumed to shift or to

evolve in tandem with changing market share. Although this is well-grounded in

the literature on finance, it nevertheless appears less relevant here since we are

using a single data set. Conceptually, market share fails to capture MSEs

characteristics that graduate from various informal arrangements and pre-existing

institutions. Additionally, the market share approach does not allow for changes in

MSEs profitability that may be associated with economies of scale, even if the

growth in market share outpaces the growth of MSEs size.

However, there are other controls or enterprise level characteristics that

determine how enterprises perform which include: financial supports or source of

microfinance (sc) which is the focus of this study: age of enterprise (age); educational

level of the enterprise head (edu); total number of employees (empl); enterprise sector

of activity (a dummy for the three sectors under consideration: production, trade,

services) (sec), capital stock per employee (capem), ownership structure (owns),

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cluster location (cluloc), and the background (apprentice activity of the entrepreneur)

(soc).

The above factors have theoretical and empirical evidence on their

relationships with enterprise performance (as shown under the a priori expectation).

Enterprise performance is measured with the profit of the enterprises. Given that the

profit shows to what extent the enterprise is actually growing, high output might not

necessarily mean growth of the enterprise if the enterprise equally records high

expenses in terms of production and indirect costs. In order to translate (3.2) into an

expression suitable for econometric analysis, the study adopts an explicit functional

form model with second-order transcendental logarithmic (‘translog’) giving rise to

the following equations classified into two models:

Model 1:

1 2 3 4 5 7 7 8 9ic fom itLn LnSC LnAGE LnEDU LnEMPL LnSEC LnCAPEM LnOWNS LnCLOC LnSOCα β β β β β β β β β ηΠ = + + + + + + + + + +………………………………………………………………………………………………3.3 Model 2a:

1 2 3 4 5 6 7 8 9ic inf itLn LnSC LnAGE LnEDU LnEMPL LnSEC LnCAPEM LnOWNS LnCLOC LnSOCα β β β β β β β β β ηΠ = + + + + + + + + + +…………………………………………………………………………………………….....3.4 Model2b:

1 2 3 4 5 6 7 8 9ic both itLn LnSC LnAGE LnEDU LnEMPL LnSEC LnOWNS LnCAPEM LnCLOC LnSOCα β β β β β β β β β ηΠ = + + + + + + + + + +…............................................................................................................................................3.5

Where LnSC stands for log of source of microfinance (for = formal for (3.3),

inf = informal for (3.4), both = both sources for equation 3.5;

LnAGE stands for log of age of enterprise;

LnEDU stands for log of educational level of the enterprise head;

LnEMPL stands for log of total number of employees;

LnSEC stands for log of sector with three sectors (production, trade and

services with production serving as the control group);

LnCAPEM stands for log of capital stock per employee;

LnOWNS stands for log of ownership structure;

LnCLULOC stands for log of cluster location with two locations: Ontisha and

Aba included while Nnewi serves as a control;

LnSOC stands for log of social capital component (membership of different

cluster groups that embark on cash contributions hence somewhat give financial

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assistance); and ηit is the standard disturbance with mean zero and variance; itηδη .2 ;

is the residual or part of the log of enterprise profitability not explained by enterprise

or cluster specific characteristics, profitability and source as well as interest on loan.

The above factors of MSE development in terms of revenue growth and net profit

growth are examined by way of statistical regression. The three equations (models)

use a multiple linear regression model while parameters are estimated by Ordinary

Least Square (OLS) method.

The results therefore could be interpreted as the profitability determinants of

the enterprise cluster once all these factors are accounted for, while differences in

profitability was the result of unobserved characteristics of the enterprises such as

skills, technology, market structure, or managerial ability.

A Priori Expectations

A priori expectation will help to show whether the sign of economic or

development theory, that is, if the sign and size of the parameters or development

relationships follow the expectation of the theory. The a priori expectations in tandem

with capital flight theory are presented in Table 3.3 below:

Table 3.3: Model 1: Microfinancing source and profitability

Regressand Regressor Relationship Π (Profit) Sc +/- Π (Profit) Age +/- Π (Profit) Edu + Π (Profit) Empl +/- Π (Profit) Sec +/- Π (Profit) Capem + Π (Profit) Owns +/- Π (Profit) Onitsha/Aba +/- Π (Profit) Soc +/-

Source: Author’s

Note that: a ‘+’ indicates that the regressand and the regressor increase (or decrease)

together in the same direction. Thus, they possess a direct relationship. On the other

hand, a ‘-’ implies an inverse relationship between the regressand and the regressor.

Thus, an increase (or decrease) in the regressor leads to a decrease (or increase) in the

regressand.

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Methods of Results Evaluation (Objectives 1 and 2)

An evaluation of the above model consists of deciding whether the estimated co-

efficient are theoretically meaningful and statistically satisfactory. For this study,

there is a need for all results to satisfy both statistical criteria (first order test) and

econometric criteria (second order test).

Statistical Criteria: First Order Test

This aims at evaluating the statistical reliability of the estimated parameters of the

model. In this case, the F-statistic, t-statistic, Co-efficient of determination (R2) and

the Adjusted R2 are used.

The Coefficient of Determination (R2)/Adjusted R2

The square of the coefficient of determination R2 or the measure of goodness of fit is

used to judge the explanatory power of the explanatory variables on the dependent

variables. The R2 denotes the percentage of variations in the dependent variable

accounted for by the variations in the independent variables. Thus, the higher the R2,

the more the model is able to explain the changes in the dependent variable. Hence,

the better the regression based VECM technique and this is why the R2 is called the

co-efficient of determination as it shows the amount of variation in the dependent

variable explained by explanatory variables. However, if R2 equals one, it implies

that there is 100% explanation of the variation in the dependent variable by the

independent variable, and this indicates a perfect fit of regression line. Where R2

equals zero, it indicates that the explanatory variables could not explain any of the

changes in the dependent variable. Therefore, the higher and closer the R2 is to 1, the

better the model fits the data. The above explanation goes for the adjusted R2.

The F-test

The F-statistics is used to test whether or not, there is a significant impact between the

dependent and the independent variables. In the regression equation, if calculated F is

greater than the table F table value, then there is a significant impact between the

dependent and the independent variables in the regression equation. While if the

calculated F is smaller or less than the table F table value, there is no significant

impact between the dependent and the independent variables.

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The t-statistic (coefficients)

This is used to determine the reliability/statistical significance of each variable

coefficient. Here, the absolute t-value of each coefficient is compared to 1.96, and if

greater than 1.96, such variable possessing the coefficient is accepted as statistically

significant and fit to be used for inferences and possibly for forecasting and vice

versa.

Econometric Criteria: Second Order Test

This aims at investigating whether the assumption of econometric method employed

are satisfied or not in any particular case. They determine the reliability of statistic

criteria and also establish whether the estimates have desirable properties of

unbiasedness, and consistency. Since survey data is used and the analyses are ran

using STATA computer package, the study did not bother much about this as STATA

computer package is known to filter any variable(s) that can lead to undesirable

properties of unbiasedness, and consistency during the different iterations. The

econometric criteria also help to evaluate the theoretical consistencies of the

estimates.

Modeling for the Determinants of the choice of Microfinance Source (Objective 3)

The concept of enterprise demand for credit refers to the variations in the quantities of

credit that an enterprise is expected to demand for, at specified (interest rate) and time

period assuming that all other pertinent factors remain constant. To analyse the

determinants of an enterprise operating in a cluster demand for credit, the starting

point is the theory of consumer behaviour. In this study, demand for credit is defined

as the probability that an enterprise answered ‘yes’ to the question “Did you apply for

credit before?” The level of credit demanded is then defined as the amount in Naira of

credit demanded by the enterprise. Total utility function can be expressed as:

( )1 2, ........ nU U X X X= …………………………………………………………

….3.6 Where, U represents the total individual/enterprise utility.

1X represents enterprise demand for credit, i =1, 2, ……, n.

If we let 1int , 2int ….intn represent the interest rate.

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Let 1f represent credit demand by an enterprise, such that ( )1 1f f C= , and let

i represent the price of credit. Then, i ifD rf= represents demand for credit, subject to

enterprise characteristics. The demand for credit can be stated thus:

( , , , )ifD f Y H V Q= ……………………………………………………………

…..3.7

Where, ifD is the demand function for credit;

Y is enterprise credit amount;

H is a vector representing enterprise characteristics including sex, age and

level of education;

V represents the credit variables for example: interest rate charge on loan and

credit distance; and

Q is the social capital dimensions.

In order to translate (3.7) into an expression suitable for econometric analysis, the

study adopted an explicit functional form model with second-order transcendental

logarithmic (‘translog’) form which therefore becomes:

Model 3:

1 2 3 4 5 6 7 8 9 10 11 12i itLnD LnC LnG LnAGE EDYU LnINT LnDS LnREL LnTRAN LnPROT LnSEC LnLEN LnSOCα β β β β β β β β β β β β ε= + + + + + + + + + + + + +…………………………………………………………………………………………

……3.8

Where LnDi represents log of demand for credit;

LnC stands for log of credit amount;

LnG stands for log of gender of the enterprise manager;

LnAGE stands for log of the age of the enterprise;

lnEDU stands for log of education level of the head of the enterprise;

LnDS stands for log of the distance to the microcredit facility(ies);

LnREL stands for relationship with the source of microfinance;

LnTRAN stands for training;

LnPROT stands for extent of protocol including collateral requirement;

LnSEC stands for different sector effects (production, trade and services);

LnLEN stands for length of repayment period offered;

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LnSOC stands for log of the social capital dimension (membership of different

cluster groups that embark on cash contributions hence somewhat give

financial assistance); and

εit is the standard disturbance with mean zero and variance; 2. itηδ ε is the

residual or part of the log of credit demand not explained by enterprise or cluster

specific characteristics, credit variables and social capital dimension.

Because of the nature of the dependent variable (binary with 0 and 1), Logit

Regression analysis is used in determining the above equation (3.8) i.e. factors

affecting demand for credit among enterprises in these clusters instead of the multiple

regression analysis. The model is used as adopted by Mpuga (2004) and Mpuga

(2008). The various sources of credit (formal and informal) from which enterprises

could access credit are classified as the dependent variables. Since this involves

multinomial logit regressions, its interpretations can only be the same as multiple

regressions when marginal odds are computed. The calculation of odds ratio of

response categories is done relative to the base line, that is, the coefficient of

probabilities. Positive coefficient implies the probability of respondent falling in

numerator category or odds are greater than the probability of falling in base category.

Chi-square distributions is used to test overall model adequacy at specific significant

level. Likelihood ratio also helps to determine whether the overall Logit model is

perfect for policy making.

It is noteworthy that Logit model is used to analyse data sets to reflect a

dichotomous category; in this case to ascertain the determinants of formal vs informal

sources. The general logit functional form according to Gujarati (2004) is stated thus:

Logitpx= log[ ]

= …………………...................................................3.9

The estimates of the above equation therefore, show the determinants of the choice of

the micro finance providers they most often go to (whether formal or informal). The

significant variables are therefore considered as the main determinants of the choice

of the microfinance source by enterprise clusters.

