SURVIVAL ANALYSIS OF SMMEs IN BOTSWANA

BAME JOSHUA MANNATHOKO

Submitted in fulfillment of the requirements for the degree

MAGISTER COMMERCII

IN THE FACULTY OF BUSINESS AND ECONOMIC SCIENCES

AT THE

NELSON MANDELA METROPOLITAN UNIVERSITY

Supervisor: Dr. Matthew Ocran

January 2011

ii

STUDENT DECLARATION

FULL NAME: Bame Joshua Mannathoko

STUDENT NUMBER: 209080678

QUALIFICATION: Magister Commercii (Economics)

In accordance with Rule G4.6.3, I hereby declare that the above-mentioned dissertation

is my own work and that it has not previously been submitted to another University or for

another qualification.

Signature: …………………………..

Date: ……………………………

iii

ACKNOWLEDGEMENTS

I wish to thank Dr. Matthew Ocran, Senior Lecturer in the Department of Economics and

Economic History, Nelson Mandela Metropolitan University, for agreeing to supervise my

research as well as for all his positive input, guidance and help in the organisation of my

dissertation. I would also like to thank my sponsor, the Ministry of Finance and

Development Planning of Botswana for offering me the opportunity to further my studies.

Special thanks to the Department of Youth (Botswana) for their full cooperation in

providing me with the necessary data to complete my study. Lastly, I thank God Almighty

for everything.

iv

ABSTRACT

This study investigates the factors influencing survival of micro enterprises funded by the

Department of Youth in Botswana. Data drawn from 271 business ventures established

between the years 2005 and 2009 was analysed by using the Cox proportional hazards

model (CPHM), a survival analysis technique.

Results from the analysis suggest that businesses operated by younger owners endure a

higher risk of failure in comparison to businesses owned by older entrepreneurs while

firm size at start-up was also a significant determinant of survival. As a component of

human capital, a personal contribution to the start-up capital and prior employment

experience were also found to be significant predictors of business survival. Regarding

gender of the business owner, the claim that female operated businesses face a higher

probability of failure when compared to businesses run by males was not supported by

the study results. The amount of funding from the DOY at start-up was found not to have

any influence on the survival or failure outcomes for the business projects.

Based on these findings, certain policy implications can be deduced. This study

recommends that policy makers focus more on human capital requirements of

beneficiaries of government business development initiatives as well as entrepreneur

contribution to start-up capital in order to increase the success rate of the business

ventures. In addition, the capacity to perform continuous monitoring and mentoring of

government funded businesses ventures, particularly SMMEs, should be increased within

the relevant departments or alternatively outsourcing of the requisite skills should be

considered. Lastly, recommendation to replicate this research, at a larger scale in future

is proposed.

v

TABLE OF CONTENTS

PAGE

DECLARATION BY STUDENT ii

ACKNOWLEDGEMENTS iii

ABSTRACT iv

TABLE OF CONTENTS v

LIST OF FIGURES viii

LIST OF TABLES viii

LIST OF ACRONYMS ix

CHAPTER 1: INTRODUCTION 1

1.1 BACKGROUND AND PROBLEM STATEMENT 1

1.2 OBJECTIVES OF THE STUDY 3

1.3 RELEVANCE OF THE STUDY 3

1.4 ORGANISATION OF THE DISSERTATION 4

CHAPTER 2: THE SMME SECTOR IN BOTSWANA 5

2.1 INTRODUCTION 5

2.2 SMME DEFINITION 5

2.3 THE EVOLUTION OF SMMEs IN BOTSWANA 6

2.4 POLICY AND INSTITUTIONAL FRAMEWORK 8

2.4.1 Entrepreneurial Development Support 10

2.4.2 Enabling Business Environment 12

2.4.3 Financial and Institutional Support 13

2.4.4 Export Promotion Support 14

2.4.5 Sectoral Support 15

2.5 CONCLUSION 15

vi

CHAPTER 3: LITERATURE REVIEW 16

3.1 INTRODUCTION 16

3.2 THEORETICAL LITERATURE 16

3.2.1 Firm Specific Factors 17

3.2.2 Environmental Factors 26

3.3 EMPIRICAL LITERATURE 28

3.4 CONCLUSION 38

CHAPTER 4: METHODOLGY 40

4.1 INTRODUCTION 40

4.2 CONCEPTS AND DEFINITIONS 40

4.3 ANALYTICAL FRAMEWORK 41

4.3.1 The Survivor Function 41

4.3.2 The Hazard Function 42

4.3.3 The Cox Proportional Hazards Model 43

4.4 EMPIRICAL MODEL 45

4.5 DATA ISSUES AND SAMPLING 47

4.5.1 Sampling 48

4.5.2 Description of Variables 49

4.6 DESCRIPTION OF THE SAMPLE 51

4.6.1 Firm and Industry Characteristics 51

4.6.2 Entrepreneur Specific Characteristics 54

CHAPTER 5: EMPIRICAL ANALYSIS 58

5.1 INTRODUCTION 58

5.2 CPHM RESULTS 58

5.2.1 Age 60

5.2.2 Gender 60

5.2.3 Human Capital 60

5.2.4 Financial Variables 62

vii

5.2.5 Firm Size 62

5.2.6 Industry Dummies 62

5.2.7 Ownership Dummies 62

5.3 DIAGNOSTICS 63

CHAPTER 6: CONCLUSION AND RECOMMENDATIONS 64

6.1 CONCLUSION 64

6.2 RECOMMENDATIONS 66

6.3 LIMITATIONS AND DIRECTIONS FOR FUTURE RESEARCH 66

LIST OF REFERECENCES 68

viii

LIST OF FIGURES

PAGE

Figure 1. Smoothed Hazard Estimate for DOY Funded Micro Enterprises 57

LIST OF TABLES

PAGE

Table 1. Dependent, Independent and Control Variables 50

Table 2. Sample Distribution by Firm and Industry Characteristics 53

Table 3. Sample Distribution by Personal Characteristics of

Entrepreneurs 55

Table 4. Descriptive Statistics of Selected Variables 56

Table 5. Cox Estimates of DOY Funded Micro Enterprises 59

ix

LIST OF ACRONYMS

AfDB – African Development Bank

BAS – Business Advisory Service

BECI – Botswana Export Credit Insurance

BEDIA – Botswana Export Development and Investment Authority

BEDU – Botswana Enterprise Development Unit

BIDPA – Botswana Institute for Development and Policy Analysis

BNPC – Botswana National Productivity Centre

BNYC - Botswana National Youth Council

BOBS – Botswana Bureau of Standards

BOCCIM - Botswana Confederation of Commerce, Industry and Manpower

BOTA – Botswana Training Authority

BTEP – Botswana Technical Education Programme

BWP – Botswana Pula

CEDA – Citizen Entrepreneurial Development Agency

CPHM – Cox Proportional Hazards Model

CSO – Central Statistics Office

DIA - Department of Industrial Affairs

DOY – Department of Youth

DVET - Department of Vocational Education and Training

EAOB - Exporters Association of Botswana

EB - Enterprise Botswana

FAP – Financial Assistance Policy

GDP – Gross Domestic Product

HR – Hazard Ratio

LEA – Local Enterprise Authority

LR – Likelihood Ratio

MOE – Ministry of Education

x

MTI – Ministry of Trade and Industry

NDP – National Development Plan

NFTRC – National Food Technology Research Centre

NGO – Non Governmental Organisation

OECD - Organisation for Economic Cooperation and Development

RB – Republic of Botswana

RBV – Resource Based View

RIP - Rural Industrialisation Programme

RIPCO – Rural Industrial Promotions Company

SBC - Small Business Council

SBPA - Small Business Promotion Agency

SMME - Small, Micro and Medium-Sized Enterprise

WBCSD - World Business Council for Sustainable Development

1

CHAPTER 1

INTRODUCTION

1.1 BACKGROUND AND PROBLEM STATEMENT

The important contribution made by small, micro and medium-sized enterprises (SMMEs)

across nations has been widely acknowledged, with a lot of empirical research suggesting

that SMMEs play a crucial role towards achieving industrial and economic development

objectives of a country.

According to the European Commission (2007), SMMEs are viewed as an engine of

growth for the European economy as they are an essential source of job creation,

entrepreneur spirit and innovation, and in that regard, seen as crucial for fostering

competitiveness and growth. The World Business Council for Sustainable Development

(WBCSD, 2004:2) reports that in the Organisation for Economic Cooperation and

Development (OECD) economies, SMMEs account for over 95 percent of firms, 60 to 70

percent of employment, 55 percent of Gross Domestic Product (GDP) and further

generate the lion’s share of new jobs. In addition, WBCSD (2004:2) reports that for

developing countries, more than 90 percent of all firms outside the agricultural sector are

SMMEs, generating a significant portion of GDP.

The Botswana Institute for Development and Policy Analysis (BIDPA, 2007) study on the

“Role of SMMEs in Botswana” notes that while their diverse nature makes their actual

contribution to the economy somewhat different, SMMEs significantly contribute to job

creation for both semi-skilled and unskilled labour. The BIDPA (2007) report indicates

that SMMEs contribute up to 75 percent of private sector employment and about 20

percent of national output (GDP). However, a major concern is that Botswana has over

the years struggled in its efforts to diversify its economy away from mining and minerals

2

processing. The mineral sector, particularly diamond mining is at the core of the country’s

economic stability. The World Bank (2007:6) suggests that diamonds are responsible for

up to 80 percent of exports while mineral revenues account for about half of government

revenue. Despite its positive contributions to the economy of Botswana, diamond mining

is highly capital intensive, asset specific and generates very little employment, accounting

for only about 5 percent of employment in the country (World Bank, 2007). The mining

sector in Botswana dominates the economy at a share of 33.3 percent of GDP followed

by other major sectors such as services at 31 percent, government - 15.5 percent,

manufacturing - 4 percent, agriculture - 2.2 percent and lastly others at consolidated

figure of 13.9 percent (World Bank, 2007:6).

Considering their documented socio-economic importance across nations, SMMEs still

face daunting challenges and in order for them to survive and grow, there is a need for a

favourable environment that allows them to prosper and expand. New small businesses

are usually considered to be vulnerable especially in the immediate post start-up period

and many will fail to develop into prospering and thriving ventures. As already

highlighted, the SMME sector is seen as an alternative avenue to contribute to a country’s

sustainable economic growth, through employment creation and poverty alleviation. The

main strategy to achieve these goals has been the promotion of entrepreneurship and

small business development. However, in spite of the concerted efforts made to promote

the development of SMMEs, small business failure in Botswana continues to increase at

a worrying rate.

According to Temtime and Pansiri (2006) the general failure rate of SMMEs in Botswana

is estimated to be at 80 percent, with over 70 percent of start-up firms failing in the first 18

months and only less than two percent of them expanding their businesses. Watson,

Hogarth-Scott and Wilson (1998) emphasise the importance of understanding the

determinants of success and failure in new small businesses. In that regard, the

founder’s motivation to setup a new business could provide valuable insight into the

3

support needs of such a small business to those institutions tasked with assisting

SMMEs.

The questions that follow from the above discussion are: (i) what are the factors driving

the setting up of SMMEs in the first place? (ii) what are the determinants of small firm

survival? and lastly (iii) what has been the impact of the start-up’s support offered by

government?

1.2 OBJECTIVES OF THE STUDY

The broad objective of this study is to examine the survival of businesses in the SMME

sector in Botswana, however, the specific objectives are as follows:

i) To review the evolution of SMMEs in Botswana and to examine the institutional

framework that supports their operations;

ii) To find out the determinants of small business survival (or failure); and

iii) To make policy recommendations based on the findings.

1.3 RELEVANCE OF THE STUDY

The present research theme is consistent with one of Botswana’s Vision 2016 strategic

goals – “A prosperous, productive and innovative nation”1. The outcome of this study will

provide additional insights into the operations and survival of SMMEs in Botswana. The

findings are expected to make a meaningful contribution to discussions about existing

government policy on promoting small, medium and micro enterprises. The work will also

contribute to the literature on SMMEs survival in Botswana and other mineral resource

dependent economies in Africa.

1 Vision 2016 is Botswana’s strategy to propel its socio-economic and political development into a competitive,

winning and prosperous nation (RB, 1997).

4

1.4 ORGANISATION OF THE DISSERTATION

The rest of the dissertation is structured as follows: in chapter 2, an overview of the

SMME sector in Botswana is discussed. Chapter 3 provides a discussion of the literature

on SMME survival. Chapter 4 focuses on the research methodology adopted in the

empirical analysis as well as the data issues considered. The main findings are given in

Chapter 5 while Chapter 6 presents the conclusions and policy recommendations that

emerge from the study.

5

CHAPTER 2

THE SMME SECTOR IN BOTSWANA

2.1 INTRODUCTION

In its effort to diversify the economy away from dependency on the mineral sector, the

government of Botswana realised the importance of supporting SMMEs in fostering

economic growth and creating jobs. Over the past two decades, targeted financial

support as well as advisory programmes to help the people of Botswana to establish their

own enterprises was introduced and implemented at different levels. In the initial stages,

“the programmes were set up more in response to specific problems encountered than as

the basis of a comprehensive and more focused government policy on SMMEs” (RB,

1999:1). However, in recent times the landscape has since changed with government

and the private sector pulling resources together in a crusade to assist the local SMME

sector. The purpose of this chapter is to provide an overview of the SMME sector by

reviewing the initiatives undertaken by government. Such initiatives include policy

formulation and the implementation of programmes intended to assist the SMME sector in

Botswana.

2.2 SMME DEFINITION

A review of the literature on definitions and categorisation of SMMEs indicates that there

is no single and standard definition for SMMEs. It is in this regard that the Government of

Botswana accepted the criteria proposed by the SMME Task Force of 1998 in defining

the three categories of enterprises, using annual turnover and the number of employees.

A micro enterprise is defined as a business with the number of employees not exceeding

6

six and a maximum annual turnover equal to or less than BWP2 60,000.00. A small

enterprise makes an annual turnover of between BWP 60, 000.00 and BWP 1.5 million

and employs not more than 25 people. A medium enterprise is defined as a firm that has

a maximum number of 250 employees and an annual turnover of between BWP 1.5

million and BWP 5 million (RB, 1999).

