i

AMAGWU, IBEAWUCHI FRANCIS

PG/Ph.D/10/54559

EFFECTIVENESS OF MICROFINANCE SOURCES ON THE PROFITABILITY OF ENTERPRISE CLUSTERS IN

SOUTH EAST, NIGERIA

INSTITUTE FOR DEVELOPMENT STUDIES (IDS)

INSTITUTE FOR DEVELOPMENT STUDIES (IDS)

Paul Okeke

Digitally Signed by: Content manager’s Name

DN : CN = Webmaster’s name

O= University of Nigeria, Nsukka

OU = Innovation Centre

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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 PROFITABILITY OF ENTERPRISE CLUSTERS IN SOUTH

EAST, NIGERIA

BY

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

BEING A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF PhD IN

DEVELOPMENT STUDIES

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

ENUGU CAMPUS, ENUGU, NIGERIA

JULY 2015

iv

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

TITLE PAGE ................................................................................................................................. iii

CERTIFICATION ......................................................................................................................... iv

APPROVAL ................................................................................................................................... v

DEDICATION ............................................................................................................................... vi

ACKNOWLEDGEMENTS .......................................................................................................... vii

TABLE OF CONTENTS ............................................................................................................. viii

LIST OF TABLES ............................................................................................ xi

LIST OF FIGURES ........................................................................................... xi

LIST OF ACRONYMS ................................................................................................................. xi

ABSTRACT ................................................................................................................................. xiii

CHAPTER ONE ............................................................................................................................. 1

INTRODUCTION .......................................................................................................................... 1

1.1 Background of the Study .................................................................................................. 1

1.2 Statement of the Problem ................................................................................................. 4

1.3 Objectives of the Study .................................................................................................... 7

1.4 Research Questions .......................................................................................................... 7

1.5 Research Hypotheses........................................................................................................ 8

1.6 Significance of the Study ................................................................................................. 8

1.7 Scope of the Study............................................................................................................ 9

1.8 Limitations and Structure of the Study ........................................................................... 9

1.9 Background Information on Development of MSEs...................................................... 10

1.10 Operational Definition of Terms ................................................................................ 11

CHAPTER TWO .......................................................................................................................... 14

REVIEW OF RELATED LITERATURE .................................................................................... 14

2.1 Introduction .................................................................................................................... 14

2.2 Conceptual Literature ..................................................................................................... 14

2.2.1 Defining Micro, Small and Medium Enterprises (MSMEs) in Nigeria ................................. 15

2.2.2 Sector Classification of MSMEs ........................................................................................... 18

2.2.3 Generic Categorization ....................................................................................................... 18

2.2.4 Technological Intensity Criterion ........................................................................................ 19

2.2.5 National Bureau of Statistics Classification ......................................................................... 19

2.3 Theoretical Literature ..................................................................................................... 19

2.3.1 The Growth Pole Theory ..................................................................................................... 21

2.3.2 The Dual Economy Models ................................................................................................. 26

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2.3.3 Sustainable Development Model ........................................................................................ 31

2.3.4 Trade-off Theory of Capital Structure ................................................................................. 33

2.3.5 Pecking Order Theory (Pecking Order Model) .................................................................... 35

2.3.6 Agency Theory ..................................................................................................................... 37

2.3.7 Micro Enterprises in Developing Economy ......................................................................... 38

2.3.8 Micro and Small Enterprises/Industrial Clusters................................................................. 39

2.4 Empirical Literature ....................................................................................................... 49

2.5 Summary of the Review of Related Literature............................................................... 55

CHAPTER THREE ...................................................................................................................... 59

THEORETICAL FRAMEWORK AND RESEARCH METHODOLOGY ................................ 59

3.1 Introduction .................................................................................................................... 59

3.2 Design of the Study ........................................................................................................ 59

3.3 Data and Sources ............................................................................................................ 59

3.4 Population of the Study .................................................................................................. 59

3.5 Instruments for Data Collection ..................................................................................... 60

3.6 Determination of Sample Size........................................................................................ 61

3.7 Validity of the Research Instrument............................................................................... 62

3.8 Reliability of the Research Instrument ........................................................................... 62

3.9 Pilot Survey .................................................................................................................... 63

3.10 Theoretical Framework ............................................................................................... 63

3.11 Models Specification, Methods of Data Analyses and Results Evaluation ................ 65

3.12 Assessment of Level of Support of Microfinance Providers for the Sustenance of Profitability of Enterprise Clutters in South-East, Nigeria (Objective 4) ................................. 71

CHAPTER FOUR ......................................................................................................................... 73

DATA PRESENTATION, ANALYSES AND DISCUSSION OF FINDINGS .......................... 73

4.1 Introduction .................................................................................................................... 73

4.2 General Enterprise Characteristics and Perceptions ....................................................... 73

4.3 Microfinance Sources and Enterprise Profitability/Objectives 1&2 (Models 1, 2a & 2b) 77

4.4: Determinants of the choice of the microfinance Source by Enterprise clusters in South East of Nigeria (Objective 3 and Model3) ................................................................................ 81

4.5 Assessing the level of support of Microfinance providers for the sustenance of profitability of Enterprise (Objective 4) ................................................................................... 85

4.6 Tests of Hypotheses ...................................................................................................... 88

4.7 Discussion of Findings ................................................................................................... 90

4.7.1 Discussion on Objective One .............................................................................................. 92

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4.7.2 Discussion on Objective Two .............................................................................................. 93

4.7.3 Discussion on Objective Three ............................................................................................ 93

4.7.4 Discussion on Objective Four .............................................................................................. 95