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3.12 Assessment of Level of Support of Microfinance Providers for the Sustenance of Profitability of Enterprise Clutters in South-East, Nigeria (Objective 4) The fourth objective of the study aims at ascertaining the level of support of micro

finance providers for the sustenance of the profitability of enterprise clusters in South

East, Nigeria. This was done in three ways. First, the study uses percentages, rates and

pie charts to measure and show the level of support for both formal and informal

financing sources on small scale enterprises in Nigeria, by comparing the number of

those that receive credit from formal sources and those that received from informal

sources. The study then used a 5-point likert scale to measure the perception of

enterprise as regards the level of support from both the formal and informal micro

finances. The 5-point likert scale is analysed such that, the average for each question

is approximated towards the nearest whole number. Then, the approximation

constitutes one of the 5-point scales as originally stated in the question. The

approximated whole number now determines the level of involvement such that 5

show very high involvement and 1 very low involvement. Finally, the study uses

percentages and pie charts to ascertain the extent to which micro finance funds have

expanded the firm’s business.

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CHAPTER FOUR

DATA PRESENTATION, ANALYSES AND DISCUSSION OF FINDI NGS

4.1 Introduction

This section of the study presents the analyses done with data generated from the

survey in line with the instruments of this study. The study conduct a cross sectional

survey of 540 enterprises across three enterprise clusters in Onitsha, Nnewi and Aba.

Of the 540 enterprises surveyed, 179 of these are based in Nnewi, 180 in Onitsha and

181 in Aba.

4.2 General Enterprise Characteristics and Perceptions

Listed in Table 4.1 are the mean, standard deviation, minimum and maximum values

of selected enterprise characteristics such as average age of the enterprises, average

number of employees that are administrators, average number of employees that are

in operations, average annual sales, average profit, average savings, average capital,

and average micro finance received.

Table 4.1: Summary of Enterprises Characteristics Variable Mean Std.

Deviation Minimum Maximum

Age of the Enterprise 11.3 6.5 1.0 33.0 Approximate number of employees (Administrators)

3.0 2.8 1.0 14.0

Approximate number of employees (Operations)

5.0 5.0 1.0 27.0

Average annual sales (N) 4,371,180.0 9,672,194.0 1,000.0 50,000,000.0 Average Profit (N) 1,834,141.0 4,396,383.0 35,000.0 30,000,000.0 Average savings (N) 412,742.1 1,206,031.0 1,000.0 10,000,000.0 Capital (N) 4,411,885.0 12,400,000.0 50,000.0 100,000,000.0 Average Micro financing (N)

514,984.0 14.7 10,000.0 10,000,000.0

Interest on loan (%) 23.3 376,494.7 5.0 40.0 Value of training Received

329,682.5 376,494.7 - 100,000.0

Source: Author’s

Other indicators include average interest on loan and the monetary value of the

training received. From the table above, the minimum age of the enterprise surveyed

is about 11 year old with the minimum and maximum standing at 1 year and 33 years

of existence respectively. The average age of 11.3 shows that most of these

enterprises have had the experience and may have had the need to borrow from either

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formal or informal sources. The average number of employees that are administrators

is just 2.45, given that administrators are usually not many, though the maximum

stands at 14.

However, the average number of operatives of the enterprises is approximately 5 with

a minimum of 1 and a maximum of 27. This shows that while some of these

enterprises are small scale with only 1 or 2 employees, others are large with up to 27

operatives. This therefore, offers a good variation of enterprise as the study examines

their credit behavior and access the way it affects enterprise performance which is

measured in this study by profitability.

The enterprises sampled across the three cities equally have huge variations in

their sales, profit, average savings and capital accumulation as deduced from their

standard deviations. The standard deviations are relatively high with capital being the

highest. This emphasizes on the high inequality that exists among micro and small

enterprises in the study area. While the mean average sales is N4,371,180, the mean

average profit is N1,834,141 just as the mean average savings is N412,742.1 with the

mean average capital as N4,411,885. In the same light, the maximum value for sales

is N50,000,000 while the maximum value for profit was N30,000,000 but for savings,

is N10,000,000 and N100,000,000 for capital. Also, the mean average micro

financing is given as N514,984 with a maximum of N10,000,000 which is far lower

than the maximum capital in the study. The mean interest on loan is 23.33%; while

the mean value of training received in the form of micro financing support is

N329,682.5 in monetary terms.

In terms of highest education attended by the enterprise heads (respondents),

the distribution shows 1% with no formal education, 4.6% with primary education,

47% with secondary education and interestingly, 44% and 3.4% with tertiary

education and post graduate studies respectively. In summary, over 90% of the

sampled enterprise heads have either secondary or tertiary education while less than

7% have either primary or non-formal education.

Indicators from the general characteristics of micro and small enterprises

sampled reveals that over 95% of the enterprise heads own the enterprises solely

while less than 4% of these enterprises are jointly owned. This means that about 97%

of the enterprises own their businesses single-handedly, bearing the risk and enjoying

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the profit. Though some of these enterprises have expanded with many employees,

they remain the sole risk bearers and managers of the business.

In terms of the sectoral compositions of these sampled enterprises, though

majority are sole proprietors, they are equally distributed into different sectors of

production that are classified into three groups namely, production, trade and services.

While 61% of these sampled enterprises are under the trade sector, 11% and 37.2%

are under production and services sectors respectively. It is also observed that some of

the enterprises in the production sector are equally in the trading sector and/or the

service sector. It is therefore evident that more than half of the enterprises are

interested in trading, while the production and service sectors are not much exploited.

Similarly, the distribution in terms of specific activities involved suggests that

based on the type of activity practiced, a greater percentage of the enterprises are

somehow involved in trading only (about 45%), with the next most exploited sector

being the textiles with about 14%, followed by Automobile and furniture and wood

work by 9% each. On the other hand, only 1% of the enterprises are in the chemicals

and plastics sector as well as the wood/paper and pulp industry while 2% are involved

in plants and machineries. However, the survey covers other types of activities like

the shoes and leather products, constituting 5% of the study, foundries,

metals/fabrication is 5%, food is 6% as well as several other types of activities.

It is also observed that though these enterprises are from different sectors of

production and economic activities, they all face the need to borrow and have had

different types of assistance from formal micro financial operatives. Credit assistance

constitutes 67%, technical support 8%, being a financial guarantor 16.4% while

financial advisory services assistance constitutes 1.2%. It is therefore evident that

micro financial institutions are more convenient with giving out credit than any of the

other forms of financial assistance.

Looking at the extent at which the credit source is generally perceived to be

reliable, it is revealed that only 7% believe that their credit source is reliable to a very

low extent and 19% to a low extent. Also18% of the respondents from the sampled

enterprises perceives that credit source is reliable to a high extent while 16%

perceives such reliability to a very high extent. On the average, the study concludes

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based on this finding (average) that the respondents generally agree that their various

credit sources are reliable.

In terms of distribution of the enterprises that have received credit from

formal, informal and from both sources, the frequency reveals that more enterprises

patronize the informal sources of financing by about 55% while enterprises who

received credit from formal Micro Finance Institutions (MFIs) are about 34%. About

11% of the respondents receive credit from both formal and informal sources.

Challenges faced by these enterprises in terms of borrowing include request for the

loan, requirement, protocol/procedure, interest on the loan and the tenure for payment.

However, it is worth noting that the type and magnitude vary between the formal and

informal sources of finance. Details of such challenges faced by enterprises while

accessing credit as observed during the Focused Group Discussion (FGD) are

presented in Table 4.2 below.

Table 4.2: Perceived Challenges faced by enterprises in accessing micro finance Formal Informal

Challenges %age of the sample

Challenges %age of the

sample Borrowing is costly 83 Can’t afford large credits 66 Request of collateral 76.8 Not reliable 32.3 Request of surety 33.4 So many phone calls 26 High interest rates 72 Takes time to build trust 37.6 Protocol 58.4 Short repayment periods 71 Not straight forward/corny 27 Lack of confidentiality 43.3 Time taking 47 The owner can easily request at anytime 27

Source: Author’s

Analysis of the above table suggests that borrowing credit from formal financial

institutions generally has its advantages and disadvantages from the borrowers’

experience. The challenge faced by the entrepreneurs as highlighted by the

respondents during the FGD is that borrowing from formal institutions is costly with

83% supporting this. Also, 76.8% of the respondents that receive formal credit show

that the collateral requested by formal institutions is the greatest challenge faced. In

addition, the request for surety is a major challenge and set back to borrowing from

formal institutions. 72% of the respondents that receive credit from formal institutions

agree that high interest rate is equally a challenge. Other constraints include protocol

at 58%, being corny and time constraints to finally get the loan. The study therefore,

86

notes the most predominant ones to be the cost of borrowing, collateral, high interest

rates and protocol, wherein more than half of the respondents that receive credit from

formal institutions perceive these as challenges.

On the other hand, the greatest challenge faced by informal micro financial

institutions is the short repayment period. About 71% of the respondents perceive that

informal institutions usually give them a very short repayment period that is usually

not even enough to make profits from the loan. The next challenge is the inability to

lend huge sums of money or give out large credits as 66% of them see it as a

challenge. Closely followed, is the lack of confidentiality as most of these informal

financial institutions are the lender’s circle of friends and made known to everyone,

all the financial transactions that have been made. Other challenges include the time it

takes to build trust and reliability, the impromptu request for credit and its interest and

close monitoring to ascertain proper usage of the credit.

4.3 Microfinance Sources and Enterprise Profitability/Objectives 1&2 (Models 1, 2a & 2b)

The first and second objectives of the study are analysed using the Ordinarily Least

Squares (OLS) estimation technique which was built to ascertain the impact of formal

and informal micro financing on the profitability of enterprises in the selected

clusters. Profitability (π) is used as the dependent variable. Three different equations

are run (see equations 3.3 - 3.5 in chapter three above), representing formal (1),

informal (2a) and both sources (2b) and the results are presented below.

Table 4.3: Effect of microfinance sources on the profitability of enterprise clusters

Model 1 Formal

Model 2a Informal

Model 2b Both

Dependent Variable (π) LnSC 0.117*** 0.529 *** 0.306*** (-3.9393) (-8.9900) (-7.4816) LnAGE -0.310** 0.179*** 0.203*** (2.3664) (-11.1875) (-13.0128) LnEDU 1.617** 1.509*** 1.478*** (-2.5831) (-3.7277) (-3.0083) LnEMPL 0.184** 0.221*** 0.182*** (-9.4358) (-8.9473) (-7.4590) TRADE 1.021*** 0.597 *** 0.582***

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Model 1 Formal

Model 2a Informal

Model 2b Both

(6.0260) (-7.2539) (-8.0497) SERVICE 0.861*** 0.697 *** 0.712*** (-3.9378) (-4.8702) (-6.5420) LnCAPEM 0.102* 0.109*** 0.113 *** (-2.0306) (-4.6982) (-4.9779) LnOWNS 0.081** 2.629*** 2.055*** (-2.4505) (-9.2533) (-5.5911) ONITSHA -0.245 -0.862** -0.887* (1.4546) (2.5848) (2.0664) ABA -0.168* -1.803* -1.062** (2.0183) (2.02595) (2.8083) LnSOC 0.647 0.788** 0.8649 (-1.5612) (-2.8945) (-1.7342) _cons 12.916*** 2.641*** 13.942*** (-8.1920) (-5.9402) (-8.1494) Sample size (n) 538 538 538 R2 0.921 0.918 0.9107 Adj. R2 0.8482 0.8427 0.8293 F-statistics 13.73 11.03 10.78 Probability (0.000) (0.000) (0.000) MSE 0.1395 0.1147 0.1273

Source: Author’s (t-statistic in parentheses; * sign shows significance @ * 0.05; ** sign shows significance @ 0.01; *** sign shows significance @ 0.001)

The OLS estimation results show that the overall model is statistically

significant. F-statistics is used to test the significance of the model - whether or not

there is a significant relationship between the dependent and the independent

variables. In the above regression equations, F calculated (13.73, 11.03 and 10.78) are

greater than table F table value (4.08) and hence, the study concludes that there is a

significant relationship between the dependent variable (profitability) and the

independent variables. Furthermore, the F-value probability of 0.000 which is less

than 0.05 shows that, the model is significant at the standard 5% significant level.