The SMME Policy (1999) notes that, even though the categorisation is retained as a

guide for broad policy purposes, the Policy leaves room to consider more specific

definitions such as when a particular government policy on e.g. taxation or direct

assistance programme is developed.

2.3 THE EVOLUTION OF SMMEs IN BOTSWANA

Although there have been efforts to document SMMEs in terms of their location, sector,

output and contribution to employment, several studies indicate that reliable, consistent

and up-to-date official statistics on SMMEs in Botswana is still lacking. Government

agencies such as Citizen Entrepreneurial Development Agency (CEDA), Local Enterprise

Authority (LEA), Central Statistics Office (CSO), Department of Youth (DOY) may

however, be credited with setting up firm data bases that can only be regarded as work-

in-progress.

According to BIPDA (2007:2), the first ever and most comprehensive nation-wide

household survey of SMMEs was conducted by Daniels and Fisseha in 1992. The study

estimated that the SMME sector consisted of 48, 000 enterprises, employing over 88, 000

people. This was followed by another estimate in 1998, documented in the Task Force

Report (2008) on the SMME policy in Botswana, indicating that there were approximately

56, 300 SMMEs operating in Botswana, employing approximately 125,000 people

2 BWP – Botswana Pula, BWP 1 = USD 0.1521; BWP 1 = ZAR 1.043 (Bank of Botswana, 06.12.2010).

7

including business owners (Task Force Report on the Policy on SMMEs in Botswana,

1998).

At the time of publication, the Task Force Report estimated the SMME sector’s

contribution to be in the range of 30 to 45 percent of GDP, while that of large firms stood

at 38 to 48 percent of GDP. It has not been fully established how much the SMME sector

currently contributes in terms of employment however, the Local Enterprise Authority

estimates that SMME sector comprise 95 percent of enterprises in Botswana while the

current contribution to GDP is estimated at 42 percent (LEA, 2008). This can therefore be

inferred that job creation is one of the most important contributions from the SMME sector

in the country.

A study by the African Development Bank (AfDB) and the Organisation for Economic

Cooperation and Development (OECD) indicated that in contrast to most other African

countries, lack of finance has not been an inhibitor for SMME growth and survival in

Botswana, as the government made resources available to SMMEs through its various

enterprise development programmes (AfDB & OECD, 2005). Two financial schemes, the

Micro Credit Scheme and the Credit Guarantee Scheme were cited as government

schemes that provided citizens with easy access to start-up capital while other initiatives

such as Citizen Entrepreneurial Development Agency (CEDA) soon followed. The AfDB

and OECD (2005) further report that SMME development in the country has over the

years suffered from other problems such as lack of premises, unfavourable laws and

regulations, lack of information on government assistance programmes, lack of business

start-up training and lack of entrepreneurial “role models”. As a result of these problems,

the survival rates of SMMEs in Botswana were reported to be very low.

8

2.4 POLICY AND INSTITUTIONAL FRAMEWORK

Government policies and programmes in Botswana are initiated within the confines of

national development plans (NDPs), which consolidate national development objectives

over six-year plan periods, and these outline the mechanisms through which policy

objectives will be achieved. Several policies, programmes and institutions intended to

assist SMMEs have been established in recent years. As one of the pioneering

assistance programmes, the Botswana Enterprise Development Programme operated by

the Botswana Enterprise Development Unit (BEDU) was set up in 1974 to provide

integrated support for citizen entrepreneurial development (RB, 1999:7). This was

followed by the Rural Industrialisation Programme (RIP) and Business Advisory Service

(BAS) to provide business support to small enterprises. The Financial Assistance Policy

(FAP) was later introduced to provide financial assistance to small scale manufactures in

1982 while in the mid-1980s, the Reservation Policy was adopted to promote the

participation of the indigenous population in the country’s economic activities (RB,

1999:7).

The Industrial Development Policy of 1998 was adopted to foster the establishment of

industries that are productive, innovative and internationally competitive. In terms of the

policy responses to the needs of the SMMEs, the following are some of what the policy

sought to achieve: improved availability of infrastructure through partnerships with local

authorities and the private sector; establishment of business assistance centres and

associations through a co-operative approach between government; local authorities and

NGOs; promotion of linkages between SMMEs and larger competitive industries; and to

promote linkages of the SMMEs with other sectors such as agriculture, mining, wildlife

and tourism in order to facilitate the production of value-added products for export

(Sentsho, Maiketso, Sengwaketse, Ndzinge-Anderson & Maiketso, 2007).

9

The Policy on Small, Medium and Micro Enterprises in Botswana was adopted in 1999

following the findings of the SMME Task Force (1998) whose job was to comprehensively

address SMME issues and guide the development of a national policy for the SMME

sector. The Task Force was mandated to hold nationwide consultations with business

owners and other interested stakeholders, consolidate their submissions and propose a

common approach to address issues peculiar to the SMMEs in the country. The Policy

presents a framework for the development of SMMEs in Botswana, including guiding

principles as well as an outline of the overall objectives of the SMME policy.

The 1999 Policy document on SMMEs estimated that 80 to 85 percent of small

enterprises in Botswana cease trading within five years of start-up. In addition, of the few

that manage to expand, only a small number manages to make the transition from

SMMEs to larger enterprises (RB, 1999:4). The SMME policy of 1999 has not yet been

reviewed and therefore it is important to point out that not all the information in the policy

document can be regarded as truly representative of the current SMME environment in

Botswana. However, the contents of the Policy indicate what massive challenges the

SMME sector experienced in the past and in recent times. Further, the Policy document

points to the urgency to which small enterprises need assistance, starting at the highest

level of decision making in government down to the grass roots level.

The Small Business Act (2003) is the most recent legislation passed with the intention to

advance the development of SMMEs in Botswana. The Act established the Local

Enterprise Authority, the Board of Local Enterprise Authority and the Small Business

Council while providing for their functions and matters incidental to them (RB, 2003). A

number of reasons motivated the above listed assistance initiatives. Chief among the

reasons was the role of SMMEs in employment creation; diversification of the economy

away from mining and traditional agriculture; support for economic activities in the rural

10

areas; support for the production of goods for export or import substitution, and to support

citizen participation and ownership of economic activities.

The initiatives dedicated to SMME support in Botswana thus far can be broadly

categorised into five categories namely: Entrepreneurial Development Support, Enabling

Business Environment, Financial Institutional Support, Export Promotion and Sectoral

Development.

2.4.1 Entrepreneurial Development Support

The entrepreneurial development aspect focuses on the institutional support for

entrepreneurship nurturing. It is based on five institutions whose common function is the

development of viable SMMEs through the cultivation of entrepreneurial capabilities of

business people. The institutions are the Department of Industrial Affairs (DIA), the Local

Enterprise Authority (LEA), Enterprise Botswana (EB), the Department of Vocational

Education and Training (DVET), and the Botswana Bureau of Standards (BOBS).

The Department of Industrial Affairs (DIA) started off by supporting SMMEs through the

Integrated Field Service (IFS) Division. IFS stations were established across the country

to promote industrialisation at the grass roots level. The IFS division was however,

phased out in 2006 and later replaced by the Local Enterprise Authority. The DIA is not

mandated to provide financial support. After potential entrepreneurs have been identified,

they are provided with relevant training, and based on the sector under which they fall,

get referred to the appropriate institutions through the DIA. Referrals will be to the

Department of Culture and Youth (if they are youths), the Department of Women’s Affairs

(if they are women) while others (the rest) will be referred to CEDA for financial support

and other business related assistance.

From its establishment in 1997, Enterprise Botswana’s (EB) main focus was on

entrepreneurial development. EB’s success was limited because while projects would be

11

funded for equipment, machinery and start-up capital, financing for working capital would

be difficult to source. EB’s role in entrepreneurship training to equip business people with

skills to access both local and external markets was viewed as a significant achievement

by the authorities (Sentsho et al., 2007). However, access to foreign markets was a

constraint to the local SMMEs because, even though export insurance cover was

provided by Botswana Export Credit Insurance (BECI), it only covers export business to

“safe” markets while emerging markets are not covered (Sentsho et al., 2007).

LEA is a co-ordinated and focused one-stop shop authority that provides development

and support services to the local industry needs of SMMEs, encompassing training,

mentoring, business plan finalisation, market access facilitation, and facilitation of

technology adaptation and adoption (LEA, 2010). LEA does not provide financial

assistance to entrepreneurs. However, the Authority facilitates access to finance as well

as provide support to SMMEs, before and after funding. The Authority's key sectors are

manufacturing, tourism, agriculture, and any services that support the three business

sectors. In particular, LEA targets women, youth, and the unemployed. Part of the

authority’s strategy is to encourage businesses to use locally available natural resources

and raw materials, within the above mentioned sectors. LEA also endeavours to build

competencies in quality and efficiency, and to encourage import substitution and export

oriented products and services. The institution also assists in the identification of

business opportunities for existing and future SMMEs, promoting domestic and

international linkages, facilitating access to markets, and exploitation of government and

large firms' procurement opportunities by SMMEs (LEA, 2010). As part of its mandate,

LEA is expected to coordinate all institutional activities relating to SMMEs in order to

improve their impact and reduce wasteful duplication of efforts currently in place.

The DVET was not created to directly assist SMMEs but has taken an internal initiative to

promote their economic development. The main programme offered at the technical

colleges is called the Botswana Technical Education Programme (BTEP). The aim of

12

BTEP is to improve access to vocational education and to produce graduates who are

trainable, employable and have the initiative and ability to start their own businesses. The

programme has been developed in close consultation with employers and industry. It

comprises practical work, projects, work attachment and a key skills component (MOE,

2010). The Botswana Bureau of Standards (BOBS) also offers subsidised training and

certification and promotional measures.

2.4.2 Enabling Business Environment

There are those institutions that are focused on providing enabling legal, regulatory and

sound business environments for growth and sustainable development of SMMEs. These

include the Small Business Promotion Agency (SBPA), the Small Business Council

(SBC), the Registrar of Companies and the Botswana Confederation of Commerce,

Industry and Manpower (BOCCIM) – a representative of the privates sector.

The Small Business Promotion Agency and the Small Business Council provide support

for SMMEs by creating an enabling business environment. These institutions also ensure

that policy and legal frameworks are in place to increase the competitiveness and

sustenance of SMMEs (MTI, 2010).

BOCCIM’s role in the development of SMMEs is that of providing advocacy and training,

small business insurance scheme, trading outlets for small business (flea markets),

organizing trade and investment missions abroad, among many other recognisable

initiatives. The members include big and small companies, with close to 80 percent of the

firms being SMMEs (BOCCIM, 2010).

The government has put in place deliberate mechanisms to encourage the development

of linkages with local firms. In the Botswana economy, commercial activities are

13

inherently linked with government budget and therefore government procurement is very

essential to the growth and prosperity of the SMMEs. The Local Procurement

Programme (LPP) is one such initiative, which requires government departments and

parastatals to reserve 30 percent of their procurement budget for local firms. In addition,

government requires central and local government departments and parastatals to

procure all their goods and services from locally based firms, “ provided that the goods

and service are locally available, competitively priced and meet tender specifications”

(RB, 1999).

2.4.3 Financial and Institutional Support

Citizen Entrepreneurial Development Agency (CEDA) was established in August 2001 in

response to calls for government to restructure its assistance and support for the

development of citizen economic empowerment. As a result CEDA, places emphasis not

only on funding, but also on the development of citizen entrepreneurs through training

and mentoring. In order to fulfil its objectives, CEDA provides the following services:

financial assistance to entrepreneurs in the form of loans which are offered at subsidised

interest rates; training and mentoring; providing management and marketing skills to the

managers of its clients in order to enhance their opportunities for success; managing and

monitoring the progress of its clients’ business operations (CEDA, 2007). In addition, the

agency provides risk capital to citizen owned projects and joint ventures between citizens

and non-citizens. As at the end of 2009, CEDA was exposed to the business sectors in

the following proportions: services sector - 44 percent, agriculture - 30 percent, retail - 14

percent, manufacturing - 9 percent and property - 3 percent (CEDA, 2009).

The National Development Bank (NDB) was established under an Act of Parliament in

1963, for promoting the economic development of Botswana. It provides services to the

agriculture, commercial, industrial sectors as well as the real estate sector. NDB is a self-

sustaining profit-making institution that provides loans ranging from BWP 20, 000 to BWP

14

60 million. NDB also plays a major role in the execution of government empowerment

schemes such as the Agricultural Credit Guarantee Scheme and CEDA Credit Guarantee

Scheme (NDB, 2008). Some local commercial banks also have special packages for

SMMEs under which their own terms and conditions apply.

The Youth Development Fund (YDF) is a government initiative through the Ministry of

Youth, Sport and Culture. The fund is aimed at empowering the youth to own businesses

and create sustainable employment opportunities for young people through the

development of sustainable projects. At inception it was called Out of School Youth Grant,

where the funding ceiling was P50 000 and it was wholly grant. The YDF caters for out of

school youth, unemployed and underemployed youth, (working youth earning less than

P600 monthly) who are citizens of Botswana, aged between 18 and 29 years (MYSC,

2010).

2.4.4 Export Promotion Support

Exporters Association of Botswana (EAOB) is a non-governmental trade organisation

registered under the Botswana Registrar of Societies Act to facilitate global trade and

enhance economic growth and diversification from traditional exports to non-traditional

exports. The non-traditional exports in Botswana’s context include manufactured goods

and services. EAOB’s target is to equip SMMEs with relevant trade and industrial skills

who wish to export; to be innovative and creative; and to increase production and market

volumes, in quality and quantity for increased profits. EAOB's programmes are designed

to deliver up-to-date counselling, advocacy, representation, training, technical assistance,

market development, foreign business match-making and joint ventures, as well as the

arrangement of viable financial packages internationally to its members (EAOB, 2010).

