Entrepreneurship in Post-Conflict Sri Lanka: Micro Level Evidence from Two Cities

Entrepreneurship in Post-conflict Sri Lanka: Micro Level Evidence from Two Cities

Kapila Chaminda Senanayake Maddumage

A thesis in fulfilment of the requirements for the degree of

Doctor of Philosophy

School of Business

University of New South Wales, Canberra

January 2015

my beloved mother, the memory of my late father,

my mother-in-law, and my wife, Nadeeka and my children, Devmina and Sithumi

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Acknowledgements

I wish to thank several people and institutions that have made this thesis possible. Most importantly, it has been a great pleasure to work with my supervisor, Prof. Satish Chand, who gave me the courage and confidence to work independently from the beginning of my candidature. I could not have asked for a better supervisor in terms of ideas, comments and suggestions and, without his guidance and advice, this thesis would not have been completed. I thank him for allowing, and reminding me, to always do what felt right. After one year of my PhD journey, I wanted to work with Prof. Premachandra Athukorala and was overwhelmed with happiness when he agreed to be my co- supervisor. My heartfelt gratitude also goes to Dr Keiran Sharpe who, with no hesitation, kindly reviewed my complete thesis.

I would like to thank the faculty and staff of the School of Business at UNSW Canberra for providing me with the logistical and necessary support. I gratefully acknowledge the receipt of an Australia Awards Scholarship (AAS) which allowed me to conduct full-time research. I also wish to thank its staff, particularly Ms Tatjana Kroll and Mr Mathew Brayen, for the efficient and kind support they extended to me during my candidature. I am also greatly indebted to Mr S.R. Attygalle, Deputy Secretary to the Treasury, Sri Lanka, who provided the support to achieve this endeavour.

I am grateful for the invaluable feedback provided by the staff of the ALL Unit, especially Dr Maria Schroder who tirelessly read my thesis. Also, my heartfelt thanks to Ms Denise Russell who offered commendable feedback and Ms Belinda Henwood for her exceptional proof-reading. I also thank the participants in the seminar at the World Conference on Entrepreneurship held in Dublin, Ireland, for their comments and suggestions regarding my conference paper and the participants in the Hong Kong conference for their feedback.

I extend my heartfelt gratitude to those who helped me in my fieldwork in Jaffna and Kilinochchi, Mr S. Mohanabawan, Mr K.K. Sivachchandran and all the anonymous entrepreneurs and research assistants involved in this project, for their precious time and tireless assistance. This research would not have been possible without their enthusiastic ground support. I am really indebted to them for caring for me despite the fact that this was my first journey to those areas and I hope to see them all soon.

Finally, I am indebted to my beloved wife, Nadeeka, and beautiful children, Devmina and Sithumi, who remained calm and supported me throughout my journey. Devmina is now aged six and Sithumi is similar to the age of my thesis, while Nadeeka played the two roles of mother and father to them. My children repeatedly asked “When are you going to play with us?” and now the time has come. Among others, I also want to thank Joan for her encouragement.

Abstract

Civil wars cause considerable harm to the local community with large adverse spill-overs on the neighbourhood and the broader international community (World Bank 2011). The cessation of conflict offers the opportunity for economic revival. The window of peace provides the space for the birth and growth of enterprises that would not have emerged otherwise. However, the contribution of private enterprises in socio-economic revival following the cessation of the conflict has received little attention in the extant literature. Also, little is known about the motivations for, and the constraints to, growth of private enterprise. This study uses the case of post-conflict Sri Lanka to investigate the entrepreneurs’ motivations for starting a business and the contributions of such enterprises to the socio-economic revival of the conflict-afflicted community. This is the first such study of the revival of private enterprise in post-conflict Sri Lanka.

The study uses data collected in 2012 via a purpose-designed survey administered to 243 emerging entrepreneurs. The data was gathered from the two cities of Jaffna and Kilinochchi in Northern Sri Lanka and analysed to: (i) examine the differences between ‘employer entrepreneurs’ and ‘solo self-employed entrepreneurs’ to decipher the motivations for starting a business; (ii) explore the differences between ‘necessity- motivated’ and ‘opportunity-motivated’ entrepreneurs, employing a probit model; and (iii) investigate the dynamics of embryonic enterprises, using least absolute deviation (LAD) and ordinary least square (OLS) regressions.

The six major findings are: (i) some 80% of entrepreneurs are necessity driven, triggered mainly by unemployment; (ii) the relationship between education and entrepreneurship is an inverse U-shape; (iii) financial constraints constitute a major obstacle to entrepreneurial activity; (iv) inter-ethnic trade with the south improves the likelihood of becoming an employer entrepreneur; (v) faster growth was experienced by businesses in the construction and related manufacturing sector which were also the first to emerge following the restoration of peace; and (vi) social networks are critical for entrepreneurial activity. The results provide a basis for policy discussion concerning the revival of entrepreneurship in, and possibly beyond, Sri Lanka.

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Publications or presentations derived from this thesis

Maddumage, K & Chand, S (in press), ‘Small firm growth in post-conflict Sri Lanka: micro-level evidence from two cities’, International Journal of Economic Policy in Emerging Economies, forthcoming (a shorter version of Chapter 7)

Maddumage, K & Chand, S 2014, ‘Emerging post-conflict entrepreneurs: evidence from Sri Lanka’ a paper presented at the ICSB World Conference on Entrepreneurship Dublin, June 11-14, under review for publication in the Journal of Business and Entrepreneurship (a shorter version of Chapter 5)

Table of Contents Acknowledgements ...... vii Abstract ...... ix Table of Contents ...... xiii List of Tables ...... xxiii List of Figures ...... xxv List of Abbreviations ...... xxvii Chapter 1 ...... 1 Introduction ...... 1 1.1 Motivation ...... 1 1.2 Entrepreneurship in a Post-conflict Setting ...... 3 1.3 The Context ...... 6 1.4 Research Objectives and Methodology ...... 7 1.5 Structure of Thesis and Main Findings ...... 8 Chapter 2 ...... 11 Literature Review: Entrepreneurship in Post-conflict Settings ...... 11 2.1 Introduction ...... 11 2.2 Civil Wars ...... 12 2.2.1 What is a Civil War? ...... 12 2.3 Causes, Duration, Consequences and Termination of Civil War ...... 13 2.3.1 Causes of Civil War ...... 13 2.3.2 Duration and Consequences of Civil War ...... 16 2.3.3 Termination of Civil War ...... 17 2.4 Post-conflict Economic Recovery ...... 19 2.4.1 Effects of Foreign Aid in Post-conflict Settings ...... 20 2.5 Peace Dividend ...... 22 2.5.1 The Impact of the Peace Dividend on Economic Outcomes ...... 22 2.6 Overview of Entrepreneurship ...... 23 2.6.1 Definitions of Entrepreneurship ...... 26 2.6.2 Definition of Entrepreneur ...... 26 2.6.3 Contributions of Entrepreneurship to Economic Development ...... 28 2.6.4 Empirical Evidence from Macro- and Micro-level Studies ...... 31 2.6.5 Empirical Evidence of Entrepreneurship in Conflict and Post-conflict Settings ...... 32 2.7 Focus of Research ...... 34 2.8 Conclusion ...... 35 xi

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Chapter 3 ...... 37 Sri Lanka’s Contextual Background ...... 37 3.1 Introduction ...... 37 3.2 Geographical, Historical, Ethnic, Political and Economic Background to the Conflict in Sri Lanka ...... 38 3.3 Economic Developments 1948-2009 ...... 43 3.4 Ending of Civil Conflict in Sri Lanka and the Aftermath in the Northeast ...... 49 3.4.1 The Costs of Conflict ...... 50 3.5 Post-conflict Economic Performance ...... 53 3.6 Conclusions ...... 58 Chapter 4 ...... 61 Research Methodology and Data ...... 61 4.1 Introduction ...... 61 4.2 Research Design ...... 62 4.3 Research Setting ...... 64 4.4 Questionnaire Design ...... 65 4.5 Sample Selection Process and Sampling Method ...... 66 4.6 Major Survey and Data Analysis ...... 67 4.7 Challenges of Data Collection in a Post-conflict Environment ...... 67 4.7.1 Security Concerns...... 68 4.7.2 Building Trust with Key Stakeholders ...... 68 4.7.3 Recruiting Research Assistants ...... 68 4.7.4 Local Context ...... 69 4.7.5 Respondents’ Attitudes ...... 69 4.7.6 Time and Costs ...... 70 4.8 Sample Description and Summary Statistics ...... 70 4.9 Conclusions ...... 73 Chapter 5 ...... 75 Emerging Post-conflict Entrepreneurs ...... 75 5.1 Introduction ...... 75 5.2 Determinants of Entrepreneurship – Brief Review of Literature ...... 76 5.2.1 Personal Characteristics ...... 77 5.2.2 Wealth and Access to Finance ...... 79 5.2.3 Business Environment ...... 81 5.3 Data and Empirical Strategy ...... 82 5.3.1 Data ...... 82 xiii

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5.3.2 Summary Statistics ...... 82 5.3.3 Empirical Strategy ...... 82 5.4 Empirical Results...... 87 5.4.1 Probit Models: the Characteristics of Employer and Solo Self-employed Entrepreneurs ...... 89 5.4.2 Estimation Results – Probit Models: Access to Finance ...... 92 5.4.3 Robustness Checks ...... 94 5.5 Discussion ...... 95 5.6 Conclusions ...... 95 Chapter 6 ...... 99 Necessity and Opportunity Entrepreneurs ...... 99 6.1 Introduction ...... 99 6.2 Theoretical Perspective – Necessity versus Opportunity Entrepreneurship ..... 100 6.3 Factors Influencing Entrepreneurial Motivation – Brief Review of Literature .... 102 6.4 Data and Empirical Strategy ...... 104 6.4.1 Data ...... 104 6.4.2 Descriptive Statistics ...... 106 6.4.3 Empirical Strategy ...... 106 6.5 Estimation Results ...... 109 6.5.1 Personal Characteristics ...... 113 6.5.2 Inter-ethnic Trade Relationship ...... 113 6.5.3 Wealth and Access to Finance ...... 114 6.5.4 Robustness Checks...... 114 6.6 Conclusions ...... 115 Chapter 7 ...... 119 Determinants of Small Firm Growth in Post-conflict Sri Lanka ...... 119 7.1 Introduction ...... 119 7.2 What are Micro and Small Enterprises ? ...... 121 7.3 Small Firm Growth – Theoretical Perspective ...... 122 7.3.1 Neoclassical Theory of ‘Optimal Size’ ...... 122 7.3.2 Penrose’s Resource-based View ...... 123 7.3.3 Managerial Approach ...... 123 7.3.4 Evolutionary Economics – ‘Growth of the Fitter’ ...... 124 7.3.5 Population Ecology Approach ...... 124 7.4 Determinants of Small Firm Growth – Brief Review of Literature ...... 125 7.4.1 Measuring Size and Growth ...... 125 xv

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7.4.2 Individual Characteristics of Entrepreneurs ...... 126 7.4.3 Characteristics of Firm ...... 128 7.4.4 Business Environment ...... 130 7.5 Determinants of Firm Growth – Empirical Evidence from Gross Income ...... 130 7.5.1 Data ...... 130 7.5.2 Univariate Distributions of Firm Size, Age and Growth ...... 131 7.5.3 Summary Statistics ...... 132 7.5.4 Methodology ...... 135 7.5.5 Empirical Strategy ...... 136 7.5.6 Empirical Results – LAD Regression ...... 136 7.5.7 Empirical Results – OLS Regression ...... 142 7.6 Determinants of Firm Growth – Empirical Evidence from Employment ...... 144 7.6.1 Summary Statistics – Employment Growth ...... 144 7.6.2 Methodology and Empirical Strategy ...... 145 7.6.3 Empirical Results – Employment Growth ...... 146 7.7 Robustness of Findings ...... 148 7.8 Conclusions ...... 150 Chapter 8 ...... 153 Conclusions ...... 153 8.1 Introduction ...... 153 8.2 Main Empirical Findings ...... 154 8.2.1 Employer Entrepreneurs versus Solo Self-employed Entrepreneurs and Necessity-motivated versus Opportunity-motivated Entrepreneurs ...... 154 8.2.2 The Non-linear Relationship between Education and Entrepreneurship .... 155 8.2.3 Entrepreneurs in Kilinochchi are ‘out of necessity’ Compared to Jaffna Entrepreneurs ...... 156 8.2.4 Financial Constraints Play a Major Role at the Start-ups...... 156 8.2.5 Increased Inter-ethnic Relationships via Trade for Building Bridges Across Communities ...... 157 8.2.6 Determinants of Small Firm Growth ...... 157 8.2.7 Faster Growth in Construction and Related Manufacturing Sector ...... 158 8.3 Theoretical and Policy Implications ...... 159 8.3.1 Theoretical Implications ...... 159 8.3.2 Implications for Policies ...... 159 8.4 Contributions of Present Study ...... 162 8.5 Limitations and Areas for Future Research ...... 163

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8.6 Concluding Statements ...... 164 Appendix A: Participant Information Statement and Consent Form………………...165 Appendix B: Survey Questionnaire………………………………...... 168 Appendix C: Correlation Matrix and Additional Results Tables……………………..180 Appendix D: Detailed Summary Statistics and Additional Results Tables…………185 Appendix E: Correlation Matrix between Key Variables – Gross Income Growth…188 References……………………………………………………………………………… ..189

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List of Tables

Table 2.1: Aspects on causes of civil war ...... 15 Table 2.2: Recurrence of violence ...... 19 Table 2.3: Summary of major contributions to economics of entrepreneurship ...... 24 Table 3.1: Population by ethnicity 1981 – 2012 ...... 40 Table 3.2: Sri Lanka and selected Asian economies – per capita GNPs relative to the USA ...... 43 Table 3.3: Chronology of major economic and political events ...... 47 Table 3.4: Estimated costs of civil conflict in Sri Lanka ...... 51 Table 3.5: Provincial share of GDP (%) and GDP by sector ...... 56 Table 3.6: Regional distribution of industrial enterprises* ...... 58 Table 3.7: Average household per capita income per month (LKR) by province ...... 58 Table 4.1: Summary Statistics (N=243)...... 72 Table 5.1: Entrepreneurial status and city distribution of total sample ...... 82 Table 5.2: Definitions of variables and descriptive statistics ...... 83 Table 5.3: Summary statistics ...... 84 Table 5.4: Business activities by solo self-employed and employer entrepreneurs ..... 86 Table 5.5: Previous labour market experience ...... 87 Table 5.6: Estimated probability of being an employer entrepreneur ...... 90 Table 5.7: Estimation results – access to finance...... 93 Table 6.1: Entrepreneurial engagements of respondents ...... 105 Table 6.2: Respondents’ motivations for engaging in entrepreneurial activities ...... 105 Table 6.3: Descriptive statistics ...... 107 Table 6.4: Estimated probability of being an opportunity entrepreneur ...... 112 Table 7.1: European Commision definitions of micro, small and medium enterprises 112 Table 7.2: Definitions of variables ...... 133 Table 7.3: Summary statistics ...... 134 Table 7.4: Determinants of firm growth (gross income) – LAD regression ...... 137 Table 7.5: Determinants of firm growth (income) – OLS regression with robust standard errors ...... 143 Table 7.6: Summary statistics – employment growth ...... 144 Table 7.7: Determinants of firm growth (employment) – OLS regression ...... 147 Table 7.8: Determinants of small firm growth – regression results from LAD and OLS – gross income (1) and (2), and employment (3) ...... 147

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List of Figures

Map 3.1: Majority ethnicity by district in Sri Lanka 2012 ...... 41

Figure 3.2: Growth in GDP (%) in Sri Lanka 1951 - 2013 ...... 44

Figure 3.3: Acute youth unemployment in 1970s and 2000s ...... 45

Figure 3.4 (a) Number of people fatalities, wounded and missing during the conflict* . 51

Figure 3.4 (b): Fatalities in the conflict in Sri Lanka ...... 52

Figure 3.5: Openness of Sri Lankan economy 1950 – 2013 ...... 55

Map 4.1: Survey area – Cities of Jaffna and Kilinochchi ...... 64

Figure 5.1 Entrepreneurs by educational attainment ...... 85

Figure 6 1: Opportunity and necessity in interactions with education ...... 108

Figure 7.1 Kernel densities of (a) size distribution (gross income 2009/2010) and (b) age distribution ...... 131

Figure 7.2: Growth rate distribution (log growth of gross income from 2009/2010 to 2011/2012) ...... 132

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List of Abbreviations

AIDS Acquired Immune Deficiency Syndrome AusAID Australian Agency for International Development BP test Breusch-Pagan test BOI Board of Investment C Celsius CDD Community Driven Development COW Correlates of War CPI Consumer Price Index CPIA Country Policy and Institutional Assessment DCS Department of Census and Statistics, Sri Lanka DS Divisional Secretariat DSI Department of Small Industries, Sri Lanka EC European Commission EU European Union F Fahrenheit FDI Foreign Direct Investment GDP Gross Domestic Product GEM Global Entrepreneurship Monitor HDI Human Development Index HIV Human Immunodeficiency Virus HREA Human Resource Ethics Advisory IDB Industrial Development Board of Sri Lanka IDP Internally Displaced Population IEP Institute for Economics and Peace IMF International Monetary Fund IPKF Indian Peace Keeping Force ISI Import Substitution Industrialisation JVP Janatha Vimukthi Peramuna LAD Least Absolute Deviation LDCs Least Developing Countries LKR Sri Lankan Rupee LSMSs Living Standards and Measurement Surveys LTTE Liberation of Tamil Tigers Eelam

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MSEs Micro and Small Enterprises NGO Non-government Organisation ODA Official Development Assistance OECD Organisation for Economic Co-operation and Development OLS Ordinary Least Square PA People’s Alliance PNG Papua New Guinea PRIO Peace Research Institute Oslo RESET Regression Specification Error Test SD Standard Deviation SLA Sri Lanka Army SLFP Sri Lanka Freedom Party SLFRD Sri Lanka Foundation for Rehabilitation of the Disabled SMEs Small and Medium Enterprises TNA Tamil National Alliance TSF Tamil Students Federation TULF Tamil United Liberation Front UCDP Uppsala Conflict Data Program UK United Kingdom UN United Nations UNDP United Nations Development Programme UNHRC United Nations Human Rights Commission UNIDO United Nations Industrial Development Organisation UNP United National Party UNSC United Nations Security Council UNSW University of New South Wales UPFA United People’s Freedom Alliance US United States USD United States Dollar VIF Variance-inflated Factor

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Chapter 1

Introduction

1.1 Motivation

The purpose of this thesis is to explore the entrepreneur’s motivations and their contributions to economic revival following the installation of peace using Sri Lanka post 2009 as a case study. Over recent decades, an enduring question concerning post- conflict recovery has been the role and effectiveness of policies in supporting sustainable economic development such that the risk of recidivism is reduced (Collier 2009; Collier and Hoeffler 2002a; Fearon, Humphreys, and Weinstein 2009; World Bank 2011). It is apparent that post-conflict economic development and peace-building are the greatest challenges for many nations (World Bank 2011), as conflicts are highly contextual and complex in nature (Collier, Hoeffler, and Soderbom 2008; Collier 2009). In addition, there is considerable historical evidence that ceased or controlled civil wars1 can reappear, posing serious challenges to human security. An empirical study by Walter (2014) found that 90% of civil wars had recurred after a period of peace. Collier, Hoeffler, and Soderbom (2008) found that about 40% of civil wars had relapsed within 10 years. Therefore, maintaining peace is a difficult task, particularly in post-conflict countries.

The impacts of conflict and post-conflict environments on economic recovery are significant. The World Bank (2011, p.1) stresses that insecurity is an alarming threat and “ …it has become a primary challenge of our time.” This insecurity is heightened by the lack of job opportunities for people living in the conflict-affected countries (World Bank 2011). The World Bank (2011) report also notes that 1.5 billion people worldwide are still suffering from conflict or are in conflict-affected countries. Recent statistics on conflict show that about 16 million lives have been lost in civil wars since the end of World War II (Regan 2009). A study by Collier (1999) estimates that gross domestic product (GDP) per capita falls at an annual rate of 2.2% during civil wars. Civil wars are also associated with unmeasured costs, with the average estimated cost of a conflict varying from USD 64 billion (Collier and Hoeffler 2007) to about USD 120 billion (Dunne 2013). Additionally, conflicts have deprived an estimated 28 million children in the world of educational opportunities (UNESCO 2011). Other social issues arising

1 A threshold of 1,000 battle-related deaths per year has commonly been used to define a ‘civil war’. 1 from armed conflicts are human suffering and the destruction of physical capital and environmental resources. Therefore, economic development is vital to create a secure society for everyone after the cessation of a conflict.

Both interstate wars (i.e. conflicts between states) and intrastate civil wars (conflicts within states) negatively affect economic development. However, as Harbom and Wallensteen (2010) argue, intrastate wars cause more human suffering, and it is difficult to revive an economy once these wars commence. For example, Fearon and Laitin (2003) estimate that intrastate wars have caused three times as many deaths as interstate wars since World War II. Some countries, such as Afghanistan, Pakistan, Syria and Nigeria, still suffer civil conflicts (Themner and Wallensteen 2014). These countries are poor and have slow economic growth. They are at a greater risk of being trapped in a vicious cycle of recurring violence compared to countries with no history of civil war (Fearon and Laitin 2003; Collier and Hoeffler 2004a; Hegre and Sambanis 2006). In addition, poor economic development which leads to an increase in unemployed men is also a common factor that increases conflict (World Bank 2013).