The square of the coefficient of determination R2 or the measure of goodness

of fit is used to judge the explanatory power of the explanatory variables on the

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dependent variables. The R2 denotes the percentage of variations in the dependent

variable accounted for by the variations in the independent variables. Thus, the higher

the adjusted R2, the more the model is able to explain the changes in the dependent

variable due to changes in the independent variables. In the above regression results,

the adjusted R2 are 0.8482, 0.8427 and 0.8293 for formal, informal and both

microfinance sources respectively which implies that, there are 84.82%, 84.27% and

82.93% explanation of the variation in the dependent variable by the independent

variables for formal, informal and both microfinance sources respectively. This

indicates a perfect fit of regression line. T-statistics which appeared in parenthesis are

used to determine the level of significance for each variable coefficient(s) in the three

equations as appeared in the three columns with variables significant at different

critical levels 0.01 (***) or 1%, 0.05 (**) or 5% and 0.10 (*) or 10%, and

insignificant when there is no star sign.

A look at the individual coefficients for formal source of microfinance (column 1)

shows that the significant determinants of profit include, age of the enterprise, level of

education of enterprise head, number of employees, ownership and ratio of capital to

employees. The indicator for social capital proxied by membership of different cluster

groups was not significant in formal microfinance source impact on profitability. Age

of the enterprise has a negative effect on profitability which suggests that for formal

microfinance source, higher age may not be an asset. Production is the control group

for the sector effect and the results suggest that trade and services fared better than

production in determining the enterprise profit. Similarly, Nnewi is the control for

cluster city effect with the results suggesting a non-significant for Onitsha and a

negative significant for Aba. This implies that Aba clusters effect fared less than

Nnewi in determining the profitability.

A look at the individual coefficients for informal source of microfinance

(column 2a) reveals that the significant determinants of profitability include, age of

the enterprise manager, level of education, number of employees, ownership, ratio of

capital to employees as well as the indicator for social capital proxied by membership

of different cluster groups. Here, age of the enterprise manager has a positive effect

on profitability which suggests that for informal microfinance source, higher age is an

added advantage. Production is the control group for the sector effect and the results

89

suggest that trade and services fared better than production in determining the

enterprise profit just as in the formal source. Similarly, Nnewi is the control for

cluster city effect with the results suggesting a significant cluster effect for Onitsha

and Aba though negative, implying that Nnewi cluster had overall better effect in

determining the profitability of enterprises within the cluster than Onitsha and Aba in

both formal and informal micro finance sources. This suggest that generally there may

be better collaboration among enterprises in different groups in the clusters in Nnewi

than in Onitsha and Aba.

Similarly, the individual coefficients for both sources of microfinance (column

2b) reveal that the significant determinants of profitability include, age of the

enterprise manager, level of education, number of employees, ownership and the ratio

of capital to employees. The indicator for social capital proxied by membership of

different cluster groups is not significant. Age of the enterprise manager here has a

positive effect on profitability which suggests that, for enterprises using both sources

(formal and informal microfinance), higher age is an added advantage. Production is

the control group for the sector effect and the results suggest that trade and services

fared better than production in determining the enterprise profit just as in the formal

source. Similarly, Nnewi is the control for cluster city effect with the results,

suggesting a significant cluster for Onitsha and Aba though negative, implying that

Nnewi cluster had better effect in determining the profitability of enterprises within

the cluster than Onitsha and Aba that use both formal and informal sources of

microfinance.

From the three equations, sector and city with the exception of Onitsha for the

formal source are considered as important determinants of profitability of the

enterprises. However, due to their categorical nature, they are examined as dummy

variables wherein, production is considered as the omitted category against trade and

services for sector, and Nnewi is considered as the omitted category against Onitsha,

and Aba for the city in which the enterprise is located. The results suggest that

enterprises involved in trade marginally contribute more to profit when compared to

those in the services and production sectors and are better when they use formal

microfinance source. This shows that enterprises involved in trade receive more

incremental profits than those in the service sector and finally, those in the production

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sector. This could be as a result of the gestation period of the enterprises in the

production sector as it may take longer time for some enterprises still expanding to

meet up with operational costs. While for the city or location effect, Nnewi

enterprises fare better than Ontisha and Aba in terms of location contribution to profit

for all sources. The results suggest that enterprises within clusters in Nnewi are likely

to make more profits than enterprises within clusters in Onitsha while enterprises

within clusters in Ontisha are more likely to make more profit than their counterparts

in Aba when they use informal source of microfinance.

Labour equally shows positive and significant effects on the profit of

enterprises within the clusters that receive credit from both formal and informal

sources. The ratio of capital to labour is also a significant determinant of the profit of

enterprises that receive credit from all the two sources (formal, informal or both).

The fact is that the relationship the enterprise has with the borrower (social

capital) did not significantly affect the profit of the enterprise when the source of

microfinance is formal but it was not so when it is an informal source. The informal

results corroborate with the cluster advantage of the entrepreneur. The advantage of

each enterprise being in a cluster and becoming members of different capital

associations does significantly improve profits of that enterprise.

It is worthy of note that the first and second objectives are to ascertain the impact of

formal and informal financial credit on the profitability of enterprises that receive it.

The variable used to proxy this is the sum total of credits received from financial

institutions that year regressed on the rate of return or the profit for that year. The t-

value for the formal and informal credit sources are (3.9393) and (8.990) and they are

both greater than 1.96 while the p-values are 0.012 and 0.009 respectively. Also, the

positive coefficients for both sources suggest that there is a positive relationship

between formal and informal credits and the return on investments of enterprises that

receive them within the clusters. In fact, from the coefficient, we can infer that as

credit increases by N1, the return on capital for enterprises that receive them,

increases significantly by N0.11, N0.529 and N0.306 or 11k, 53k and 31k for formal,

informal and both sources respectively.

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4.4: Determinants of the choice of the microfinance Source by Enterprise clusters in South East of Nigeria (Objective 3 and Model3) To ascertain the determinants of choice of microfinance source, the study employs

two methods; first, by estimating multinomial logit regression that investigates the

determinants of the choice of the microfinance provider, and second, by analyzing the

perception of determinants of the choice of the microfinance source. The logit

regression uses binary dependent variable due to the qualitative nature of the study.

The dependent variable is a categorical variable and is designed such that 1 represents

enterprises that receive (proxy for choice) formal credit while 0 represents informal

credit. The significant determinants of profitability include amount of credit needed,

gender of the enterprise head, age of the enterprise head, level of education of the

enterprise head, interest rate, reliability, training, protocol extent, discrimination of

sectors, and length of repayment period offered. Indicators of social capital and sector

are regressors to the regressand, that is, the choice of the micro financial provider.

The logit estimation results are presented Table in 4.4 below.

Table 4.4: Determinants of the choice of micro financial sources for enterprises

Dependent variables Marginal effects Odds ratio LnC -0.6464 1.98000 (5.58)*** LnG 0.2447 1.2773 (0.46) LnAGE 0.4004 1.4924 (2.25)** LnEDU 0.0292 1.0380 (0.67) LnINT 0.1353 1.1449 (4.35)*** LnDS 0.1366 1.1463 (0.308)*** LnREL 1.3368 3.8071 (2.88)** LntRAIN -0.03209 0.9684 (-0.31) LnPROT 0.9998 2.717 (3.66)***

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Dependent variables Marginal effects Odds ratio TRADE 2.0435 4.70076 (3.35)*** SERVICES 3.08779 1.01485 (3.98)*** LnLEN 0.8031 2.232561 (3.87)*** LnSOC 0.0138 2.98628 (-4.11)*** _cons -18.1842 (-4.03)*** Sample size s 539 Pseudo R2 0.6311 (0.0012) Chi-square 214.15 (0.0027) Log likelihood -308.564 Source: Author’s Absolute value of z statistics in parentheses, * significant at 10%, ** significant at 5% and *** significant at 1%. Omitted categories in the dependent variables are the (comparison enterprises who did not borrow credit)

Estimation results in the above table show an overall significance given by the

probability of chi-square to be 0.0000 which is less than 0.05 hence, significant at 5%

significant level. This is evident as the probability chi-square calculated (214.15) is

greater than the probability of chi-square tabulated (4.574) with 11 degrees of

freedom. The Pseudo R2 in this non-linear model is often considered to be usually low

and it constrains this model for that characteristics. However, the Pseudo R2 of the

model estimated above is still relatively high at 0.6311.

The significant determinants for the choice of the microfinance source as shown in the

table above include amount or volume of credit needed, age of enterprise head,

distance to microfinance facility, relationship with the microfinance provider,

problematic extent of protocols, interest rate, length of repayment period offered,

sector discrimination and the social capital component. On the other hand, the non-

significant determinants include gender of the enterprise head, training offered by the

provider and level of education. Nevertheless, the positive determinants are the age of

enterprise head, reliability of the provider, problem extent of protocol, interest rate,

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length of repayment period offered, cluster advantage and social capital while the

negative determinants are amount or volume of credit needed and training, though

training was not significant.

Given the complicated and unfriendly nature of the marginal effect, the study

estimates odds ratio of the logit model that is equally presented in the above table.

The odds ratio is, however, the antilog of the logit and is less than 1 when the

marginal effects is negative and, greater than 1 when the marginal effect is positive.

For a unit increase in the amount or volume of credit needed by the entrepreneurs, the

odds in favour of choosing informal sources over formal sources decrease by 0.6464

or 36.4%. This variable is statistically significant given its probability value in terms

of the amount or volume of credit needed. However, enterprises prefer formal

sources.

The relationship with the credit provider equally and significantly determines

the choice of the credit source. The positive coefficient suggests that a unit increase in

the extent of relationship with the credit provider increases the odds in favour of

choosing an informal credit source. Training is not a significant determinant of the

choice of credit source, though units increase on the training received decreases the

odds in favour of choosing an informal source by 0.9684 (3.2%). Therefore, the odds

in favour of choosing formal sources also increased, though not significantly.

The extent of protocol as a problem is equally a significant determinant of the

choice of credit source among enterprises in the South East of Nigeria. The absolute

z-value is 3.66 which is greater than 1.96 hence, a unit increase in the extent of

protocol as a problem increases the odds in favour of an individual choosing an

informal credit provider. This is expected a priori given that; there exist more

protocol in the formal sector than in the informal sector. The extent to which the

credit providers discriminate on specific sectors of activities does not significantly

determine the choice of the credit source, though the odds are in favour of choosing

formal sectors by 0.319 or 68.1%.