15

2.4.5 Sectoral Development

With regards to sectoral development, there are government departments and units that

have been established and tasked with the appropriate financial, technical and manpower

capabilities to assist SMME development. They include Rural Industrial Promotions

Company (RIPCO), National Food Technology Research Centre (NFTRC) and Integrated

Programme for Arable Agriculture Development (ISPAAD).

2.5 CONCLUSION

For SMMEs to realise their full potential as drivers of economic growth, employment

creation and poverty reduction, it is important to create a favourable environment that

allows them to thrive. The past two decades have witnessed the introduction and

implementation of targeted financial support, advisory programmes and institutional

capacity to help the people of Botswana establish their own enterprises. An interesting

revelation from a study by the AfDB and OECD (2005) indicated that in contrast to most

African countries, lack of finance has not been an inhibitor for SMMEs progress in

Botswana, as the government made resources available to SMME’s through its various

enterprise development programmes. The Government of Botswana’s past and present

day efforts to create a conducive and supportive environment for SMMEs are reported to

have made significant gains in the small business sector. However, some studies

continue to reveal an existence of a business environment that still needs a lot of

improvement.

16

CHAPTER 3

LITERATURE REVIEW

3.1 INTRODUCTION

The purpose of this chapter is to review the related literature on small business survival

analysis. The literature comprises both theoretical and empirical studies. The theoretical

review looks at factors that are considered most likely to impact small business outcomes

(i.e. survival or failure) while the empirical literature dwells on research estimations. This

part of the study helps in identifying factors that possibly determine small business failure

as well as the estimation techniques used in survival analysis studies conducted in other

parts of the world, including countries in Africa

The literature on firm survival identifies two major groups of determinants of firm survival. The

broad categories may be described as firm specific, entrepreneur specific and environmental

factors respectively. This part of the dissertation presents a discussion of these factors. The

section also presents a number of studies that test these various drivers of firm survival

empirically.

3.2 THEORETICAL LITERATURE

A meaningful discussion of the theoretical determinants of firm survival (or failure)

depends on how the term failure is defined. Failure of a business is synonymous with

closing of its operations on a permanent basis. From the literature, most authors chose

not to define a firm that is sold or changes ownership as failed. If a firm remains in

operation and keeps offering the same type of goods and services within the duration of

observation, it is regarded as still existing. The literature identifies a large number of firm

specific, entrepreneur specific and environmental factors that are likely to influence

17

business failure. The following presents a discussion of the most important factors found

in the literature.

3.2.1 Firm Specific Factors

Firm Age

According to Industrial Organisation literature, one stylized fact about firm entry and exit is

that age and size are positively correlated to the probability of firm survival. This is

reflected in the work of Geroski (1995). In regards to firm age, new entrant firms are said

to face a phenomenon known as the “liability of newness” effect, proposed by

Stinchcombe (1965) (as cited in Geroski, Mata & Portugal, 2007), who argued that new

firms face a greater risk of failure as compared to older ones. At their infancy, most firms

are expected to face the challenges of attaining an organisational structure and a level of

efficiency that enables them to be at par with competitors. The theory of noisy selection

by Jovanovic (1982) proposes that efficient firms survive and grow while inefficient ones

ultimately decline and fail. Jovanovic’s (1982) view is supported by Perez, Llopis and

Llopis (2004) who argue that new entrant firms have no knowledge of their efficiency

levels before entering the market and also that they do not know yet if they possess traits

necessary to adapt themselves to the competitive environment in order to survive. The

studies continue to report that as time elapses, firms will learn about their relative

efficiency and competitiveness, reducing the risk of failure while those that learn of their

inefficiencies exit the market.

Other studies have however, found that the probability of failure may increase with firm

age. The “liability of adolescence” effect by Fichman and Levinthal (1991) and the

“liability of senescence” effect by Hannan (1998) explain this relationship. The liability of

adolescence effect posits that new firms are shielded from failure by initial resource

endowments and the strategic choices made by these firms upon entering the market

(Perez et al., 2004:254). As firms age, they may no longer be protected by their

18

endowments and strategic choices which become less adequate as they are confronted

with a new market environment, leading to increased firm exit-risk during adolescence

(Perez et al., 2004:254). The risk of failure is however, expected to decline after the

period of adolescence when firms consolidate their position in the market (Perez et al.,

2004:254).

Regarding the “liability of senescence”, Hannan (1998) indicates that with time, older

firms will face a relatively high chance of market exit. This may be due to factors such as

outdated technology and products, obsolete management strategies and business

concepts, while owner-managed firms will face challenges in finding a successor for the

business once the owner/founder is no longer available to run the business (Perez et al.,

2004:255).

Start-up Size

A lot of theoretical literature reports that larger firms are predicted to experience higher

probabilities of survival than their smaller counterparts (e.g. Mata & Portugal, 2002;

Bates, 1995). The arguments put forward for the likelihood of smaller firms exiting the

market early is that larger firms, unlike their smaller counterparts, are more likely to be

closer to the minimum efficient scale required to operate efficiently in the market. Larger

firms are also cited to be in a superior position to access funds and the labour market. In

addition, larger firms are viewed to be more diversified thus having more options in one

market should investment activities go bad in another (Bates, 1995). Further, larger firms

are reported to also benefit from varied managerial capabilities which translate to higher

performance, increased efficiencies and reduced costs (Lucas, 1978). The hypothesis

derived from this literature is that “larger firms have lower probabilities of exit”.

Comparatively, new entrant firms tend to start at a relatively smaller size and thus

Freeman, Carroll and Hannan (1983) associate the liability of being new with the liability

19

of smallness. Smaller firms are viewed as facing cost disadvantages compared to the

more established firms operating at minimum efficient scale.

Relative Efficiency, Investment in Research and Development (R&D) and

Economic Activity

Other factors that are considered to be important determinants of survival are a firm’s

relative efficiency and competitiveness, economic activity and investment in research and

development (R&D). As an aspect of economy activity, the export market is regarded as

considerably tough and thus, Perez et al. (2004:255) argue that exporting firms are

associated with high efficiency and hence a higher likelihood of survival. In regards to

research and development, there is an assumption that R&D activities are positively

related to the competitive advantage of a firm and so to its prospects for survival. Perez

et al. (2004) report that the chances of avoiding market exit depends on the ability of firms

to innovate, which improves their level of competitiveness and hence their chances of

survival.

As for economic activity undertaken by firms, the expectation is that firms whose main

activity is the production of final products will most likely experience a higher risk of failure

in comparison to those firms that produce intermediate3 and capital goods. Perez et al.

(2004) argue that finished goods producers normally face higher market competition and

high uncertainty in demand, which my lead to increased chances of failure.

Initial Resources

According to scholars who support the resource based view (RBV), the characteristics of

a firm’s initial resources will determine its ability to survive in a competitive market

environment (Aspelund, Berg-Utby & Skejevdal, 2005). The RBV perspective further

indicates that the entrepreneurial process is one where entrepreneurs acquire and

3 Intermediate goods are used in the production of other products.

20

develop resources and thus, the new entrant firm’s outcome will be largely dependent on

the nature of the resources the entrepreneurs are able to acquire (Dolliger, 1999 as cited

in Aspelund et al., 2005:1338)

In examining the relationship between initial resources and survival of new ventures,

propositions were made by Aspelund et al. (2005). The main proposition was that initial

resources controlled by entrepreneurs at inception are significant predictors of survival.

The initial resources according to Aspelund et al. (2005) comprise: initial team size,

degree of heterogeneity in founding team, entrepreneurial experience and technological

radicalness.

Regarding initial team size, Aspelund et al. (2005) propose that the more individuals

involved in the founding team, the greater the probability of survival of a new venture.

The assumption is that larger teams are usually associated with more resources and

resourceful teams are known for their ability to mobilise new competencies. Larger teams

are also viewed as being able to have more value adding to the decision making

processes and allow for more specialisation, providing a new venture with added

advantage for progress and survival. However, Aspelund et al. (2005) point out that the

combination of varying competencies in the founding team may as well create hampering

and deteriorating conflicts which may ultimately affect the firm negatively.

The presence of past entrepreneurial experience in the founding team is considered a

valuable resource since team members would have faced similar challenges in their past

entrepreneurial activities. It is in this regard that Apelund et al. (2005:1339) hypothesises

that “entrepreneurial experience present in the team yields a greater probability of survival

for a new venture”.

Another hypothesis made under initial resource endowment is that “a greater degree of

embedded radicalness in the initially controlled technology leads to a higher probability of

survival for a new venture” (Apelund et al., 2005:1340). New firms are required to come

21

up with new inventions to have a sustained competitive edge and growth. “The more

radical the core technology of the new venture, the lesser the advantage held by

competitors” and hence the improved chance for survival (Apelund et al., 2005:1339).

Advancing a similar view is Audretsch (1991), who argues that the probability of making

an innovation influences a business’s decision to exit or stay on in an industry. Audretsch

(1991) points out that being innovative will lead to growth and the attainment of minimum

efficient scale (MES). The understanding from this theoretical view is that those firms that

successfully innovate can expect growth in the future, while those that are unable to

successfully innovate are more likely not to survive. In addition, high-MES industries,

which also happen to be capital intensive, are identified as ones that are particularly

subject to low incidents of failure while newly formed firms that are unable to innovate are

forced to exit the market (Audretsch, 1991:445).

The amount of start-up capital is expected to elevate the chances of firm survival

(Vinogradov & Isaken, 2007:26). A substantial amount of literature reports that more

initial capital affords time for the entrepreneur to learn and overcome challenges during

the early days of start-up. More financial capital is said to provide a liquidity buffer for firm

survival during periods of low performance (Vinogradov et al., 2007:26).

Vinogradov et al. (2007) also point out that firms set up by teams have a higher chance of

survival compared to those firms that are founded by sole entrepreneurs. Team based

ventures are said to benefit from certain advantages that result in their higher rates of

survival. Firstly, a team is capable of assembling more resources and rely on a broader

variety of expert opinion as compared to a sole trader (Ucbasaran, Wright & Westhead,

2003). Secondly, having many partners contributes to the venture’s credibility, thereby

making the business more attractive to lenders and other potential investors (Vinogradov

et al., 2007). Thirdly, it is argued that multiple partners may proxy for more commitment

to a successful enterprise resulting in increased chances of survival (Astebro &

Bernhardt, 2003).

22

Another predictor of business survival is said to be holding a bank loan at start-up.

Astebro et al. (2003:308) advance three reasons for expecting that holding a bank loan at

start-up will be a valid predictor for business venture survival. The first reason given is

that managing to secure a bank loan lifts the burden of financial constraints on investment

activities. Secondly, by securing a bank loan, a business may increase its credibility with

potential suppliers and customers. Thirdly, a loan signals the commitment of the founder

to put in more effort in running a successful business that will be able to make profits and

pay back its creditors.

Human Capital

Previous studies on human capital by authors such as Mata and Portugal (2002), and

Gimeno, Folta and Woo (1997) found human capital to be a good predictor of survival.

Possession of valuable knowledge and skills can prove to be very valuable in improving

the survival chances of firms. Barney (1991) reports that a firm’s ability to survive and

successfully compete in the market is largely based on the extent to which a business

develops firm specific assets, and because these assets cannot be imitated by

competitors, they provide a basis for increased competitive advantage. A hypothesis by

Geroski et al. (2007:14) that “firms employing more skilled labour have lower probabilities

of exit” was formed in relation to the above captioned theoretical views.

A study by Becker (1993) (as cited in Vinogradov et al., 2007) made a distinction between

general and specific human capital. General human capital was described in the context

of years of education and work experience, which is associated with factors expected to

increase an individual’s productivity for a wide range of work-related activities

(Vinogradov et al., 2007). In contrast, specific human capital is reported to be applicable

only to a specific domain such as managerial, industry specific and self-employment

related experience (Bosma, van Praag & de Wit, 2004).

23

According to Bruderl and Preisendorfer (1992) human capital may influence the survival

of ventures in several ways. Enhanced human capital increases the founder’s

productivity resulting in higher profits, thus increasing business survival. Secondly,

education may be used by customers, investors and other stakeholders as a screening

device on which firm survival is dependent. Thirdly, people with higher human capital are

expected to start larger and financially stable businesses due to their higher previous

earnings as employees. Further, based on their broader levels of experience, such

people are expected to identify the most lucrative entrepreneurial opportunities for their

businesses. Lastly, people possessing higher human capital are said to be rarely forced

into self-employment by a desperate need for income, therefore enjoy the luxury of taking

time to develop superior strategies before going into business (Vinogradov et al., 2007).

Firm Strategies

According to Geroski et al. (2007), there are two divergent views regarding the impact of

firm strategies on survival, with ecological and economic arguments on opposite ends.

Geroski et al. (2007) indicate that ecologists emphasise inertia, stressing that survival will

favour those firms that do not change, that is, the greater the magnitude of change, the

higher the likelihood of firm exit. Economic theory literature on the other hand bases its

argument on the adaptive role of change. Economic intuition dictates that firms that are

faced with suboptimal conditions are bound to change and adapt to new techniques in

order to stay in business (Geroski et al., 2007). Those firms that succeed in changing

and adapting to new ways of doing business will survive while those that do not, fail and

exit the market. In forming their strategies, Geroski et al. (2007) indicates that firms

should consider their size at start-up as well their human capital.

24

Location

Location of a business is an important factor that can go a long way in influencing the

availability of resources and access to customers. Mayer and Goldstein (1961) mention

that the choice of location may be based on factors such as vacancy of premises,

nearness to home, familiarity with the neighbourhood and availability of a business for

sale. Even though the just mentioned reasons may seem to business owners as good

and sufficient, the mistake they often make is failing to make objective evaluations of the

location’s potential as a good site for doing business (Mayer et al., 1961).

Mayer et al. (1961) indicate that often entrepreneurs overlook the signals that the location

could be declining. Examples of this decline could be that: there is population relocation

due to major developments in the area, the area could be unsuited for the type of goods

and services offered or that the same goods and services are already adequately

supplied by different firms already established in the area.