Most studies have been devoted to understanding the socio-economic and political aspects of civil wars including causes, consequences and challenges of post-conflict economic development. Such studies provide insights about civil wars but have several limitations. Firstly, there is lack of adequate theoretical framework for understanding a conflict and, as there is also lack of clear definition of it, the findings from empirical studies remain unclear (Sambanis 2004; Blattman and Miguel 2010; Bazzi and Blattman 2014). A common definition is ‘a threshold of 1,000 battle-related deaths’ per year (Bazzi and Blattman 2014; Blattman and Miguel 2010). However, some studies use 25 battle- related deaths per year. Blattman and Miguel argue that although substantial progress has been achieved in theorising about conflict over the last 20 years, the existing theoretical framework with regard to its origins requires further empirical testing. The lack of a clear definition is due to the issue that reliable data on world conflict is also sparse and data collection in intensively conflict-affected areas is a cumbersome process. Also, outsiders find it difficult to collect data from such contexts for various reasons, such as suspicion and fear of harm. As such, using different definitions of a civil war makes it difficult to compare the empirical results.

Secondly, in terms of causes, economic variables such as income levels and economic growth rates are more robustly associated with the onset of a conflict (Blattman and Miguel 2010; Bruckner 2011; Bazzi and Blattman 2014). In addition, social divisions,

2 inequality, ethnicity, colonial history, territory, political grievances and resource abundance have been identified as potential factors for civil wars despite their statistically weaker empirical support.

Thirdly, in terms of the consequences of conflicts with regards to human capital, macro- level and micro-level studies provide contradictory results. For example, the macro-level study by Miguel and Roland (2011) found that human capital converges rapidly over a period, while an increasing number of micro-level studies reveal that conflicts have negative impacts on human capital (e.g. Blattman and Annan 2010; Alderman, Hoddinott, and Kinsey 2006). Furthermore, they are long-lasting and have significant spill-over effects stemming particularly from globalisation which allows free movement of skills and capital across borders (Hegre and Sambanis 2006; Blattman and Miguel 2010). Civil wars not only suppress the economic growth of the countries directly involved, they also impact the economic growth of the neighbouring countries (Murdoch and Sandler 2002). For example, Miguel, Satyanath, and Sergenti (2004, p.740) estimate that “a one- percentage-point decline in GDP increases the likelihood of civil conflict by over two percentage points”. Thus, economic variables are significantly associated with civil war onset. Given this backdrop, I next explain the role of entrepreneurship in enhancing economic growth in conflict-affected countries (Acs and Naude 2011; Naude 2011a; Tobias, Mair, and Barbosa-Leiker 2013).

1.2 Entrepreneurship in a Post-conflict Setting

Entrepreneurship is a driving force behind innovation (Schumpeter 1934) and is ‘an engine of change’. As an economic process, it has recently been considered a vital driver of economic growth in both developed and developing countries (Audretsch, Keilbach, and Lehmann 2006; Koellinger, and Thurik, 2012). The three main notions of the entrepreneurial process are human actions that are catalysts for economic growth: (i) arbitrage – entrepreneurs who are alert to opportunities (Kirzner 1973); (ii) entrepreneurs who introduce ‘new innovations’ into various entrepreneurial activities (Schumpeter 1934); and (iii) the historical view of entrepreneurs ‘betting on ideas’, that is, being risk- takers and innovators (Brenner and Reuven 1985; Mokyr and Joel 1992). Although entrepreneurship is a subject of interdisciplinary analysis, no clearly established theory or model has been developed as a general theoretical framework (Parker, 2009). However, existing theories, particularly robust empirical findings, offer the potential to draw some implications for a post-conflict setting.

Entrepreneurship has always been defined according to the context of the research undertaken. Baumol (1990) found that entrepreneurial talent may be deployed not only for productive purposes but also unproductive ones, such as rent-seeking, or destructive ones, such as illegal activities. Entrepreneurship is also defined as “any attempt at a new business or new venture creation, such as self-employment, a new business organisation, or the expansion of an existing business, by an individual, a team of individuals, or an established business” (Bosma, Wennekers, and Amoros 2012, p.9). Further, Bruck, Naude, and Verwimp (2011, p.163) define entrepreneurship as “the actions through which individuals or communities perceive potentially profitable opportunities and create new firms and employment opportunities to pursue these opportunities”. I use the definition of Bruck, Naude, and Verwimp (2011) in this thesis.

Entrepreneurs play an important role in peace-building activities as they draw ethnic groups together in trade and exchange, which is part of a broader characterisation of the peace dividend whereby “the potential positive effects that peace would yield for the business community” are realised (Bruck, Naude, and Verwimp 2011, p.164). This concept of the peace dividend is broader than that of a simple reduction in military outlays (Barker, Dunne, and Ron 1991; Knight, Loayza, and Villanueva 1996; Krishnamurty and Shome 2008), as the peace fostered through the binding of business is a public good which benefits society at large. Thus, identifying the drivers of business start-ups after a conflict can have ramifications for future peace and economic development.

Entrepreneurship provides three channels through which it affects the rate of economic growth. Entrepreneurs are classified in terms of their motivations for starting a business. Given the importance of the micro foundation of entrepreneurship, ‘Schumpeterian innovative entrepreneurs’ may be either ‘defensive or necessity entrepreneurs’ (Baumol 1990). The former start businesses due to market opportunities, exploit their innovative ideas and are known as ‘opportunity-motivated’ entrepreneurs. The latter enter into business due to the need to earn an income for living and ‘subsistence’ and are viewed as ‘necessity-motivated’ entrepreneurs (Vivarelli 2013). In developing countries, these types of ‘survival-driven’ entrepreneurs are motivated to be self-employed (Naude 2010).

Self-employment appears to be driven more by necessity than opportunity because necessity-motivated entrepreneurial activity dominates overall entrepreneurship in developing countries (Acs 2006) where entrepreneurship has several key benefits. It is

4 considered a catalyst for economic development (Acs et al. 2012), although there is no consensus on its effect, and it is also an important vehicle for economic growth (Naude 2011a). Firm creation can be beneficial to economic development through employment generation and unemployment reduction in both developed and developing countries (Ayyagari, Demirguc-Kunt, and Maksimovic 2011). The positive relationship between entrepreneurship and employment generation is observed in developing countries only after excluding purely self-employed and informal types of companies (Ghani, Kerr, and O'Connell 2011).

Also, entrepreneurship can provide employment opportunities for unemployed youths in post-conflict countries. The importance of promoting entrepreneurship in these economies is also acknowledged in recent literature (Acs, Desai, and Hessels 2008; Bennett 2010; Bruck, Naude, and Verwimp 2011; Demirguc-Kunt, Klapper, and Panos 2011; Gries and Naude 2010; Naude 2010, 2011b). After the conflict ceases, entrepreneurship creates an environment for exploiting business opportunities in potentially high-earning activities such as construction. However, this is not always true as the poor are trapped in a vicious cycle of poverty and subsistence living due to external reasons, such as their borrowing constraints and lack of initial wealth (Ghatak and Nien-Huei Jiang 2002; Rosa, Kodithuwakku, and Balunywa 2006). Despite this, entrepreneurship plays a pivotal role in post-conflict situations (Demirguc-Kunt, Klapper, and Panos 2011).

Entrepreneurship also has a ‘transformative potential’ in terms of the processes of economic and social change (Venkataraman 2004; Hitt et al. 2001; Sen 2001). Although very little is known about this potential, entrepreneurship assists in generating socio- economic change in post-conflict countries to conflict reduction and peace-building (Tobias, Mair, and Barbosa-Leiker 2013), with recent literature identifying it as ’peace through commerce’ (Wilson and Wilson 2006; Westermann-Behaylo 2009; Williams, 2008; Tobias, Mair, and Barbosa-Leiker 2013). Furthermore, Tobias, Mair, and Barbosa- Leiker (2013) argue that entrepreneurship played a key role in prosperity and conflict reduction in conflict-affected Rwanda. Therefore, entrepreneurship may help to improve the lives of the poor (Sridharan et al. 2014; Tobias, Mair, and Barbosa-Leiker 2013).

Entrepreneurship is a driving force behind innovation (Schumpeter 1934) as new firms contribute to creating employment, energising innovation and promoting competition (Wennekers and Thurik 1999; Malchow-Moller, Schjerning, and Sorensen 2011). More

5 importantly, endogenous growth theorists emphasise the importance of innovations in technology and human capital as underlying drivers of production growth (Romer 1986, 1990; Grossman and Helpman 1991; Aghion and Howitt 1998). Given the importance of entrepreneurship as a contributing factor to endogenous growth, it is proposed as a third driver of economic growth and employment creation (Acs et al. 2012; Braunerhjelm et al. 2010). Thus, entrepreneurship has a potential to increase economic growth in post- conflict countries. However, little is known about this phenomenon. As such, Sri Lanka provides a good case study to understand the emergence of entrepreneurs after the cessation of conflict.

1.3 The Context

This thesis sets out to address questions relating to the emergence of entrepreneurship for the socio-economic revival of post-conflict Sri Lanka. Twenty-five years of conflict ended in 2009 when the government defeated the separatist force with a decisive military victory. Given the importance of economic revival in conflict-affected areas, the cessation of the conflict in Sri Lanka raised expectations of the revitalisation of economic growth and the peace dividend (Athukorala and Jayasuriya 2013; Coyne, Dempster, and Isaacs 2010). Some of these expectations have been realised in the immediate post-conflict phase. For example, growth in GDP has doubled, unemployment and poverty have reduced and the tourism industry has recovered. Also, Sri Lanka’s position in international rankings has improved, as the World Bank upgraded its ranking on the ‘Ease of Doing Business Index’ to 85 in 2014 (from 105 in 2010). However, the post- conflict phase is fragile, thus there is no guarantee that such recovery will prevail in the medium and long term (Collier, Hoeffler, and Soderbom 2008; Collier and Hoeffler 2004a; World Bank 2011).

The end of the conflict has provided avenues for entrepreneurship that could not have emerged otherwise. The role of entrepreneurship is important, particularly for the conflict- affected areas, given the 27.4% of unemployment among the eligible workforce (aged 15-59) in the severely conflict-affected northern province (Wickramasinghe 2014). Entrepreneurship provides employment, necessary goods and services and generates income for the affected population. As apparent from Sri Lanka’s contextual background (Chapter 3), the open economic policies led by the private sector could be of benefit (Athukorala and Jayasuriya 2013). This supports the argument that promotion of entrepreneurship in a post-conflict setting can yield higher socio-economic outcomes.

1.4 Research Objectives and Methodology

The objective of this thesis is to explore entrepreneurs’ motivations and their contributions to economic revival under peace achieved through the military victory. This thesis attempts to address the main research gaps in the literature, as explained in the literature review (Chapter 2). So far, most studies have focused on cross-country level analysis, particularly in Sub-Saharan Africa. Despite their importance, these studies neglect the nuances of the particular context and the regional aspect of conflict. Moreover, entrepreneurship in both developed and transitional countries has been extensively studied but is relatively under-researched in developing countries. Also, very little is known about this phenomenon in countries involved in active conflict or in an immediate post-conflict phase (Bruck, Naude, and Verwimp 2013). Additionally, much research on determinants and motivations for entrepreneurship focuses on the data drawn, particularly from household-level surveys. Thus, this study employs enterprise- level data. Furthermore, to the best of my knowledge, there appear to be no such comparable studies in post-conflict areas in Sri Lanka conducted immediately after the end of the conflict. Based on the identified research gaps, this thesis addresses the following objectives:

1. to examine the factors influencing entrepreneurship and identify the characteristics of different types of entrepreneurs, particularly, the ‘employer entrepreneurs’ who create employment for others, and the ‘solo self-employed entrepreneurs’ who are self-employed

2. to identify the characteristics of individuals who are motivated by the need to generate income (necessity-driven) vis-à-vis those who do so to take advantage of an opportunity to make a profit (opportunity-driven)

3. to examine the determinants of the growth of micro and small enterprises (MSEs) in two cities following the cessation of conflict.

In order to narrow the focus, the following overarching research questions were formulated to obtain a thorough understanding of entrepreneurship in post-conflict Sri Lanka:

1. Who are the emerging entrepreneurs in post-conflict Sri Lanka?

i. What are the factors affecting emerging entrepreneurship? 2. How do they contribute to a post-conflict economic recovery? ii. What are the factors affecting necessity-driven entrepreneurs vis-à-vis those who are driven by the opportunity to earn profits? iii. What are the determinants of growth of small firms (i.e. MSEs)? This thesis applies a comprehensive economic approach to examine the entrepreneurs’ motivations and contributions of their enterprises in revitalising the conflict-affected northern part of Sri Lanka, drawing on enterprise-level data. The analyses are solely based on a purpose-designed survey administered to 243 entrepreneurs in 2012 in two post-conflict cities, namely Jaffna and Kilinochchi. The study adopts quantitative design as the main method of data collection and uses STATA (version 13) econometric software for the analysis. I employ probit regressions, Least Absolute Deviation (LAD) and Ordinary Least Square (OLS) regressions to answer the three research sub- questions.

1.5 Structure of the Thesis and the Main Findings

This thesis consists of eight chapters. Following this introductory chapter, Chapter 2 reviews the literature, beginning with defining a civil war, discussing causes, consequences and termination of civil war, describing post-conflict economic recovery and expected peace dividend. This is followed by theoretical knowledge which highlights that there is no accepted theoretical framework for understanding entrepreneurship. Among other major gaps, it identifies that little is known about entrepreneurship in post- conflict countries. The identified research gaps serve as the basis for this study.

Chapter 3 provides a contextual overview to set the background for this study. The chapter evaluates Sri Lanka’s complex geographical, historical, ethnic, political and socio-economic situation and confirms that private-sector driven open market economic policies, rather than closed inward-oriented economic policies, could benefit the Sri Lankan economy. Finally, it shows that regional economic development is timely to reduce regional disparities and highlight the importance of promotion of entrepreneurship to revive the conflict-affected regions.

Chapter 4 details the methodology used to examine the motivations and contributions of emerging entrepreneurs in the two post-conflict cities. It describes the research design, setting and questionnaire, sample selection process and method, challenges of data

8 collection in a post-conflict environment, and also presents a snapshot of the data obtained.

Chapter 5 investigates the different factors related to two types of entrepreneurs, namely ‘employer entrepreneurs’, who create employment for others, and ‘solo self-employed entrepreneurs’, who are self-employed workers without employees. It differentiates the two groups in terms of personal characteristics, access to finance and inter-ethnic trade relationships using descriptive statistics and probit regression. Personal characteristics, such as gender, age, whether an able-bodied person and education, and household- related factors, such as own wealth, have been identified as important in affecting employer entrepreneurs over sole self-employed entrepreneurs. Contrary to the ‘U- shaped’ relationship between entrepreneurship and education found by Poschke (2013) in the United States (US), this study indicates inverse U-shaped relationship. Individuals with an intermediate level of education are more likely to become entrepreneurs than individuals with either high or low levels of education. Access to finance and the extent of inter-ethnic trade relationships are also different between employer entrepreneurs and solo self-employed entrepreneurs. These results address the importance of personal characteristics of entrepreneurs, the business environment and institutions in this post- conflict setting.

Chapter 6 investigates the factors affecting entrepreneurial motivations. This study finds that approximately 80% of entrepreneurs were motivated ‘out of necessity’, that is, the need to generate an income (necessity-motivated), rather than to take advantage of an opportunity to make a profit (opportunity-motivated). Conversely, opportunity entrepreneurs are largely engaged in the construction and related manufacturing sector which is the first to emerge to exploit new market opportunities after the conflict ceases. The main differences between opportunity and necessity entrepreneurs are their residency status, entrepreneurial family background, education and inter-ethnic trade relationships with the south.

Chapter 7 examines the determinants of small firm growth in post-conflict Sri Lanka. It employs LAD and OLS regressions and different indicators of growth, such as gross income and employment. The results indicate that an entrepreneur’s education, social networks, firms’ age and size (smaller and older firms), the construction and related manufacturing sector, sole proprietorship and the formality of business are important determinants.

Finally, Chapter 8 brings together the major empirical findings, highlights the main findings and conclusions drawn from this thesis, and discusses policy measures that could assist growth and expansion of embryonic small firms in post-conflict Sri Lanka. It also identifies the contributions of this research to existing knowledge, its limitations and avenues for future research. As this is the first such study of its kind conducted in post- conflict Sri Lanka, it establishes a framework for further research in these areas.

Chapter 2

Literature Review: Entrepreneurship in Post-conflict Settings

2.1 Introduction

The main objective of this chapter is to review the theoretical and empirical literature on entrepreneurship in post-conflict environment. This review provides an overview of two major areas of research, that is, entrepreneurship and the peace dividend, and it identifies the knowledge gaps that are addressed later in this thesis.

Research on the causes of civil wars, as well as the means of ending conflicts and sustaining peace, is still in its infancy (Blattman and Miguel 2010). Most studies have used cross-country level analyses, drawing particularly on data from Sub-Saharan Africa. Although important for identifying broad correlations, they neglect the nuances of the particular context. Even within a country, most civil wars occur ‘locally’, that is, in a part of a region on an international border or far away from the capital city (Rustad et al. 2011). However, the major challenge for such studies is the lack of available data (Abadie and Gardeazabal 2003; Bozzoli, Bruck, and Sottsas 2010; Blattman and Miguel 2010), which highlights the importance of collecting primary data in post-conflict settings. While entrepreneurship in developed countries has been widely studied and there is growing interest in it in developing countries, it is an under-researched area in post- conflict situations. Also, factors affecting entrepreneurship and entrepreneurs’ motivations for starting businesses have previously been studied using household data whereas this study uses enterprise-level micro-level data for analysis.

This chapter is organised as follows. Section 2.2 gives an overview of civil wars and provides definitions of a civil war. Section 2.3 discusses the possible causes and consequences of a conflict, and the forces that lead to its cessation. Section 2.4 reviews the literature on post-conflict economic recovery. Section 2.5 discusses the peace dividend that can be enjoyed after a conflict. Section 2.6 defines entrepreneurship and describes its importance in a post-conflict setting. Section 2.7 identifies the knowledge gaps revealed through this literature review, which are then used as a basis for the empirical chapters in this thesis, that is, Chapters 5, 6 and 7. Section 2.8 concludes the chapter.

2.2 Civil Wars

A civil war or internal conflict and its consequences have detrimental effects on the global economy, security and, importantly, the community (World Bank 2011). For example, Fearon and Laitin (2003) estimate that since World War II intrastate wars have ‘robbed’ three times as many people of their lives as interstate wars. Despite fears of the latter in some regions, the former are still the major economic and security threats in the 21st century (World Bank 2011). Since the end of the Cold War, research has turned from interstate to intrastate conflicts because of the extent of mass violence and human suffering they cause. For example, the highest number of deaths recorded in Sri Lanka resulted from the intense fighting between the Sri Lankan government and the Liberation of Tamil Tigers Eelam (LTTE) during 2008 and 2009 which was followed by violence in Pakistan, Afghanistan, Iraq, Rwanda and Somalia (Harbom and Wallensteen 2010). Thus, civil wars remain a serious threat to economic development and security, particularly in poor countries and their surrounding regions.

Although active conflicts are declining compared to the 1990s, there are still civil conflicts and alarming security threats to the conflict-affected countries themselves and across regions. It has been recorded that, from 1946 to 2013, 254 internal conflicts occurred in 155 locations around the world (Themner and Wallensteen 2014). As of 2013, 33 armed conflicts with a minimum of 25 battle-related deaths a year were active in 25 locations, of which seven were wars recording 1,000 or more battle-related deaths in a year (Themner and Wallensteen 2014). This is a considerable reduction compared with 52 active conflicts in 1991 (Themner and Wallensteen 2011). The highest number of battle- related deaths – of nearly 80,000 – were recorded from Ethiopia and the Horn of Africa- Eritrea in 1991 and 1999, respectively (Themner and Wallensteen 2014). Although, the lowest number of fatalities were recorded in 2005, the death tolls increased after 2010 when conflicts erupted in the Middle East, such as in Syria, Yemen and Iraq (Themner and Wallensteen 2014). As civil wars attract international involvement and sometimes lead to regional or international crises (Cockayne, Mikulaschek, and Perry 2010), they remain a threat to the economic development and security of conflict-affected countries and associated regions.

2.2.1 What is a Civil War?

An understanding of the definition of a ‘civil war’ is crucial to the focus of this thesis. Although it is defined by researchers in many ways, the common definition is that a civil

12 war is an intrastate conflict causing 1,000 battle-related deaths per year (Bazzi and Blattman 2014; Hoeffler, Ijaz, and von Billerbeck 2011; Blattman and Miguel 2010). Sarkees and Wayman (2010) identify four programs for collecting data on internal conflicts, which are: (i) the Uppsala Conflict Data Program (UCDP)-Peace Research Institute Oslo (PRIO), (ii) Fearon and Laitin (2003), (iii) Sambanis (2004), and (iv) the Correlates of War (COW). The availability of four programs implies the absence of a clearly accepted definition of a civil war. The most commonly used, UCDP-PRIO, defines an armed conflict as “a contested incompatibility that concerns government or territory or both where the use of armed force between two parties results in at least 25 battle- related deaths in a calendar year” (Themner and Wallensteen 2014, p.541), where at least one party has to be the government of a state.2

According to the UCDP-PRIO dataset, a minor armed conflict comprises at least 25 battle-related deaths in a year, whereas a high-intensity conflict has at least 1,000 deaths. Although the United Nations Security Council (UNSC) follows the same definition as the UCDP-PRIO, it uses a threshold of 500 battle-related deaths per year (Cockayne, Mikulaschek, and Perry 2010). This absence of a clear definition of a civil war adversely affects comparisons of cross-country empirical findings.