Interest rate and the length of repayment period are both significant

determinants of the choice of credit source by the entrepreneurs. A unit increase in the

extent to which the respondents perceive interest rate as a problem increases the odds

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in favour of choosing a credit provider by 1.1449 while a unit increase of length of

repayment period increases the odds in favour of choosing an informal source for

credit provision.

Social capital component is equally a significant and positive determinant of

the choice of the credit provider, and the odds in favour of choosing an informal credit

source is on the increase. The fact that an enterprise enjoys such cluster advantage due

to the location of the firm significantly affects the choice of the credit source. And

such positive sign of the cluster advantage shows that the odds increase in favour of

choosing the informal sector for every unit increase for those with a cluster advantage.

This is expected, given that social capital component are more predominant in the

informal sector, and more importantly, it improves the confidence the provider has in

the borrower.

Surprisingly, education is not a significant determinant of the choice of the

credit provider. This suggests that it is not on the basis of education that the

enterprises choose where to get credit from. A unit increase in the educational level,

however, reduces the odds in favour of choosing an informal source by 6.2%. Sector

is considered as a categorical variable and so the production sector is omitted and

used as a reference category for trade and services. The result for both categories is

significant and positive, suggesting that the odds in favour of choosing a provider

increases for enterprises in the trade and service sectors when compared to the

production sector.

In other words, there exist significant determinants of the choice of

microfinance sources by enterprise clusters in South East, Nigeria and, these

determinants include: the amount or volume of credit needed, relationship with the

microfinance provider, problematic extent of protocols, interest rate, length of

repayment period offered, social capital component including cluster advantage. It is

equally noteworthy that factors such as age of the enterprise, interest rate, relationship

with the microfinance provider, extent of protocol including collateral availability,

being engaged in trade and services, the length of repayment period and the social

capital dimension (membership of different cluster groups that embark on cash

contributions hence somewhat give financial assistance) have odds ratio that are

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higher than one (1). This implies that these are the main factors that affect enterprises

in the selected clusters from moving from formal to informal microfinance sources.

The likelihood of moving from formal to informal are highest with enterprises

engaged in trade as well as relationship with the microfinance provider, social capital

and extent of protocols.

Analysis of the perception of determinants of the choice of the microfinance

sources using some selected factors as depicted in Figure 4.1 below shows that

respondents perceive that the quick response is the most deterministic factor for their

micro financing choice.

Figure 4.1: Perception of determinants of choice of microfinance choice

Source: Author’s Evidence from the above figure shows that 41% of the sample opine that their choice

for credit source depends on quick response, 27% of the sample perceive interest rate

as a significant determinant of microfinance sources, 17% says it depends on the

reliability of the credit source to choose their source of micro financing, 9% of them

choose a particular credit source because they are favoured by the credit supplier

based on the cluster in which they are while only 6% of the respondents choose a

particular credit supplier because they have a relationship with the provider.

4.5 Assessing the level of support of Microfinance providers for the sustenance of profitability of Enterprise (Objective 4) The fourth objective of the study is to assess the level of support of microfinance

providers for the sustenance of profitability for enterprises that use both formal and

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informal sources in enterprise clusters in South East, Nigeria. This objective is

examined from three perspectives. First, the study investigates the perception of the

extent to which micro financing has been supportive to the enterprises of formal and

informal recipients. Also, it examines the level of support using the likert scale and

lastly, it examines the extent to which microfinance providers have expanded their

businesses.

To examine the perception of the extent to which micro financing has been

supportive to the enterprises that receive credit from formal sources, the study uses

the chart below which is extracted from the questionnaire for demonstration.

Figure 4.2: Extent of microfinance support perceived from formal and informal sources

Source: Author’s

In examining the extent to which microfinance support enterprises that receive

funds from formal microfinance providers, the chart above shows that 53% of the

recipients from formal sources perceive that microfinance providers support them

averagely. Only 7% believe that microfinance providers support them to a very high

extent and 26% to a high extent, while 7% perceive that microfinance providers

support them to a very low extent, and 7% to a low extent. On the average, the study

states that the recipients of formal credit generally agree that microfinance providers

support them averagely.

In assessing the extent to which microfinance providers support enterprises that

receive funds from informal microfinance sources, the chart above shows that 18% of

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the recipients from informal sources perceive that microfinance providers support

them averagely. Up to 24% believe that they support them to a very high extent and

21% to a high extent, while 16% perceive that microfinance providers support them to

a very low extent, and 21% to a low extent.

To show the level to which the respondents perceive that the micro financial funds

have been supportive, the study uses a likert scale for both formal and informal

sources. As stated above, the 5-point likert scale is analysed such that, the average for

each of the sources is approximated toward the nearest whole number. Then, the

approximation constitutes one of the 5-point scales as originally stated in the question.

The approximated whole number now determines the level of involvement such that

‘5’ shows very high involvement and ‘1’, very low involvement. The results are

therefore shown below.

Table 4.5: Average Perception of Micro Financial Support from Micro Financial

Sources for Enterprises in South East, Nigeria

Variable Obs Mean Std. Deviation Approximation of the mean

Conclusion

Formal Sources

266 3.198795 0.9225779 ≈ 3 Average

Informal Sources

272 3.661832 1.306656 ≈ 4 High extent

Source: Author’s

Analysis of Table 4.5 above clearly shows that, while the respondents on the

average perceive that formal micro financial institutions support enterprises in South

East, Nigeria, enterprises that receive funds from informal micro financial institutions

perceive that the institutions have supported them to a high extent.

Finally, to examine the extent to which funds have expanded businesses in the

sector, the study employs the doughnut chart below.

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Figure 4.3: Perceived extent to which funds have expanded business

Source: Author’s

In examining the extent to which microfinance funds have expanded

businesses of small scale enterprises as perceived by the respondents, a doughnut

chart is used to show the percentage representative for each level or extent. The chart

above suggests that 25% of the sample perceive that the funds expanded their

businesses averagely. Only 9% believe that microfinance funds expanded their

businesses to a very low extent, and 11% to a low extent. While 38% perceive that

microfinance funds expanded their businesses to a high extent, 17% perceive that the

funds have expanded their businesses to a very high extent. On the average, the study

states that the respondents generally agree that microfinance funds have expanded

their businesses significantly.

4.6 Tests of Hypotheses

The study recalls the following research hypotheses as presented in their null forms

thus:

1. There is no significant impact of the formal microfinance sources on the

profitability of enterprise clusters in South East of Nigeria,

2. There is no significant impact of the informal microfinance sources on the

profitability of enterprise clusters in South East of Nigeria,

3. There exist no significant determinants (i.e. amount, interest, extent of

protocols including collateral availability, relationship with the provider) of

the choice of microfinance sources by enterprise clusters in South East,

Nigeria.

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4. There is no high involvement of the microfinance providers for the sustenance

of profitability of enterprise clusters in South East Nigeria.

Decision Rule for Hypotheses Testing:

The stated hypotheses are tested at 0.05 level of significance. The null hypothesis is

rejected if the probability (p-value) at which the t-value for hypotheses 1 and 2 or the

z-value for hypothesis 3 is significantly less than the chosen level of significance,

otherwise, the alternative hypothesis will be accepted. In other words:

1. If the calculated t-value for the variable coefficient for hypotheses 1 and 2 is >

1.96, the study does not accept the null hypothesis, and accepts the alternate

hypothesis.

2. If the calculated z-value for the variable coefficient for hypotheses 3 is > 1.96,

the study does not accept the null hypothesis, and accept the alternative

hypothesis.

3. If the calculated approximation value of the mean is 3≥ in the 5 likert scale,

the study does not accept the null hypothesis, and accept the alternative

hypothesis.

Hypothesis 1 and 2:

The t-value for the formal and informal credit sources are (3.9393) and (8.990) and

they are both greater than 1.96 while their p-values are 0.012 and 0.009 respectively

which are less than 0.05 hence, we reject the null hypothesis which implies that

formal and informal sources of microfinance significantly affect the profitability of

the enterprises that receive them. Also, the positive coefficients for both sources

suggest that there is a positive relationship between formal cum informal credits and

the return on investments of enterprises that receive them within the clusters. In fact,

from the coefficient, we can infer that as credit increases by N1, the return on capital

for enterprises that receive them, increases significantly by N0.11, N0.529 and

N0.306 or 11k, 53k and 31k for formal, informal and both sources respectively.

The above findings mean that we do not accept the first and second null hypotheses of

the study. In other words, the study concludes that there is significant impact of the

formal and informal microfinance sources on the profitability of enterprise clusters in

South East of Nigeria. In summary, the first and second null hypotheses of the study

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were rejected and hence, formal and informal sources of microfinance significantly

affect the profitability of the enterprises that receive them in the clusters of South

East, Nigeria.

Hypothesis 3

The third hypothesis of the study states that, exist no significant determinants (i.e.

amount, interest, extent of protocols including collateral availability, relationship with

the provider) of the choice of microfinance sources by enterprise clusters in South

East, Nigeria. From the study findings, the amount or volume of credit needed (5.58),

relationship with the microfinance provider (2.88), problematic extent of protocols

including collateral requirement (3.66), interest rate (4.35), length of repayment

period offered (3.87), social capital component including cluster advantage (-4.03) are

clearly significant determinants of the choice of microfinance sources by enterprise

clusters in South East, Nigeria. This is because these variables all have the absolute

value of their z-value > 1,96 hence, the third null hypothesis of the study is equally

rejected.

In other words, the study concludes that, there exist significant determinants of

the choice of microfinance sources by enterprise clusters in South East, Nigeria and,

these determinants include: the amount or volume of credit needed, relationship with

the microfinance provider, problematic extent of protocols, interest rate, length of

repayment period offered, social capital component including cluster advantage.

Hypothesis 4

The fourth null hypothesis says there is no high involvement of the microfinance

providers for the sustenance of profitability of enterprise clusters in South East

Nigeria. Unlike the above three hypotheses, this hypothesis can only be accepted

rejected based on the decision rule around the approximation value of the mean being

3≥ in the 5 likert scale. Evidence from table 4.5 which shows the average perception

of micro financial support from micro financial sources for enterprises in South East,

Nigeria shows that both formal (~3) and informal (~4) microfinance sources have

mean values at either 3 or above. This implies that the fourth null hypothesis is

rejected and the study concludes that there is high involvement of the microfinance

providers both formal and informal in sustaining profitability of enterprise clusters in

South East Nigeria.

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4.7 Discussion of Findings

Findings of the statistical analysis from the respondents show that a great proportion

of the heads of the enterprise are educated as more than 94% have a minimum of

secondary education. This means that, on the average, the respondents to these

questions have the basic knowledge of the formalities or processes of borrowing from

formal or informal sources. That is, the study assumes that about 94% can read and

write. The role of education is not really an issue as it is not a significant determinant

of the choice of the microfinance provider as shown in Table 4.4 above. Also, 97% of

the respondents are sole proprietors meaning that the risks are very high for most of

these enterprises as they are owned and controlled by individuals, thereby leaving the

fate of the business on the welfare of the individuals.