Newness versus Previously Owned Business

Whether or not a business is new or purchased from a previous operator can as well be

used as a predictor for survival. Astebro et al. (2003) reported that an already established

business with new owners has a greater likelihood of surviving than a completely new

business. The argument for this view is that market uncertainties and production costs

will be reduced for an on-going business but not for newly established businesses.

Gender

Overt discrimination is viewed as an inhibitor to survival and success of female-owned

businesses (Cater, Williams & Reynolds, 1997:129). Denied access to capital markets is

viewed as one of the influential factors leading to the demise of female-owned ventures.

Three reasons are given by Tiggers and Green (1994) on why women may be

25

disadvantaged in the capital markets. First, women tend to have less experience and

equity in their businesses in comparison to their male counterparts. Secondly, women

may be discriminated against by lenders on the basis of out-dated gender role beliefs.

Thirdly, their belief that they will likely receive differential treatment may reduce the rate of

lending applications among potential and existing women business owners. Cater et al.

(1997) therefore hypothesised that “women-owned firms have lower levels of human

resources and less financial resources from outside sources than men-owned

businesses, increasing the odds they will discontinue”.

Access to Credit

The extension of credit to customers is one of the means by which small businesses can

stay in operation. However, this can become a problem to a number of small ventures

and in some cases contributing heavily to failure. According to Mayer et al. (1961:128)

the weak capital structure of small businesses requires that extension of credit be

carefully controlled. The reason given for this cautious approach is that, if customers do

not pay their debt on time or not pay at all, this will result in small businesses not being

able to pay their debts or replenish their stock (Mayer et al., 1961). The expectation is

therefore that if the small business does not receive money, they will have to close down

in order to avoid getting deeper into debt.

Ownership Structure

Literature on firm survival has also interrogated the effect of the ownership structure of

the firm on its survival prospects. According to Perez et al. (2004), limited liability firms

face a higher likelihood of market exit than firms with other legal structures. Stiglitz and

Weiss (1981) point out that entrepreneurs in limited liability firms pursue projects with

relatively higher expected returns which are in-turn associated with higher risk of failure.

Astebro et al. (2003) remark that sole proprietorships are expected to have higher survival

26

rates than other ownership structures because theoretically the costs of failure accrue

entirely to the owner and thus, this may influence the sole trader to maximise his or her

effort in a bid to survive. However, a differing view is that being a sole proprietor may not

afford one the sufficient resources in the form business partners who are expected to

bring along other value adding skills and expertise (Perez et al., 2004).

3.2.2 Environmental Factors

Interesting work regarding environmental conditions on the performance and survival of

new firms has been done over the years. According to Geroski et al. (2007), there are

two diverging streams of research regarding literature on firm survival. The first stream

originates in organisational ecology proposed by Hannan and Freeman (1977), whose

main argument is based on the concept of density (i.e. the population of firms relative to

the market size). This concept suggests that exit rate will rise with increases in the level

of competition during periods when markets are crowded and decrease with increases in

the legitimacy of businesses already in the market. The second stream of research is

based on evolutionary economics proposed by Nelson and Winter (1982) (as cited in

Geroski et al., 2007). Researchers in this field argue that during their life cycles,

industries go through a number of stages where technology and market conditions vary,

setting the standard as to how easy or difficult it would be for firms to enter and survive in

a particular market condition (Suarez & Utterback, 1995).

The environmental conditions as a basis for determining firm survival can further be sub

divided into industry specific and macro-economic conditions.

Industry Specific Conditions

Geroski et al. (2007) reports that organisational ecology scholars view concentration as a

force that increases mortality. At lower levels of density, a rise in the number of firms in

the market is expected to favour survival whereas after a certain threshold, further

27

increases in the number of firms may lead to increased competition and hence increased

chances of mortality (Geroski et al., 2007). Industrial organisation literature however,

advances a different view point on concentration (or density). On one side it argues that

collusion may arise as a result of market concentration thus creating room for economic

profits making it easier for firms to survive (Geroski et al., 2007). Entrants that manage to

survive their infancy period in the highly concentrated market would likely be accepted in

the group of more established firms and become protected under the umbrella of

concentration (Geroski et al., 2007).

On the other hand, more established firms in concentrated markets have higher profits to

defend and hence coordination among them gets easier (Geroski et al., 2007). The firms

in the concentrated market may therefore retaliate against new entrants creating a hostile

environment and making it difficult for them to survive (Bunch & Smiley, 1992). Geroski

et al. (2007) however, mention that because of the conflicting effects, the available

evidence linking market concentration and firm survival is somewhat inconclusive.

Regarding the extent of entry into a market, organisational ecologists are of the view that

markets with high entry rates are those in which the highest exit rates are expected

(Geroski et al., 2007). The argument is based on the idea that mass entry flows increase

the density in the market and hence the expectation of high exit rate as a consequence.

Industrial organisation economists on one hand argue that the same entry barriers such

as the magnitude of investment required, which make entry difficult in the first place also

hinder exit (Eaton & Lipsey, 1980). Lastly, evolutionary economists argue that there are

distinct stages in the industry evolution, with each stage exhibiting different entry and exit

rate characteristics (Geroski et al., 2007).

Macro-economic Conditions

The macroeconomic environment as one of the components of environmental conditions,

at the time of founding is also discussed. It is widely appreciated that the overall state of

28

the economy has a significant bearing on the outcome of business success or failure.

One major reason why current macroeconomic conditions matter is because current

conditions prevailing will most likely influence expectations in the future. Unfavourable

macroeconomic conditions such as declining GDP, high levels of: inflation, taxation,

unemployment and interest rates, associated with the inability to secure funds for survival

may lead some firms to cut their losses and exit the market. In view of this proposition,

Geroski et al. (2007:10) hypothesises that “unfavourable current macroeconomic

conditions decrease the probability of survival” or “favourable macroeconomic conditions

at founding persistently increase the probability of survival”.

3.3 EMPIRICAL LITERATURE

In their study focusing on the determinants of survival for Spanish manufacturing firms,

Perez et al. (2004) discovered that the probability of exit is higher for small firms as well

as for both younger and mature firms. In the Spanish study, firms between 11 and 25

years old and ages between 26 and 56 endured a significant lower risk of exit than those

suffered by the youngest firms of ages between 6 and 10 years old. The results were

consistent with existing empirical evidence on new firm survival by authors such as Mata

and Portugal (1992) and Dunne et al. (1989). Regarding firms older than 50 years, it was

found out that they faced worse survival conditions than younger firms, consistent with the

theory of liability of senescence by Hannan (1998). Further, the authors found out that,

exporting firms and firms involved in research and development (R&D) activities enjoyed

better survival prospects. The results on exporting activities were in line with findings by

Kimura and Fujii (2003) and Bernard and Jenson (2002) while those of investment in R&D

echoed Audretsch (1995) empirical findings. Another factor considered in the study was

that of economic activity performed by the firms. The results indicated that those firms

whose main activity was the production of finished goods endured a higher risk of failure

than firms producing intermediate goods. Perez et al. (2004) arrived at their conclusion

29

using both non-parametric techniques and the estimation of a Cox proportional hazards

model.

Aspelund et al. (2005) investigated to what extent the resources controlled by

entrepreneurs at a firm’s inception affect the new business’s ability to survive in Norway.

The overall results supported the hypothesis that initial resources influence a firm’s ability

to survive during adolescence. Contrary to the expectation that a larger team size

positively influences the probability of survival in new technology-based firms, the study

results indicated that smaller teams increased the likelihood of survival. Regarding the

hypothesis suggesting that entrepreneurial experience initially present in the founding

team would increase the probability of survival, the results from analysing the difference

between survivors and non-survivors were inconclusive in terms of entrepreneurial

experience. As for the hypothesis that the degree of technological radicalness increases

the chances of survival, the results came out as expected supporting the hypothesis. The

Cox regression model was used in the statistical analysis.

Another study in the context of Norway was done by Vinogradov et al. (2007). The

researchers investigated the survival rates of businesses founded by immigrants and

Norwegian natives. Entrepreneur’s human capital and venture characteristics were

expected to explain the differences between survival rates of immigrant and native

(Norwegian) founded businesses. The overall conclusion from the study indicated that

firms established by immigrants were less likely to survive compared to those founded by

their native counterparts. In regards to human capital characteristics, the logistic

regression analysis indicated that the level of entrepreneur education failed to explain the

differences in survival rate of immigrant and native founded businesses. Differences in

business ownership experience and work experience as measures of survival were also

found to be insignificant. Vinogradov et al. (2007) reported that the Norwegian study

results contradicted the propositions of the positive relationship between survival and

human capital, particularly in relation to the level of education attained.

30

In relation to venture start-up characteristics, the research findings from Norway indicated

that urban located firms and higher perceived product/service novelty were associated

with the lower survival rates of immigrant owned firms. The reason for perceived novelty

was raised because immigrants tend to introduce untraditional products and services to

the host country market, this leading to lower levels of success. Explanations were also

given for the negative relationship between urban location and survival. The first reason

cited was that major urban centres provide more alternatives when compared to rural

locations, and therefore immigrants in urban areas can easily abandon their businesses

for salaried jobs. Another reason provided was that the relatively dynamic urban

economic life and high levels of competition could be used as an explanation of increased

closure or exit by immigrant owned firms. The proposed hypotheses in the study were

tested using the logistic regression models.

A study on SME growth and survival in Vietnam by Hansen, Rand and Tarp (2004)

provided some interesting results. Regarding skills level of the entrepreneur, it was

discovered that there is no significant influence from being medium skilled or possessing

low entrepreneurial skills on survival rates of small to medium businesses in Vietnam.

The negative effect of being small on survival was also absent. In addition, gender of the

owner was not a significant contributor to growth or survival, contradicting Cater et al.

(1997) hypothesis that women-owned firms are often discriminated against and have

lower levels of human resources and less financial resources than men-owned

businesses, thus increasing their odds of discontinuance.

In relation to location, Hansen et al. (2004) found out that the probability of survival in

rural areas was higher than in urban areas. The reason given for this outcome was that

competition in urban locations is much higher than in rural areas and therefore firms view

this situation as a constraint to growth and survival in Vietnam. Lastly, the higher survival

rates in rural Vietnam were reported to be helped by the fact that firms in those areas

(rural) were oriented towards serving local markets thereby escaping some of the survival

31

risks exposed to larger and outward orientated firms located in urban areas. Little

significance on survival emerged when considering innovations and diversification,

contradicting other studies (e.g. Perez et. al, 2004) on the subject. The authors gave

reasons that Vietnam’s small firms are not yet subjected to severe competition pressures

from the outside world, and as such a domestic innovator will be copied by other local

firms, limiting the premium that could have been gained from innovation. The same result

was reflected for the degree of diversification in relation to firm survival. The findings of

the study were derived from sample selection models, also identified as probit and logit

models used in the data analysis.

Another identified predictor for small business survival in Africa and Latin America was

the sector in which the venture operated. Most small businesses in the retail sector faced

the highest risk of closure when compared to their counterparts in other sectors. In the

countries surveyed, location also played an important role in influencing small firm

survival. Small businesses in urban areas were reported to have a 25 percent greater

chance of survival than their rural counterparts. This is in contradiction to Hasen et al.

(2004) Vietnam study. In addition, small businesses located in commercial districts were

reported to be more likely to survive than those that operated from home. Another

revelation from the study was that female headed businesses were less likely to survive in

the long run as compared to the male-headed ones. The study notes that the relatively

high rates of closure for female headed small businesses were due to personal and non-

business failure reasons. However, gender of the entrepreneur was found to no longer

be a significant determinant when closures caused by pure business failures were

analysed, thus leading to a conclusion that in terms closures, both female-headed and

male-headed small business faced the same chances of survival or failure. The data

from countries in the study were analysed by estimating an ordinary least square model

for firm growth while survival was investigated using the Heckit model.

32

Bates (1995) analysed the survival rates among franchise and independent small

businesses in the United States using Logistic regression models. The results indicated

that the franchise characteristic, holding other things constant, was a highly significant

determinant of firm survival. Franchises, according to the study are much more likely to

go out of business than a cohort of independent firms. Further, the surviving firms were

those headed by highly educated owners who worked full time, affirming Bruderl et al.

(1992) propositions about the advantages of being a highly educated entrepreneur. The

surviving firms were larger in the sense that they began operation with greater owner

financial capital and labour input. When analysing retail firms separately, the results

reveal that the franchise characteristic is the strongest predictor of firm discontinuance i.e.

the retail franchise firms are more likely to fail than independent young retail businesses.

Astebro et al. (2003) investigated the relation between holding a bank loan at start-up and

the survival of new small businesses in the United States. Their results indicated that an

unconditional correlation between having a bank loan and the survival of small

businesses was negative. However, a regression model of a start-up business including

owner human capital, loan sources, industry and company characteristics showed that

having a bank loan, ceteris paribus, is a significant positive predictor of start-up business

survival. In contrast, unconditional correlation (correlation excluding other variables)

between having non-bank loans and survival come out positive. The study reported a

substantial number of start-ups with high survival rates that did not use bank loans, rather

opting for more use of other sources of borrowed capital. Evidence of self-selection

against commercial bank loans was observed on owners with high levels of human

capital, that is, a higher level of education, work experience and equity. Astebro et al.

(2003) however, mentioned that banks do not discriminate against loan applicants with

high levels of human capital and equity but rather start-up owners with high human capital

self-select against banks. Probit regression model was estimated for data analysis in the

study.

33

Audretsch’s (1995) study on the impact of technological innovation on entrant firms found

out that conditional upon having survived for a considerable amount of time (eight years),

firms still remaining in a highly innovative industry have a greater probability of surviving

for an additional number of years. This confirmed Geroski’s (1995:21) hypothesis that the

growth and survival of new firms will depend on their ability to learn about their

environment and how they link their strategies to the way that particular environment is

configured. Audretsch (1995) remarked that this provides new entrants with a higher

possibility of finding a niche and therefore compensating for scale economies and other

size-related disadvantages, but only if they had survived the first few years of entry. The

study results also indicated that neither the innovative environment nor the scale

economies, as proxied by the mean size of the largest firms in the industry, exerted any

influence on both the probability of survival and growth rates. The conclusion from this

study was that structural characteristics such as scale economies and product

differentiation (innovation) may serve as barriers to survival within the first few years after

entry however, the impact of these barriers are not permanent and diminish over time

subsequent to entry. The study results were arrived at by estimating logistic regression

models.