2.3 Causes, Duration, Consequences and Termination of Civil War

Understanding the causes and consequences of a conflict and the factors responsible for the ending of a conflict are important in terms of post-conflict economic development. Most research on civil wars has considered three major aspects, their causes, duration, and consequences and termination (peace building), as thoroughly reviewed by Blattman and Miguel (2010) and Bozzoli, Bruck, and Sottsas (2010). In addition, these aspects have been studied using different disciplinary lenses, including economics, political science, sociology, anthropology, psychology and law. This review is mainly based on the economic aspect.

2.3.1 Causes of Civil War

Empirical studies have revealed that civil wars occur in poor countries which are vulnerable to income shocks and have weak institutions, poor governance, sparsely populated bordering regions and mountainous terrain (Blattman and Miguel 2010). However, no consensus has yet been reached on their causes. Research has found that

2 See http://www.pcr.uu.se/research/ucdp/definitions for the definition. 13 economic variables, such as low income levels and slow economic growth, are significantly associated with civil war (Fearon and Laitin 2003; Collier and Hoeffler 2004b; Hegre and Sambanis 2006; Miguel, Satyanath, and Sergenti 2004; Collier, Hoeffler, and Rohner 2009). Also, inequality and poverty are associated with conflict (Blattman and Miguel 2010). Apart from economic variables, social factors such as ethnically related dominance, polarisation and concentration also play important roles in explaining civil wars (Fearon, Kasara, and Laitin 2007; Goldstone et al. 2010; Denny and Walter 2014; Montalvo and Reynal-Querol 2010; Esteban, Mayoral, and Ray 2012). This empirical evidence suggests that the root causes of a civil war are complex and extend beyond economic variables.

While it is difficult to attribute any one cause to the emergence of a conflict, a plethora of literature identify several factors. Dixon (2009) found the factors range from geographic, demographic and economic to historical and political. After reviewing 46 quantitative studies of the causes of civil wars occurring from 1998 to 2007, Dixon (2009) identified 203 independent variables used to detect the causes, some of which are listed in Table 2.1. One reason for this is that there is no widely accepted theory which explains the causes and the studies’ findings are subject to omitted variable bias or reverse causality (Bazzi and Blattman 2014; Dixon 2009). In addition, these explanatory variables demonstrate correlations rather than causes (e.g. Blattman and Miguel 2010). For example, Djankov and Reynal-Querol (2010) found that, using fixed country effects, GDP per capita levels have no significant impact on the emergence of a civil war whereas Bruckner (2011) argues that there is a significant relationship between income changes and risks of conflict. Thus, economic variables can play a more significant role in explaining the possible correlations of conflict than other factors. However, as causes are specific to a particular context, it is difficult to generalise using cross-country analyses.

Several studies have identified the effect of exogenous shocks, proposing that slower economic growth causes conflict. For example, some studies find that rainfall and climate shocks, such as global warming, increase the risk of conflict (Burke et al. 2009; Hsiang, Meng, and Cane 2011; Miguel, Satyanath, and Sergenti 2004). Others have examined export price shocks to gauge how they affect the occurrence of conflict. However, the relationship between conflict and export prices shows mixed results, including: (i) a negative relationship, that is, conflict increases with declines in export prices (Bruckner and Ciccone 2010); (ii) a positive one, that is, conflict increases with increases in export prices (Besley and Persson 2008); and (iii) no relationship (Deaton and Miller 1996;

Bazzi and Blattman 2014). These contrasting results are due mainly to the different export commodities and years, definitions of civil war and models considered (Bazzi and Blattman 2014).

Table 2.1: Aspects on causes of civil war

Category Variable Direction Demographics Population size Positive Population density Positive Ethnic dominance Positive Social factorisation Negative Ethnic heterogeneity Positive Geography Asia Positive Middle East Positive Neighbour warring country Positive Neighbour democratic country Negative Non-contiguous Positive Large area Positive Mountains Positive Environment Soil degradation Positive Drought Positive Climate for heavy grass agriculture Negative Resources Primarily diamonds Positive Oil exports Positive Economy Prosperity Negative Growth Negative Investment Negative Trade as % of GDP Negative Primarily commodity exports Curvilinear (inverted U) History and insecurity Peaceful years Negative Distinct civil war in previous year Negative New state Positive Refugees Positive Rivalry and interstate war Positive Regime and policies Democracy Curvilinear (inverted U) Regime instability Positive Mass education Negative

Source: adapted from Dixon (2009) Apart from economic factors, political studies have identified the relationship between civil war and territory or territorial borders. For example, Toft (2014) found that 73% of civil conflicts are ethnically ‘identity-based’ and are fought to gain more autonomy for territory or ‘homeland’. Conflicts over territory seem to be more persistent and difficult to end than other issues (Fuhrmann and Tir 2009; Dreyer 2010) and are exacerbated when the territory becomes ‘symbolic’ of the historical, religious and ethnic connections between the territory and the ‘homeland’ (Hassner 2006). This relationship also has an economic basis, as evidenced by research revealing that territorial fighting is more common in poor countries while non-territorial

15 conflicts appear to erupt in middle-income countries (de la Calle and Sanchez-Cuenca 2012).

Geographic concentrations of ethnic groups in a country also affect civil conflicts. Regionally concentrated ethnic groups are more likely to act against the government than regionally dispersed groups (Weidmann 2009; Weidmann, Rød, and Cederman 2010), particularly if they perceive the territory as their ‘homeland’ (Toft 2002; Toft 2003). Therefore, territory is a significant factor in explaining the causes of civil war and the nature of peace (Toft 2014). Although these studies are important, it appears that they may have presented superficial causes of conflicts. For example, economic reasons may have triggered the mobilisation of ethnic groups to claim territory, as in the case of Sri Lanka (Abeyratne 2004).

2.3.2 Duration and Consequences of Civil War

Some conflicts last longer than others. For example, Fearon (2004) found that the violence associated with coups and insurgencies appears to last for short periods while those relating to territorial and ‘sons-of-soil’ conflicts, that is, fought by the local majority ethnic group against in-migrants, seem to last longer. The common consensus is that ethnically divided and polarised countries suffer longer conflicts (Sambanis and Elbadawi 2000; Montalvo and Reynal-Querol 2010). Despite their important insights, these studies suffer from measurement errors, as identified in the causes of conflict (Blattman and Miguel 2010). However, these studies suggest that civil wars are more persistent than other types of conflict.

Another important characteristic of a civil war is its consequences, which affect not only the countries involved but also neighbouring countries and regions as well as the globe (Murdoch and Sandler 2002; Bozzoli, Bruck, and Sottsas 2010). Civil war has a negative impact on economic growth, as thoroughly reviewed by Sambanis (2002) and Bozzoli, Bruck, and Sottsas (2010). The costs of conflicts have been examined mainly in terms of their effect on GDP levels (DiAddario 1997; Abadie and Gardeazabal 2003; Kelegama 1999) or growth rates (Collier 1999; Murdoch and Sandler 2004). For example, Collier (1999) estimates that a conflict-affected country loses about 2.2 percentage points of economic growth for each year of conflict. In contrast, using rainfall variations as an instrumental variable for economic growth in African countries from 1981 to 1999, Miguel, Satyanath, and Sergenti (2004) found that growth is robustly negatively correlated with civil conflict. They estimate that “a one-percentage-point decline in GDP increases the

16 likelihood of civil conflict by over two percentage points” (Miguel, Satyanath, and Sergenti 2004, p.740).

Civil wars also have detrimental effects on neighbouring countries and related regions, with implications for violence spreading to both rich and poor countries (de Groot 2010; Murdoch and Sandler 2004). For example, every year Tanzania lost 0.7% of its national wealth due to conflicts in neighbouring countries (World Bank 2011). Murdoch and Sandler (2004) claim that if there are three or more civil wars near a country, it is likely the country itself will have a war. Thus, the literature finds that spill-overs from civil war not only reduce the GDP of both the country involved and neighbouring countries but also have other consequences, such as spreading diseases like malaria and HIV/AIDs, causing refugees to flee, lawlessness, drug trafficking and escalating the costs of trade due to the tightening of trade barriers (Ghobarah, Huth, and Russett 2003; Murdoch and Sandler 2004; Montalvo and Reynal-Querol 2007; Schindler and Bruck 2011).

Although these studies provide useful information, they are in their infancy, quite fragmented and lack links to previous studies (Bozzoli, Bruck, and Sottsas 2010). For example, so far, few efforts have been made to relate macro- and micro-level estimates of costs (Bozzoli, Bruck, and Sottsas 2010). It is important to note, however, that the consequences of conflicts are not limited to economic losses and also have impacts in the aftermath of civil wars (Ghobarah, Huth, and Russett 2003).

2.3.3 Termination of Civil War

This sub-section describes the end of conflict and its impact on economic development and the resultant peace. The termination of a civil war can be achieved in one of two ways, namely, through a victory (by the government or rebels) (Walter 1997; Licklider 1995; Wagner 1993; Luttwak 1999; Toft 2010; Fortna 2004a; Herbst 2000; Kant 1939; Stedman 1997; Licklider 2009) or a peace agreement or negotiated settlement (Fortna 2004b; Collier and Hoeffler 2004a; Doyle and Sambanis 2000; Doyle and Sambanis 2006; Hartzell 2009). Luttwak (1999) suggests a theory of ‘give war a chance’ because he claims that decisive military victories could create the conditions for a lasting peace. Prior to Luttwak, the German philosopher Immanuel Kant (1724-1804) believed that war could only be legally embarked on as a defensive measure (Kant 1939). Kant claims that ceasefires represent a ‘postponement of conflict’ and not a peace, and he argues that perpetual peace means ‘the end to all hostilities’ (Kant 2006). Both Wagner (1993) and Luttwak (1999) argue that allowing wars to ‘reach their natural conclusion’ leads to a likelihood of a permanent peace. Wagner (1993) claims that a decisive military victory is 17 more stable than negotiated settlements because the losers would be too weak to restart a conflict. Although negotiated settlements are the most dominant form of termination, the probability of ending a conflict through a government or rebel victory appears to be approximately the same (Kreutz 2010).

However, military victory is called a ‘peace of the grave’ because of the persistence of political violence, including genocide, after the fighting (Doyle and Sambanis 2000; 2006). Licklider (1995) also claims that political violence follows military victory. Doyle and Sambanis find little evidence for military victories leading to peace. In contrast, Toft (2010) points out that negotiated settlements have proven to be ineffective and are more likely to relapse than military victories. The above two camps clearly show that there is no agreement as to which avenues lead to a permanent peace.

Although civil wars end in one of the above two ways, they have higher risks of being repeated by for example: (i) previous belligerents; (ii) splinter factions of ex-combatants; or (iii) the emergence of a completely new rebel group (Kreutz 2012). However, there is no consensus about the relationship between the termination and possible risk of recurrence of a conflict; for example, Kreutz (2010) found that a civil war is less likely to be repeated after a government victory or the deployment of peacekeepers than when it is terminated by rebel victories. In contrast, Toft and Nathan (2011) argue that there is little chance of a relapse when a civil war is terminated by a rebel victory (6%) if one considers the wars between 1940 and 2000. Conversely, the likelihood of recurrence is 12% when civil war is ended by a military victory and 22% following a negotiated settlement (Toft and Nathan 2011). These findings suggest that political stability is important for avoiding or stopping conflict and reducing an individual’s incentive to fight in a current conflict (Bazzi and Blattman 2014; Fearon 2003; Licklider 1995; Licklider 2009). However, these studies, which are significant and appear to focus on the durability of peace, produce mixed results, with the literature on the termination of war has omitted variable biases (Blattman and Miguel 2010) and reverse causation.

Post-conflict peace is fragile. The fragility is highlighted by the fact that nearly half of all civil wars are recurrences of previous conflicts (Collier, Hoeffler, and Soderbom 2008). This is a major concern and is often referred to in the literature as being due to ‘dysfunctional institutions or poor governance’ and fragile states3 predicted to ‘fail’ or

3 Although measuring fragility is difficult, research has used the World Bank Country Policy and Institutional Assessment (CPIA) index in which a state is considered ‘fragile’ if its aggregate score falls in the bottom 40% of the sample. 18

‘collapse’ (Fearon 2010). They tend to revert to conflict because of their inability to react to internal and external shocks (Piffaretti 2010). Additionally, Walter (2011) claims that civil wars have a high rate of risk of recurrence, about 57%. For example, as demonstrated in Table 2.2, although 57% of civil wars originating in the 1960s were new conflicts, this declined to 10% in the 2000s.

Table 2.2: Recurrence of violence

Decade Onsets of violence in Onsets of violence in Number of countries with no countries with previous onsets previous conflict (%) conflict (%) 1960s 57 43 35 1970s 43 57 44 1980s 38 62 39 1990s 33 67 81 2000s 10 90 39 Note: ‘previous conflict’ includes any major conflict since 1945 Source: adapted from Walter (2011)

In contrast, as Table 2.2 demonstrates, 90% of the civil wars initiated in the 21st century were recurrences of previously ended or controlled conflicts and were also concentrated in the poorest and most fragile regions of the world, particularly Sub-Saharan Africa and Asia. As the World Bank (2011) highlights, the post-conflict phase is embedded with fragility and faces the risk of resurgence.

2.4 Post-conflict Economic Recovery

This section explores the literature related to how conflict-affected countries can revive their economies after the cessation of conflict. Although Bozzoli, Bruck, and Sottsas (2010) argue that post-conflict economic development depends on the characteristics of the conflict and the policy strategies adopted to accelerate growth, performance is mixed across countries. Post-conflict situations bring economic opportunities for: high returns on investment in infrastructure projects; high potential for commodity exports; and initiatives for economic reforms to stimulate growth (Collier 2009). This is because post- conflict countries have the potential to grow faster than ‘normal’ ones. For example, their GDP growth is, on average, 3% per year, one percentage point higher than that of non- conflict-affected countries (Hoeffler, Ijaz, and von Billerbeck 2011). Also, Chen, Loayza, and Reynal-Querol (2008) found that the average growth rate of their GDP per capita increases by about 2.4 percentage points, which is particularly influenced by increases in their investment rates, as supported by Elbadawi, Kaltani, and Schmidt-Hebbel (2008). In addition, Collier and Hoeffler (2004a) found that post-conflict countries register

19 approximately 1% higher than average GDP growth rates, as confirmed by Hoeffler, Ijaz, and von Billerbeck (2011). In contrast to the above macro-level studies, Chand (2013) analysed peace-building in Bougainville, Papua New Guinea (PNG), and found that in the post-conflict phase, per capita income recovered to 40% of its pre-conflict level.

Generally, the post-conflict stage sees faster growth due to the rebuilding of infrastructure within a short period (Cerra and Saxena 2008). Hoeffler, Ijaz, and von Billerbeck (2011) claim that, at this time, countries receive more international official development assistance (i.e., aid) and are more democratic than they were before the conflict. They further suggest that there is a slow recovery in the first three years which becomes faster from the fourth to sixth years if no violence occurs during that period. Therefore, the absence of violence is important for ensuring economic recovery and peace in a post-conflict phase (Doyle and Sambanis 2000).

However, these short-term growth prospects might not be sustainable in the medium to long term (Collier 1999; World Bank 2011). Although a post-conflict country is expected to be more politically stable or better governed after its war, its institutions will be weaker (Blattman and Miguel 2010). Such countries encounter greater security and socio- economic development challenges (Hoeffler, Ijaz, and von Billerbeck 2011), such as low economic growth, unemployment, destruction of infrastructure, poor health and increased inequality and insecurity, than countries not exposed to conflict (Collier 1999; Hoddie and Smith 2009). In addition, on average, their military expenditures increase from 3% during a period of conflict to 3.5% in a post-conflict phase (Hoeffler, Ijaz, and von Billerbeck 2011). Conversely, inequality remains and human rights violations continue and often increase (Hoeffler, Ijaz, and von Billerbeck 2011). Therefore, a ‘distinctive policy approach’ is important in such an economic recovery due to the high risks of further violence (Collier 2009).

2.4.1 Effects of Foreign Aid in Post-conflict Settings

Due to the lack of domestic finance sources in post-conflict countries, foreign aid is important, particularly for finance development projects, as it can play a key role in rebuilding infrastructure and replenishing household assets (Collier and Dollar 2002). Post-conflict reconstruction in poor countries requires long-term commitment by donors because it is unlikely that the incumbent governments could meet reconstruction costs in less than a decade or two (Chand and Coffman 2008). Although the assessment of its impact on economic development is ambiguous in the literature, foreign aid can play an important role in post-conflict economic recovery. The common view is that it is positive 20 and can help to reduce conflict and sustain peace (World Bank 2011). The role of the international community has been important in reducing the risk of conflict in countries such as Sierra Leone and Liberia (Collier 2007). Furthermore, Collier and Hoeffler (2004a) found that aid is more effective for generating economic growth in post-conflict countries than in those that have not experienced conflict. Foreign aid is an important determinant of economic growth (Elbadawi, Kaltani, and Schmidt-Hebbel 2008) and increasingly it indirectly reduces risk of conflict (Collier and Hoeffler 2002b). Also, although the effectiveness of foreign aid for the economies of post-conflict countries is not great in the first three years, it doubles after the third year in the post-conflict phase (Collier and Hoeffler 2004a). Additionally, de Ree and Nillesen (2009) found that Official Development Assistance (ODA) reduces conflict.

In contrast, some studies argue that foreign aid appears to fuel conflict (Anderson 1999; Polman 2010), with Nunn and Qian (2014) finding that US food aid increased conflicts in recipient countries. Similarly, Crost, Felter, and Johnston (2014) argue that a Community-driven Development (CDD) program in the Philippines increased violent conflict. Additionally, Besley and Persson (2011) found a positive relationship between World Bank-funded foreign aid and conflict in the Philippines while Dube and Naidu (2010) note a positive relationship between US military aid and conflict in Colombia.

However, Collier and Hoeffler (2002a) found that total ODA has no impact on conflict and Hoeffler, Ijaz, and von Billerbeck (2011) confirm that, on average, foreign aid does not increase economic growth in post-conflict countries. Although the benefits of foreign aid have been extensively studied (Burnside and Dollar 2000; Easterly 2003; Easterly, Levine, and Roodman 2004), its impact depends on the strategic or economic preferences of donor countries (Alesina and Dollar 2000; Nunn and Qian 2012).

In summary, most research has used cross-country data to distil lessons for building peace. However, these analyses suffer from two major problems: (i) ignorance of other factors associated with violence, such as natural disasters (e.g., drought, tsunami, flooding) and elections; and (ii) the assumption that the distribution of conflict across a country is equal whereas the majority of civil wars occur far away from capital cities and along international borders (Rustad et al. 2011). This suggests the importance of conducting studies at a regional level within a country using micro-level data (Bazzi and Blattman 2014).

Despite the vast number of economic empirical studies conducted, there is little consensus on the most effective policies for preventing conflict or promoting post-conflict 21 reconstruction (Blattman and Miguel 2010). Furthermore, the causes of conflict and its consequences have been investigated mainly at the macroeconomic level, with only a few examined at the micro level (Collier and Duponchel 2013). Therefore, it is important to incorporate the micro-level aspect of individual and firm decisions into the formal modelling of a civil war (Blattman and Miguel 2010).

2.5 Peace Dividend

This section provides a definition of the peace dividend that can be expected following the cessation of conflict and its impact on economic outcomes which provides a platform for the emergence of entrepreneurship. In a conventional approach, the peace dividend is referred to as ‘catch-up effects’ (Knight, Loayza, and Villanueva 1996). Although there are several definitions of this concept, there appears to be no common one in the literature. Most studies define it as the savings resulting from a reduction in military outlay (Barker, Dunne, and Ron 1991; Knight, Loayza, and Villanueva 1996; Krishnamurty and Shome 2008), with those to date often focusing on military expenditure being re-allocated to other productive sectors.

However, entrepreneurs play an important role in peace-enhancing activities as they build bridges between ethnic groups through trade and exchange. This role is part of a broader characterisation of the peace dividend whereby “the potential positive effects that peace would yield for the business community” are realised (Bruck, Naude, and Verwimp 2011, p.164), a definition this study follows. Therefore, identifying the drivers for business start-ups after a conflict can have ramifications for future peace and economic development.