A greater part of the micro and small enterprises (MSEs) that were

interviewed are involved in trading and other service sectors, with very few in the

production sector. This means that most of the enterprises are not involved in

converting raw materials to semi-finished or finished products which is one of the

major indicators of a fast growing economy or the age of high mass production. This

could be explained by several reasons. First, it could be explained by the fact that the

gestation period is very short for traders, and they only need to buy and sell and make

gains. On the other hand, enterprises in the production sector wait for a longer time

for the production to take place, including packaging and some other sub-processes

before it gets to the market, and finally gets converted into liquidity/cash. Some banks

tend to prefer traders as well as those in the service sectors that are predominantly

consultants/contractors who service and repay their loans easily than those in the

production sector who may last for months or even years before recording profit on

their investments let alone the loans.

Also, trading seems more lucrative and does not need a very large capital base

to start-up/expand, and so, its reflection on the loans requested are favourable to the

Microfinance Institutions (MFIs) and therefore, encourages them to give out loans

with the expectations that they will also pay back within a short period. Results

suggest that microfinance institutions may have preferred those in the service sector to

those in the production sector. This can be attributed to the quick gain expected in the

service sector unlike the production sector that takes lots of time to mature.

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Finally, the production sector is mostly risky in this part of the globe as they

depend on several other factors to be productive. In addition, given the poor

investment climate in Nigeria coupled with the lack of government intervention in

case of a recession, most microfinance institutions shy away from giving out credits to

enterprises in the production sector. Average micro finance source to this sector is

11.6% from the result of the survey.

The most practical way that microfinance providers support enterprises is by

giving actual credit or loans. The enterprises do not benefit up to 20% of the other

forms of microfinance assistance that constitutes technical support, financial

guarantors and financial advisory. There are, however, several other cheaper and

efficient means by which micro financial institutions can assist their customers in

securing future loans. Some of them are training and technical support. From the

statistics also, the study notes that a greater percentage have received credits more

from informal sources than formal sources. This could be explained by the fact that,

most informal sources are closer to the people and understand their peculiar needs vis-

à-vis the nature of the businesses they are engaged in. This may also explain why the

distance to MFIs is a significant determinant of microfinance source as seen in Table

4.4 above.

The challenges faced by entrepreneurs in receiving credit from formal sources

are quite different from those they faced from the informal sources. Entrepreneurs that

borrow from the formal sources face challenges such as high cost of borrowing,

request for collateral, and sureties, high interest rates, tiring protocol/bureaucracy,

corny and usually time constraints. On the other hand, entrepreneurs that receive

assistance from informal sources face challenges such as inability to lend out large

credits, unreliability, receiving so many phone calls from the lenders, taking time to

build trust with the lender, giving short repayment periods and lack confidentiality.

These challenges are also highlighted during the focus group discussions as the major

hindrances to accessing credit from formal and informal institutions.

4.7.1 Discussion on Objective One

The first specific objective is to assess the effect of formal microfinance sources on

the profitability of enterprises in South East, Nigeria. The findings show that formal

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sources significantly and positively affected the profitability of its recipients. The

study, therefore, accepts the hypothesis and concludes that, there is a significant

differential impact of the formal microfinance sources on the profitability of

enterprise clusters in South East of Nigeria. This is expected a priori as the credit

taken is usually intended to expand the business and so, should ordinarily reflect on

the profit of the enterprise. This result is similar to that of Nimoh, Kwasi and Tham-

Agyekum (2011) who examined the effect of formal credit on the performance of the

poultry industry in urban and peri-urban Kumasi of the Ashanti region in Ghana. They

found that, formal credit has a positive effect on the net income of large-scale poultry

farmers in the urban and peri-urban Kumasi.

The other significant determinants of profitability of firms that receive formal

credit are capital, education, sectors (trade and services), labour (number of

employees) and age of enterprise head as well as cities of operation (Aba and Nnewi

only). Social capital including cluster advantage and the city of Onitsha are not

statistically significant determinants of the enterprise profit. The significance of the

determinants is expected a priori for most of the variables.

Capital and labour are equally significant and positive as shown in Table 4.3.

They are both considered important factors and determinants of output as well as

profit as theoretically shown on most theories of the firm and evident in numerous

empirical researches. The age of the enterprise depicts experience in the field. The

older an enterprise becomes, the better it gets at, in minimizing its cost and finding

newer strategies of maximizing it profit thereby, enhancing its efficiency. On the

other hand, relationship with providers and the cluster advantage (social capital) are

not significant determinants of the enterprise profit. This is not surprising as formal

institutions have discrete directives and work with the minimum requirements, such

that the relationship with the providers and the cluster advantage (social capital) do

not have any room for favouritism.

4.7.2 Discussion on Objective Two

On the other hand, the second objective seeks to ascertain the effect of informal

microfinance sources on the profitability of enterprises in South East, Nigeria. Hence,

the study concludes that there is a significant differential impact of the informal

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microfinance sources on the profitability of enterprise clusters in South East of

Nigeria. The results are very similar to the model that assesses the effect of formal

sources on the profitability of its recipients. The total annual average credit from

informal sources equally, significantly and positively affects the profitability of its

recipients as expected a priori.

Though enterprises that receive credit from informal sources complain about

the inability to raise huge funds, such firms have the advantage of being able to

borrow as many times as possible with fewer protocols as requested by the formal

credit providers. The sum total by the end of the year might, therefore, be enough to

impact on the profitability of the enterprises as is the case in the study. Also, the other

determinants that significantly affect the profits or the return on investment are

capital, labour (number of employees), age and cluster advantage (social capital) as

well as level of education of the enterprise head, sector and city of operation.

This finding is consistent with the result of the study by Loca and Kola (2013),

which used qualitative and quantitative tools to show that lending practices have a

positive effect on entrepreneurial activities in increasing employee salaries, job

creation or generating employment and profit margin of enterprises in Albania. These

are in line with the results of the findings of this study that shows that formal and

informal credits are significant determinants of profitability for enterprises in the

South East, Nigeria. Several other studies show similar findings. An example is

Wanambisi and Bwisa (2013) who used descriptive statistics and logistic regression to

demonstrate that, the amount of loans is significantly and positively related with

performance of MSEs in Kitale Municipality.

4.7.3 Discussion on Objective Three

In assessing the third objective, the study finds that the significant determinants for

the choice of micro financial sources as shown in Table (4.4) above are: the amount or

volume of credit needed, relationship with the credit provider, problematic extent of

protocols, interest rate, length of repayment period, the cluster advantage (social

capital component) and the categories of the sectors. The non-significant determinants

are: training offered by the provider, discrimination on sectors, gender and education.

105

Most of the significant variables are expected a priori as is evident in other empirical

and theoretical literature.

An example is seen in Olowe et al. (2013) who investigated the impact of

microfinance on MSEs growth in Ibadan metropolis of Nigeria. The results showed

that high interest rate, collateral security and frequency of loan repayment could

cripple the expansion of MSEs in Nigeria. These are also the challenges that were

noted in accessing credit in the South East, especially in accessing credit from formal

channels. The present study, therefore, notes that the most predominant ones are the

cost of borrowing, collateral, high interest rates and protocol, wherein more than half

of the respondents receiving credit from formal institutions perceive these as

challenges. On the other hand, short repayment periods, inability to lend huge sums of

money, lack of confidentiality, much time to build trust, unreliability, impromptu

request for credit and its interest and then so many phone calls are the challenges

faced in accessing credit from the informal sector.

The amount or volume of credit is in favour of formal institutions given that,

they are in the best position to give out large loans. So, the higher the amount or

volume of the credit needed, the greater the odds in favour of choosing a formal credit

provider.

The relationship with the microfinance provider and the extent to which the

protocol and interest rates are problematic are both positive and significant. It implies

that the odds in favour of choosing formal sources increase with their units increase.

This is because the burdensome protocol/bureaucracy and high interest rates have

plagued formal institutions for a long period of time and have become one of the

reasons for which some small scale enterprises choose informal institutions over

formal institutions.

Also, the extent to which the length of the repayment period is offered is

equally a positive and significant variable. Hence, it increases the odds in favour of

choosing the informal source over the formal source, given that it is a challenge for

both the formal and informal institutions. However, it apparently affects the formal

institutions more than the informal institutions. The trade and service categories of the

sectors of activity are positive and significant relative to the production sector. Hence,

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they equally increase the odds in favour of choosing from the informal sector. This

could be explained by the fact that, most entrepreneurs in the trade and service sector

need smaller amounts or credits more frequently, thus, they patronize informal credit

providers, unlike the production sector.

The results of the logit regression of this study depict that the significant

determinants for the choice of the microfinance sources are the amount or volume of

credit needed, relationship with the microfinance provider, problematic extent of

protocols, interest rate, length of repayment period offered, the cluster advantage

(social capital) and the trade and service categories of the sectors. On the other hand,

the non-significant determinants are gender, training offered by the provider,

discrimination on sectors and education. Essien et al. (2013), which examined both

formal and informal credit sources and the role of social capital to small scale agro-

based enterprise in the Niger Delta region of Nigeria, is the closest to the present

study. Their results reveal that gender, age, and social capital are significant

determinants of informal credit while gender, education, age, size and collateral are

significant determinants of formal credit. While this study examines the determinants

of choosing between formal and informal credit, Essien et al investigate the

independent determinants of formal and informal credit. Though the results are

different, Essien et al suggest that education is a determinant for both formal and

informal credit which is not the case in this study.

4.7.4 Discussion on Objective Four

The last objective is to assess the level of support of microfinance providers for the

sustenance of profitability of enterprise clusters in South East, Nigeria. The analysis

shows that formal credits support enterprises that benefit from them averagely while

informal credits support their beneficiaries to a high extent. This means that informal

credit sources support micro and small enterprises much more than the formal

sources. In agreement with these findings is the study by Akinbola et al. (2013) who

examined the extent to which micro financing and marketing techniques have

contributed to entrepreneurial development of the customers of ten microfinance

banks located in Ojo Local Government Area (LGA) of Lagos State. Their result

suggests that microfinance banks have contributed significantly to the entrepreneurial

development in Nigeria. Again, Suberu et al. (2011) assess the impact of

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microfinance institutions on small scale enterprises in Nigeria and showed that,

majority of the small scale enterprises acknowledged positive contributions of

microfinance institutions’ loans towards promoting their market excellence and

overall economic company competitive advantage.

This is equally confirmed by the focus group discussions that were held in

these clusters as the informal sectors could give credit faster and frequently more than

the formal sectors. This submission is, however, paradoxical to one of the principal

challenges faced by enterprises that benefit from them as they stated that the inability

to give out large loans is problematic. Nevertheless, this could be explained by the

fact that, though the informal credit providers may not be able to provide huge sums,

they are more likely to give out as many credits as possible. The credits may make a

significant impact in the long term.

The submission above is consistent with Babajide (2011), who investigated

the effects of micro-financing on micro and small enterprises (MSEs) in South West,

Nigeria and the results suggest that, micro financing enhances the survival of micro

and small enterprises but it is not sufficient for growth and expansion of such micro

and small enterprises. Also, the study shows that micro-financing is not financially

effective and practiced in Nigeria as many MFB’s grant more individual loans than

group based loans, thereby increasing their running cost and putting their portfolio at

risk. Though, the study was done in South-South, Nigeria while the present study

focused on the South East, Nigeria, the findings of both studies are similar and

consistent.