Carter et al. (1997) motivation for their study was based on the discrepancy between the

number of businesses owned by women and men, and their performance differences that

would ultimately lead to survival or discontinuance. The study particularly examined

whether the performance and survival differences could be explained by variations in

initial resources and founding strategy. The results were contradictory to expectations

that the lack of prior start-up experience in starting new businesses would negatively

affect survival of women businesses. The probability of male-owned businesses surviving

was significantly influenced by owner’s prior experience on new business start-up while

such experience did not impact female-owned businesses.

34

Cater et al. (1997) study results did not support the view that female-owned businesses

would be disadvantaged by starting their businesses ventures without business partners.

Actually neither forms of start-up resources allowed for in the study (industry experience,

start-up team, number of employees, credit from formal sources) impacted on the odds of

survival of female-owned businesses in retail. However, in male-owned retail businesses

that were started by a team, 73 percent of them discontinued. The study also considered

the nature of strategy-gender interaction. Women-owned businesses that adopted a

super achievers4 strategy showed a higher likelihood of survival. For male-owned

businesses, the choice of strategy had an insignificant impact on the discontinuance of a

business.

Geroski et al. (2007) observed and analysed the effects of founding conditions on the

survival of new firms in Portugal for a period of ten years. The researchers wanted to

investigate which founding conditions matter most and how long their effects would last.

The results of the analysis indicated that firms that are larger in size, in their initial year of

founding will survive longer and that any subsequent increases in firm size further

improves survival prospects. The finding was cited as being consistent with Jovanovic

(1982) view that firms adjust their choice of size as a result of observed performance in

past periods. The influence of human capital on firm survival was discovered to be

significant and nearly permanent. However, the subsequent changes in human capital

component did not impact expected survival chances.

The Portuguese study revealed that concentration at the time of entry had a strong effect

on the probability of firm exit. The concentration effect was said to vanish almost

immediately after entry had occurred, with the impact of subsequent changes in market

concentration being positive for firm survival, a finding that is consistent with the “trial by

fire” hypothesis by Swaminathan (1996). In regards to rate of entry, firms that were

founded in periods when other firms are also entering the industry in large numbers were

4 Businesses adopting the super achiever strategy attempt to be all things to all customers, exploiting a diverse

set of resources in the quest to attract and retain customers.

35

most likely to fail, with their survival prospects even lower if subsequent entry rates keep

on increasing. Lastly, Geroski et al. (2007) reported that survival prospects of firms born

during times of rapid economic growth were higher than for those founded in a period of

declining economic activity. The Cox proportional hazards model was estimated in the

data analysis and the reasons advanced for this choice was that such a model enables

the user to characterise the exit process more rigorously than is possible with

conventional approaches such as Probit and Logit models.

Audretsch (1991) study, based in the United States was aimed at testing the hypotheses

that new-firm survival is attributable to the technological regime, market structure, and the

extent of scale economies characterising an industry. The overall results supported the

hypotheses that new-firm survival is positively influenced by the technological regime.

There was also evidence that firm survival is significantly impacted by the extent of scale

economies and capital intensity characterising an industry. However, there were

differences emerging between the determinants used in the short run and the long-run,

that is, the influence of the technological regime and market structure on firm survival

varied with the time interval under observation.

According to Audretsch (1991), technological regime had no significant influence on firm

survival within a shorter period (four-years) subsequent to the founding of a new firm.

The positive and statistically significant coefficients of the measures of scale economies,

capital intensity and market concentration all indicated that high-minimum efficient scale

(MES) markets positively impact the ability of start-up firms’ survival in the short run.

When the observation time was extended, the results took a different direction. For high-

MES markets, the existence of high market concentration, scale economies and capital

intensity in the long run, were seen to impede firm survival, that is, a negative relationship

between survival and capital intensity, concentration and scale economies was

established. For each time period observation, the technological regime had either a

positive or negative influence on firm survival, which Audretsch (1991) suggested may be

36

attributed to a stage at which firms are positioned within the business cycle. Because the

dependent variable in the study varied between one and zero (dichotomous), ordinary

least squares estimation (OLS) was disqualified in favour of logit estimation, in view that

OLS would produce inefficient variances of the estimated coefficients rendering the

hypothesis tested unreliable.

Bekele and Worku (2008) sought to identify influential factors that affect the survival of

small enterprises in Ethiopia. The author’s main interest was to asses the degree of

importance of social capital for promoting viability and long term survival of small

business ventures in Ethiopia. Results from this study indicated that financial and non-

financial ]services obtained from social networking have highly benefited small firms. The

services facilitated the sharing of business skills, innovative ideas and alleviated the acute

shortage of finance. Based on the findings, Bekele and Worku (2008) reported that

participation in social capital and networking (iqqub schemes) was critically important for

long-term survival and that those businesses that did not regularly participate in the

schemes were found to be 3.5 times more likely to fail in comparison to businesses that

participated. Bekele et al. (2008) further reported that social capital has the potential for

enhancing performance in small businesses in three major aspects: it provides free input

of knowledge and business skills to production, reduces the cost of borrowing funds for

investment and research, and imposes informal pressure on entrepreneurs to work hard

and avoid failure. A semi-parametric model, that is, Cox proportional hazards model was

used in the data analysis.

Another study carried out on Ethiopia was done by Shiferaw (2009). The study objective

was to analyse the risk of exit for privately-owned manufacturing firms in Ethiopia. The

study took into account establishment and industry level characteristics to determine the

probability of market exit. The results from discrete-time hazard models indicated that: (i)

larger establishments are more likely to survive than smaller businesses which is similar

to what is observed in developed and few other African countries, (ii) surviving firms face

37

hazard rates that decline over time revealing that firms learn survival skills by staying in

the market, (iii) improving firm level productivity also significantly reduces the risk of exit,

and that (iv) hazard estimates show that women entrepreneurs are better at preventing

firm closure when compared to their male counterparts.

Liedholm (2001) investigated the determinants of survival and growth among small and

very small enterprises in Africa and Latin America. Among the determinants that

Liedholm (2001) considered were firm initial size, business sector, location and gender of

the entrepreneur. The study indicated that small business failure was at its peak before

the end of the first year in Botswana and Swaziland, and between the first and second

years in Kenya and Zimbabwe. The expected direct relationship between a small firm’s

initial size and its chances of survival, predicted by Jovanovic (1982) learning model of

firm growth was not supported by the empirical results. In countries such as Botswana,

Malawi, Swaziland and the Dominican Republic, firm size had no significant influence on

firm survival. Another significant finding from the study was that growing SMMEs were

more likely to survive than those that remained the same size. This was supported by a

case study from Zimbabwe where it was discovered that for every 1 percent increase in

the firm’s employment level, the SMME reduced its likelihood of closing down during the

year by approximately 5 percent (McPherson, 1996). SMMEs in the retail sector were

reported to face the highest risk of closure than their counterparts in real estate,

wholesale trade and wood processing sectors. In regards to location, SMMEs situated in

urban and commercial districts were reported to be more likely to survive than those in

rural areas. Liedholm (2001) indicates that the studies conducted in Africa and Latin

America utilised hazard analysis to ascertain the key determinants of firm closure and

survival.

Thus far, it appears there is little work on Botswana in the literature regarding studies on

SMME survival. One of the few papers that have looked at the issue of firm performance

is the work by Acquah and Mosimanegape (2006). The study sought to identify factors

38

that influence the performance of small business enterprises in Botswana. An ordinary

least squares (OLS) regression model was used to analyse the primary data collected in

order to determine the factors that influenced the performance (measured in total

revenue) of the business enterprises in Gaborone and surrounding towns and villages.

The results showed that start-up funds and total costs incurred by the business were

significant in having a positive influence on the performance of the business. In addition,

the results also indicated that improvements in business premises, finance, and business

start-up training contributed positively to total revenue, while improvements in

entrepreneurial skills, security and management competence contributed positively to the

performance of a business.

In another study by Tentime and Pansiri (2006), the authors investigated whether small

businesses need marketing to survive in Botswana. The study discovered four marketing

and finance problems that affect SMME performance. Findings from the study indicated

that local small businesses equate marketing with advertising and hence do not pull all

the necessary marketing tools to maintain and develop their customer base. Further, the

study indicated that the businesses lacked product and service marketing. Lastly, lack of

market research and information gathering were discovered to be prominent while

customer relationship management was identified as very weak.

3.4 CONCLUSION

As already highlighted in the analytical framework, there is no single pattern but rather a

multitude of factors that can influence business outcomes, particularly for start-up

ventures. A number of authors have used varying methodologies to predict firm survival

and failure. Some of the results from studies done affirmed what had been theoretically

hypothesised. However, the outcomes of some firm survival analyses were not

consistent with both the theoretical literature and results of empirical studies done in other

parts of the world.

39

From the literature, the main analytical methods used in firm survival analyses comprised

both parametric (logit, probit and ordinary least squares) and non-parametric (Cox

proportional hazards) techniques. The use of a particular survival model was based on

each individual or group of researcher's preference. Those who opted for the semi-

parametric, cox proportional hazards model justified their choice by indicating that the use

of other methods (parametric) leads to wastage of information and biases due to leaving

out censored5 cases. In the case for firm failure, the standard econometric analysis, such

as the ordinary least squares are said to ignore firms or units that are outside the

observation window and count them as if they have closed down and thus producing

misleading results. Non parametric models on the other hand are reported to allow the

researcher to control for both the occurrence of an event (i.e. whether a firm fails or not)

and the timing of the event (i.e. when market exit takes place), hence taking into account

the evolution of the failure risk and its determinants over time. Based on its reported

flexibility and accommodative nature, the Cox proportional hazard model will be estimated

in this study (Geroski et al., 2007, Bekele et al., 2008, Shiferaw, 2009).

5 A censored case refers to an event of interest (i.e. failure) not occurring within the observation period.

40

CHAPTER 4

METHODOLOGY

4.1 INTRODUCTION

This chapter presents the analytical framework adopted for the study as well as data

issues considered. Following earlier studies in the literature, the empirical work is based

on the survival analysis technique. The approach is deemed the most suitable in

empirically examining the determinants of firm survival (Audretsch & Mahmood, 1995;

Perez, Llopis & Llopis, 2004; Aspelund, Berg-Utby & Skjevdal, 2005).

4.2 CONCEPTS AND DEFINITIONS

Survival analysis techniques are used to examine the probability that a certain event will

occur within a specified time range or a given duration. According to Melnyk, Pagell,

Jorae and Sharpe (1995), survival analysis possesses two aims. On the one hand, the

aim is to estimate the time period during which an event6 can happen, while on the other

hand the interest is to describe the time distribution of the event and estimate

quantitatively the impact of independent variables (covariates) on the event. In utilising

the techniques of survival analysis, Perrigot, Cliquet and Mesbah (2004) identify the main

concepts of the methodology as: event, measurement window and censorship.

In survival analysis, a researcher seeks to model both the duration of time spent in the

initial condition and the transition to a subsequent state, that is, the event. The period of

time during which a researcher makes their observation defines the measurement

window. Choosing the length of a measurement window in an area of study is normally a

6 The event here refers to business failure.

41

personal and arbitrary choice of a researcher and as various studies indicate, results vary

as a consequence of the length of the observation window.

The other important concept is related to censorship. Censorship here, refers to

incomplete information regarding units under observation. Perrigot et al. (2004) remark

that the term censored means that the exact length of the duration is ignored because the

initial event date or the final event date is unknown. There are two main types of

censorship in survival analysis studies, right and left censorships.

Right censorship implies that the final date of the period separating the two events

studied cannot be determined. According to Perrigot et al. (2004), when data are right

censored, there is no ending point but just a starting time because the event has not yet

ended. For example, firms that are still operational or open at the end of the

measurement window will indicate the presence of right censored data.

Left censorship implies that the initial date of the event studied cannot be determined.

Left censorship presents a situation whereby there is data for which there is an ending

point but no information regarding when the item studied was initially exposed to the risk

(Melnyk et al., 1995).

4.3 ANALYTICAL FRAMEWORK

Literature on event history modelling or statistical analysis of failure-time-data indicates

that the core of the survival analysis methodology is based on two key concepts. These

two theories are the survivor and hazard functions respectively.

4.3.1 Survivor Function

The survivor function reflects the cumulative survival probabilities throughout the

observation time or measurement window (Perrigot et al., 2004). It is applied in settings

where the interest is in describing how long the study subjects are “alive” than how

42

quickly they “die” (Hosmer & Lemeshow, 1999). The survivorship function is therefore

defined as the unconditional probability that an event has not yet occurred at the time

period t. Therefore, )(tS , the survivor function may be represented as:

TobtFtTobtS (Pr1)(1)(Pr)( <

t

duuft0

)(1) (1)

where )(tF is the cumulative distribution function of the variable time T . )(tS

represents the proportion of units surviving beyond time t . At the start of observation

time, 0t and 1)0( S , indicating that all units under observation are surviving.

However, as time passes, the proportion of surviving units must decrease as units under

observation start to fail hence, )(tS is a strictly decreasing function (Box-Steffensmeier

& Jones, 2004). How the concept of failure and survival are linked to one another is

captured by the hazard rate (Box-Steffensmeier et al., 2004).

4.3.2 Hazard Function

The hazard function has two components. The first one is the concept of set at risk,

which is the set of units, individuals, organisations etc. in a sample exposed to risk, in

relation to an event occurring at a certain point in time (Perrigot et al., 2004). The second

concept is the hazard rate, also referred to as conditional failure rate. The hazard rate

gives the rate at which units under observation fail (or durations end) at time t , given that

the unit had survived until time t (Box-Steffensmeier et al., 2004). Using an example of

firm exit, the hazard rate may be defined as the probability that a firm exits the market at

time t having survived until a given time t conditional on a vector of covariates iX ,

which may involve both time varying and time constant variables. Given that )(th is

dependent on time as well as covariates, the hazard rate can be expressed as:

43

dt

XtTdttTtobXth i

dti

),/(Prlim),(

0

(2)

where T is a non-negative continuous random variable (failure time duration) and )(th is

an instantaneous exit rate (Perez et al., 2004).