CHAPTER FIVE .......................................................................................................................... 97

SUMMARY, POLICY RECOMMENDATIONS AND CONCLUSION ................................... 97

5.1 Summary of Findings ..................................................................................................... 97

5.2 Policy Implications of Findings ..................................................................................... 98

5.3 Policy Recommendations ............................................................................................... 99

5.4 Contribution of this Study to Knowledge on the Subject Matter ................................. 100

5.5 Suggestions for Further Research ................................................................................ 101

5.6 Conclusion .................................................................................................................... 102

REFERENCES ........................................................................................................................... 103

ANNEX I: QUESTIONNAIRE ON MICRO AND SMALL ENTERPRISES (MSEs) ............ 111

ANNEX II: INTERVIEW SCHEDULE ..................................................................................... 118

ANNEX III: GUIDE QUESTIONS FOR THE FOCUS GROUP DISCUSSIONS ................... 119

ANNEX IV: ABA, NNEWI AND ONITSHA CLUSTERS DIRECTORY .............................. 120

ANNEX V: COMPUTATION OF SPEARMAN’S CORRELATION COEFFICIENT ........... 121

USING TEST-RETEST RESULTS ........................................................................................... 121

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LIST OF TABLES PAGES

Table 2.1: Cross-Country approaches to defining SMEs 16

Table 2.2: Different classification of MSMEs in Nigeria 17

Table 3.1: Estimated Number of Enterprises across selected clusters in South-East 60

Table 3.2: Sample Size of Enterprises across Clusters in South-East Nigeria 62

Table 3.3: Model 1: Microfinancing source and profitability 67

Table 4.1: Summary of Enterprises Characteristics 73

Table 4.2: Perceived Challenges faced by enterprises in accessing micro finance 76

Table 4.3: Effect of microfinance sources on the profitability of enterprise clusters 77

Table 4.4: Determinants of the choice of micro financial sources for enterprises 81

Table 4.5: Average Perception of Micro Financial Support from Micro Financial

Sources for Enterprises in South East, Nigeria 87

LIST OF FIGURES PAGES

Figure 2.1 Simple Illustration of the Value Chain 42

Figure 4.1: Perception of determinants of choice of microfinance choice 85

Figure 4.2: Extent of microfinance support perceived from formal and informal sources 86

Figure 4.3: Perceived extent to which funds have expanded business 87

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

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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 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”. 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

3

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 money to individual firms. 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 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

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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.

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

5

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 bureaucratic costs and

government seeming lack of interest in small businesses. All these have great damaging

effects 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,

6

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.

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

7

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 Micro and Small Enterprises (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,

(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

of profitability of enterprise clusters in South East, Nigeria?

8

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 Micro and Small 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 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

9

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 Micro and Small Enterprise (MSE) 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 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.

10

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 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

11

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

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.

12

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 (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

13

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 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.

14

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 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

15

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.

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).

16

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 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

17

Country Classification approaches and criteria

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, 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.

18

• 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 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 (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).

19

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, 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

20

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 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.

21

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 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).

22

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).

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 of 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.

23

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 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.

24

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 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:

25

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.

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

26

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

earnings of labour.

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 formation increases in the industrial sector. In such a

27

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 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

28

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 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

29

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 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

30

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 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

31

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.

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

32

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 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

33

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 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

34

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 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.

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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

36

equity, investors believe that managers think that the enterprise is 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 is less, the enterprise first draws down its cash

balance or sells its marketable securities rather than reduce dividends.

37

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:

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

38

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.

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

39

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 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.

40

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 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

41

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 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

42

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 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

Design

Production

Marketing

Consumption

Recycling

43

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 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

44

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 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

45

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 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.

46

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 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,

47

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 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

48

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. 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

49

infrastructure as an 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 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

50

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.

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

51

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 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

52

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, 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

53

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 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,

54

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

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

55

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

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;

56

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.

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

57

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.

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 at how microfinance

58

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.

59

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.

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

60

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.

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.

61

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

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;

62

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

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,

63

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.

Out of the 70 respondents, 60 questionnaires were correctly completed and retrieved.

The pilot survey was considered as of 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 uses 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

64

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 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

65

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;

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

66

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 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), 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;

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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 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 +

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Regressand Regressor Relationship 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.

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.

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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.

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:

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( )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.

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);

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LnLEN stands for length of repayment period offered;

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.

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.

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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 FINDINGS

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 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.

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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 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

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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 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.

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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, 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

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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*** (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

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Model 1 Formal

Model 2a Informal

Model 2b Both

(-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 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.

79

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 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

80

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 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.

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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.

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)

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Dependent variables Marginal effects Odds ratio 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)*** 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.

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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, 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

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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 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

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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.

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 informal sources in

enterprise clusters in South East, Nigeria. This objective is examined from three perspectives.

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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 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.

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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.

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

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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.

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.

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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 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.

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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.

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

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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.

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

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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 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

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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 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

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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. 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, they equally increase the odds in

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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

microfinance institutions on small scale enterprises in Nigeria and showed that, majority of

the small scale enterprises acknowledged positive contributions of microfinance institutions’

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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, interest rate, length of repayment period offered, the cluster advantage (social

capital) and the trade and service categories of the sectors.

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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

Specific policy issues 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 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

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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.

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.

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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 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

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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.

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

102

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.

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.

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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).

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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

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ANNEX I: QUESTIONNAIRE ON MICRO AND SMALL ENTERPRISES

(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[ ]

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) ……………………

116

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 any)…………………………………………………………………….

…………………………………………………………………….…………………………………

…………………………………………………………………….…………………………………

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 DISCUSSIONS 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