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CHAPTER FIVE

SUMMARY, POLICY RECOMMENDATIONS AND CONCLUSION

5.1 Summary of Findings

The study was motivated by the fact that micro enterprises are referred to as the arm

of the industry that could be used to reach out to relatively low scale investors and

develop the home industries of any economy. In Nigeria, it could be said to be the

‘sleeping drug of the sleeping giant’ given that a revamp of the micro enterprises

would expand the businesses to boost the manufacturing sector, increase production,

increase exports and then lead a nation to a stage of high mass production. The study

examined the effectiveness of the microfinance sources on the profitability of

enterprise clusters in South East, Nigeria. In order to achieve the central aim, the

study had the following specific objectives: to assess the effect of formal

microfinance sources on the profitability of enterprise clusters in South East of

Nigeria, to ascertain the effect of informal microfinance sources on the profitability of

enterprise clusters in South East of Nigeria, to examine the determinants (i.e. amount

or volume, interest, process, accessibility, product/ sector concentration, connection

with provider) of the choice of the microfinance source by enterprise clusters, and to

assess the level of support of microfinance providers for the sustenance of

profitability of enterprise clusters in South East, Nigeria.

The study employed multiple regression techniques, logit regressions and

descriptive analysis to attain these objectives. The statistics showed that a greater

proportion of the samples were more of trade and services than the production sector.

Of these enterprises, more of them (about 55%) received credit from informal sources

than the formal sources. The results of the first and second objective showed that

formal and informal credits were significant determinants of the profit of the

enterprises or return on investment. However, other significant determinants included

capital, labour and age for both formal and informal sources. The cluster advantage

(social capital) was equally a significant determinant of the profit of enterprises that

was used as a proxy for performance. The results of the logit regression suggested

that, the significant determinants for the choice of the microfinance sources were the

amount or volume of credit needed, reliability, problematic extent of protocols,

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interest rate, length of repayment period offered, the cluster advantage (social capital)

and the trade and service categories of the sectors.

To achieve these objectives, the study further analysed the perception of the

entrepreneurs to establish what were considered significant determinants. The

respondents perceived that the most deterministic factor for their choice of micro

financing was the credit provider that gave them quick response, followed by the

interest rate, reliability, and finally the relationship with the provider (social capital).

Finally, the study used descriptive analysis to show that, formal credits

supported the enterprise recipients much more than the informal credits. But both of

them supported the enterprise to a reasonable extent. Also, the credits generally

expand the enterprises averagely, so the credits could be encouraged and the access

smoothened to cover a larger span.

5.2 Policy Implications of Findings

A specific policy issue from the first two objectives is that formal microfinance

sources significantly and positively affected the profitability of its recipients but not

as much as the informal sources for enterprise clusters in South East of Nigeria. This

is expected a priori as credit taken is usually intended to expand the business and so,

should ordinarily reflect on the profit of the enterprise. Other determinants crucial for

policy is the high impact of social capital including cluster advantage. There is the

need for policy direction to tap from such social capital including cluster advantage. It

is equally necessary for microfinance banks to begin to come closer to their clients as

the study found that relationship with credit providers and the cluster advantage

(social capital) are not significant determinants of the enterprise profit when such

enterprise is using the formal microfinance source and vice versa.

Another policy question from the finding is why has Nnewi performed better

than Onitsha and Aba? Are there inherent qualities and strategies that need to be

harnessed in order for Aba and Onistha to measure up with Nnewi?

The study also found that the amount or volume of credit needed, relationship

with the service provider, problematic extent of protocols, interest rate, length of

repayment period, the cluster advantage (social capital component) and the categories

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of the sectors are all determinants for choosing a particular source. It is equally

important to note that any positive improvement on the part of policy makers on any

of these variables sway enterprise from informal sources to formal sources and vice

versa. Also, the extent to which the length of the repayment period is offered is

equally a positive and significant variable. Hence, it increases the odds in favour of

choosing the informal source over the formal source, given that it is a challenge for

both the formal and informal institutions. However, it apparently affects the formal

institutions more than the informal institutions. The trade and service categories of the

sectors of activity are positive and significant relative to the production sector. Hence,

they equally increase the odds in favour of choosing from the informal sector.

Findings from the last objective means that informal credit sources support

micro and small enterprises much more than the formal sources. This is equally

confirmed by the focus group discussions that were held in these clusters as the

informal sectors could give credit faster and frequently more than the formal sectors.

This submission is, however, paradoxical to one of the principal challenges faced by

enterprises that benefit from them as they stated that the inability to give out large

loans is problematic. Nevertheless, this could be explained by the fact that, though the

informal credit providers may not be able to provide huge sums, they are more likely

to give out as many credits as possible. The credits may make a significant impact in

the long term.

5.3 Policy Recommendations

The findings of this study have, to a large extent, revealed what most micro and small

enterprises (MSEs) face and equally provided empirical evidence to help inform

policies that address their situation. Some of these include;

i) The main weaknesses of accessing formal credit are the amount or volume of

credit, interest rate and its tenure. These can only be bridged by the Central

Bank of Nigeria and development institutions through interventions such

as grants, donors, and/or soft loans. The recent N220 billion for micro and

small scale enterprises by the CBN should be focused on the micro

enterprises as was originally designed.

111

ii) Accessing credit in the formal sector is equally seriously plagued by

documentation and burdensome protocol in an effort to reduce the number

of bad loans and divergence of credit given. This challenge can be

minimized if banks empower their branches to move closer to the sectors

and clusters in order to exploit better, the social capitals that exist amongst

these clusters. By this policy, specific credit could be granted to micro and

small enterprise within the sector using their social capital dynamics as

collateral.

iii) The study equally recommends that, as the microfinance banks are relocating

to these sectors and clusters using the cluster advantage, they should be

supported in any form from the regulatory authorities. This is consistent

with the objectives of the rural banking scheme of the 1970s that is no

longer in operation. The scheme provided that, a minimum of 45% of total

deposit liabilities of bank branches located in local areas be given to the

location as credit facilities.

iv) Credits can therefore be given on the basis of social capital and group

dynamics to the informal sectors that act as the backup for securities. Once

the fear for physical and tangible security is not there, it would encourage

enterprises to borrow and create more confidence in formal financial credit

providers.

v) Formal microfinance providers should develop products with less emphasis on

physical and tangible securities as collateral and should rely more on their

social dynamics, discipline and trust, to avail them credit facilities within

their locations.

vi) The study recommends that Microfinance banks should move closer to the

clusters in order to exploit the social capital and group dynamics that exist,

and should be supported always by the regulatory authorities.

5.4 Contribution of this Study to Knowledge on the Subject Matter

The findings of the study are consistent with existing literature on the subject matter

and align with empirical findings earlier mentioned in Chapter Two. It, however,

makes additional contribution to knowledge on the subject by revealing the positive

impact of both formal and informal microfinance sources on the profitability of MSEs

in the South East. It goes further to reveal the differential impact of these sources on

112

each of the economic sectors (production, trade and services) within the clusters.

Although both formal and informal sources affect profitability of MSEs significantly,

the study reveals that microfinance providers shy away from the production sector,

hence granting only 11.6% of total credit to this sector within the period covered by

the study.

This finding is instructive and explains why despite the positive contribution

of microfinance sources to the profitability of MSEs over the years, the production

sector has not witnessed any significant growth. It, therefore, means that there is still a

huge gap in the financing profile of the production sector, hence a call for a robust

and dedicated model of financing to galvanize the production sector of the MSEs.

The computation of odds ratio in the study revealed major factors and the

magnitude at which factors such as age of the enterprise manager, interest rate,

relationship with the microfinance provider, extent of protocol, being engaged in trade

and services, the length of repayment period and the social capital dimension

(membership of different cluster groups that embark on cash contributions hence

somewhat give financial assistance) have odds ratio that are higher than one (1). This

implies that these are the main factors that affect enterprises in the selected clusters

from moving from formal to informal microfinance sources. The likelihood of moving

from formal to informal are highest with enterprises engaged in trade as well as

relationship with the microfinance provider, social capital and extent of protocols.

Some studies may have found few of these factors as key to the choice of

microfinance sources but this study went beyond that to estimate the odds ratio which

is quite useful to every policy maker.

Additionally, the study goes beyond the economic benefits (i.e. profitability)

to reveal the psychological and social benefits and the spread mechanism of micro

financing sources on MSEs in the clusters. These benefits come in the form of social

capital existing within the clusters which could be used to cushion or mitigate the

collateral gap in financing these enterprises for growth and sustainability. The focus

of the study on major enterprise clusters in the cities of South East like Aba, Nnewi

and Onitsha further reveals why certain businesses are easily associated with certain

communities, like Nnewi is associated with spare parts business, and Aba with textile

businesses in the South East economy.

113

With these findings, it is possible to develop a micro financing model for

MSEs to align with the social dynamics and capital as a substitute for collateral

instead of relying heavily on physical asset from operators which discourages them

from seeking financing assistance from the providers whenever in need.

5.5 Suggestions for Further Research

This study has been able to add value to existing literature, but more importantly to

stimulate further debate on the subject under consideration. The role of micro

financing is very significant in developing countries and even more significant in

Nigeria given the size of her informal sector and the number of small scale

enterprises. This study could equally be carried out in other economies and countries

as there is the need to continually improve the impact of micro financing on the

performance/profitability of MSEs.

Further studies could equally examine other aspects of social capital and group

dynamics in considering other ways in which they can be exploited as means of

enhancing securities and reducing risks. Other clusters could equally be examined to

investigate to what extent the clusters can be an advantage to micro financial

institutions. Other methodologies could equally be used to verify if they adheres to the

findings of this study in an effort to appreciate better the recommendations.

5.6 Conclusion

This study was motivated by the slow state of manufacturing as it was deduced to be

caused by the inability of domestic enterprises to thrive and grow into manufacturing

giants. This led the study to evaluate the effectiveness of the microfinance sources on

the profitability of enterprise clusters in South East, Nigeria, paying particular

attention to Onitsha, Aba and Nnewi industrial clusters. Amongst several findings, the

results showed that formal and informal credits were significant determinants of firm

profitability in South East, Nigeria. While the results of the logit regression suggested

that, the significant determinants for the choice of the microfinance sources are the

amount or volume of credit needed, reliability, problematic extent of protocols

including collateral availability, interest rate, length of repayment period offered,

relationship with the credit provider including cluster advantage (social capital) and

concentration on the trade and service categories of the sectors.

114

The findings further showed that the respondents perceived that, the main

factor for their choice of micro financing is the credit provider who provided loans

quickly. This was followed by the interest rate, reliability and the relationship with the

provider (social capital). And finally, the study used descriptive analysis to show that,

though both the formal and informal credit providers supported the firm at least

averagely and the credits generally expanded the enterprises averagely, formal credits

supported the enterprise recipients much more than the informal credits.

Therefore, within the limits of the scope and coverage of the study, the

findings were consistent with the objectives of the study. The study is confident that

the research is an interesting and worthy exercise and, thereby presents the report as a

contribution to the knowledge base on the subject matter.