Various statistical models may be constructed to describe the hazard rate, however,

modelling the hazard rate is dependent on the research question. This study focuses on

the relationship of an event (firm failure) and covariates of theoretical interest and less

interested in time dependency of the event. Cox proportional hazard model was favoured

over fully parametric methods because the distributional form of the duration times is left

unspecified. Geroski et al. (2007) indicate that the Cox proportional hazards model

enables the user to characterise the failure process more rigorously than is possible with

conventional approaches such as Probit and Logit models. Aspelund et al. (2005) also

argue that the method is advantageous because it avoids biases associated with

censoring and that the method is “informationally efficient”.

4.3.3 The Cox Proportional Hazards Model (CPHM)

The Cox proportional hazards model is also referred to as the Cox model, the

Proportional hazards model or the Relative risk model. According to Kalbfleish and

Prentice (2002), the Cox model has a nonparametric aspect because it involves an

unspecified function in the form of an arbitrary baseline hazard function. It is further

referred to as semi-parametric because it incorporates a parametric modelling of the

relationship between the failure rate and specified covariates.

By letting ),.....,,( 21 pi XXXX denote a collection of p explanatory variables that

affect survival time, the hazard function for the Cox proportional hazards model is

represented by:

44

)exp()(),( 0 ii XthXth (3)

However, in order to have a positive hazard function without any constraints on the

coefficient values, the Cox model takes the form:

p

i

ii XthXth1

0 exp)(),( (4)

The econometric measure of effect in the Cox regression is the hazard ratio, which

involves only vector coefficients of . Estimates of the s' are the maximum likelihood

estimates while )(0 th represents the baseline hazard function. The baseline function

involves t , but not the X variables. For the Cox proportional hazards model, )(0 th is

obtained by replacing all the variables in ),( Xth by zeroes (i.e. when 0iX ). Hosmer

et al. (1999) note that )(0 th characterises how the hazard function changes as a function

of survival time while )exp( ii X characterises how the hazard function changes as a

function of covariates.

The assumption of the proportional hazard requires that the hazard rate is constant over

time or similarly, the hazard for one individual is proportional to the hazard for any other

individual, where the proportionality constant is independent of time (Bekele et al., 2008).

As already indicated, the Cox model is non-parametric because )(0 th is unspecified and

in the Cox model, the hazard ratio )(HR for a subject with a set of predictors *X

compared to a subject with a set of predictors X is estimated by the following

expression:

p

i

iii XXXth

XthHR

1

**

)(ˆexp),(ˆ

),(ˆ (5)

45

Because the baseline hazard rate is left unspecified, the Cox regression models do not

have an intercept term. Expressed in a scalar form the Cox model appears in the form:

)(ˆ.....)(ˆ)(ˆexp)( *

2

*

221

*

11 pppi XXXXXXth (6)

where ̂ is a constant of proportionality and does not depend on the time t .

4.4 EMPIRICAL MODEL

The empirical model estimated for this study is given in this sub-section. The dependent

variable )(th represents the probability that a firm will exit the market at time t having

survived until a given time t , conditional on a vector of covariates represented by X .

The empirical model is based on the standard proportional hazards regression:

)

exp()(

161615151414131312121111101099

8877665544332211

XXXXXXXX

XXXXXXXXth

(7)

where )(th represents the hazard rate, - the coefficient estimates and X - the

covariates represented by:

1X = Age

2X = Age2

3X = Size

4X = (Gender) Female entrepreneur

5X = Work experience

6X = Self-employment experience

7X = Entrepreneurship training

8X = Secondary education

46

9X = Initial funding support

10X = Entrepreneur contribution

11X = Retail firm dummy

12X = Manufacturing firm dummy

13X = Agriculture firm dummy

14X = Construction firm dummy

15X = Sole proprietorship dummy

16X = Partnership dummy

The duration in the model is defined by: 0t , t and d where, 0t is the start date, t -the end

of the observation window and d represents failure. In this study, 0t is the starting date

for observing DOY funded firms, t - time at which observation ends and d represents

date at which firms are reported to have permanently exited the market. Because the

incomplete nature of the observation occurs in the right tail of the time axis, such

observations are said to be right censored. Our censoring indicator, represented by d is

defined as follows:

censoredrightif

exitmarketfailurefor

d0

/1

The choice of covariates to be included in the analysis has been determined by our prior

expectations based on theory and previous empirical studies. Their expected effects on

firm survival or failure are outlined below:

Our first prior expectation is that firms operated by older entrepreneurs face a lower risk of

failure when compared to firms run by younger entrepreneurs. Our second expectation is that

47

female-owned firms have lower levels of human resources and less financial resources than

male-owned businesses, increasing the odds that they will have shorter survival times. The

third expectation is that a new business venture owned by an entrepreneur with prior work

experience faces a higher likelihood of failure. Fourth; entrepreneurial experience possessed by

the business owner at start-up yields a greater probability of survival for a new business

venture. Fifth; a new firm with a highly educated owner will experience a lower probability of

failure than one with an owner with lower levels of education. Sixth; larger firms have lower

probabilities of failure when compared to smaller ones. Seventh; a higher amount of start-up

funding elevates the chances of firm survival. Eighth; entrepreneur personal contribution of

capital7 to the business at start-up increases the chances of survival for a new business

venture. Lastly; limited liability firms are expected to face a higher likelihood of market exit than

firms with other legal/ownership structures due to their risk taking tendencies.

From the empirical analysis results in chapter 5 our prior expectations will either be rejected or

be supported.

4.5 DATA ISSUES AND SAMPLING

The dataset used in this study was constructed using information extracted from the

Department of Youth (DOY) database. The DOY, is a department that falls within

Botswana’s Ministry of Youth, Sports and Culture. The database captures both individual

business and entrepreneur characteristics. The DOY database contains up to 1200

business ventures and fewer than 300 failed businesses as at the end of December 2009.

Considering that the database includes business projects that started from as far back as

2005 and that at the end of 2009, the Out of School Youth Grant scheme was phased out

and replaced by another similar but upgraded Youth Development Fund scheme, the

decision was made for the study observation window to fall between the beginning of year

2005 and the end of year 2009. The exact number of businesses is not provided

7 In accounting terms, capital refers to the amount of resources, fixed assets and liquid assets, supplied by the

business owner.

48

because the database updating process was reported by the administering organisation

to be irregular and thus the information in the database could not be fully relied upon.

Because of the gaps in the database, which is hosted by the headquarters office in

Gaborone, a decision to go out of Gaborone and visit district offices was made. The visits

to a selected number of districts was intended to consult the data files of those districts, in

an effort to collect additional details or fill in gaps in the central database.

Unlike the common practice where questionnaires are administered, either through face-

to-face interviews or by post etc., the data collection process was mostly done by

consulting the data files of the business ventures in the department. In addition to the

data obtained from the data base, a follow up was made by visiting district offices where

the benefit of looking at and verifying from files containing individual business and

entrepreneur information was made possible.

4.5.1 Sampling

The sampling was based on a two stage process. First, the urban and rural

categorization of locations, within which the district offices are located, was done.

Following the urban-rural stratification, a number of DOY district offices were identified.

The selection criterion for district offices was based on the perceived level of economic

activity and the population density in the targeted locations. In total there are 23 DOY

district offices and out of that number, seven district offices were selected for this study.

The issue of resource constraint inhibiting a nationwide survey also contributed

significantly in location selection. Gaborone and Francistown were selected to represent

the urban locations by virtue of the being the capital city and the second largest city in the

country respectively. The geographical positioning of the cities also brought some

variability to the study in that one city (Gaborone) is located in the southern part of the

country and closer to the Botswana-South Africa border while the other (Francistown) is

situated in the northern part, nearer to the Botswana-Zimbabwe border.

49

The locations under the rural category are Molepolole (Kweneng district), Serowe

(Central district), Palapye (Central district), Mochudi (Kgatleng) and Tlokweng (South

East). Owing to the resource constraint factor, not all district offices, especially the ones

in the rural category, were visited because some of the districts were considered to share

similar qualities such as infrastructural developments and population density with those

that had not been selected. Hence, the selected rural towns can be said to be a good

representation of the rural part of Botswana.

Regarding the business ventures sampled, we included all the businesses within the

selected districts that had failed. The economic activities represented in the database are

the services, agriculture, manufacturing, construction and retail sectors. For those

businesses that were in operation at the time of data collection, the basis for selection

was grounded on an attempt to mirror the failed firms in terms of the type of economic

activity. While there were a large number of records covering individual firms, not all the

business ventures made it into the final sample due to incomplete information

characterising some of the records. In all, a total of 271 businesses were sampled, with

122 failed firms and 149 currently operating.

4.5.2 Description of Variables

Table 1 below presents the selected variables and the description of the categorical and

continuous variables used in the analysis.

50

Table 1. Dependent, Independent and Control Variables

Variable Variable description

Dependent variable

Business survival Business reported to have survived until 2009.

(survived = 0, failed = 1)

Independent variables

Business specific characteristics

Age Number of months to failure or operating.

Size Number of employees at start-up.

Initial financial capital Start-up funding from DOY ranging between

BWP4,000.00 and BWP90,000.00.

Human capital characteristics

Highest education attained

Secondary education (=0), post-secondary education

(=1)

Work experience Yes (=1), no (=0)

Self-employment Yes (=1), no (=0)

Entrepreneurial training at start-up Yes (=1), no (=0)

Owner contribution at start-up Yes (=1), no (=0)

Entrepreneur age Age in years

Control variables

Location

Urban(=0), rural (=1)

Urban = municipality of100,001 people or more.

Rural = municipality of 10,000 people and less than

100,001.

Gender Male (=0), female (=1)

Industry dummies Retail (=1), otherwise (=0),

Services (=1), otherwise (=0)

Manufacturing (=1), otherwise (=0),

Agriculture (=1), otherwise (=0)

Construction (=1), otherwise (=0)

Ownership structure Sole proprietorship (=1), otherwise (=0)

Partnership (=1), otherwise (=0)

51

4.6 DESCRIPTION OF THE SAMPLE

The sample of 271 business ventures identified for the empirical analysis was made up of

122 (45 percent) failed businesses as at the end of 2009. On the other hand, 149 (55

percent) businesses were still in operation going into the year 2010. The summary

statistics below are used to summarise our observations and they are presented in two

broad themes. The themes are based on firm and industry specific characteristics as well

as entrepreneur specific characteristics.

4.6.1 Firm and Industry Characteristics

Table 2 presents a profile of businesses based on firm and industry characteristics.

There are various business sectors present in the sample, namely: retail, manufacturing,

services, agriculture and construction. The services sector was the most dominant at

(63.47 percent) of the sample while agriculture followed at (23.25 percent). Generally

Botswana’s manufacturing industry is still under developed and this showed in our

sample, at a figure of (8.86 percent). The retail sector was represented at a low (3.69

percent) while construction was even lower at (0.74 percent).

Given the urban and rural stratification of the sample, a total of 115 (42.44 percent)

businesses were from the urban centres whereas 156 (57.56 percent) businesses were

located in rural areas. Seven districts from where the businesses were sampled are also

represented. The percentage contributions of the each district to our sample are given in

the following order: Francistown (26.57 percent), Gaborone (15.87 percent), Mochudi

(9.93 percent), Molepolole (15.87 percent), Palapye (13.65 percent), Serowe (12.92

percent), Tlokweng (5.9 percent).

The ownership structure was mostly populated by businesses organised as sole

proprietorships, at a comparatively larger figure of (86.72 percent). There was not much

52

difference, in terms of percentage contribution to the sample, between the other types of

business ownership structures. Partnerships constituted only (7.01 percent) while private

limited companies were just behind at (6.27 percent). The recording of the each

business’s age was done in months. Business age depicts the number of months to time

of failure (or market exit) as well as the number of months for firms still operating as at

end of the observation window. In terms of size at start-up, all businesses sampled fell

under the category of micro enterprise (i.e. an enterprise which has less than six

employees including the owner), when using the Botswana SMME definition.

53

Table 2. Sample Distribution by Firm and Industry Characteristics

Characteristic Total number Percentage total (%)

Firm Status

Operating

149

54.98

Failed

122

45.02

Firm Location

Urban

Gaborone

72

26.57

Francistown

43

15.87

Rural

Mochudi

25

9.23

Molepolole

43

15.87

Palapye

37

13.65

Serowe

35

12.92

Tlokweng

16

5.9

Business sector

Retail

10

3.69

Manufacturing

24

8.86

Services

172

63.47

Agriculture

63

23.25

Construction

2

0.74

Ownership structure

Sole proprietorship

235

86.72

Partnership

19

7.01

Limited company

17

6.27

Firm size at start

1 employee

99

36.53

2 employees

129

47.60

3 employees

39

14.39

4 employees

4

1.48

Firm age

12 months and less (1 year)

17

6

13 - 24 months (2 years)

75

28

25 - 36 months (3 years)

79

29

37 - 48 months (4 years)

71

26

49 - 61 months (5 years) 29 11

54

4.6.2 Entrepreneur Specific Characteristics

Table 3 gives us a sample distribution by personal characteristics of entrepreneurs. First,

looking at the education and entrepreneurial training variables, just above a third (38

percent) of the entrepreneurs had acquired a certificate as their highest level of

education, constituting the average-highest level of education attained. Those who had

studied up to senior secondary level followed at (34.3 percent). Degree holders, the most

highly educated, comprised (20 percent) of the sample while business owners who

progressed as far as junior secondary, the least educated group, contributed a mere (0.7

percent). Basic entrepreneurial and management training prior to or just after the

business had started was also deemed an important variable in determining business

survival. There were 102 (37.6 percent) of entrepreneurs who had attended training

organised by the DOY while the numbers of those who did not get trained or where there

was no proof that they had indeed gone for training stood at 169 (62.4 percent).