2.5.1 Impact of the Peace Dividend on Economic Outcomes

The impact of the cessation of conflict has been studied in terms of different economic outcomes. The relationship between military outlay and economic growth has been widely investigated and is popularly termed ‘guns or butter’ (Bernstein 1996). However, there is no consensus regarding the relationship in developed and developing countries (Stewart 1991; Benoit 1973; Murdoch, Pi, and Sandler 1997; Smith 1980; Gerace 2002; Wijeweera and Webb 2009, 2010). Despite the importance of the above findings, the impact of the end of conflict (peace dividend) on broader economic outcomes has rarely been studied (Besley and Mueller 2012; Coyne, Dempster, and Isaacs 2010). So far, the peace dividend has been examined in terms of different outcomes: (i) increased life satisfaction (Frey, Luechinger, and Stutzer 2009); (ii) increased value of gold (Willard, 22

Guinnane, and Rosen 1996); (iii) returns from asset prices and stock markets (Zussman and Zussman 2006; Zussman, Zussman, and Nielsen 2008; Guidolin and Ferrara 2007; Coyne, Dempster, and Isaacs 2010); and (iv) property and house prices (Collins and Margo 2007; Abadie and Dermisi 2008; Besley and Mueller 2012). However, the effect of the cessation of conflict on entrepreneurs’ start-ups has rarely been understood in the extant literature.

2.6 Overview of Entrepreneurship

Entrepreneurship has been prominent in recent years, particularly in developing countries. More importantly, promotion of entrepreneurship in post-conflict countries has been highlighted as being able to achieve the expected peace dividend in the aftermath of war (Bruck, Naude, and Verwimp 2011). This practical motivation has led to an increase in studies of entrepreneurship and economic development among scholars (Acs, Desai, and Hessels 2008; Bennett 2010; Bruck, Naude, and Verwimp 2011; Demirguc-Kunt, Klapper, and Panos 2011; Gries and Naude 2010; Naude 2010, 2011b; Audretsch, Keilbach, and Lehmann 2006; Koellinger, and Thurik, 2012; van Praag and Versloot 2007).

So far, a great deal of research has been devoted to analysing the role of entrepreneurship in terms of economic growth setting. Despite the large number of studies conducted in developed countries using different models and theories, entrepreneurship lacks well-defined theories (Parker, 2009) and its role in economic ‘collapse’ or ‘stagnation’ due to conflicts has rarely been studied (Bruck, Naude, and Verwimp 2011). This is due to the assumption of peace in the existing models, the absence of theories regarding the causes and consequences of conflict, the difficulty of collecting data in conflict areas and the dominance of macro-level analyses (Bruck, Naude, and Verwimp 2011). More importantly, the lack of studies conducted in post- conflict settings is a significant gap in the current literature.

An extensive literature review of the ‘economics of entrepreneurship’ is provided by Parker, (2009). Table 2.3 presents a summary of the major contributions to the field of entrepreneurship which will assist in narrowing down the wide array of existing knowledge in order to scope this research. Details are provided throughout this thesis, where appropriate, with relevant theories and empirical literature discussed in Chapters 5, 6 and 7.

Table 2.3: Summary of major contributions to economics of entrepreneurship

Key areas of Key contributors Implications/views of research (in chronological order, entrepreneurship where appropriate) Definition of Cantillon (1755) Arbitrage, taking risks and uncertainty entrepreneur Jean-Baptiste Say (1828)- Combine and coordinate factors of production Knight (1921) Risk adjusted relative rewards Schumpeter (1934), Innovation and “creative destruction” Schumpeter (1939) Leadership and motivation, and personal and psychological traits Kirzner (1973), Kirzner Alertness (1985) Entrepreneur’s ability Lucas (1978) Entrepreneurial abilities (human capital) - how they perform Kihlstrom and Laffont KL79 model- (1979) Heterogeneous aversion to risk

Holmes (1990) Least able entrepreneurs manage existing firms whereas most able ones start new businesses Lazear (2005) Entrepreneurs ‘jacks of all trades’ - fit for all jobs Newman (2007) Risk aversion relies on entrepreneur’s wealth Impact of institutions Baumol (1990) Productive, unproductive and on entrepreneurial destructive entrepreneurship allocation Bhagwati (1982) Allocation of entrepreneurial activities Government failures - toward unproductive activities rent seeking behaviours Entrepreneurs face Stiglitz and Weiss (1981) Underinvestment in entrepreneurship financial constraints at Evans and Jovanovic Amount of capital depends on the start-ups and their (1989) entrepreneur’s assets expansion Parker (2004), Paulson Importance of wealth means and Townsend (2004), businesses under-financed. Kerr and Nanda (2011) Factors affecting Johnson (1986), Bates ‘Escape from unemployment’ entrepreneurship (1990), Storey (1994b), Reynolds et al. (2001) Evans and Leighton Human capital (1989) Family background Evans and Leighton (1989), Blanchflower and Oswald (1998), Djankov et al. (2006b), Chlosta et al. (2012) Earle and Sakova (2000), Labour market characteristics Wang (2006), Demirguc- Kunt, Klapper, and Panos (2011), Millan, Congregado, and Roman (2012) Taylor (1996), Psychological factors Blanchflower and Oswald (1998), Benz and Frey (2004)

Key areas of Key contributors Implications/views of research (in chronological order, entrepreneurship where appropriate) Shleifer and Vishny Business environment (1993), Klapper, Laeven, and Rajan (2006), Sanders and Weitzel (2013) Entrepreneurs’ origins Johnson and Darnell ‘Push and pull’ motivation (1976), Amit and Muller (1995), Cooper and Dunkelberg (1986), Segal, Borgia, and Schoenfeld (2005) Reynolds et al. (2002), Necessity-motivated (push) and Kodithuwakku and Rosa opportunity-motivated (pull) (2002), Block and entrepreneurs Sandner (2009) Determinants of firm Gibrat (1931) Law of Proportionate Effect growth Viner (1932) ‘Optimal size’ Coase (1937), Williamson ‘Transaction costs theory’ (1975) Alchian (1950) ‘Fitter firms survive’ Penrose (1959), Winter ‘Resources’- knowledge through (2003) learning Marris (1963), Baumol Importance of ‘firm size’. (1967) Hannan and Freeman ‘Competition for resources’ (1977)

Source: author’s compilation based on key literature reviews, such as those of Parker (2009), Storey (1994a), Coad (2009), Nichter and Goldmark (2009) and Vivarelli (2013)

Entrepreneurship plays a vital role in economic growth and development. It has been identified as a catalyst for economic development through spill-over knowledge (Acs et al. 2012), the process of structural change and industrialisation (Gries and Naude 2010) and employment creation (Malchow-Moller, Schjerning, and Sorensen 2011). However, understanding entrepreneurship in fragile states, including post-conflict countries, has been overlooked in the academic literature (Guglielmetti 2010). In addition, several models which pay particular attention to destructive entrepreneurship, that is, illegal businesses, such as drug trafficking (Desai, Acs, and Weitzel 2013) and mis-allocations of talents, in post-conflict phases (Sanders and Weitzel 2013) have recently been developed. However, they may need further empirical testing. Although previous research has analysed entrepreneurship and economic growth at country, regional or cross-country levels, it is an under-researched area in terms of the emergence of new enterprises and communities after the cessation of conflict.

2.6.1 Definitions of Entrepreneurship

Entrepreneurship has been defined in many ways. The most popular definitions are that it is ‘a driving force behind innovation’ (Schumpeter 1934) and an ‘engine of growth’ due to its role in the promotion of competition, innovation and creation of new firms and sectors (Wennekers and Thurik 1999; Dejardin 2011), and generation of employment (Acs, Carlsson, and Karlsson 1999; Malchow-Moller, Schjerning, and Sorensen 2011). Entrepreneurship has recently been considered a third driver for both economic development and employment creation (Vivarelli 2013) as it contributes to new and spill- over knowledge through the formation of new entrepreneurial firms (Acs et al. 2012; Braunerhjelm et al. 2010). Knowledge spill-overs can be twofold: (i) directly via the introduction of new products and improvement of existing ones; and (ii) indirectly via innovation and increasing productivity with higher competition (Baptista, Escaria, and Madruga 2008; Bosma, Stam, and Schutjens 2011). Therefore, entrepreneurship can be defined as “the process by which new entrepreneurs are founded and become viable” (Vivarelli 2013, p.1456). Furthermore, particularly in terms of a post-conflict setting, Bruck, Naude, and Verwimp (2011, p.163) define entrepreneurship as “the actions through which individuals or communities perceive potentially profitable opportunities and create new firms and employment opportunities to pursue these opportunities”, a definition followed by this study.

2.6.2 Definition of Entrepreneur

This study follows the economics definition, particularly in terms of what an entrepreneur does which can be grouped into behavioural, occupational and synthesis (Naude 2013). As summarised in Table 2.3, entrepreneurship is ‘creative destruction’ and an entrepreneur is defined as the ‘coordinator of production’ and ‘agent of change’ (Schumpeter 1950, 1961), that is, an innovator. Other definitions in relation to the behavioural aspect of entrepreneurship provide a background to understanding the role played by entrepreneurs, particularly in post-conflict countries. Richard Cantillon (1755) regards an entrepreneur as an ‘arbitrager or speculator’ and, according to his ‘risk theory of profit’, successful entrepreneurs continue their businesses while taking risks whereas unsuccessful ones exit from business. Based on this view, Kirzner (1973, 1985) considers the role of an entrepreneur as that of a middleman, a facilitator who identifies opportunities for profitable arbitrage, an aspect noted in the recent literature as ‘opportunity-grabbing-for-profit’ (Shane and Venkataraman 2000). Knight (1921) emphasises the importance of risk and uncertainty for entrepreneurial entry, with his key

26 contribution identifying that an individual’s occupational choice of being a worker or entrepreneur depends on the risk-adjusted relative returns inherent in that choice. Similarly, Kanbur (1979) defines an entrepreneur as one who ‘manages the production function’ by paying wages, taking risks and dealing with uncertainties. This definition can apply to developing countries which largely have small- and medium-scale enterprises (Naude 2013).

In contrast, institutions, that is, the ‘rules of the game’, impact on the particular allocation of resources. Baumol (1990) found that entrepreneurial talent may be deployed not only for productive purposes but also unproductive (i.e., rent-seeking) or destructive (i.e., illegal) activities. He defines entrepreneurs as “persons who are ingenious and creative in finding ways that add to their own wealth, power and prestige” (Baumol 1990, p.895). Coyne and Leeson (2004) found that institutional weaknesses rather than an insufficient supply of entrepreneurs lead to under-development.

An entrepreneur can also be defined in terms of his/her occupational choice between wage employment and self-employment (Lucas 1978; Evans and Jovanovic 1989; Murphy, Shleifer, and Vishny 1991). For example, Lucas (1978) first identifies that an entrepreneur can be different from a worker in terms of his/her entrepreneurial ability. Following Lucas (1978), Jovanovic (1982) argues that entrepreneurs learn their abilities by observing their business performances. Audretsch (2003) explains this choice model as being individuals making decisions as to whether they will be unemployed, a paid worker or self-employed after weighing the costs and benefits related to these three choices. Therefore, the likelihood of starting a new firm depends on the increased expectation of obtaining more profit from self-employment than from wages from paid employment or the expected benefits when unemployed (Naude 2013). However, there are numerous variations and extensions of this argument (Calvo and Wellisz 1980; Lazear 2005; Murphy, Shleifer, and Vishny 1991; Nocke 2006; Holmes 1990; Jovanovic and MacDonald 1994) and it may have limited scope in a post-conflict setting due to the lack of available employment choices immediately after the cessation of conflict.

However, entrepreneurship is not really a choice and is often determined by the necessity or opportunity motivations of entrepreneurs (Reynolds et al. 2002). Necessity might ‘push’ individuals towards entrepreneurial activities due to a lack of alternative employment options (necessity-motivated entrepreneurs). Conversely, some individuals are ‘pulled’ to start businesses in order to exploit opportunities to maximise their profits (opportunity-motivated entrepreneurs) (Reynolds et al. 2005; Acs 2006). A detailed

27 discussion of these two distinct concepts is provided in Chapter 6. The next section discusses the contributions of entrepreneurship to economic development.

2.6.3 Contributions of Entrepreneurship to Economic Development

Entrepreneurship assists in increasing economic and industrial growth, productivity and employment generation ( for example, Acs, Desai, and Hessels 2008; Szirmai, Naude, and Alcorta 2013; Van Praag and Versloot 2007; Koellinger, and Thurik, 2012). However, entrepreneurship is rarely related to a reduction in poverty, inequality and conflict, particularly in post-conflict countries (Tobias, Mair, and Barbosa-Leiker 2013; Bruton, Ketchen, and Ireland 2013).

Major insights have been presented in discussions of the contributions of entrepreneurship to economic development, including Naude (2013) recent review from the perspective of development economics. Firstly, the role of entrepreneurship in structural transformation is important. For example, Lewis (1954) introduces ‘a dual economy’ model to explain the structural transformation of developing economies from the traditional to modern sectors, which is further developed by Murphy, Shleifer, and Vishny (1991), Peretto (1999), Rada (2007) and, more recently, Gries and Naude (2010). The model of Murphy, Shleifer, and Vishny (1991) postulates that a firm’s size and growth as a function of an entrepreneur’s ability is important for assimilating technology, research and development, and increasing returns from physical and human capital (Nelson and Pack 1999; Michelacci 2003).

Peretto (1999) model is a modified endogenous growth model which postulates that an economy can transit from capital-accumulated to knowledge-accumulated growth in the long term. However, Rada (2007) argues that entrepreneurs transit from traditional to modern sectors by increasing their investment in the latter once they perceive its profitable opportunities. More recently, Gries and Naude (2010) developed a dual model which incorporates micro aspects of households, firms and the labour market and, importantly, identifies the differences between mature and start-up entrepreneurs, and between necessity- and opportunity-motivated entrepreneurs. Transformation from a traditional to modern economy takes place through changes to production methods and entrepreneurial processes, that is, by increasing productivity and jobs, providing innovative intermediate inputs and allowing specialisation (Gries and Naude 2010).

Structural changes in an economy have implications for entrepreneurship and economic development (Naude 2013). For example, Ciccone and Matsuyama (1996) argue that, if 28 an economy produces new intermediate goods, there will be an increase in demand for producers of final goods which will provide incentives for fresh start-ups, that is, new enterprises. Moreover, entrepreneurs provide positive externalities with new technologies while releasing new goods to the market (Hausmann and Rodrik 2003).

The duality of the traditional and modern sectors is observed in the formal and informal sectors of an economy (Maloney 2004). Informality is a common feature in developing countries where individuals are attracted to entrepreneurship ‘out of necessity’ (Demirguc-Kunt, Klapper, and Panos 2011; Bennett 2010). While formal sector wages seem to be higher in the urban areas of developing countries due to poor regulatory environments, the informal sector seems to be less efficient, more likely to be dominated by entrepreneurs with low abilities and attract individuals in search of the opportunity to earn a livelihood (Lewis 1954; Lucas 1978; Loayza 1996). In contrast, Maloney (2004) argues that informal entrepreneurs play an important role in economic growth in developing countries. These entrepreneurs contribute to poverty reduction (Tamvada 2010), empower women (Minniti and Naude 2010) and, finally, work as incubators for the formal sector in a country’s transitional period (Demirguc-Kunt, Klapper, and Panos 2011).

The formal and informal sectors and their experiences regarding entry and growth are debatable. Informality can be a result of ‘aversive’ entrepreneurship which evades taxes or regulations (de Paula and Scheinkman 2007). The literature indicates that increased regulation and related enforcement lead to a large informal sector in a country (Johnson et al. 1997; Johnson, Kaufmann, and Zoido-Lobaton 1998; Friedman et al. 2000). However, entrepreneurial human capital in the informal sector is important for the development process (Hausmann and Rodrik 2003) and, as entrepreneurs perform many tasks, they are seen as ‘jacks of all trades’ (Lazear 2004). This debate has policy implications for economic development whereby the informal sector can be treated as a potential means of latent entrepreneurial development through enhancing skills and capabilities (Naude 2013).

Secondly, in contrast to the above structural transformation, entrepreneurship has often been considered a social change aimed at finding solutions to poverty and inequality, and generating social and economic value (Miller et al. 2012). Therefore, the second contribution of entrepreneurship is that it has a ‘transformative potential’ which increases both economic growth and social change (Venkataraman 2004). Entrepreneurship will assist post-conflict countries to build bridges between previous warring factions or

29 communities through trade and commerce which is commonly known as ‘peace through commerce’ (Williams, 2008; Wilson and Wilson 2006; Tobias and Boudreaux 2011). It has a ‘transformative potential’, for example, to change the lives of entrepreneurs in Rwanda (Tobias, Mair, and Barbosa-Leiker 2013).

Thirdly, entrepreneurship has not been limited to the perspective of economic development. Previous literature has often focused on only the relationships between entrepreneurship and development in terms of economic growth, productivity enhancement and employment growth (Naude 2013). However, it has multi-dimensional contributions (Acs, Desai, and Hessels 2008) as it improves capabilities (Gries and Naude 2011), human skills (Sen 2001), national happiness (Naude, Amoros, and Cristi 2014), job satisfaction (Bradley and Roberts 2004; Millan et al. 2013) and health (Demirguc-Kunt, Klapper, and Panos 2011; Block and Koellinger 2009).

Fourthly, institutions play a key role in economic development, with formal ones providing the rules and regulations accepted as a guide to frame a society. Tonoyan et al. (2010) note that these institutions involve property rights, contractual enforcements, the rule of law and personal freedom in economic decision-making, and influence the decision- making processes of both individuals and organisations by framing their choices, norms and behaviours within a society. These institutions play a role of either supporting or undermining activities. Conversely, informal institutions are the traditions, customs, social norms, culture and other codes of conduct (Baumol 1990; North 1990), and their roles also address issues of social interaction and coordination (North 1990). Importantly, post-conflict countries provide conducive settings for institutional change (Collier 2009). North, Wallis, and Weingast (2009) explain that the central role of institutions is to contain violence and they distinguish between two types of social order, the ‘natural state’ and ‘open access society’ which, generally, are developing and developed countries respectively. Unlike natural state countries, open access societies are based on equality, competition across all sectors and impersonality which provide impetus for ‘creative destruction’ and, thereby, yield prosperity through markets. North, Wallis, and Weingast (2009) believe that it is important to develop their institutions, policies and organisations for natural state countries, such as post-conflict countries.

Although the role of government in supporting entrepreneurship while improving institutions and reducing the barriers to business is significant, it is still debated. Some argue that reducing regulations increases start-ups (Hausmann and Rodrik 2003) while others claim that regulations create negative externalities, such as higher borrowing

30 costs (de Meza and Webb 1987). However, the role of government in industrial policy has recently been emphasised following the global financial crisis of 2008/2009 which was due to market failures (Szirmai, Naude, and Alcorta 2013; Acs and Naude 2013).

2.6.4 Empirical Evidence from Macro- and Micro-level Studies

Although both macroeconomic and microeconomic studies provide insights into understanding the drivers of entrepreneurship, this literature review is based mainly on the microeconomic drivers. Although entrepreneurship is widely acknowledged as an important vehicle for economic development at the macro level (Acs et al. 2012; Acs, Audretsch, and Strom 2009), there is no consensus on whether it influences growth, productivity or employment (Naude 2013). Despite the mixed results found in the literature, Naude (2011a) argues that the relationship between entrepreneurship and a country’s economic development, measured by GDP per capita, appears to be U-shaped which implies that higher levels of entrepreneurial activity can be observed in low-income countries than middle-income countries (Wennekers et al. 2005). This could be due to entrepreneurs in these countries being more necessity motivated and engaged in less innovative entrepreneurial activities (Acs, Desai, and Hessels 2008). Further, more ‘innovative’ types of opportunity-motivated entrepreneurial activities can be observed in high-income countries (Acs 2006) which demonstrates a U-shaped relationship. However, as to whether there is a causality of this type of relationship between entrepreneurship and economic development or vice versa suggests the need for more empirical testing. Most studies of entrepreneurship have used cross-country or country- level analyses, with little attention paid to regional- or community-level studies. However, the role of entrepreneurship in conflict reduction and the socio-economic revival of conflict-affected countries has been under-studied.

In contrast, micro-level studies have focused mainly on the impact of entrepreneurship on the individual characteristics of entrepreneurs and are twofold: (i) the factors affecting entrepreneurship and entrepreneurs’ motivations (Chapters 5 and 6); and (ii) the determinants of small firm growth (Chapter 7). Personal characteristics play a pivotal role in the entrepreneurial decision-making process, as summarised in Table 2.3. For example, Knight (1921) and Schumpeter (1934) emphasise the importance of the characteristics of the founders of firms. The literature identifies the importance of the labour market characteristics of entrepreneurs, such as whether they were previously employed, unemployed or are ex-entrepreneurs (Vivarelli 1991, 2013; Storey 1994a). In this respect, an entrepreneurial decision is a choice between becoming a wage earner

31 or making a profit by starting a new firm (Vivarelli 1991; Reynolds 1997; Levesque and Shepherd 2004). Therefore, the formation of a new firm may be an alternative to unemployment or a response to an uncertain employment environment (Evans and Leighton 1990; Storey 1994a). Personal characteristics, such as gender, age, marital status, previous work experience, family background, financial status (own wealth and unexpected financial gains such as a lottery win or job bonus) as well as personal motivation and psychological factors can influence start-up decisions (Vivarelli 2013, 2007; Chlosta et al. 2012; Evans and Jovanovic 1989; Djankov et al. 2006b).

The rates of growth of new firms also depend on different characteristics identified in the literature, such as: a firm’s size and age; an entrepreneur’s attributes, such as education, human capital, previous job and previous unemployment; and innovation and credit constraints (Vivarelli 2013; Coad 2007, 2009). More specifically, recent literature identifies four major factors affecting small firm growth in developing countries: (i) a firm’s characteristics, such as its size and age, formality and access to finance; (ii) an entrepreneur’s personal characteristics, such as gender, education and work experience; (iii) relational factors such as social networks, that is, relationships between individuals; and (iv) contextual factors such as the business environment (Nichter and Goldmark 2009).