115

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enterprises in developing countries. Vienna: UNIDO. UNIDO, (2003). A Path out of Poverty: Developing rural and women

entrepreneurship. Vienna: UNIDO. UNIDO (2006). SME Cluster and Network Development: Principles and Practices.

Enugu: UNIDO. Wanambisi A. N. and Bwisa H. M. (2013). “Effects of Microfinance Lending on

Business Performance: A Survey of Micro and Small Enterprises in Kitale Municipality, Kenya”. International Journal of Academic Research in Business and Social Sciences, 3(7): 56-67

Wang, G.Y. (2010). The Impacts of Free Cash Flows and Agency Costs on Firm

Performance, Journal Service Science and Management, 3: 408-418. Wellalage, N.H. (2012). An Empirical Investigation of Agency Costs and Ownership

Structure in Unlisted Small Businesses, New Zealand Journal of Applied Business Research, 10(2).

World Bank (2003). Rural and Micro Finance Regulation in Ghana: Implications for

Development and Performance of the Industry, Africa Region Working Paper Series No. 49, http://www.worldbank.org/afr/wps/wp49.pdf

Zaman, H. (2000). “Assessing the Poverty and Vulnerability Impact of Micro-Credit

in Bangladesh: A case study of BRAC”. URL:www.worldbank.org/html/dec/Publications/Workpapers/wps2000series/wps2145/wps2145.pdf

123

ANNEX I: QUESTIONNAIRE ON MICRO AND SMALL ENTERPRIS ES (MSEs) Institute for Development Studies, University of Nigeria, Enugu Campus, Enugu. May, 2014 Dear Respondent, This is a pubic survey questionnaire which is aimed at identifying and collecting data on the problems, concerns and issues that affect the operations and performance of our Micro and Small Scale Enterprises (MSSEs). Your kind and objective e-response will significantly contribute towards reducing if not totally removing the problems militating against this all-important sub-sector of our economy. In order to ensure confidentiality, do not put down your name on the questionnaire but please answer the questions as honestly and objectively as possible. Section A: Demographic Characteristics 1. (a) Study Area

Name of Area ………………………………………………………………….. Town/Village: ………………………………… LGA ……………………………. State: …………………………… Geopolitical Zone: …………..………………. (b) Identification of Enterprise Name: …………………………………………………………………………….. Address: …………………………………………………………………………..

2. How long has the Enterprise/Firm been in operation? (Years) ………………….. 3. What is the highest level of education of the enterprise head? No formal education______ Primary education____ Secondary education ________Tertiary Education (Degree, OND, HND, etc.)_______ Post graduates________

4. Is this firm owned by a sole proprietor? Yes……………No…………….

5. Were you an apprentice before? Yes……………No…………….

5. Total Number of employees: Managerial and Administrative ……….…...

Operatives ……..……...

6. In which sector is this enterprise?

Production…….…Trade…………...Services….………

7. Type of activity

i. Food [ ]

ii. Textiles, Clothing & Garments[ ]

iii. Oil & Gas [ ]

iv. Wood/Paper/Pulp [ ]

v. Furniture and wood work [ ]

vi. Chemicals and Plastics [ ]

vii. Automobile [ ]

viii. Foundries, Metals/Fabrication[ ]

124

ix. Shoes and Leather Products [ ]

x. Plants and machineries [ ]

xi. Agro Processing [ ]

xii. Solid Minerals (Mining) [ ]

xiii. Bio Fuels [ ]

xiv. Petro Chemicals [ ]

xv. Community-based craft [ ]

xvi. Quarrying [ ]

xvii. Trading [ ]Others (specify) ……………………

122

Section B: Finance, Credit and Association

8. What is the value of your sales on the average?

………………….……………………Naira

9. How much is the average annual profit of this enterprise? ……….…………………

10. How much is the average annual savings of this enterprise? ………….…………

11. What is your estimated capital at present? …………….………………………

12. Which of these forms of micro financial support have you received most? (����)

Credit……. Technical support ………. Financial guarantor ……… Financial leaving

13. How much credit have you received from micro finances?

Year Number of Credit formal informal Total Amount 2004 …………………… ……… .……… ………… 2005 …………………… .……… ……... ………… 2006 …………………… ……… ……... ………… 2007 …………………… ……… …….. ………… 2008 …………………… ……… ……... ………… 2009 …………………… ……… ……... ………… 2010 …………………… ……… ……... ………… 2011 …………………… ……… ……... ………… 2012 …………………… ……… ……... ………… 2013 …………………… ……… ……... …………

14. What is the average annual income received in the form of assistance from

microfinances over the

years……………………………………………………………………………………

15. What was the interest on the

loan?................................................................................Naira

16. Which of these do you participate most in?(����) self-help

group………Esusu……..MFB……Friends and

Relatives…….Others(specify)………………

17. Which of these have given you the most

credit?....................................................................

18. Name (if any) the Business Membership Organization (BMOs) your enterprise

belongs

to:………………………………………………………………………………………

19. Why do you prefer the channel used?.....................................................................

20. What was the length of repayment of the last credit given? ……….……….months

21. Have you received any training or other sources (specify) of support from MFIs

and DFIs: yes………………..No………………

22. If ‘yes’ what is the value of the assistance (support) that was

given?..........................Naira

23. Enterprise profitability

Year Total credit N Total assets N PBT N ROI %

Section C: Entrepreneurs’ Perception

(Likert is a 5 point scale where, A is very high extent and E is very low extent)

24. To what extent is your credit source reliable? A[ ] B[ ] C[ ] D[ ] E[ ]

25. How can you rate the extent to which the funds have expanded the business in this

area? A[ ] B[ ] C[ ] D[ ] E[ ]

26. To what extent have the protocols been a hindrance in accessing credit from

microfinance sources? A [ ] B[ ] C[ ] D[ ] E[ ]

27. What determines your use of micro finance source tick (����)[ ] reliability [ ]interest

rate [ ]quick response [ ] relationship with provider [ ] focus of provider on sectors [ ]

support from provider [ ] Accessibility [ ]others (specify)

28. What is the extent to which your microfinance provider has been supportive?

A [ ] B[ ] C[ ] D[ ] E[ ]

29. To what extent is the credit source reliable? A[ ] B[ ] C[ ] D[ ] E[ ]

30. Do you have any personal or family relationship with your credit supplier?

Yes…No…

31. What are the three greatest challenges faced in accessing credit from formal

institutions

1………………………………….2……………………………………3……………

32. What are the three greatest challenges faced in accessing credit from informal

providers

1………………………………….2……………………………………3……………

33.General comment (if………………….……………………

Thanks for your patience and cooperation

AMAGWU, IBEAWUCHI FRANCIS

ANNEX II: INTERVIEW SCHEDULE Interviewer Interviewee

S/N Questions Responses 1. What is your name Sir/Madam? 2. What is your profession? 3. What position do you occupy? 4. How long have you been in your profession? 5. Are you aware of Micro Finance Bank(s) (MFB) within the location? 6. What is the closest MFB to your business location? 7. Have any of them assisted you to access finance? 8. If ‘Yes’ what is the impact of such assistance on your business? 9. How do you access the assistance, as a business person or group (cluster)? 10. Is this assistance on a regular basis or once in a while? 11. Do you think your group (i.e. clusters) have also benefited? 12. How far has assistance impacted on your business and the cluster? 13. Has there been any increase in your business activities and benefits since you

started accessing formal MFB loans?

14. What other support services are available to you as a business and a cluster? 15. How regular are these support services? 16. What form of collateral do you pledge? 17. Does the group have any influence on the offer and acceptance of financial

assistance to you and/or the cluster?

ANNEX III: GUIDE QUESTIONS FOR THE FOCUS GROUP DISC USSIONS 1) What are the various ways through which microfinance sources affect

profitability of enterprises?

2) To what extent has their assistance improved your profitability?

3) Do microfinance institutions prefer giving support to individuals or the cluster

as a whole?

4) Do you prefer formal or informal sources of micro financing?

5) Why or why-not?

6) What are the key determinants for micro financing from formal sources?

7) What are the key determinants for micro financing from informal sources?

8) What are the key constraints for micro financing from formal sources?

9) What are the key constraints for micro financing from informal sources?

10) Do microfinances privilege some sectors of activity than others?

11) Does one need to have a personal or family relationship with your credit

supplier to access credit?

12) How reliable is your credit supplier?

13) How much does the growth of your enterprise depend on microfinancing?

14) What policy options could be incorporated to improve profitability and

output?

ANNEX IV: ABA, NNEWI AND ONITSHA CLUSTERS DIRECTORY

S/No Name of Cluster

1. A.M.E. Shoe Makers Cluster

2. Omemma workers/traders Cluster

3. Building materials/Allied Cluster

4. Aba Leather product/Garments common facility center 18 industrial road Aba

5. National Board for technology incubation center

6. Nkwo Ngwa Allied workers Cluster

7. Aba North Shoe Plaza Cluster

8. Ugwu Mango Furniture Cluster

9. Aba belts makers Cluster, Ugwu mango-Ariaria

10. ATE Bags makers Cluster

11. Trunk box manufacturers Cluster

12. Pionerering shoe manufacturers Cluster

13. Cluster of shoe parts dealers

14. Timber and Allied market

15. Nigerian Automobile Technicians Cluster

16. Asa Nnentu motor spare parts Cluster

17. Factory Road Crunchies

18. GSM and Allied components Aba central

19. Aba woodworkers Cluster(Town ship unit) St. Georges

20. Plant dealers/Technicians Cluster Aba

21. Eziukwu Block moulders Cluster

22. Railway tennants corridors Cluster hair weavers

23. Effort printers

24. Aba motorcycle spare parts dealers Ass. (ASPADA)

25. Nnewi Technology Incubation Centre

26. Habour/Niger Bridge Ind Layout

27. Osakwe Ind Layout

28. Awka Industrial Layout

29. Tinkers Dealers Cluster

30. Aluminium village, Onitsha

31. Shoe manufacturing Cluster of Anambra State (SMAAS), Onitsha

32. Nnewi Automobile Cluster

33. Global Systems Mobile Network (GSM) and Allied Components Cluster, Nnewi

34. Global Systems Mobile Network (GSM) and Allied Components Cluster, Onitsha

ANNEX V: COMPUTATION OF SPEARMAN’S CORRELATION COEFFICIENT

USING TEST-RETEST RESULTS

Resp. 1st Test 2nd Test R1st R2nd d d2 1 88 85 2 3 -1 1 2 78 80 9 8 1 1 3 83 82 4 5 -1 1 4 85 86 3 2 1 1 5 82 80 5 8 -3 9 6 89 88 1 1 0 0 7 77 81 10 7 3 9 8 79 82 8 5 3 9 9 80 79 7 10 -3 9

10 82 83 5 4 1 1

Total 41

r =

r =

r =

r =

r =

r = 0.7515

sum

Variable | Obs Mean Std. Dev. Min Max

-------------+--------------------------------------------------------

age | 500 11.288 6.52612 1 33

employmana~r | 500 2.457203 2.791527 1 14

employoper~s | 500 4.45898 4.967895 1 27

averagesales | 500 4371180 9672194 10000 5.00e+07

averagepro~t | 500 1834141 4396383 35000 3.00e+07

annualsaving | 500 412742.1 1206031 1000 1.00e+07

capital | 500 4411885 1.24e+07 40000 1.00e+08

avassistin~e | 500 514984.9 1264916 10000 1.00e+07

intereston~n | 500 23.3342 14.6875 5 40

whatisthea~s | 500 329682.5 376494.7 0 1000000

. tab town

town where |

the |

respondent |

resides | Freq. Percent Cum.