Entrepreneur age in terms of years was represented in the following order: 20 to 24 (7

percent), 25 to 29 (66.4 percent) and 30 to 34 (26.6 percent). Based on gender

categorisation, the number of males was slightly higher at 143 (52.8 percent) compared

to those of females who were 128 (47.2 percent of the sample). Work experience and

self-employment elements ware also considered. The number of entrepreneurs who had

no prior work experience before starting a business stood at 175 (64.6 percent) against

those who were previously employed for a continuous period of 6 months and more. The

number of entrepreneurs who were previously employed stood at 96 (35.4 percent). Self-

employment experience of a potential business owner (entrepreneur) was also

documented. Entrepreneurs who had owned a business prior to applying for the DOY

grant were only 39 (14.4) compared to those with no self-employment experience, whose

number stood at 232 (85.6 percent).

55

Table 3. Sample distribution by Personal Characteristics of Entrepreneurs

Characteristic Total number Percentage total (%)

Entrepreneur age (years)

20 - 24

19

7

25 - 29

180

66.4

30 - 34

72

26.6

Entrepreneur gender

Male

143

52.8

Female

128

47.2

Highest education level

Junior secondary

2

0.7

Senior secondary

93

34.3

University/college certificate

103

38

University/college diploma

54

20

University/college degree

19

7

Work experience

Yes

96

35.4

No

175

64.6

Self-employment

Yes

39

14.5

No

232

85.6

Entrepreneurial training

Yes

102

37.6

No 169 64.4

Table 4 outlines the descriptive statistics on variables such as start-up funding,

entrepreneur age, firm age and firm size. Regarding start-up funding, the businesses

secured funding ranging from a minimum amount of BWP 4,000.00 and up to a maximum

amount of BWP 90,000.00. On average, BWP 44,333.59 was spent or disbursed as

start-up finance to establish the businesses. The entrepreneur ages, for those who

operated either a failed or operating business, ranged between 20 (minimum age) and 34

(maximum age) while the average entrepreneur age was 28 years (mean age). Most

businesses at start up operated with just 2 employees, representing the average number

of employees per business. For some businesses, only one person (minimum number)

was employed while others started with a slightly higher number of 4 employees

(maximum employee number). The maximum number of months while a business was in

56

operation is 61 (approximately 5 years) while the minimum number of months for an

operating business was 9 months. On average most business survived up to 31 months

(an equivalent of 2 years and 6 months).

Table 4. Descriptive Statistics OF Selected Variables

Measure Funding (BWP) Age (years) Months Firm Size

Mean 44333.59 27.89 30.80 2

Median 48857 28.00 30.00 2

Maximum 90000 34.00 61.00 4

Minimum 4000 20.00 9.00 1

Std. Dev. 10082.83 2.62 12.15 1

Skewness -1.65916 0.02 0.12 1

Kurtosis 8.023888 2.77 2.10 3

Jarque-Bera 409.3312 0.64 9.87 14

Probability 0 0.73 0.01 0

Sum 12014404 7559.00 8348.00 490

Sum Sq. Dev. 2.74E+10 1849.90 39858.63 144

Observations 271 271 271 271

From Figure 1 below, the smoothed hazard estimate illustrates the risk of failure

associated with firm age or time spent in the market. The ascending graph indicates an

increasing level of market exit-risk for firms. The level of risk increases up to 22 months

where the hazard is at its peak (measuring at 0.027) before it starts to drop. The level of

exit-risk continues to drop for firms that have spent more time in the market (i.e. for a

duration of more than 22 months) until it reaches a point where it is considered minimal.

57

Figure 1. Smoothed Hazard Estimate for DOY Funded Micro Enterprises.

58

CHAPTER 5

EMPIRICAL RESULTS

5.1 INTRODUCTION

In this chapter, results from our analytical framework are presented. The relationship

between our selected covariates and the outcome of interest, that is, business failure or

survival is investigated. The results, as indicated in the first chapter are intended to

contribute to policy decision making in Botswana’s SMME sector as well as to contribute

to the literature on SMME survival analysis in sub-Saharan Africa.

5.2 CPHM REGRESSION RESULTS

The Cox proportional hazards model (CPH) is estimated and the effects of covariates on

business survival are presented in terms of hazard ratios8. According to Box-steffensmier

et al.(2004), when the CPH model estimates take the form of a hazard ratio (HR),

interpretation of the results is such that: if the hazard ratio is less than one, an increase in

the variable associated with the coefficient reduces the risk (or hazard), thus resulting in

longer survival time. In contrast, a hazard ratio greater than one implies that the risk is

rising with an increase in the variable associated with the given coefficient estimate,

leading to reduced survival time. For hazard ratios closer to one, the resulting implication

is that the hazard rate is non-responsive to changes to the covariate, or the variable of

interest has no influence on the increase or decrease of the hazard.

Table 5 shows the main results obtained from estimating the CPH model and they are

discussed below. All coefficient estimates are interpreted while controlling for other

variables.

8 The hazard ratios are exponential coefficients rather than the coefficient estimated themselves.

59

Table 5: Cox Estimates of DOY Funded Micro Enterprises

Variable HR Std. err z p > z 95 % conf. interval

Entrepreneur age

Age 2.85*

1.83 1.63 0.10

(0.81, 10.00)

Age2

0.98*

0.01 -1.88 0.06

(0.96, 1.00)

Gender

Female entrepreneura

0.76 0.14 -1.45 0.15 (0.53, 1.10)

Human capital

Work experience 2.08***

0.42 3.65 0.00

(1.40, 3.09)

Self-employment experience 1.19 0.34 0.62 0.53 (0.68, 2.08)

Entrepreneurial training 1.50**

0.29 2.07 0.04

(1.02, 2.19)

Secondary education 0.87 0.19 -0.65 0.52 (0.56, 1.33)

Financial variables

Start-up grant

Contribution from entrepreneur

1.00**

0.56**

0.00

0.13

2.07

-2.42

0.04

0.02

(1.00,

(0.35,

1.00)

0.90)

Size at start-up 0.79*

0.11 -1.70 0.09

(0.61, 1.04)

Industry dummies

Retail

b 0.41

* 0.22 -1.67 0.10 (0.15, 1.17)

Manufacturingc

0.61 0.22 -1.35 0.18 (0.30, 1.25)

Agricultured

1.07 0.29 0.24 0.81 (0.63, 1.81)

Constructione

1.17 1.22 0.15 0.88 (0.15, 9.98)

Ownership dummies

Sole proprietorship

f 0.87 0.31 -0.38 0.70 (0.43, 1.77)

Partnershipg

0.68 0.32 -0.83 0.41 (0.27, 1.71)

N 271

Mean firm age (months) 30.8

Log likelihood -618.67

LR chi2

56.34

Global test (d.f.) 0.29

Link test

0.98 Notes: The first column gives the results from a CPH model. Coefficients are expressed in hazard ratios. The

Breslow method was used within the Cox framework to cope with the existence of ties and to get unbiased and

consistent estimates. Level of significance: * indicates 10 percent level;

** 5 percent;

*** 1 percent (2 tailed).

aThe

reference group is females; bthe reference category is the retail sector;

cthe reference category is the

manufacturing sector; dthe reference category is the agriculture sector;

ethe reference category is the

construction sector. For ownership dummies: fthe reference category is the sole proprietorhip set-up and

gthe

reference category is the partnership set-up. HR = Hazard Ratio.

60

5.2.1 Age

In relation to entrepreneur age, the hazard ratio value is 2.84. The hazard ratio value

indicates that businesses owned by younger entrepreneurs are twice more likely than

business operated by older owners to fail or exit the market. This is probably due to the

inexperience, high mobility and lack of commitment by young people, who are constantly

seeking out other opportunities. The evidence for such a claim in this study is supported

at 10 percent statistical significant level. Even though the hazard ratio is close to 1,

transforming the age variable to age2 showed significant evidence that firms run by older

entrepreneurs endure a 2 percent less risk of exit than the risk encountered by smaller

firms.

5.2.2 Gender

Regarding entrepreneur gender, female owned businesses are found to suffer a lower

risk of exit than male owned businesses,. The results indicate that the probability of

market exit for female owned businesses is 24 percent lower than that of male owned

businesses. The literature on gender-based firm survival, which reports that at times

there is overt discrimination against female owned businesses (Carter et al., 1997:127)

and that women may be disadvantaged in the capital markets (Tiggers & Green, 1994), is

refuted by the regression results. However, there is no significant evidence to support or

reject these results.

5.2.3 Human Capital

Our significant results, at 10 percent level, indicate that those businesses with owners

possessing employment experience (6 months and more) are twice (HR = 2.08) more

likely to close down in comparison to businesses owned by entrepreneurs with lower

levels of work experience. The argument behind this outcome may be due to that those

61

entrepreneurs who struggle in business will not be motivated stay because they have an

option to go for paid employment.

Regarding prior self-employment characteristics of business owners, the results

interestingly imply that business owners who have prior self-employment face 19 percent

more risk of failure than those who did not possess self-employment experience before

starting a new business. This is in contradiction to van Praag’s (2003:10) view that

experience in the same industry as the business venture gives a better chance of

survival. Examining these results closely, one can reason that when the opportunity to

source grant funding from the DOY arose, a lot of young people who were supposedly

operating struggling small business thought that by getting more money, their businesses

would expand and thrive. Further, this result illustrates that having some experience in

running a business in a particular sector or location does not translate into capability to

successfully run a business in a new sector which might also be in a new environment.

However, the results are not supported at 5 percent level of significance.

In relation to basic entrepreneurial training at start up, businesses run by trained

entrepreneurs endure a 50 percent higher risk of exit when compared to businesses

operated by untrained owners. This result can be explained by highlighting that the DOY

are required to organise entrepreneurial training for all grant recipients and hence there is

a large number of failed businesses which were also operated by trained owners.

Another argument for this result is that entrepreneurial training does not guarantee

business success but rather there are various other critical success factors in running a

thriving business. This finding is not significant at the standard level of significance.

Businesses owned by entrepreneurs where the highest level of education is secondary

school face a 13 percent lower exit risk than the risk suffered by those with a higher level

of education even though the results proved to be insignificant.

62

5.2.4 Financial Variables

The results of the CPHM regression suggest that start-up finance from the DOY had no

effect on survival or failure, neither refuting nor supporting our expectations in hypothesis

7. This is due to the hazard ratio being equal to one. Personal contribution of working

capital from the entrepreneur is regarded as important in estimating the risk of failure. In

that regard, there is significant evidence indicating that businesses which the owner had

made a contribution face a 44 percent lower risk of failure than the risk endured by those

businesses where no contribution from the owner was made. .

5.2.5 Firm Size

Results from the size variable indicate that larger businesses face a 21 percent lower risk

of market exit than the risk suffered by firms which are smaller in size, supporting

hypothesis 6. This finding is significant and echoes the liability of smallness theory (Mata

& Portugal, 2002; Bates, 1995).

5.2.6 Industry Dummies

Looking at the retail sector, businesses in this sector face a 59 percent lower exit risk

than the risk endured by non-retail businesses. The results are significant at 10 percent

statistical level. Apart from retail trade, the nature of economic activity or business sector

had no significant effect on the risk of failure.

5.2.7 Ownership Dummies

Firms under the sole proprietorship set up face a 13 percent lower failure rate when

compared to non-sole proprietorship businesses. However, these results are

insignificant. Still on the business orientations, partnerships endure an insignificant 32

percent lower market exit risk than the risk suffered by non-partnerships. Results from

63

both ownership dummies (sole proprietorships and partnerships) suggest that businesses

of the opposite ownership structure endure a higher probability of failure even though the

results are not statistically significant. It can therefore be inferred that the results from the

ownership structure covariates, as determinants of firm survival or failure, are

inconclusive.

5.3 DIAGNOSTICS

For an empirical study such as this one, it is necessary to do some specification tests.

First, the proportional hazards assumption which posits that the effect of a given covariate

does not change over time is tested. If this assumption is violated, the CPH model is

considered invalid. The global proportional hazards test is used and yields a p value of

0.29, which is not significant, implying that there is an absence of evidence to contradict

the proportionality assumption. The likelihood ratio (LR) test was used to assess the

overall fit of the model. The p value from the likelihood ratio test was small (0.0000 <

0.01), thereby showing that all the 16 variables constituting the fitted CPH model were

significantly related to survival time at the 1 percent level of significance. Lastly, the

“Link” test specification uses the fitted values from the CPH model to predict the

dependent variable. Failing the link test is a sign that the model is incorrectly specified.

The link test results produced a p value of 0.98, failing to reject the null hypothesis that

the model is correctly specified.

64

CHAPTER 6

CONCLUSION AND RECOMMENDATIONS

6.1 CONCLUSION

The motivation to conduct this study was based on getting an insight into the SMME

sector in Botswana and to document the efforts, both institutional and personal, put in

place to try and promote local businesses, particularly the smaller ones. Specifically, it

was even more important to find out what influences small business survival or failure in a

mineral resource dependent country such as Botswana. The expectation is that upon

completion of the study, significant results would be produced and recommendations

made in order to inform policy decision making based on the findings. Data collected

from a cohort of micro enterprises supported by the DOY was used in the analysis.

Survival analysis techniques, the estimation of the CPH model in particular, were used to

produce results that inform us of the influential factors contributing to survival or failure of

micro enterprises in Botswana, specifically the ones that were given institutional

assistance and monitored over time.

This study took into account entrepreneur, firm and industry specific characteristics as

predictors of market exit and survival. The significant results show that businesses run by

younger entrepreneurs face a higher risk of market exit than the risk faced by those run

by older entrepreneurs. Regarding gender of the business owner, the claim that female

operated businesses face a higher probability of failure when compared to businesses

run by males was not supported. The study’s results indicate that female owned

business suffer a lower risk of market exit than the risk suffered by male owned

businesses. As a component of human capital, a personal contribution of business

premises, equipment or other tangible contributions at start-up, by the entrepreneur

proved to be a significant predictor for firm survival. Businesses where the owner had

65

made a contribution stood a better chance of survival in comparison to businesses where

no contribution by the owner was made.