2.6.5 Empirical Evidence of Entrepreneurship in Conflict and Post-conflict Settings

The following discusses the effects of conflict and post-conflict situations on entrepreneurs. However, there are few studies in conflict-affected countries due to: (i) the assumption of peace in the theoretical model; (ii) the absence of theories about the causes and consequences of conflict; (iii) the difficulty of collecting data in conflict- affected countries; and (iv) the dominance of macro-level studies (Bruck, Naude, and Verwimp 2013; Bruck et al. 2013; Bruck, Naude, and Verwimp 2011).

Conflict may directly or indirectly benefit entrepreneurship through relative increases in the prices of goods commonly used during war (security services and alcohol) or a decline in market entry requirements (Bennett 2010). Furthermore, by constructing a detailed panel census of the entrepreneurial activity of manufacturers between 1993 and 2004, Camacho and Rodriguez (2013) found that an increase in the number of attacks in a year causes a decrease in the number of employees hired the next year. In addition, conflicts have dire impacts such as: the loss of human capital (e.g., Rwanda); the destruction of capital assets and inaccessibility to land due to landmines (e.g., 32

Mozambique and Angola); insecure property rights; or a decline in consumer demand (Bruck, Naude, and Verwimp 2011; Bruck and Schindler 2009; de Walque and Verwimp 2010; Nagler and Naude 2014). They also reduce the productivity of enterprises and increase the constraints of doing business (Naude and Rossouw 2010). Elements of all these features have been found to be the case in Sri Lanka (see Sarvananthan (2007)).

Although macro-level evidence suggests that violence has a detrimental effect on development, micro-level studies indicate that farmers reverted to subsistence agriculture when confronted with violence in Burundi (Nillesen and Verwimp 2010) and Uganda (Deininger 2003). Surprisingly, Nillesen and Verwimp (2010) find that farmers increased their incomes from export and cash crop-growing activities, invested more in public goods and revealed a higher subjective welfare evaluation during the conflict. Furthermore, studying 11 production firms in Liberia using semi-structured interview techniques, MacDougal (2010) follows the adaptation strategies of firms affected by the ongoing civil war and identifies three determinants of entrepreneurial decisions: (i) the proximity of the battlefront; (ii) the actions of the combat forces against a firm; and (iii) the market value of the goods produced. He also finds that diversifying supply lines was one of the key entrepreneurial activities in that uncertain environment.

Entrepreneurship is important in the aftermath of conflict, particularly for peace building, although there are few studies of such settings. Collier and Duponchel (2013) use firm- level data to assess the negative impact of civil war on firms in conflict-intensive areas in Sierra Leone. They find that the loss of human capital during the war led to firms facing the problem of a lack of skilled workers, a phenomenon they describe as ‘forgetting by not doing’ which replicates Arrow’s theory of ‘learning by not doing’ (Arrow 1962). Therefore, war depletes human capital, results in the destruction of physical capital and brings about technical regression with declining incomes.

However, the amount of physical destruction caused by violence is relatively low in developing countries due to little capital being at risk (Miguel and Roland 2011). Collier and Duponchel (2013) argue that the disruption of production due to various factors, such as electricity shortages, fear of looting and a return to inefficient production techniques, significantly affects economic recovery in a post-conflict situation. They find that there is a positive correlation between the intensity of the conflict and the reported willingness of employers to invest in the training of their employees which is reflected in a shortage of skilled workers. They also determine that entrepreneurs who conducted business for many years in conflict situations generated smaller incomes and

33 employment opportunities than firms of the same age operating in non-conflict areas. Although their findings are important, their study was hampered by a lack of data.

The informal sector plays a pivotal role in economic growth in post-conflict transition as it stimulates sharing of the peace dividend. Demirguc-Kunt, Klapper, and Panos (2011) investigate the factors that caused individuals to switch to self-employment and the determinants of subsequent economic survival in post-conflict Bosnia and Herzegovina using household data from 2001 to 2004. Their study suggests that informal sector employment can work as an incubator of formal sector employment because a positive correlation is observed between previous informal activity and the probability of transition into formal self-employment and retention in the first year. Also, individuals in wealthier households are more likely to become entrepreneurs which implies that there are financial constraints on entrepreneurial activity. However, although access to formal finance, such as bank loans, does not significantly affect start-ups, it has a significant positive effect on the survival of new entrepreneurs. Moreover, analysing the contributions of grassroots businesses in Liberia, Tarway-Twalla (2011) find that these businesses provide goods and services to remote villages.

2.7 Focus of Research

This review, covering both theoretical and empirical findings, identifies major gaps and challenges in the literature. Most research on civil wars has focused on cross-country- level analyses using macroeconomic data, particularly in Sub-Saharan Africa. However, it is difficult to compare the empirical results due to the lack of one common definition of a civil war. As the majority of civil wars occur far from capital cities and along international borders (Rustad et al. 2011), this suggests the need for micro-level studies at a regional level within a country, as only limited areas may be severely affected (Bazzi and Blattman 2014).

Conversely, micro-level analyses conducted in Least Developing Countries (LDCs), particularly those using surveys of households and rebel organisations, are compelling (Verwimp and Nillesen 2010; Bundervoet, Verwimp, and Akresh 2009; Bellows and Miguel 2009, 2006; Annan, Blattman, and Horton 2006; Verwimp 2005). However, there is little in the extant literature about how a conflict affects firms (Collier and Duponchel 2013) or how firms contribute to post-conflict economic recovery.

Entrepreneurship in developed countries has been widely studied and there is a growing interest in examining it in developing countries. However, few studies have been 34 conducted in conflict or post-conflict countries due particularly to the difficulty of data collection and lack of longitudinal data. Also, little is known about the impact of peace on the emergence of entrepreneurship on a sub-national scale, that is, at individual and community levels. Macro-level studies fail to provide evidence of the impact of conflict or post-conflict situations in particular contexts due mainly to the fact that conflict usually affects only a region or area of a country; for example, the most severely conflict-affected areas of Sri Lanka are the north and east. Therefore, as analysing the emergence of entrepreneurship in conflict-affected areas is important for policy formulation, micro-level data is required. However, the lack of reliable and timely data for conflict-affected countries in turn affects policy-making in their post-conflict phases. Thus, it is important to collect primary data from such contexts to investigate the micro-foundations of economic growth.

As the existing academic literature focuses on aggregate analyses, these results cannot be applied to a specific context. Although a few studies of post-conflict countries used household data, there is a dearth of those using firm-level data. To fill this gap, this study uses primary data collected through a purpose-designed survey conducted in severely conflict-affected areas. The data is analysed to investigate entrepreneurs’ motivations and their contributions to the socio-economic revival of post-conflict Sri Lanka.

2.8 Conclusion

This chapter reviewed the literature on entrepreneurship in a post-conflict setting and revealed the following: (i) to date, empirical research has been based on cross-country regressions, particularly in Sub-Saharan Africa; (ii) as there is no clear definition of conflict, the countries included in studies differed; (iii) most analyses were conducted at the macro-level, with few micro-level studies; (iv) most studies neglected ‘locally’ occurring conflict at the regional level where most of it occurs; (v) although entrepreneurship in developed countries has been extensively studied and there is a compelling interest in it in developing countries, little is known about this phenomenon in post-conflict countries; (vi) to date, entrepreneurship has lacked an established theoretical framework; and (vi) few firm-level analyses of constraints on the growth of entrepreneurship in post-conflict situations have been conducted. In this study, which concentrates on two severely conflict-affected cities in Sri Lanka, an emerging post- conflict country in South Asia, some of these identified shortcomings are addressed. It uses enterprise-level micro-data to examine entrepreneurs’ motivations and their

35 contributions to economic revival after the cessation of the conflict via a military victory in 2009. The next chapter provides the background to its analyses.

Chapter 3

Sri Lanka’s Contextual Background

3.1 Introduction

The objective of this chapter is to provide a brief account of Sri Lanka’s geographical, historical, ethnic, political and economic background, and to evaluate its current economic conditions after the cessation of the conflict. Sri Lanka, ‘the pearl of the Indian Ocean’ (Tambiah 1986) was transformed to a ‘tear drop’ (Juergensmeyer 1990) during a quarter of a century of civil conflict from 1983 until 2009. On 18th May 2009, the Sri Lankan Army defeated the separatist force, the LTTE. It is reported that at least 7,000 people lost their lives during the last few months of the conflict (Hoglund and Orjuela 2011), and altogether 80,000 were directly involved and innocent civilians were killed (Fearon and Laitin 2011). In addition to the human suffering, Sri Lanka missed opportunities for economic development (Rajasingham-Senanayake 2005; Hoglund and Orjuela 2011; Kelegama 2000). For example, Ganegodage and Rambaldi (2014) argue that average annual economic growth rate would have been nearly to 14% if there had not been a conflict. Also, the total costs of the conflict ranged from USD 1.99 billion to USD 22.34 billion regardless of the time period considered (Bozzoli, Bruck, and Sottsas 2010).

The conflict has its own complex historical, ethnic, socio-economic and political causes which clearly reflect nearly 450 years of colonisation and a series of economic policy errors. These policy errors include, among others, a welfare system adopted in the closed economic policy regime, failure to maintain liberal economic policies implemented after 1978 and dismantling of the rural sector (Athukorala and Jayasuriya 1994; Abeyratne 2004; Dunham and Jayasuriya 2000; Richardson 2005; Wilson 1988).

This chapter is organised as follows. Section 3.2 provides a geographical, historical, ethnic, political and economic background to the conflict. Section 3.3 presents an overview of Sri Lanka’s economic developments from 1948 to 2009. Section 3.4 describes the factors that led to the cessation of the civil conflict and subsequent economic and political situation in the northeast. Section 3.5 explores the opportunities for post-conflict economic development with special reference to the northeast, and Section 3.6 provides a conclusion to this chapter.

3.2 Geographical, Historical, Ethnic, Political and Economic Background to the Conflict in Sri Lanka

Sri Lanka’s civil conflict has an embedded geographical, socio-economic and political background. Sri Lanka is a small island, known as Ceylon until 1972, with a total area of 65,610 sq. km and situated about 29 km off the southeastern coast of India. It is also located at a strategic point in the world map, in the Indian Ocean sea lanes which helped most of the previous invaders find it. With a tropical climate, Sri Lanka has some flat lands, varied terrain and mountainous formations in the south-central area. The average temperature is 860F to 910F (280C to 310C), with a minimum temperature of 60.80F (160C) in the central part of Sri Lanka. The majority Sinhalese are mostly located in the resource- rich and wettest central and southern parts of Sri Lanka, while minority Tamils mostly reside in resource-poor and drier broader geographical regions in the north and east, particularly around Jaffna (Wayland 2004).

In 2012, Sri Lanka’s population was 20.3 million and from 1981 to 2012 it had an annual population growth of 1.1%4 and a density of 325 persons per sq. km. Despite its low income level, Sri Lanka has been an ‘outlier’ in human development indicators, such as life expectancy, health status and literacy rates, vis-à-vis other developing countries since the 1950s (Anand and Ravallion 1993). It is known as the ‘best in South Asia’ (Wriggins 1960). In 2011, life expectancy was 72 for men and 78.6 for women. In 2009, infant and maternal mortality rates were 9.7 and 7.2 per 100,000 live births respectively; and, in 2012, its literacy rate was 95.7%. All these human development indicators are better than for other South Asian countries and are on a par with some East Asian countries.

Sri Lanka’s colonial history in the modern world started with the Portuguese who ruled the country from 1505 to 1658, followed by the Dutch (1658-1796) and the British (1796- 1948) (Wayland 2004). The latter brought Indian Tamils to Sri Lanka as plantation workers in the 18th century. The majority of the Tamil population was concentrated in the northeastern part of the country when the first Europeans, the Portuguese, invaded Sri Lanka in 1505 (DeVotta 2000; Singer 1992). Tamils were advantaged with civil service opportunities and English education under the British, which resulted in the Sinhalese feeling they were being discriminated against. Given this background, a series of discriminatory policies against Tamils was implemented after independence in 1948. For example, disenfranchisement of the Tamil plantation workers brought from India by the

4 No complete population census has been conducted between 1981-2012 due to the conflict. 38

British after independence, the declaration of Sinhala as the official language in 1956, restrictions on the admission of Tamil youths to university in 1971, and the settlement of landless Sinhalese in Tamil-dominated areas via government-sponsored irrigation projects, such as the Gal-oya irrigation scheme in the 1970s (Orjuela 2003). Additionally, despite the fact that Sri Lanka is a multi-religious country, Buddhism was affirmed as the state’s primary religion in 1972. These policies made the minority Tamils feel like ‘second-class citizens’ under the majority Sinhalese government (Orjuela 2003). Given this backdrop, Sri Lanka implemented inward-oriented economic policies while the welfare system, which included free education and health for all, generated extra demand for resources and opportunities (Abeyratne 2004). As a consequence, unemployment among both Sinhalese and Tamil youths increased which led them to act against the government in the 1970s (Abeyratne 2004).

Ethnicity has an important dimension for the conflict. Both Sinhalese and Tamils claim ‘sons of the soil’ status (DeVotta 2007). The conflict between migrant and indigenous people has been referred to as a ‘sons of the soil’ situation (Weiner 1978). This concept has two main elements: (i) conflict between minority ethnic groups in some regions and the majority ethnic groups in others in the same country; and (ii) the minority ethnic groups perceiving themselves to be indigenous to the homelands (Fearon and Laitin 2011). For example, Sinhalese perceive themselves as ‘sons of the soil’ and Tamils as the ‘cultural threat’ to their soil (Fearon and Laitin 2011). As such, the LTTE claimed that the northern province is the traditional homeland of Tamils (Uyangoda 2005). Sinhalese claim they were the first to arrive in the country, based on more than 2,500 years of Sri Lanka’s history recorded in Mahavansa. Prince Vijaya and 700 followers are believed to have arrived in Sri Lanka from North India in 543 BC (DeVotta 2007). On the other hand, the traditional Tamil Kingdom can be traced back to the 13th century (Warnapala 1994). It has also been documented that there was an antagonism between Sinhalese and Tamils demonstrated by the historical battle between Duttagamini (Sinhalese King) and Elara (Tamil King) (DeVotta 2007). The victory of the Sinhalese King over the Tamil King has brought imaginaries into modern Sri Lankan politics and “Tamils are perceived as the current equivalent of King Elara” (Frerks 2013, p.26).

Historical and cultural reasons have made Sri Lanka a diverse, multi-ethnic, multi- religious, multi-linguistic and multi-caste society. As illustrated in Table 3.1, in 2012, the Sinhalese constituted 75% of the total population. The two major minorities, the Tamils and Muslims (commonly referred to as Moors), comprised 15.2%, and 9.3%, respectively. Although, Muslims speak the Tamil language, their primary identity is their

39 religion. Furthermore, the Tamils are divided into two groups: Sri Lankan Tamils (comprising 11.1% of the population) resident in the northeast who perceive themselves as indigenous to Sri Lanka (Fearon and Laitin 2011); and the descendants of Indian Tamils or ‘up-country Tamils’ (4.1% of the population), “the lower cast offspring of tea estate workers” (Wayland 2004, p.412). Despite the Sri Lankan Tamils and Indian Tamils having a common language and culture, Sri Lankan Tamils do not consider themselves as Tamils because of their lower caste status and they are more recent settlers (Wayland 2004). Over the years, particularly after the conflict, the Sinhalese and Sri Lankan Moor populations have increased, while the number of Tamils has drastically declined as demonstrated in Table 3.1. After the eruption of conflict in 1983, over 200,000 Tamils fled overseas (Chandrakanthan 1994).

Table 3 1: Population by ethnicity 1981 – 2012

Ethnic group 2012 Census 2001 Census* 1981 Census Excess or Total no. % Total no. % Total no. % shortfall** of people of people of people Sinhalese 15,250,081 74.9 14,011,734 74.5 10,979,561 74.0 184,096 Sri Lankan Tamil 2,269,266 11.1 2,233,624 11.9 1,886,872 12.7 -316,383 Indian Tamil 839,504 4.1 859,052 4.6 818,658 5.5 -280,265 Sri Lanka Moor 1,892,638 9.3 1,561,910 8.3 1,046,926 7.0 467,477 Burgher 38,293 0.2 38,388 0.2 39,374 0.3 -22,785 Malay 44,130 0.2 55,352 0.3 46,963 0.3 -16,948 Other 25,527 0.1 37,197 0.2 28,398 0.2 -15,192 Total population 20,359,439 100 18,797,257 100 14,846,750 100 Note: * based on estimated populations in Jaffna, Mannar, Vavuniya, Mullaitivu and Kilinochchi in the north and Batticaloa and Trincomalee in the east ** excess or shortfall of population in 2012 relative to 1981 Source: Statistical Abstract (various years), Department of Census and Statistics, Sri Lanka

The majority of Sinhalese are Buddhists (70.1% of the population) and Tamils are mostly Hindus (12.6%) (DCS 2012a). Roman Catholics and other Christians comprise 7.6% across the ethnic boundaries, while the Moors and Malays are Muslim and comprise 9.7% of the population. An all-island census of 1981 shows the population in terms of religion as: 69.3% Buddhist, 15.5% Hindu, 7.6% Christian, and 7.5% Muslim. However, one of the possible motivations for the conflict is not religious diversity, but linguistic differences (Wayland 2004).

As per the above discussion, the civil conflict in Sri Lanka has its own ethnic background. As illustrated in Map 3.1, the northern province contains the majority of Tamils who reside in districts such as the Tamil heartland of Jaffna (99.2%), Kilinochchi (98.2%), Mullaitivu (88.2%), Vavuniya (83.1%) and Mannar (81.1%) in addition to Batticaloa district (72.2%) in the eastern province, while the majority of Muslims live in the eastern province in districts such as Ampara (43.4%) and Trincomalee (41.8%) (DCS 2012a). The majority 40 ethnic group in Nuwara Eliya is the Indian Tamils (53.1%) and the rest of the 16 districts are majority Sinhalese.

Map 3.1: Majority ethnicity by district in Sri Lanka 2012

Source: Amy Griffin, UNSW Canberra, used by permission

Fearon and Laitin (2003) claim that 57% of the civil wars between 1945 and 2008 were related to ‘ethnic’ issues. The literature shows that ethnic divisions have negative effects on economic outcomes, economic growth, public provisions and the quality of institutions (Alesina and La Ferrara 2000; La Porta et al. 1999; Collier 2000) due to the lack of consensus among ethnic groups on economic outcomes (Cooray 2014). These geographically concentrated and politically divided ethnic groups have a chance to mobilise easily around any deprivation or grievances (Denny and Walter 2014). Socio- economic and political grievances, such as youth unemployment and marginalisation of the minority from the political process, can aggravate tensions between Sinhalese and

Tamils (Abeyratne 2004). Given this, the geographical distribution of ethnicity among districts or provinces is important in terms of post-conflict economic development.

Sri Lanka’s political history is embedded with its colonial history. The political structure of Sri Lanka immediately after independence is characterised by a centralised state, with its bureaucratic structure stemming from a nationalistic ideology at the expense of a multi-ethnic and multi-religious society (Fernando and Moonesinghe 2012). This ideology coupled with its electoral system led Sri Lanka to ethnic political divisions (DeVotta 2007) which ultimately led to the creation of armed groups such as the LTTE (Fernando and Moonesinghe 2012). Sri Lanka’s major political parties are ethnically based. For example, the Sinhalese are represented by two main parties, the United National Party (UNP) and United People’s Freedom Alliance (UPFA) led by the Sri Lanka Freedom Party (SLFP), and the Tamils by the Tamil National Alliance (TNA). The current ruling party, the UPFA which is majority Sinhalese with more than a two-thirds majority in the parliament.5 Sri Lanka followed the Westminster system (Moore 1994) after it gained independence from the British on February 4th 1948, having had prior experience of ‘universal adult franchise’ in the early 1930s. It now follows an executive presidential system and is divided into 9 provinces with 25 administrative districts.

Sri Lanka has undergone frequent regime changes. The exceptions are the 17-year rule by the UNP during 1977-94 and the UPFA ruling from 1994 to the present day. After independence, the UNP ruled the country from 1948 to 1956. However, the Hartal organised by the SLFP against the UNP government led the SLFP into government in 1956. After 1956, there were four government terms, the SLFP (1956-59, 1960-65 and 1970-77) and the UNP (1965-70). The left-wing SLFP ruled the country from 1970-77 when it suffered a massive defeat. The UNP rule ended in 1994 with a landslide victory for the People’s Alliance (PA) led by the SLFP. The PA government was defeated in the parliamentary election held in December 2001 and the UNP ruled the country until 2004, after which the UPFA regained control and has ruled the country since.