------------+-----------------------------------

Nnewi | 169 33.80 33.80

Onitsha | 170 34.00 67.80

Aba | 161 32.20 100.00

------------+-----------------------------------

Total | 500 100.00

. tab state

state where |

the |

respondent |

reside | Freq. Percent Cum.

------------+-----------------------------------

Anambra | 350 70.00 70.00

Abia | 150 30.00 100.00

------------+-----------------------------------

Total | 500 100.00

. tab education

what is the highest education level of |

the head of the enterprise | Freq. Percent Cum.

----------------------------------------+-----------------------------------

no formal education | 5 1.00 1.00

primary education | 23 4.60 5.60

secondary education | 235 47.00 52.60

tertiary education(degree, OND, HND etc | 220 44.00 96.60

postgraduates | 17 3.40 100.00

----------------------------------------+-----------------------------------

Total | 500 100.00

. tab ownership

is this |

business |

owned by a |

sole |

proprietor? | Freq. Percent Cum.

------------+-----------------------------------

yes | 483 96.60 96.60

no | 17 3.40 100.00

------------+-----------------------------------

Total | 500 100.00

. tabsectorproduction

is this |

firm into |

production? | Freq. Percent Cum.

------------+-----------------------------------

yes | 55 11.00 11.00

no | 445 89.00 100.00

------------+-----------------------------------

Total | 500 100.00

. tabsectortrade

is this |

firm into |

trading? | Freq. Percent Cum.

------------+-----------------------------------

yes | 306 61.20 61.20

no | 194 38.80 100.00

------------+-----------------------------------

Total | 500 100.00

. tabsectorservices

is this |

firm into |

services? | Freq. Percent Cum.

------------+-----------------------------------

yes | 186 37.20 37.20

no | 314 62.80 100.00

------------+-----------------------------------

Total | 500 100.00

.

. reg averageprofit capital labour amtformalcredit age education i.sector i.tow

> n relationship clusteradvan

Source | SS df MS Number of obs = 123

-------------+------------------------------ F( 9, 113) = 27.69

Model | 1.9165e+15 9 2.1294e+14 Prob > F = 0.0000

Residual | 8.6915e+14 113 7.6916e+12 R-squared = 0.6880

-------------+------------------------------ Adj R-squared = 0.6631

Total | 2.7856e+15 122 2.2833e+13 Root MSE = 2.8e+06

------------------------------------------------------------------------------

averagepro~t | Coef. Std. Err. t P>|t| [95% Conf. Interval]

-------------+----------------------------------------------------------------

capital | .6319159 .0500798 12.62 0.000 .5326988 .7311331

labour | 94393.14 31475.48 3.00 0.003 32541.7 156244.6

amtformalc~t | .6620369 .268835 2.46 0.014 1.191592 .1324817

age | 136422.9 40793.25 3.34 0.001 55604.16 217241.7

education | 933814 524751.7 1.78 0.078 1973442 105813.7

sector |

trade | 450732.9 433001.4 1.04 0.299 402199.7 1303665

services | 155422 598228.8 0.26 0.795 1022978 1333822

|

town |

Onitsha | 331530 621170.5 0.53 0.594 892061.1 1555121

Aba | -78551.76 391973.3 -0.20 0.841 -693563.1 850666.7

relationship | 105212.5 276298 0.38 0.704 -439043.5 649468.4

clusteradvan | 112335.9 254720.7 0.44 0.660 -389416.8 614088.5

_cons | -6079524 4187497 -1.45 0.149 -1.44e+07 2216662

----------------------------------------------------------------------------

. reg averageprofit capital labour amtinformalcredit age education i.sector i.

> town relationship clusteradvan

Source | SS df MS Number of obs = 254

-------------+------------------------------ F( 11, 242) = 17.83

Model | 6.6465e+14 11 6.0423e+13 Prob > F = 0.0000

Residual | 8.2017e+14 242 3.3891e+12 R-squared = 0.4476

-------------+------------------------------ Adj R-squared = 0.4225

Total | 1.4848e+15 253 5.8688e+12 Root MSE = 1.8e+06

------------------------------------------------------------------------------

averagepro~t | Coef. Std. Err. t P>|t| [95% Conf. Interval]

-------------+----------------------------------------------------------------

capital | .3823681 .0843017 4.54 0.000 .2163093 .5484268

labour | 158036 53869.82 2.93 0.004 51922.39 264149.6

amtinforma~t | .7237205 .2610987 2.77 0.006 1.237995 .2094464

age | 115758.5 24799.99 4.67 0.000 66907.09 164609.9

education | 209296.7 199959.9 1.05 0.296 603180.7 184587.3

|

sector |

trade | 450732.9 433001.4 1.04 0.299 -402199.7 1303665

services | 155422 598228.8 0.26 0.795 -1022978 1333822

|

town |

Onitsha | 331530 621170.5 0.53 0.594 -892061.1 1555121

Aba | 78551.76 391973.3 0.20 0.841 -693563.1 850666.7

|

relationship | 2670319 1526209 1.75 0.083 353376.8 5694014

clusteradvan | 1432049 722721.5 1.98 0.050 207.389 2863891

_cons | -1626049 1069139 -1.52 0.130 -3732055 479956.3

------------------------------------------------------------------------------

. logit creditsource creditass creditreliable i.training protocol discsectors interestrate lengthofcredit relationship clusteradvan education sector

Iteration 0: log likelihood = -168.37609

Iteration 1: log likelihood = -74.479889

Iteration 2: log likelihood = -64.845136

Iteration 3: log likelihood = -61.666716

Iteration 4: log likelihood = -61.305632

Iteration 5: log likelihood = -61.303429

Iteration 6: log likelihood = -61.303429

Logistic regression Number of obs = 255

LR chi2(11) = 214.15

Prob > chi2 = 0.0000

Log likelihood = -61.303429 Pseudo R2 = 0.6359

------------------------------------------------------------------------------

creditsource | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

creditass | -.4491102 .1398679 -3.21 0.001 -.7232462 -.1749741

creditreli~e | 1.336885 .4640955 2.88 0.004 .4272747 2.246496

2.training | -.032095 .1050466 -0.31 0.760 -.2379824 .1737925

protocol | .999844 .2730717 3.66 0.000 .4646333 1.535055

discsectors | .3188975 .8861363 0.36 0.719 -1.417898 2.055693

interestrate | 1.01501 .1988301 5.10 0.000 .62531 1.40471

lengthofcr~t | .8031496 .2072845 3.87 0.000 .3968795 1.20942

relationship | 1.445636 .509865 2.84 0.005 .4463189 2.444953

clusteradvan | 1.616448 .5527644 2.92 0.003 .5330497 2.699846

education | -.0642644 .2666639 -0.24 0.810 -.5869161 .4583872

sector |

2 | 2.043405 .6091559 3.35 0.001 .8494815 3.237329

3 | 3.087797 .7759164 3.98 0.000 1.567029 4.608565

|

_cons | -17.59081 4.081958 -4.31 0.000 -25.5913 -9.590324

------------------------------------------------------------------------------

Note: 0 failures and 16 successes completely determined.

. logit creditsource creditass creditreliable i.training protocol discsectors interestrate lengthofcredit relationship clusteradvan education sector, or

Iteration 0: log likelihood = -168.37609

Iteration 1: log likelihood = -74.479889

Iteration 2: log likelihood = -64.845136

Iteration 3: log likelihood = -61.666716

Iteration 4: log likelihood = -61.305632

Iteration 5: log likelihood = -61.303429

Iteration 6: log likelihood = -61.303429

Logistic regression Number of obs = 255

LR chi2(11) = 214.15

Prob > chi2 = 0.0000

Log likelihood = -61.303429 Pseudo R2 = 0.6359

------------------------------------------------------------------------------

creditsource | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]

-------------+-------------------------------------------------------

creditass | .6381958 .0892631 -3.21 0.001 .4851747 .8394787

creditreli~e | 3.807167 1.766889 2.88 0.004 1.533074 9.454548

2.training | .9684146 .1017286 -0.31 0.760 .7882165 1.189809

protocol | 2.717858 .7421701 3.66 0.000 1.59143 4.64158

discsectors | 1.37561 1.218978 0.36 0.719 .2422227 7.812249

interestrate | 2.759391 .54865 5.10 0.000 1.868825 4.074344

lengthofcr~t | 2.232561 .4627753 3.87 0.000 1.487177 3.351539

relationship | 4.24455 2.164148 2.84 0.005 1.56255 11.53001

clusteradvan | 5.035174 2.783265 2.92 0.003 1.704122 14.87745

education | .937757 .2500659 -0.24 0.810 .5560394 1.581521

sector |

2 | 7.716842 4.70076 3.35 0.001 2.338434 25.46561

3 | 21.92872 17.01485 3.98 0.000 4.792388 100.3401

------------------------------------------------------------------------------

Note: 0 failures and 16 successes completely determined.

.3303049 .1751423 -2.09 0.037 .1168343 .9338127

. tab determinantcredit1

does |

reliability |

of credit |

source |

determine |

your use of |

micro |

finance |

source? | Freq. Percent Cum.

------------+-----------------------------------

yes | 81 16.53 16.53

no | 409 83.47 100.00

------------+-----------------------------------

Total | 490 100.00

. tab determinantcredit2

does |

interest |

rate of |

credit |

source |

determine |

your use of |

micro |

finance |

source? | Freq. Percent Cum.

------------+-----------------------------------

yes | 123 25.36 25.36

no | 362 74.64 100.00

------------+-----------------------------------

Total | 485 100.00

. tab determinantcredit3

does quick |

response of |

credit |

source |

determine |

your use of |

micro |

finance |

source? | Freq. Percent Cum.

------------+-----------------------------------

yes | 191 39.38 39.38

no | 294 60.62 100.00

------------+-----------------------------------

Total | 485 100.00

. tab determinantcredit4

does |

relationshi |

p with the |

provider |

determine |

your use of |

micro |

finance |

source? | Freq. Percent Cum.

------------+-----------------------------------

yes | 30 6.19 6.19

no | 455 93.81 100.00

------------+-----------------------------------

Total | 485 100.00

. tab determinantcredit5

does focus |

of favour |

of sectors |

determine |

your use of |

micro |

finance |

source? | Freq. Percent Cum.

------------+-----------------------------------

yes | 40 8.25 8.25

no | 445 91.75 100.00

------------+-----------------------------------

Total | 485 100.00

. tab determinantcredit6

does |

support |

from |

provider of |

credit |

determine |

your use of |

micro |

finance |

source? | Freq. Percent Cum.

------------+-----------------------------------

no | 485 100.00 100.00

------------+-----------------------------------

Total | 485 100.00

Sources researcher 2014

Sources researcher 2014

Sources researcher 2014

Sources researcher 2014