The significant results also indicated that possessing employment experience had a

negative impact on business survival. The argument presented for this outcome is that if

one has had an opportunity to be employed and earned a salary, options are always

going to be available if a change in vocation does not work out. Such is the case that

entrepreneurs who struggle in business will not be motivated to stay because they have

an option to go back to salaried employment. The educational attainment variable failed

to explain the survival differences between entrepreneurs who had only studied up to

secondary school and those who were tertiary institution educated.

Before starting a business, an entrepreneur ought to be adequately prepared and if

necessary undergo some kind of entrepreneurial and managerial training as this can

positively influence business survival. Contrary to this view, the significant results from

this study showed that businesses run by those who had undergone basic training in

entrepreneurship and managerial skills endured a higher risk of failure. However, it could

not be established as to how relevant this kind of training was to the needs of the owners

at the time. Further, one can argue that the training organised in a seminar or workshop

setting could not have adequately prepared the owners to deal with real life challenges of

running a successful business. Managing to secure a substantial amount of start-up

funding is supposed to lift the burden of financial constraints on investment activities and

other activities that require financial expenditure. We see from the study results that the

amount of start-funding, either a higher or lower amount, had no influence on survival or

failure prospects of the business projects.

66

6.2 RECOMMENDATIONS

As a result of this study, a number of practical policy responses are suggested. There is

a greater need for policy makers to focus on human capital requirements of beneficiaries

of government assistance schemes in order to increase the success rate of SMMEs.

Firstly, instead of just providing general basic training to beneficiaries, relevant training

should be matched with the proposed business projects. Even though the beneficiaries

are out-of-school and unemployed youth, a greater commitment to their ventures is

required. This can be done by making some kind of personal contribution to the business

as the study has already highlighted the benefits of doing this.

The government of Botswana through the DOY has taken a positive step by motivating

beneficiaries to stay committed to their business projects by replacing the full grant

scheme with a 50 percent loan and 50 percent grant structure. Lastly, the capacity to

perform continuous monitoring and mentoring of the small DOY funded businesses

should be increased within the department or alternatively outsourcing of the requisite

skills should be considered. It is important to ensure that initial decisions taken at

founding are the correct ones. Studies have indicated that these decisions have a long

lasting effect upon survival of new businesses and if correct decisions are not made in the

initial stages it may be insufficient to produce the desired improvements to increase the

chances of survival.

6.3 LIMITATIONS AND DIRECTIONS FOR FUTURE RESEARCH

Resource constraints inhibited a much broader and comprehensive study that would have

covered the entire districts represented in the DOY database. In addition, the businesses

in our sample only included micro enterprises, presenting a missed opportunity to analyse

other businesses that fall within the SMME sector. Further, a preferable sampling strategy

would have been to randomly select firms at both location and sector level, however, due

67

to the information gaps characterising the DOY database from which the information was

collected, certain firms had to be selected while leaving out others from the sample. As

such, this introduced some element of subjectivity in the sample selection process

discussed in Chapter 4 and in that regard, it can therefore be inferred that the findings

from study are only applicable to the group of firms in the sample. In conclusion, a similar

research by other business development organisations such as LEA and CEDA, which

are mandated by the government to promote SMME development, should be replicated in

future in order to unravel the determinants of success and failure facing local SMMEs.

68

LIST OF REFERENCES

Acquah, B.K. & Mosimanegape, B. 2007. Factors contributing to the performance of small

business enterprises in Botswana. a case study of Gaborone and environs,

Gaborone, 6 June 2007, 1-23. Gaborone: BIDPA.

AfDB & OECD. 2005. African Economic Outlook: Botswana. [Online] Available:

http://www.oecd.org/dataoecd/14/36/40573959.pdf [Accessed 5 February 2010].

Aspelund, A., Berg-Utby, T. & Skejevdal, R. 2005. Initial resources influence on new

venture survival: A longitudinal study of new technology-based firms.

Technovation, 25: 1337-1347.

Astebro, T. & Bernhardt, I. 2003. Start-up financing, owner characteristics, and survival.

Journal of Economics and Business, 55: 3030-3319.

Audretsch, D.B. 1991. New-firm survival and the technological regime. The Review of

Economics and Statistics, 73(3): 441-450.

Audretsch, D.B. 1995. Innovation, growth and survival. International Journal of Industrial

Organization, 13: 441-457.

Audretsch, D.B. & Mahmood, T. 1995. New firm survival: new results using a hazard

function. Review of Economics and Statistics, 77(1): 97-103.

Barney, J. 1991. Firm resources and sustained competitive advantage. Journal of

Management, 17: 34-56.

Bates, T. 1995. Analysis of survival rates among franchise and independent small

business start-ups. Small Business Economics, 7(377): 377-388.

69

Bekele, E. & Worku, Z. 2008. Factors that affect the long-term survival of micro, small

and medium enterprises in Ethiopia. South African Journal of Economics, 76(3):

548- 567.

Bernard, A.B., & Benson, J.B. 2002. The deaths of manufacturing plants. NBER Working

Paper Series, number 9026, Cambridge.

BIDPA. 2007. Small, medium and large enterprises database, sector mapping and

validation in Botswana: findings and strategies for Local Enterprise Authority in the

Republic of Botswana.

Bosma, N, van Praag, M. & de Wit, G. 2004. The value of human and social capital

investment for the business performance and start-ups. Small Business

Economics, 23(3): 227-236.

BOCCIM. 2010. Services offered by BOCCIM. [Online] Available:

http://www.boccim.co.bw/boccim-about-us.php?title=Services [Accessed 10 March

2010].

Box-Steffensmeier, J.M. & Jones, B.S. 2004. Event history modelling: a guide for social

scientists. Cambridge: University Press.

Bruderl, J., Preisendorfer, P. & Ziegler, R. 1992. Survival chances of newly founded

business organisations. American Sociological Review, 57: 227-242.

Bunch, D. and Smiley, R. 1992. Who deters entry? evidence on the use of strategic entry

deterrents. Review of Economics and Statistics, 74: 509-521.

Carter, N.M., Williams, M. & Reynolds, P.D. 1997. Discontinuance among new firms in

retail: the influence of initial resources, strategy and gender. Journal of Business

Venturing, 12: 125-145.

CEDA. 2007. Annual Report.

70

CEDA. 2009. Annual Report.

Dunne, T., Roberts, M.J., & Samuelson, L. 1989. The growth and failure of US

manufacturing plants. Quarterly Journal of Economics, 104: 671-698.

EAOB. 2010. Services. [Online] Available: http://www.exporters.bw/servfac.htm

[Accessed 15 March 2010].

Eaton, B. & R. Lipsey. 1980. Entry barriers are exit barriers: the durability of capital as a

barrier to entry. Bell Journal of Economics, 11: 721-729.

European Commission. 2005. The new SME definition: user guide and model declaration.

[Online] Available:

http://ec.europa.eu/enterprise/policies/sme/files/sme_definition/sme_user_guide_e

n.pdf [Accessed 6 February 2010].

Fichman, M. & Levinthal, D. 1991. Honeymoons and the liability of adolescence: a new

perspective on duration dependence in social and organisational relationships.

Academy of Management Review, 16: 442-468.

Freeman, J., Carroll, G.R. & Hannan, M.T. 1983. The liability of newness: age

dependence in organizational death rates. American Sociological Review, 48(5):

692-710.

Geroski, P. 1995. What do we know about entry? International Journal of Industrial

Organisation, 13: 421-440.

Geroski, P.A., Mata, J. and Portugal, P. 2007. Founding conditions and the survival of

new firms. Druid Working Paper, 07-11:1-38.

71

Gimeno, J., Folta, T. and Woo, C. 1997. Survival of the fittest? entrepreneurial human

capital and the persistence of underperforming firms. Administrative Quarterly, 42:

750-783.

Hannan, M. 1998. Rethinking age dependence in organisational mortality: logical

formalisations. American Journal of Sociology, 104: 126-164.

Hannan, M. and Freeman, J. 1977. The population ecology of organizations. American

Journal of Sociology, 82: 929-964.

Hansen, H., Rand, J. & Tarp, F. 2004. SME growth and survival in Vietnam: did direct

government support matter? Discussion Papers 04-13, University of Copenhagen.

Department of Economics. [Online] Available: http://www.econ.ku.dk/ [Accessed 10

March 2010].

Hosmer, D.W. & Lemeshow, S. 1999. Applied survival analysis: regression modelling of

time to event data. New York. Wiley-Interscience.

Jovanovic, B. 1982. Selection and the evolution of industry. Econometricia, 50: 649-688.

Kalbfleish, J.D., & Prentice, R.L. 2002. The statistical analysis of failure time data. New

Jersey. John Wiley & Sons.

Kimura, F. & Fujii, T. 2003. Globilization activities and the rate of survival: panel data

analysis of Japanese firms. NBER Working Paper Series 10067, Cambridge.

Liedholm, C. 2001. Small firm dynamics: evidence from Africa and Latin America. World

Bank Institute, stock no. 37189.

Lucas, R. E. 1978. On the size distribution of business firms. Bell Journal of Economics,

9(2), 508-523.

72

LEA. 2008. The Dichotomy within the institutional framework of SMME development in

Botswana. [Online] Available:

www.africagrowth.com/2008%20SMME%20presentations/LEA.ppt [Accessed 5

February 2010].

LEA. 2010. Services. [Online] Available: http://www.lea.co.bw/article.php?id_mnu=2

[Accessed 20 February 2010].

NDB. 2008. Annual Report.

Mata, J. & Portugal, P. 2002. The survival of new domestic and foreign owned firms.

Strategic Management Journal, 23: 323-343.

Mayer, K.B. & Goldstein, S. 1961. The First Two Years: Problems of Small Firm Growth.

Washington: Small Business Administration.

McPherson, M.A. 1996. Growth of micro and small enterprises in Southern Africa. Journal

of Development Economics, 48: 253-277.

Melnyk, S.A., Pagell, M., Jorae, G. & Sharpe, A. S. 1995. Applying survival analysis to

operations management: analyzing the differences in donor classes in the blood

donation process. Journal of Operations Management, 13: 339-356.

Perez, S.E., Llopis, A.S. & Llopis, J.A.S. 2004. The determinants of survival of Spanish

manufacturing firms. Review of Industrial Organisation, 25: 251-273.

Perrigot, R., Cliquet, G. & Mesbah, M. 2004. Possible applications of survival analysis in

franchising research. International Review of Retail, Distribution and Consumer

Research, 14(1): 129-143.

Republic of Botswana. 1997. Long term vision for Botswana. Gaborone: Government

Printer.

73

Republic of Botswana. 1999. Policy on small medium and micro enterprises in

Botswana: Government White Paper no. 1. Gaborone: Government Printer.

Republic of Botswana. 1998. Small, medium and micro enterprises Task Force Report.

Gaborone: Government Printer.

Republic of Botswana. 2003. Small business act, no. 7 of 2004.

Republic of Botswana. Ministry of Education. Department of Vocational Education and

Training. 2010. Botswana technical education programme. [Online] Available:

http://www.moe.gov.bw/index.php?id=537 [Accessed 17 March 2010].

Republic of Botswana. Ministry of Trade and Industry. 2010. Small Business Council.

[Online] Available:

http://www.mti.gov.bw/index.php?option=com_content&view=article&id=12:mm-

small-business&catid=2:about-mm [Accessed 12 March 2010].

Republic of Botswana. Ministry of Trade and Industry. 2010. Small Business Promotion

Agency. [Online] Available:

http://www.mti.gov.bw/index.php?option=com_content&view=article&id=78:small-

business-promotion-agency&catid=54:about-entrepreneurship [Accessed 12 March

2010].

Republic of Botswana. Ministry of Youth, Sports and Culture. Department of Youth. 2010.

Youth Development Fund. [Online] Available:

http://www.mysc.gov.bw/youth/index.php/youth-development-fund [Accessed 12

March 2010].

74

Sentsho, J., Maiketso, J.T., Sengwaketse, M., Ndzinge-Anderson, V. and Maiketso, T.

2007. Performance and competitiveness of small and medium sized manufacturing

enterprises in Botswana. Gaborone: Printing & Publishing Company Botswana.

Shiferaw, A. 2009. Survival of private sector manufacturing establishment in Africa: the

role of productivity and ownership. World Development, 37(3): 572- 584.

Stiglitz, J. and Weiss, A. 1981. Credit rationing in markets with imperfect information.

American Economic Review, 71: 393-410.

Suarez, F. and Utterback J. 1995. Dominant designs and the survival of firms. Strategic

Management Journal, 16: 415-430.

Swaminathan, A. 1996. Environmental conditions at founding and organizational

mortality: a trial by fire mode. Academy of Management Journal, 39: 1350-1379.

Tentime, Z.T. & Pansiri, J. 2006. A proactive marketing and financial management for

small and medium enterprises. BIAC Journal, 53-67.

Tiggers, L.M. & Green, G.P. 1994. Small business success among men and women

owned firms in rural areas. Rural Sociology, 59(2): 289-310.

Ucbasaran, D., Lockett, A., Wright, M. & Westhead, P. 2003. Entrepreneurial founder

teams: factors associated with member entry and exit. Entrepreneurship Theory

and Practice, 28(2): 107-127.

Van Praag, M. 2003. Business survival and success of young small business owners.

Small Business Economics, 21: 1-17.

Vinogradov, E. & Isaksen, E.J. 2008. Journal of Developmental Entrepreneurship, 13

(1): 21-28.

75

Watson, K., Hogarth-Scott, S. & Wilson, N. 1998. Small business start-ups: success

factors and support implications. International Journal of Entrepreneurship

Behaviour & Research, 4(3): 217-223.

World Bank. 2007. An Assessment of the investment climate in Botswana: Main Report,

vol. 1.

WBCSD. 2007. Promoting small and medium enterprises for sustainable development.

[Online] Available:

http://www.wbcsd.org/Plugins/DocSearch/details.asp?DocTypeId=25&ObjectId=Mj

U1MTM [Accessed 10 February 2010].