5 With this political power, the previous two-term limit of being a president has been abolished. Checks on presidential power were also abolished with the new 18th Amendment to the constitution (DeVotta 2014). 42

3.3 Economic Developments 1948-2009

This section explains the socio-economic development in Sri Lanka between 1948-2009, with special reference to the conflict. At the time of independence in 1948, Sri Lanka outperformed other Asian countries in terms of economic development; it was a country with good levels of infrastructure and social development. (Rotberg 1999). Snodgrass (1998, p.89) asks: “What more could a newly independent nation want?”. de Silva (1982, p.489) describes Sri Lanka as an “oasis of stability, peace and order” and Jiggins (1976, p.1) claims it would prove to be “the best bet in Asia” of the post-colonial economies. Its economic and political development immediately after independence stemmed from increased prices for goods such as tea, rubber and coconut. As shown in Table 3.2, in the 1950s, Sri Lanka ranked well above most Asian countries, such as South Korea and Thailand in terms of per capita income (Athukorala and Jayasuriya 2013). In addition, its social welfare system aimed at education and health support raised its social indicators relative to its income (de Silva 1982; Sen 1981). However, this masked the underlying tensions among ethnic groups (Athukorala and Jayasuriya 1994; Athukorala and Jayasuriya 2013).

Table 3.2: Sri Lanka and selected Asian economies – per capita GNPs relative to the USA

Country 1950 1960 1970 Sri Lanka 11.4 12.5 9.3 India 7.1 7.4 6.0 Pakistan 9.0 6.8 8.1 Indonesia -- 5.8 4.8 Thailand 9.6 9.6 11.9 Malaysia 14.6 15.1 15.6 South Korea 7.6 8.7 12.8 Singapore -- 16.6 24.2 Source: Heston, Summers, and Aten (2002) Note: -- data not available

Although tea prices increased from time to time, Sri Lanka experienced a balance of payments problem and declining economic growth, as illustrated in Figure 3.2. In 1953, a movement known as ‘Hartal’ erupted asking for reduced subsidies, increasing prices of rice, electricity, transport and postal services, and withdrawing free meals for school children. The new government declared Sinhala as the official language which caused the Tamils to commence ‘Satyagraha’ (non-violent acts) to which the Sinhalese reacted violently (Fearon and Laitin 2011). However, the SLFP agreed to autonomy in the northeast and later accepted Tamil as the administrative language. With this Sinhalese Buddhist backed government, in 1956 the Tamils lost their political and economic power

43 base in the capital Colombo. As a result, Tamils claimed autonomy for the northern and eastern regions within a federal framework (Bose 1995). The SLFP-led government rejected the Tamil’s demand and this, with other economic reasons, led Tamil youths to dream of a separate Tamil state named ‘Eelam’. Tensions among the Tamils led to the formation of a rebel group called the LTTE which became a guerrilla organisation demanding a separate state. The Tamil diaspora became the main funding source for the LTTE (ICG 2010).

Figure 3.2: Growth in GDP (%) in Sri Lanka 1951-2013

9.0 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0

-1.0

1957 1983 1951 1953 1955 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 -2.0

Source: Statistical Appendix, Annual Report 2013, Central Bank of Sri Lanka

These inward-oriented economic policies continued in the 1960s under the influence of the majority Sinhalese left-wing party, and economic growth started to decline from the late 1960s. Although reasons for this are debatable, one is that ‘consumer subsidies’ and free education and health crippled private sector investment, and another is that the ISI trade policies hindered private sector investment (Osmani 1994; Athukorala and Jayasuriya 2013). Economic policies were not targeted to increase private sector development due to an ‘anti-export bias’ stemming from strict trade controls and overvalued real exchange rates (Cuthbertson and Athukorala 1990). The composition of the major political parties displayed ethnic characteristics in the mid-1960s despite the fact that until independence they had been based on class rather than ethnicity (Athukorala and Jayasuriya 2013).

During the post-colonial period (1948-77), the import substitution strategy (control of import trade and foreign exchange payments) led to the expansion of the government’s

44 role in the economy. During 1970-77, the economy was driven by aggressive state intervention and trade restrictions, with the aim of achieving a ‘socialist society’. This had enormous consequences for the domestic economy, which led to youth uprisings in 1971, and which intensified after the first oil shock in 1973. In 1978 this restrictive policy regime changed to a liberalised one directed towards an export-oriented economy. The government’s economic policies were accompanied by political reforms, changing the constitution to introduce an executive presidency, and then economic growth was maintained at a 5% level with decreasing unemployment.

As illustrated in Figure 3.3, economic policy errors led to a country with high youth unemployment, both the welfare system and liberalisation reforms in Sri Lanka were unable to fulfil the expectations of youth which led to the prolonged civil war (Abeyratne 2004). In 1963, the unemployment rate was 16.6 % which increased to 18.7 % in 1971 and caused both Sinhala and Tamil youths to protest, and then further increased to 19.7 % in 1975. After the liberalisation reforms of 1977 this rate rose from 14.8 % in 1978 to 17.9 % in 1981. Dunham and Jayasuriya (2000) claim that dismantling social welfare, neglecting the rural sector and causing a broadening inequality in the liberalised trade regime propelled the country towards civil war.

Figure 3.3: Acute youth unemployment in the 1970s and 2000s

70 Emergence of Sinhalese and Tamils political crisis 60 50 Sinhalese JVP uprising 40 30 20 10

Pecentage Pecentage ofLabour Force 0 1963 1973 1978/79 1981/82 1986/87 1996/97 2003/04 Unemployment/labour force aged 14 and above Male Unemployment 14-18 years 19-25 years

Source: Based on data compiled from Central Bank of Sri Lanka, Annual Report (various years)

During 1977/78, Sri Lanka was among the first group of Asian countries, including China, India and Vietnam, to experiment with open liberal economic policies, which marked a significant change from previous policies (Cuthbertson and Athukorala 1990; Dunham 45 and Kelegama 1997; Athukorala and Rajapatirana 2000). Economic growth increased, on average, from 2.9% during 1970-77 to 6% during 1978-83 (Athukorala and Jayasuriya 2013). This was accelerated by the establishment of free trade zones, a massive flow of foreign aid for mega-infrastructure development, such as the Mahaweli irrigation project, and investor-friendly constitutional reforms. However, it suffered setbacks due to low economic returns from foreign-funded infrastructure projects and increasing employment in public enterprises (Athukorala and Jayasuriya 2013). In addition, massive public expenditure led to inflationary pressures which weakened the impact of trade liberalisation. As this ‘authoritative’ system disenfranchised the ex-Prime Minister, Mrs Sirimavo Bandaranaike, and frightened both Sinhala and Tamil opposition groups, Tamil parties started to act non-violently against the government (Athukorala and Jayasuriya 2013). Meanwhile, public sector workers, particularly those belonging to left-wing parties, continued to participate in strikes in 1980, with 10,000 dismissed from their jobs (Athukorala and Jayasuriya 2013).

Given this backdrop, ethnic tensions emerged as the result of a series of discriminatory policies whereby agricultural trade liberalisation and land development were targeted mostly to Sinhalese while the minority Tamils were neglected (Bandara and Jayasuriya 2009; Dunham and Jayasuriya 2000). In addition, the government’s industrialisation process focused particularly on western provinces which discriminated against areas where Tamils lived as well as other rural areas. This further increased the Sinhalisation of political parties, which resulted in the exclusion of Tamils from the major parties.

These actions fuelled the Tamils’ radical movement which was driven by a firm determination to establish a separate state through armed conflict (Athukorala and Jayasuriya 2013). The killing of 13 Sinhalese soldiers by the LTTE in Jaffna in July 1983 (Black July) signalled the beginning of the civil war. As a result, anti-Tamil violence occurred, particularly in Colombo, where houses and shops were destroyed and nearly 300 Tamils (according to government figures) or 3,000 Tamils (according to Tamil figures) died (Wayland 2004). The government did not actively control this violence (Wayland 2004).

The second wave of political tension was started in 1988/89 by Sinhalese youths in the southern part of Sri Lanka and spread across the country except the northeast. After suppressing them in 1989, the government was able to bring these youths into the political system. The massive human and economic losses caused by this insurrection

46 led to an economic crisis which required assistance from the International Monetary Fund (IMF) (Athukorala and Jayasuriya 2013).

The second raft of economic reforms began in 1994 with the development of an open market economy and a liberal economic policy regime (Dunham and Kelegama 1997). As privatisation occurred in other countries in the 1990s, Sri Lanka also became involved in this process by introducing economic reforms, such as easing restrictions on foreign direct investment (FDI), simplifying tariffs, and improving its economic structure while adopting an export-oriented industrialisation policy (Cuthbertson 1997). However, between 1994 and 2009, Sri Lanka was adversely affected by the 1997/98 Asian financial crisis, 2004 tsunami, surge in global oil prices, increased food prices in 2007/08 and the 2008/09 global financial crisis. These events had an adverse effect on economic growth. Negative growth in the GDP was recorded in 2001 due mainly to fiscal and inflationary pressures on domestic prices coupled with the flight of capital and a currency crisis in 2000. Despite these issues, the economic reforms have shown positive effects on economic growth in Sri Lanka (Athukorala and Rajapatirana 2000). The major economic and political events are chronologically summarised in Table 3.3.

Table 3.3: Chronology of major economic and political events

Year Major event Colonial Invasion 1505 The first Europeans (Portuguese) arrive in Colombo and control coastal areas 1658 The Dutch arrive and force out Portuguese. Control all the country except Central Kingdom of Kandy 1796 The British arrive and continue to invade the country 1815 The British conquer the Kingdom of Kandy and begin to bring workers from southern India to work in the tea, rubber and coconut plantations 1833 The British control the whole island 1931 The British grant the right to vote and share power with Sinhalese-led Cabinet Independence and post-independence 1947 The first Prime Minister of Ceylon, D.S. Senanayake, elected under the UNP 1948 Independence from the British which continues to fund public welfare programs; UNP rules the country 1950’s Implements more liberal market policies and becomes one of the prosperous nations Sinhalese nationalist ideology 1956 SLFP comes to the power, with S.W.R.D. Bandaranaike as the Prime Minister and follows inward-oriented ISI policies with high government involvement in development Declares Sinhala as the sole official language: more than 100 Tamils killed after the Tamils protest against the language Act 1958 Sinhalese act violently against Tamil rioters who protest against the official language: over 200 Tamils killed and thousands are displaced 1959 Prime Minister S.W.R.D. Bandaranaike assassinated by a Buddhist monk 47

Year Major event 1965 UNP comes to the power and attempts to reverse nationalisation policies 1970 SLFP comes to the power, with Sirimavo Bandaranaike as Prime Minister, wife of the late S.W.R.D Bandaranaike; she was the world’s first female Prime Minister; implements policies such as food rationing and cuts consumer subsidies; forms the Tamil Students Federation (TSF) Ethnic tensions 1971 Sinhalese insurrection led by the People’s Liberation Front – Janatha Vimukthi Peramuna (JVP) begins and the government supresses it 1972 Ceylon renamed Sri Lanka; Buddhism becomes the primary religion; Tamil youth movement, TSF, renamed the Tamil New Tigers 1975/76 The Tamil New Tigers becomes LTTE Introduction of liberal economic policies 1977 UNP comes to power and introduces executive presidency, with J.R. Jayewardene as the President; introduces open economic policies; the Tamil United Liberation Front (TULF) with support for the Tamil Eelam in 1975 wins all parliamentary seats in Tamil-majority areas. As a result, Sinhalese starts anti-Tamil riots claiming over 100 deaths 1981 The Jaffna library is burnt down Civil war begins 1983 13 Sri Lankan soldiers (Sinhalese) killed in Jaffna. As a result, anti- Tamil riots start, called ‘Black July’; 300 or 3,000 Tamils killed, 200,000 of them fled to the western countries and India 1985 First attempt of peace talks fail 1987 Northern and eastern provinces are created under the Indo-Lanka Peace Accord and Indian Peace Keeping Force (IPKF) is deployed 1988/89 Second wave of Sinhalese insurrection by the JVP starts. It ends with the killing of the JVP leader, Rohana Wijeweera. 1990 IPKF withdraws from Sri Lanka. The LTTE expels Muslims from the north 1991 LTTE assassinates former Indian Prime Minister Rajiv Gandhi by a suicide bomb attack 1993 The LTTE kills Sri Lankan President Ranasinghe Premadasa 1994 The UNP loses power after 17 years and the PA, with the SLFP, comes to power; the government initiates privatisation policies; Chandrika Bandaranaike Kumaratunga becomes the President, a daughter of Bandaranaike 1995 Eelam War III begins 1997/98 The effects of Asian financial crisis Peace agreement 2002 The UNP comes to the power and signs the 2002 ceasefire agreement 2003 The LTTE pulls out of the peace talks 2004 2004 tsunami kills more than 30,000 people; Karuna, the LTTE commander in the east, breaks from the LTTE 2005 Prime Minister Mahinda Rajapakse is elected as the President, Foreign Minister Laksman Kadirgamar is assassinated by the LTTE War intensifies 2006 Geneva peace talks fail 2007/08 The government pulls out from the 2002 ceasefire; massive fighting in the north and east; world oil and food prices increase LTTE defeated and expectation of the peace dividend 2009 The LTTE headquarters in Kilinochchi is captured by the army in January 2009 after 10 years; Karuna becomes a government Minister; 18th May, the LTTE leader is killed and ends Sri Lanka’s 25 years of conflict; local election held in August and TNA wins; impact of the global financial crisis

Year Major event 2010 GDP growth more than doubles. President Mahinda Rajapakse re- elected as the President; the term limits on the executive presidency abolished; the powers of the President increased under the 18th amendment. UN human rights against possible war crimes 2011 The United Nations (UN) calls for international investigations for possible war crimes; TNA wins local council election 2012 The UN Human Rights Council (UNHRC) adopts a resolution against possible war crimes; however, the government dismisses it 2013 TNA wins the first northern provincial council election; the UNHRC again adopts a critical resolution 2014 The President says the investigators of the UNHRC will not be permitted to enter the country Source: Compiled by the author

3.4 Ending of Civil Conflict in Sri Lanka and the Aftermath in the Northeast

This section describes the history of the ending of the conflict with special reference to the northeast. After independence, Sri Lanka was a peaceful country with favourable prospects for growth and was considered a model British colony (DeVotta 2014). However, in the 1970s it descended into a violent conflict, particularly with the eruption of the separatist force of Tamil youth, it was often known as an ‘ethnic conflict’ between the majority Sinhalese and minority Tamils. The terrorist organisation LTTE came out of this conflict in 1976 and it continued its objective of achieving a separate state from July 1983 to May 2009, to which the government responded both militarily and through negotiated settlements.

This civil conflict had four major phases (Samarasinghe 2009): (i) Eelam conflict I (1983- 87); (ii) Eelam conflict II (1990-94); (iii) Eelam conflict III (1995-2001); and (iv) Eelam conflict IV (2006-09) during which several attempts were made to settle it, in 1985, 1989/90, 1994/95, 2002/03 and 2006. In 1987, India intervened by sending the IPKF but it withdrew in 1990 following a change of government in India (Rogers, Spencer, and Uyangoda 1998). In December 2001, a ceasefire agreement between the Sri Lankan government and LTTE was signed following mediation by the Norwegian government but this, commonly called ‘2002 Ceasefire’, lasted for just one year (Rupesinghe 2006).6 This was due to many ceasefire violations and subsequent withdrawal by the LTTE (Höglund and Orjuela 2011) coupled with criticisms of the agreement, particularly from the south (Samarasinghe 2009). In 2004, demonstrating intra-Tamil conflict, the LTTE split into two sections which created long-standing boundaries between northern and

6 Rupasinghe (2006) mentions that the agreement recognised the LTTE’s control over the territory and granted LTTE as the sole representative of the Tamils in peace talks. 49 eastern Tamils, with the latter often claiming that the former treated them as ‘second- class citizens’ (DeVotta 2005). Then, the eastern ‘Karuna Group’7 broke away from the main group and assisted government troops. The last attempt at peace talks commenced in Geneva in 2006 but did not yield positive results, leading to Eelam War IV. The decisive victory of the military forces over the LTTE in May 2009 brought the war to an end (Clarke 2011).

After the victory, local government elections were held in the conflict-affected north and east and were followed by presidential and general elections in 2010 (Uyangoda 2010). For the first time after the civil conflict, a provincial council election was also held in the north in 2013. As the primary representative of the Tamil population, the TNA won by a landslide, claiming 30 of the 38 seats in the province, with the ruling party, the UPFA, gaining 7 seats. However, many academics have since criticised the economic development in the north after the victory, claiming that Sinhalisation (e.g., land grabbing and settlement of Sinhalese in the north and east) and militarisation occurred (Goodhand 2010; Frerks 2013; DeVotta 2014).

3.4.1 The Costs of Conflict

Sri Lanka incurred enormous costs for its 25 years of conflict. The conflict not only crippled political and social development and damaged, but important skills were lost due to the brain drain, with about one million people displaced. Also, the Sri Lankan conflict claimed 80,000 lives (Fearon and Laitin 2011) and the country missed opportunities for economic development (Rajasingham-Senanayake 2005; Hoglund and Orjuela 2011; Kelegama 2000). Ganegodage and Rambaldi (2014) argue that the conflict has stolen average annual growth of 14% if there had not been a conflict. As illustrated in Table 3.4, the total costs of the conflict ranged from USD 1.99 billion to USD 22.34 billion regardless of the time period considered (Bozzoli, Bruck, and Sottsas 2010).

7 The former militant of the ‘Karuna Group’, Vinayagamoorthy Muralitharan, called Karuna Amman, is now the Deputy Minister of Resettlement in the UPFA Government in Sri Lanka. 50

Table 3 4: Estimated costs of civil conflict in Sri Lanka

Richardson Grobar and Harris Kelegama Arunatilake, and Gnanaselvam (1999) (1999) Jayasuriya, Samarasinghe (1993) and Kelegama (1991) (2001) Conflict years 1983-88 1983-88 1983-92 1983-87 1984-96 covered by and study 1990-94 Total costs 6.15 1.99 6.31 16.74 22.34 (in 2000 USD billion ) Average per 1.02 0.33 0.63 1.72 1.93 annum (in 2000 USD billion) Source: Bozzoli, Bruck, and Sottsas (2010)

Additionally, both the government and the LTTE were actively engaged with fighting and both parties were blamed for committing human rights violations during the civil war (Höglund and Orjuela 2011; Wayland 2004). The LTTE became “an elite group of suicide bombers” (Wayland 2004, p.414).

Figure 3.4 (a): Number of fatalities, wounded and missing during the conflict*

8000

7000

6000

5000

4000

3000

2000

1000

1993 2005 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2006 2007 2008 2009

Fatalities Wounded Missing Total

Source: Humanitarian Factual Analysis, 2011, Ministry of Defence, Sri Lanka, Available at http://www.defence.lk/news/20110801_Conf.pdf. Note:* the fatalities, wounded and missing committed only by the LTTE

The LTTE engaged in suicide bomb attacks assassinating many Sri Lankans despite their ethnicity (Figure 3.4 (a)). For example, the Tamil Tigers assassinated many

51 politicians starting with the Member of Parliament and Mayor of Jaffna, Alfred Duraiappa and the Indian Prime Minister Rajiv Gandhi in 1991, the Sri Lankan President Ranasinghe Premadasa in 1993, the leader of the opposition Gamini Dissanayake in 1994, the Minister for Foreign Affairs Lakshman Kadiragamar in 2005, among others, and bombing civilian targets, economic centres and Buddhist temples (MOD 2011). DeVotta (2009, p.1022) questions, “how did a ragtag group that numbered less than 50 members before the civil war began in 1983 become ‘among the most dangerous and deadly extremists in the world’?”. The LTTE conducted ethnic cleansing: about 120,000 Muslims were forced to leave the LTTE-controlled area in the 1990s (Frerks 2013), making the northern region purely Tamil (Orjuela 2003). Also the LTTE used violence against their own Tamils during the conflict, against both civilians and rivals to establish their control over the territory (Lilja and Hultman 2011). Figure 3.4 (b) shows the total recorded fatalities from 2000 to 2014. As demonstrated in the figure, fatalities increased in the 2008-09 period due to insensitivity of conflict. It is important to note that the effect of conflict on civilians was the highest in 2009 before the end of the conflict.

Figure 3.4 (b): Fatalities in the conflict in Sri Lanka

16000 14000 12000 10000 8000 6000 4000 2000 0

Civilians Security Force Personnel Terrorists Total

Source: South Asia Terrorism Portal (2014), Accessed on 14/11/2014, Available at http://www.satp.org/satporgtp/countries/shrilanka/database/annual_casualties.htm

3.5 Post-conflict Economic Performance

Sri Lanka’s military victory created widespread domestic and international expectations. Domestically, it engendered high expectations of the peace dividend (Athukorala and Jayasuriya 2013; Coyne, Dempster, and Isaacs 2010) and, within a short span of five years in the immediate post-conflict phase, some of these were realised. For example, GDP growth more than doubled, increasing from 3.5% in 2009 to 8% and 8.2% in 2010 and 2011, respectively (see Figure 3.1). However, it plummeted to 6.3% in 2012 but then recovered to 7.3% in 2013, thereby remaining robust at an average of 7.45% over the 2010-13 period. Further, GDP registered 7.6%, 7.8% and 7.7% growth rates in the first, second and third quarters of 2014, respectively. Sri Lanka’s per capita income increased from USD 2,057 in 2009 to USD 3,280 in 2013 and it graduated to the status of a lower- middle income country in 2010.

Also, poverty and unemployment were reduced. The head-count poverty ratio declined from 8.9% in 2009/10 to 6.7% in 2012/13 (DCS 2014) and unemployment decreased from 5.9% in 2009 to 4.3% in 2013. The budget deficit to GDP ratio narrowed from 9.9% in 2009 to 5.9% in 2013. There was also a surge in infrastructure projects after the conflict. The number of projects, such as roads, railroads, power stations, ports and airports, continued to increase during the post-conflict phase, with the first toll expressway from Colombo to the southern city of Galle completed in 2011, and the second from Colombo to Bandaranaike International Airport, Katunayake and Negombo city in 2013. Importantly, to connect the north and south, a train service from Colombo to Jaffna resumed on 13 October 2014 after a lapse of 24 years.

Cessation of the conflict helped to rebuild sectors such as tourism, foreign aid and foreign reserves, the stock market and the wider business environment. Tourist arrivals increased from 0.44 million in 2009 to 1.27 million in 2013. Total foreign financing disbursements jumped from USD 1,621 million in 2009 to USD 2,058 million in 2012, with development partners including China and India rather than traditional western countries and Japan. Foreign reserves surged from USD 2,401 million in 2008 to USD 7,495 million in 2013. Sri Lanka’s stock market was the world’s second best performer in 2010 (Athukorala and Jayasuriya 2013). All these developments created a business- friendly investment climate, while taxes were simplified and administrative hurdles to investment were reduced (Goodhand 2012).

As previously mentioned, Sri Lanka also improved its international status in the post- conflict phase. Its ranking on the ‘Ease of Doing Business Index’ of the World Bank was upgraded from 105th in 2010 to 85th in 2014, well above other South Asian countries. Also, its value on the Human Development Index (HDI) of the United Nations Development Programme (UNDP) increased from 0.705 in 2010 to 0.750 in 2014, making it 73rd of 185 states. In addition, its ranking on the Global Peace Index (GPI) of the Institute for Economics and Peace (IEP) improved from 126th in 2009 to 105th in 2014.

Despite these positives, a post-conflict phase can be fragile (Walter 2011). The challenge for contemporary Sri Lanka is to sustain its momentum of peace and prosperity, although the literature suggests that there is no guarantee that post-conflict economic recovery will prevail in the medium to long term (Collier, Hoeffler, and Soderbom 2008; Collier and Hoeffler 2004a; World Bank 2011). Numerous hurdles hindered Sri Lanka’s post-conflict economic development, such as high debt and interest payments, an overstaffed public sector, a high budget deficit, corruption, pressures from pay hikes and subsidies, the cost of rehabilitating the northeast, and, on the international front, accountability issues concerning the possible war crimes raised by western countries (Goodhand 2012). Furthermore, “In Sri Lanka’s case, it did not take long for the early optimism to fade” (Athukorala and Jayasuriya (2013, p.2). Concerns have also been raised regarding the high cost of living, with inflation (measured as the change in the consumer price index (CPI) with 2006/07 as the base year) increasing from 3.5% in 2009 to 6.9% in 2013. Although the current account deficit as a percentage of GDP increased from 0.5% in 2009 to 7.8% in 2011, it fell to 3.9% in 2012 and 2013. In addition to economic concerns, a new outbreak of violence occurred on 15th June 2014 when Sinhalese Buddhist mobs attacked Muslim homes and businesses on the basis of a Buddhist monk having been assaulted by Muslims (Economist 2014).

The government that took office in 2005 promised to end the conflict. A medium-term policy document, Mahinda Chinthana: A Vision for New Sri Lanka – Ten Year Horizon Development Framework, was formulated with the aim of achieving ‘balanced growth’ through infrastructure development, recognising both market economic policies and government involvement, and promoting small and medium enterprises with an emphasis on the government’s role in economic development (Athukorala and Jayasuriya 2013). Reflecting economic ‘dirigisme’ (as Lal (2006) refers to state intervention in economic activities), the privatisation of public enterprises, such as state banks, electricity, petroleum, transport and railways and ports, was abandoned. As illustrated in Figure 3.5, trade openness measured by the trade to GDP ratio has been

54 deteriorating since 2004 due to the increased non-tariff barriers since 2000 coupled with the import licensing of food items such as rice, onions and potatoes (Athukorala and Jayasuriya 2013). Additionally, as there are concerns about the investment approval process and tax concessions for FDI, it is important to formulate an outward-oriented economic policy in order to drive post-conflict Sri Lanka forward. In this sense, private sector development plays a pivotal role as it creates employment opportunities and contributes to economic growth.

Figure 3.5: Openness of Sri Lankan economy 1950 – 2013

0.8

0.7

0.6

0.5

0.4

0.3

0.2 Trade/GDP (%) 0.1

1980 1995 2010 1962 1965 1968 1971 1974 1977 1983 1986 1989 1992 1998 2001 2004 2007 2013 1959 Source: Special Statistical Appendix, Annual Report 2013, Central Bank of Sri Lanka

The end of a conflict provides an opportunity for major political, economic and social change in conflict-affected areas. The role of cities and regions and the impact of past policies (Fujita, Masahisa, and Venables 1999; World Bank 2009) were important for the revival of the conflict-affected north and east. The GDP growth in the north was 22% in 2012, which contributed 4% to the national economy. Also, infrastructure development has been dedicated to conflict-affected areas where roads, railroads, education, health and public service delivery are being implemented, with two major programs for the north and east provinces: Uthuru Wasanthaya (Northern Spring) and Negenahira Navodaya (Eastern Revival). During the 2009-13 period, the government, with the assistance of donors, spent LKR 221 billion (approximately USD 1.85 billion) on infrastructure in the northern province (MOFP 2013). Most importantly, according to the Ministry of Resettlement of Sri Lanka, 97% of Internally Displaced Populations (IDPs) have been resettled in the north and east, while about 95% of mined areas have been cleared and approximately 12,000 ex-combatants rehabilitated (MOFP 2013). Expenditure on national security as a percentage of GDP declined from 3.9% in 2009 to 2.4% in 2013, 55 which allowed these savings to be allocated to other productive sectors, such as education, health and private sector development. After the conflict, land prices also increased, as did tourism and construction activities. For example, in 2013, construction activities increased by 14.4%. In addition, 431 bank branches and outlets were established in the north and east, and the north opened its markets to southern traders.

Entrepreneurship in Sri Lanka does not well-recognised in society compared to white- collar employments since independence in 1948 due to the dominance of government in economic activities. Nationalisation of banks, plantation companies and price controls created bottlenecks for the stat-ups and growth of enterprises. Although Sri Lanka opened its economy in 1977, socialist psyche has not faded away from the Sri Lankan society. Based on the survey conducted in 1999 and 2009, Hettige (2009) finds that 70% of youths (aged 18-25) seek public sector employments rather than starting a business.

The impact of conflict on the severely conflict-affected areas, that is north and east, has not yet been fully understood. Due to the severity of the conflicts and the transition out of conflict, socio-economic data is lacking, particularly in the north and east provinces, as this study observes. He suggests the reasons for the decline may include: economic restrictions imposed by the government, such as establishment of high security zones, restrictions on fishing, the LTTE’s illegal taxation and extortion, land mines on agricultural lands, lack of infrastructure, such as electricity, and lack of law and order. In the 1990s, the agrarian economy of the north changed to a service-dominated economy, with 38% of the provincial GDP stemming from public administration and the defence sector in 2003 (Sarvananthan 2007).

Table 3.5: Provincial share of GDP (%) and GDP by sector

Year/Provin W C S Nort East NW NC Uva Sab ce h 2008 45.4 9.8 10.5 3.2 5.6 9.9 4.7 4.5 6.4 2009 45.8 9.8 10.5 3.2 5.8 9.6 4.6 4.5 6.1 2012 42.8 10.2 11.0 3.7 6.3 10.0 5.0 4.8 6.2 GDP by sector (%) 2009 Agriculture 2.8 19.2 17.3 18.3 22.8 18.8 30.7 30.7 19.9 Industry 33 27.1 32.1 9.3 33.5 29.5 19.9 20.4 25.7 Services 64.2 53.7 50.6 72.4 43.7 51.7 40.6 48.9 54.4 2012 Agriculture 2.8 15.8 13.1 19.5 19 14.5 20.7 27.5 18.1 Industry 35.1 29 33.7 23.5 29.2 30.1 24.8 23.4 26.9 Services 62.1 55.2 53.2 57 51.8 55.4 58.5 49.1 55.0 Source: Special Statistical Appendix, Annual Report 2013, Central Bank of Sri Lanka Key: W – western; C – central; S–- south; NW – north western; NC – north central; Sab – Sabaragamuwa

Economic integration between the north and south could be expanded with the completion of the A9 road and the railway line from Colombo to Jaffna. As demonstrated in Table 3.5, the contribution of the industry sector to GDP increased from 9.3% in 2009 to 23.5% in 2012. In terms of its share of GDP, the service sector dominated in the northern province due mainly to government services and public administration. Thus, the government-led economic growth has been prominent in the provinces as well as Sri Lanka as a whole (Sarvananthan 2011).

On the other hand, many industries are concentrated in the western province, with the northern province having fewer major industries than the other provinces. However, the total number of industries in the north increased from 4 in 2009 to 19 in 2013, as shown in Table 3.6.

Table 3.6: Regional distribution of industrial enterprises*

Province 2009 2010 2011 2012 2013 Western 3,541 3,638 3,737 3,722 3,720 Central 247 257 266 281 289 Southern 267 263 272 279 287 North Western 234 180 255 263 261 Sabaragamuwa 111 120 124 124 129 Eastern 25 32 37 42 59 North Central 39 45 46 47 53 Uva 37 45 45 47 53 Northern 4 6 8 11 19 Note: * includes industries registered under both Ministry of Industry and Commerce and Board of Investment (BOI) Source: Special Statistical Appendix, Annual Report 2013, Central Bank of Sri Lanka

MSEs are one of the drivers of economic development in Sri Lanka. The Annual Industrial Survey of Industries (2011) conducted by the Department of Census and Statistics notes that 86% of industries comprise firms that employ fewer than 25 persons. Promoting the private sector has been a major policy initiative since the introduction of the open-market economy in 1977. However, these policies have concentrated on and benefitted some areas, particularly the urban western province, while neglecting other rural regions, which has resulted in increased regional disparity. For example, as illustrated in Table 3.5, the western province contributed 43.4% to GDP in 2012, while the other eight each contributed on average 7% (although this was 4% when considering the conflict-affected northern province). Naranpanawa, Bandara, and Selvanathan (2011) emphasise the importance of implementing short-term complementary policies to compensate low-income populations in rural areas, which is particularly pertinent to the conflict-affected northern province as it has the largest number of Tamils in Sri Lanka. In

57 this context, it is important to develop policies to support MSEs in creating employment, reducing regional disparity and rejuvenating the conflict-affected economy.

As illustrated in Table 3.7, the per capita income of the northern province was LKR 8,339 in 2012/13, the lowest of all the provinces and well below the national average, although it approximately doubled after the cessation of conflict. It is also noteworthy that one of the poorest provinces, Uva, no longer had the lowest per capita income. As income levels are correlated with the onset of conflict, it is important to increase the income levels of people through entrepreneurship.

Table 3.7: Average household per capita income per month (LKR) by province

Province 2003/04 2009/10 2012/13 Western 5,999 11,561 16,124 Central 3,222 8,040 10,104 Southern 3,060 8,035 10,973 Northern 3,208 5,515 8,339 Eastern 2,905 5,663 7,622 Northwestern 3,872 9,352 11,596 North central 3,814 9,280 9,877 Uva 2,570 7,343 9,382 Sabaragamuwa 2,894 9,132 10,718 Sri Lanka 3,968 9,104 11,819 Source: Household Income and Expenditure Surveys (various years), Department of Census and Statistics, Sri Lanka

3.6 Conclusions

Luttwak (1999) refers to a decisive military victory as setting the scene for sustained peace with his proposition to ‘give war a chance’. Post-conflict Sri Lanka, and its economic recovery, provides a test case for Luttwak’s thesis. Despite the brutality and handling of the conflict over its last few months, Toft (2010) claims that peace following a military victory is more stable than that resulting from a negotiated settlement. However, the current literature acknowledges that post-conflict peace is fragile due to the high risks of reverting to conflict (Walter 2011; Walter 2014; Kreutz 2010).

The end of civil conflict provided opportunities for economic development in Sri Lanka, particularly in the northeast, and the correction of previous policy errors to facilitate faster economic growth. The civil war in Sri Lanka was characterised by complex socio- economic and political factors (Athukorala and Jayasuriya 1994; Abeyratne 2004; Dunham and Jayasuriya 2000; Richardson 2005; Wilson 1988; Athukorala and Jayasuriya 2013). This chapter explained these complexities and the series of policy errors, such as ISI policies and welfare systems, set against the background of the

58 conflict. It also highlighted that private sector-led open-market economic policies could benefit the economy, which supports the argument that private sector development in post-conflict settings can yield higher economic outcomes. In addition, it showed the significance of political processes for addressing the grievances of minority ethnic groups. On the other hand, regional economic development is also important in reducing the regional disparity among provinces (Bandara and Jayasuriya 2011) discussed above. In order to secure peace and prosperity, it is important to increase economic development through the promotion of entrepreneurship (Sarvananthan 2011). However, so far, no systematic quantitative research has been undertaken to analyse the motivations and contributions of entrepreneurs in the northern province at the micro- level. The next chapter outlines the methodology and data in conducting the research to address these questions.

Chapter 4

Research Methodology and Data

4.1 Introduction

The aim of this study is to examine the entrepreneurs’ motivations for starting a business and the contributions of these businesses to economic revival after the cessation of conflict. In order to ensure a comprehensive research method, an integrated, multidisciplinary approach that involves the analysis of data from different aspects, such as economics, business, sociology and anthropology, is adopted. Thus, the conclusions drawn will contribute to research in each of these diverse areas.

This chapter discusses the research methodology used to collect and analyse data, while empirical analyses are presented in Chapters 5, 6 and 7. The research method selected to analyse answers to the research questions conforms to best practice in the literature. Given the nature of the research, and the difficulties of data collection and lack of available research on entrepreneurship, particularly in conflict-affected areas, a purpose- designed survey of emerging entrepreneurs is necessary (Verwimp, Justino, and Bruck 2009). Although the effect of entrepreneurship on peace-building has recently been acknowledged, there is little information in the extant literature on how firms contribute to economic revival and peace. This is mainly due to the fact that it is more difficult to obtain enterprise-level than household-level data (Verwimp, Justino, and Bruck 2009; Bruck et al. 2013). This study addresses the absence of firm-level analysis in the post- conflict literature by administering a purpose-designed survey in two post-conflict Sri Lankan cities in 2012.

The remainder of this chapter is structured as follows: Section 4.2 presents methods used to analyse the impacts of conflict on the behaviours of individuals and households at the micro level, that is, the use of a cross-sectional purpose-designed survey; Section 4.3 explains the research settings and reasons for their selection; Section 4.4 describes the questionnaire design process; Section 4.5 discusses the sample selection process and sampling method; Section 4.6 focuses on the survey and the method of data analysis; Section 4.7 reports the difficulties involved in data collection in a post-conflict environment; Section 4.8 presents sample descriptions and summary statistics; and Section 4.9 discusses the conclusions drawn.

4.2 Research Design

The two most important types of surveys used in the literature are cross-sectional and longitudinal, with data collected through questionnaires or interviews in addition to lab and/or field experiments (Creswell 2009). This study uses a quantitative approach employing a cross-sectional purpose-designed survey.

Cross-sectional surveys: in contrast to longitudinal surveys, these collect data from informants at a given point in time, which is then used to make inferences about the population of interest. Cross-sectional data is a “snapshot of characteristics of each element of a subset of the population” (Cameron and Trivedi 2005, p.47). The cross- sectional survey has been used to examine the impacts of conflict on individuals or households in various disciplines, such as economics, sociology, political science and anthropology (Bruck et al. 2013), and allows researchers to test micro-economic hypotheses and evaluate economic policies (Wooldridge 2009). Therefore, cross- sectional data is important for examining individual or firm behaviours in a population of interest. However, it has some drawbacks which, in particular, arise from violating the assumptions of the random sampling technique for obtaining data, that is: (i) excluding a particular attribute from a subset of data; and (ii) taking a larger sample relative to the population. This is not a major issue in this study due to the adherence to the random sampling technique.

Most research conducted in post-conflict countries has used cross-sectional data because of the lack of continuous and complete data for all measurements over a long period. There are two main reasons for this limitation: firstly, it is impossible to conduct research at the time of intensive conflict because of security concerns; and, secondly, there are various selection biases and recall errors in the data collection. The latter occurs due to the removal of an important sub-group, such as those who migrated, or access to an area being restricted, and/or respondents being repressed and/or refusing to recall unpleasant memories (Bruck et al. 2013). Additionally, in contrast to a longitudinal survey (a panel data set which repeatedly measures each cross-sectional member in the data over time), a cross-sectional survey does not allow control for unobserved characteristics of individuals, households, firms and cities, or for determining causal relationships. Therefore, it is impossible to examine the impact of economic policies using cross-sectional data (Wooldridge 2009).

Recent multi-disciplinary studies have used mainly two research designs to understand the effect of conflict on human behaviour: (i) purpose-designed surveys; and (ii) socio- economic surveys, such as living standards and measurement surveys (LSMSs), household surveys, censuses, and demographic and health surveys (Bruck, Naude, and Verwimp 2013). This thesis used a purpose-designed survey, as explained in the following sections.

Purpose-designed surveys: these have been used to examine the causes and impacts of conflict at the micro level, with their main advantage being that the direct impacts can be identified using a questionnaire (Bruck et al. 2013). Reviewing conflict surveys, Bruck et al. (2013) classified the main purpose-designed surveys into categories, such as ex- combatants, genocides and atrocities, displaced populations, post-conflict reconstruction and health. However, they did not identify surveys of emerging entrepreneurs, as there are only a few in the extant research. This study addresses the gap in terms of enterprise- level surveys.

As the focus of this research was to examine the motivations for starting a business and the contributions of these businesses to economic revival at the micro level, a purpose- designed survey, which enables the researcher to use a questionnaire, was conducted. Additionally, due to the lack of data there is a gap in the literature regarding how violence/peace affects firms and/or entrepreneurs. The difficulty of obtaining data is due to firms are unwilling to provide information, and the absence of regular data collections during the time of intense conflict (Bruck, Naude, and Verwimp 2013). Consequently, the roles of firms and/or entrepreneurs in socio-economic revival following the cessation of civil conflict have received little attention in the literature (reported in Chapter 2). Regarding time and cost constraints, the purpose-designed survey was chosen as the appropriate method for data collection in this setting. Further, due to the sensitive nature of the research and concerns for the security of the researcher, the Human Resource Ethics Advisory (HREA) Committee of the University of New South Wales (UNSW) granted its approval to conduct field surveys within city limits in the proposed research districts (see Appendix A for participant information statement and consent form). All these reasons led to a purpose-designed survey as the appropriate method. However, despite its advantages, it has some drawbacks, such as the previously discussed selection bias and recall errors which has minimised by using the random sampling technique and enterprise-level data.

4.3 Research Setting

This research is set in post-conflict Sri Lanka. Sri Lanka provides an opportunity to understand emerging post-conflict entrepreneurs after the cessation of the conflict. Sri Lanka embedded with conflict due its complex geographical, historical, ethnic, political and economic reasons, as discussed in Chapter 3. The purpose-designed survey was administered to 243 entrepreneurs in the two post-conflict cities of Jaffna and Kilinochchi in the northern province, Sri Lanka, from June to October 2012 (see Map 4.1). These cities were severely affected by the conflict and the main ethnic groups in the area are the country’s minority Tamils (see Chapter 3 for more details).

Map 4.1: Survey area – cities of Jaffna and Kilinochchi

Source: Amy Griffin, UNSW Canberra, used by permission

Of the conflict-affected areas in the north, Jaffna Peninsula deserves special attention because it is considered the ‘epicentre’ of the separatist force (Shastri 1990). Jaffna, the capital city of the northern province, has the highest population density (599 per sq. km), is isolated from the rest of Sri Lanka in terms of infrastructure, and is only connected to the mainland by a small strip of land called Elephant Pass. Jaffna belongs to a dry zone (1,025 sq. km) surrounded by sea from the north, east and west, and is encircled by the Jaffna lagoon and Kilinochchi District in the south.

The northern province has the potential to develop industries for fisheries, aquaculture and tourism, particularly in Jaffna (PSI 2012). In addition, its Point Pedro harbour, A9

64 road and Palali airport provide a logistical setting for potential industries (Sarvananthan 2007). The region is rich in limestone deposits and the government is currently investigating possible oil deposits off the Gulf of Mannar. However, as it does not have a single perennial river, Jaffna is exposed to scarcity of water for drinking and agricultural purposes. It was mainly under government control during the conflict, except between 1990 and 1995 when it was controlled by the LTTE.

Adjacent to Jaffna, Kilinochchi (1,237 sq. km) has two coastal belts and a mostly sparsely populated area (99 per sq. km) with forest cover. As it has irrigation tanks fed by perennial rivers, paddy cultivation is the dominant sector. It was one of the districts most affected by the conflict as, for many years, it was controlled by the LTTE which established its de-facto government there.

Jaffna District has a total population of 583,882 (comprising 99.2% Tamils, 0.39% Sinhalese and 0.37% Muslims) and Kilinochchi District 113,510 (98.2% Tamils, 1.2% Sinhalese and 0.5% Muslims), with both districts divided into divisional secretariats (DS) (DCS 2012a). The Jaffna DS, which includes Jaffna city, has a population of 50,789 (95% Tamils, 3.3% Muslims and 1% Sinhalese) and Karachchi DS, which includes Kilinochchi city, 61,137 (98% Tamils, 0.87% Sinhalese and 0.29% Muslims). As explained in Chapter 3, the demographics of these cities are completely different from the whole country. Of the 20.3 million Sri Lankans, the largest ethnic group is the Sinahalese (75%) followed by the Tamils (15.2%) and Muslims (9.3%).

4.4 Questionnaire Design

This study employed four steps in the process of questionnaire design: an expert review; language translation; pre-testing; and a fieldwork survey.

Expert review: the questionnaire was designed based on the World Bank’s enterprise ‘doing business’ survey 2011 and those commonly used by other studies (e.g, Bruck et al. 2010; Bruck et al. 2013; Tarway-Twalla 2011; Demirguc-Kunt, Klapper, and Panos 2011). Two economics professors were asked to examine the initial questionnaire designed by the author. Their comments regarding wording were incorporated and a section on diaspora involvement in entrepreneurship in terms of emerging entrepreneurs in a post-conflict setting was added.

Translation of questionnaire: as this study was conducted in the northern part of Sri Lanka, the questionnaire was translated from English to Tamil. For this process, this 65 study used two professional bilingual translators, and a back translation technique, which “translates back from the target language to the source language” (Nurjannah et al. 2014, p.3). This method was used so that the translated and original questionnaires were consistent.

Pilot testing of questionnaire: this was conducted in the local language (Tamil) with 10 entrepreneurs in different locations within Jaffna’s city limits. The results were used to modify the questionnaire, particularly in terms of language, sensitivity and content, but are not included in the main survey. The questionnaire had five main components: (i) personal characteristics (gender, ethnicity, religion, education, residence and marital status); (ii) general information about the business (type, ownership, year of establishment, reason/s for start-up, membership in any business network, number of employees and income received at both start-up and the time of the survey); (iii) inter- ethnic trade relationships; (iv) barriers to doing business (finance, business registration, taxes and infrastructure, i.e., electricity, water and telecommunications and security); and (v) diaspora involvement in business. In addition, the respondents’ perceptions of the post-conflict business environment, obstacles to doing business and suggestions for improving their present business situation were gathered for analysis.

The main problem with the pilot questionnaire was the ambiguous meanings of certain questions and some sensitivity; for example, when respondents were asked about their labour market engagements prior to starting their businesses, some were reluctant to declare that they were ex-combatants, had been rehabilitated or had received grants from non-government organisations (NGOs), and the word ‘peace’ was not clear to them. To solve these problems, some questions were added/replaced, and the final questionnaire is presented in Appendix B.

4.5 Sample Selection Process and Sampling Method

Using a stratified random sample technique, 130 firms were approached to be interviewed, of which 126 in Jaffna participated. Due to the absence of an updated public register of enterprises in Jaffna, one entrepreneur was selected at random from every cluster of five counted from the beginning of each of the main streets to the city limits. This approach was applied to avoid potential bias in the results that could be caused by demographic or other differences among the entrepreneurs surveyed. Unlike Jaffna, Kilinochchi was completely devastated after the 2008 intense conflict; all the firms were

66 new. A sample of 123 was approached, of which 117 participated. The survey was conducted exclusively for this thesis with the sample firms.

4.6 Major Survey and Data Analysis

After finalising the questionnaire, sample selection and sampling technique, the survey was administered in Jaffna and Kilinochchi under the guidelines of the UNSW HREA process. Interviews were conducted firm by firm by trained research assistants under the supervision of the researcher. These assistants had prior fieldwork experience after graduating from universities such as the University of Jaffna and University of Peradeniya. They were trained on the objectives of the study, the research methodology, concerns of confidentiality and administration of the questionnaire.

In the survey 243 entrepreneurs participated and 10 did not; a response rate of 96.04%. After data collection, the responses were manually entered into a computer for data analyses which were conducted using the STATA 13 software.

4.7 Challenges of Data Collection in a Post-conflict Environment

Collecting data in a post-conflict setting presents numerous challenges, with the following seven identified in the context of post-conflict Sri Lanka:

(i) security of the researcher and sensitivity of the study; (ii) building trust with key stakeholders which required local expertise; (iii) respondents’ attitudes towards the researcher and project; (iv) post-conflict context; (v) recruiting skilled research assistants for the project; (vi) obtaining a representative sample of firms (explained Section 4.5 above); and (vii) time and research funding, for example, interviews sometimes lasted several hours and required multiple visits.

It is important to consider the challenges for future research identified by Chand (2011) in his household survey conducted in post-conflict Bougainville, PNG: (i) ensuring the security of the researcher; (ii) gaining entry into the community; (iii) drawing on local knowledge and capacity; (iv) sampling; and (v) determining data collection methods and costs.

4.7.1 Security Concerns

The HREA Committee’s main concern was the security of the researcher given the sensitivity of the research for the respondents. Although the researcher is Sri Lankan, he had not lived or travelled in those areas prior to the fieldwork. This concern was heightened by the fact that a Sinhalese (this author) would be conducting a survey in a Tamil-dominated area. Before commencing the survey, the officer in charge of the Jaffna police was informed about the survey and no security-related incident took place during the researcher’s stays in the cities.

Security concerns can be illustrated by explaining this researcher’s travel experience and observations made during the survey. One of the major government-controlled checkpoints, Omanthai, was active at the time of the researcher’s survey. On the way to Jaffna, passengers’ identity cards were checked and, on their return, people were asked to get off the bus to have their bags checked, which demonstrated that security checking of passengers and vehicles continued during the post-conflict phase. There was a snack bar at the checkpoint operated and staffed by the Sri Lanka Army (SLA) to cater for passengers.

4.7.2 Building Trust with Key Stakeholders

Prior to this researcher’s departure for Sri Lanka, he discussed his plans with stakeholders and, during the first month there, familiarised himself with the area and culture. He established trust with all the stakeholders, including local government officials, business communities and agencies, with the help of his colleagues who work for the government in the two cities. Thereafter, the District Secretariat officials in Jaffna and Kilinochchi, as well as trade associations, expressed their utmost cooperation with the survey.

4.7.3 Recruiting Research Assistants

The next task was to select research assistants to administer the survey, which was a challenge as the researcher had little knowledge of Tamils and the local context and culture. The pre-departure plan was to recruit a few undergraduate students from the University of Jaffna as research assistants. However, after consultations with the relevant planning units, graduate students who had joined the government service as trainees, of whom there was a large number, were recruited as research assistants. An interview was conducted to select the best for the project. Ten graduates who spoke 68

English and had experience in fieldwork were chosen from the pool. Eight, four males and four females, were employed and the remaining two were placed on a waiting list in case of the sickness or absence. These eight were given a roster for conducting the survey under the researcher’s personal supervision and were paid a fixed rate (nearly USD 2) for each completed questionnaire. The assistants’ previous fieldwork experiences helped the researcher gain access to the respondents. A similar process was carried out in Kilinochchi.

4.7.4 Local Context

Understanding the local context was important for conducting the survey, with language one of the major challenges because the local language is Tamil. The researcher’s mother tongue is Sinhalese, and only a few respondents spoke either Sinhalese or English. However, this barrier was overcome with the help of the assistants and the questionnaire design which focused mainly on gathering quantitative data.

Infrastructure development projects, such as roads, electricity, market development, telecommunications and health, which are the most important for entrepreneurial activities, were being implemented in the areas. Also, institutions such as banks, trade associations and government agencies were being established. These projects and the establishment of institutions provide a setting for the emergence of entrepreneurs in the conflict-affected areas.

4.7.5 Respondents’ Attitudes

Respondents’ attitudes towards the project were also of concern. During the pilot survey in Jaffna, entrepreneurs showed some inquisitiveness about this researcher and the project because this survey was the first such study conducted in both cities after the conflict. However, after having the project explained and the ethics approval letter and UNSW consent form presented to them, the respondents were convinced that the project was an independent study leading to a research degree. During this period, relevant trade associations in the two cities were consulted and they supported this research.

4.7.6 Time and Costs

Another challenge was related to time and funding. Although the anticipated time to answer all the questions in the questionnaire was about 40 minutes, it took more than this because some entrepreneurs were busy with customers in the morning and doing their banking in the afternoon. In fact, several visits had to be made to collect information from one respondent. Also, as the cost of basic accommodation ranged between LKR 800 and LKR 2,000 per night (USD 8 – 20), it was difficult to find a suitable hotel with adequate security in Kilinochchi. The required funding for the survey was provided by the School of Business, UNSW, and the Australian Agency for International Development (AusAID).

4.8 Sample Description and Summary Statistics

The survey data was collected in the fields of: (i) individual characteristics; (ii) firm characteristics; (iii) access to finance; (iv) business environment; and (v) inter-ethnic trade links with the south after the conflict. Related reviews of the literature on these factors are presented in empirical Chapters 5, 6 and 7.

Table 4.1 presents the summary statistics. The main objective is to provide an overview of the data, which was primary in nature. The categorical variables were coded as ‘1’ if ‘yes’ and ‘0’ if otherwise, and reflected the characteristics of respondents, firms and business environments, as described below.

Personal characteristics: the respondents’ personal characteristics included ethnicity, gender, age, children, health (able-bodied individuals), level of education, entrepreneurial background (entrepreneurial family members and previous business experience), social capital (trade membership) and labour origins (ex-entrepreneur, employed – formal and informal, unemployed). These variables are important as they affect entrepreneurship (Demirguc-Kunt, Klapper, and Panos 2011; Djankov et al. 2006b)

Firm characteristics: these included the number of employees, gross income, age, ownership (private, partnership or company), trade membership, sector and ownership of business (private/partnerships) and rental of premises. These are considered the main factors that affect firm growth (Coad 2009; Storey 1994a; Coad and Tamvada 2012).

Access to finance: home ownership was included as a measure of the wealth of the respondents because it was considered a good proxy for measuring the wealth of individuals living in a developing country (Lu and Tao 2010). To measure access to finance, the following variables were determined as important for start-ups: savings deposits, pawning, micro loans, informal and bank loans, and remittances received from abroad (Paulson and Townsend 2004; Demirguc-Kunt, Klapper, and Panos 2011).

In addition, to examine the business environment, businesses were classified into three main sectors: (i) trade; (II) services; and (iii) construction and related manufacturing. Further, the variable TRADESOUTH was included to examine the inter-ethnic trade relationships between north and south (Tobias and Boudreaux 2011). Finally, the city dummy was included to correct city-specific effects on the dependent variable.

Summary statistics: these were drawn from the whole sample and are presented in Table 4.1. The sample included small firms with a maximum of 21 employees and an average of approximately two. The firms were young, on average, three years old and, although the majority started after 2009, some were set up before then. Of the entrepreneurs in the Jaffna sample, 25.19% established their businesses during the conflict (in the government-controlled period) and restarted or continued them after 2009. However, the firms in Kilinochchi were established after 2009.

Even though the selected firms performed a wide range of business activities, this study classified them into three sectors according to their main ones: (i) trade – retail and wholesale (41.56%); (II) services (34.15%); and (iii) construction and related manufacturing (9.05%).

About 93% of owners were Tamils, only 5% Muslims and only 1% Sinhalese, with less than 1% of firms owned by both Tamils and Muslims. There respondents were 91% male and the rest female, with approximately 43% having only a lower secondary education and 30% upper secondary level, and graduates and higher degree holders accounting for 7.8 % of the sample. About 13% of respondents received formal entrepreneurial training from private enterprises, NGOs or government agencies after the conflict. Despite having received training themselves, only 17% of owners were willing to train their employees.

Table 4.1: Summary Statistics (N=243)

Variable Mean Standard Deviation (SD) City dummy KILINOCHCHI -Dummy Variable (DV=1/0) 0.4814 0.5006 Individual Characteristics of Entrepreneur Ethnicity TAMIL (DV) 0.9300 0.2556 MUSLIM (DV) 0.0534 0.2254 MALE (DV) 0.9094 0.2875 AGE ( years) 37.40 10.61 CHILDREN (DV) 0.7695 0.4219 Education PRIMARY (DV) 0.1234 0.3296 LOWER SECONDARY(DV) 0.4367 0.4969 UPPER SECONDARY(DV) 0.2962 0.4575 GRADUATE & POST (DV) 0.0781 0.2690 ABLE-BODIED (DV) 0.9300 0.2556 ENTREPRENEURIAL FAMILY(DV) 0.3415 0.4752 PAST BUS EXP- previous business experience (DV) 0.7283 0.4457 Labour market characteristics EX-ENTREPRENEURS (DV) 0.2208 0.4156 FORMAL SECTOR EMPLOYED (DV) 0.3083 0.4627 INFORMAL SECTOR EMPLOYED (DV) 0.1458 0.3536 UNEMPLOYED (DV) 0.2333 0.4238 EX-COMBATANT (DV) 0.0250 0.1564 Social Networks TRADE MEMBERSHIP (DV) 0.5390 0.4994 Sector TRADE- retail and wholesale trade (DV) 0.4156 0.4938 SERVICES – hotel, restaurant, communications (DV) 0.3415 0.4752 CONST& MANU - construction and related manufacturing 0.0905 0.2875 (DV) TRADESOUTH -inter-ethnic trade relationships (DV) 0.3744 0.4849 Wealth HOME OWNERSHIP (own a house) (DV) 0.6543 0.4765 Financial Characteristics Self-help finance SAVINGS (DV) 0.5020 0.5010 External Finance INFORMALLOAN (DV) 0.3443 0.4761 MICROLOAN (DV) 0.0414 0.1998 BANKLOAN (DV) 0.5394 0.4994 Remittances REMITABROAD – Remittances from abroad (DV) 0.0539 0.2263 Other finance PAWNING (DV) 0.1618 0.3690

Of the total respondents, 30.4% were unemployed before starting their businesses and another 22% were ex-entrepreneurs. Of the 31% in the sample who were formal sector employees, interestingly, only 2.5% were ex-combatants.

Approximately 65% of the whole sample reported that they were homeowners and only 20.57% owned additional properties as investments. However, about half the respondents reported having financial difficulties and, as a result, relied on external finance to support their seed capital for their start-ups. Considering all the seed capital sought, the majority (33%) of respondents had bank loans, 31% personal savings and 21% informal loans, while 16 % used pawning and 5.3% remittances as seed capital. About 37% of respondents had a trade link with the south. In addition, the qualitative data found that a lack of finance and electricity were the two major two barriers to conducting business.

4.9 Conclusions

This chapter presented the methodology adopted to test the hypotheses. A purpose- designed survey was administered to 243 entrepreneurs in two post-conflict Sri Lankan cities in 2012 to examine their motivations for starting a business and their contributions to economic revival. The challenges involved in undertaking fieldwork in post-conflict environments are many, some of which are security concerns, the difficulty of sample selection, a lack of local knowledge and the time required to collect data from firms. The summary statistics show that, on average, within three years of their establishment, firms were still small and the majority were owned by Tamils. The details of the data analysis, interpretation and reporting of data for the study are discussed in the next three empirical Chapters, 5, 6 and 7.

Chapter 5

Emerging Post-conflict Entrepreneurs

5.1 Introduction

The importance of promoting entrepreneurship in developing countries is acknowledged by both practitioners and academics, as discussed in Chapter 2 (Audretsch, Keilbach, and Lehmann 2006; Koellinger, and Thurik, 2012). This is because of the role that entrepreneurs play in job creation, poverty reduction, economic development and peace generation (Wilson and Wilson 2006; Naude 2011b; Tobias, Mair, and Barbosa-Leiker 2013) despite the sizes of their enterprises (Ayyagari, Demirguc-Kunt, and Maksimovic 2011). Although entrepreneurship is doubly important in terms of economic recovery following a civil war, little is known about its role in socio-economic revival in a post- conflict environment. This chapter attempts to address the under-studied area of the emergence of entrepreneurs and small businesses using the case of post-conflict Sri Lanka. It investigates the motivations for business start-ups and the contribution small businesses make to economic recovery.

Entrepreneurs play an important role in peace-building activities as they draw ethnic groups together in trade (Tobias and Boudreaux 2011), with the bonds fostering a public good which benefits society at large. Thus, identifying the drivers for business start-ups after a conflict can have ramifications for future peace and economic development. Examining empirical studies in more detail, most use household data or the Global Entrepreneurship Monitor (GEM) dataset to distinguish between entrepreneurs and non- entrepreneurs (Djankov et al. 2006a, 2006b; Ardagna and Lusardi 2008; Lu and Tao 2010; Chowdhury 2011; Demirguc-Kunt, Klapper, and Panos 2011). Departing from this literature and using enterprise-level data collected through the purpose-designed survey discussed in Chapter 4, this study distinguishes the different factors related to ‘employer entrepreneurs’, who create employment for others, and ‘solo self-employed entrepreneurs’ who are self-employed workers without employees,.

The data gathered from the survey allows this research to distinguish between these entrepreneurs following the approach of van Stel, Wennekers, and Scholman (2014). Using ‘becoming an employer entrepreneur’ as the dependent variable, this study investigates the differences in terms of personal characteristics, access to finance and

75 inter-ethnic trade relationships in the post-conflict settings. The data is analysed using descriptive statistics and probit regression.

The findings suggest that significant proportions of entrepreneurs undertake entrepreneurial activities ‘out of necessity’ and are subject to financial constraints. Nevertheless, these activities contribute to peace generation. They also show the positive effects of entrepreneurship on the formation of social capital between ethnic groups which has implications for reducing the risks of conflict recidivism (Gedajlovic et al. 2013).

This chapter provides two major contributions to the literature. Firstly, it distinguishes between the contributions and motivations of both employer and solo self-employed entrepreneurs in a post-conflict setting. Secondly, it analyses firm-level data in a post- conflict context for which data is currently sparse, thereby providing new insights into entrepreneurship.

This chapter is organised as follows: Section 5.2 discusses the theoretical framework; Section 5.3 specifies the empirical strategy; 5.4 describes the data; Section 5.5 provides summary statistics; Section 5.6 presents the estimation results and discusses the robustness checks; Section 5.7 provides a discussion of the results; and Section 5.6 draws conclusions.

5.2 Determinants of Entrepreneurship – Brief Review of Literature

The determinants of entrepreneurship have been studied in a variety of disciplines, including economics, business management, sociology and psychology. Despite many attempts to develop a more general theoretical framework, there is no clearly established theory or model of entrepreneurship (Parker 2009). However, empirical analyses reveal some robust correlations between the factors affecting entrepreneurship and start-ups. The many factors influencing individuals to participate in entrepreneurship include: (i) personal characteristics, psychological and socio-economic factors (Djankov et al. 2006a, 2006b; Chowdhury 2011; Demirguc-Kunt, Klapper, and Panos 2011); (ii) labour market characteristics (Earle and Sakova 2000; Wang 2006; Millan, Congregado, and Roman 2012); (iii) financial constraints (Paulson and Townsend 2004; Kerr and Nanda 2011; Demirguc-Kunt, Klapper, and Panos 2011); and (iv) the nature of the business environment (Shleifer and Vishny 1993; Klapper, Laeven, and Rajan 2006; Aterido, Hallward-Driemeier, and Pages 2011; Sanders and Weitzel 2013; Sebigunda 2013). 76

5.2.1 Personal Characteristics

This section outlines the main personal characteristics, such as ethnicity, gender, age, education, previous business experience, health, family background, number of children and previous labour market experience of, in particular, unemployed and formal sector employees in the contexts of both developed and developing countries.

Ethnicity is one of the main drivers of the formation of new firms in developing countries (Iyer and Schoar 2010; Mavoothu 2009). The majority of such studies concentrate on understanding self-employment entry by immigrants (Simoes, Moreira, and Crespo 2013); for example, the study by Salaff, Greve, and Ping (2002) of educated and experienced Chinese immigrants to Canada. These studies found that these people are forced into self-employment because of their inability to find a formal job. Ram (1994) highlights the importance of social networks among minority ethnic groups which help to overcome obstacles such as credit constraints, information asymmetries and high transaction costs (Kilby 1983). Therefore, it is important to examine the roles played by ethnic groups, such as Tamils, Muslims and Sinhalese, in the conflict-affected areas of Sri Lanka.

Gender also plays a major role in whether an individual has the propensity to become either an employer or solo self-employed entrepreneur (Warnecke 2013). Women are less likely to become self-employed than men in Organization for Economic Co-operation and Development (OECD) countries, post-conflict Bosnia-Herzegovina and also cross- country analyses (Blanchflower 2000; Demirguc-Kunt, Klapper, and Panos 2011; Klyver, Nielsen, and Evald 2013), whereas Co, Gang, and Yun (2005) found that Hungarian women are more likely to become self-employed. Given that there are generally more families headed by women after a conflict, it is important to investigate the role of gender in business start-ups in post-conflict situations.

Regarding age and entrepreneurship, the literature presents mixed results. Empirical studies of age as a variable include both age and age squared in their econometric models (Simoes, Moreira, and Crespo 2013). According to the GEM 2011 (Kelley, Singer, and Herrington 2012), start-up entrepreneurs are more likely to be aged between 25 and 44 because older people have higher human capital, more financial capital, better networks and increased employment flexibility than younger people (Simoes, Moreira, and Crespo 2013). In contrast, Hintermaier and Steinberger (2005) found a negative relationship between age and start-ups in the US and Evans and Leighton (1989), Co,

Gang, and Yun (2005) and Yang, Cheng, and Gao (2008) determine that age has an insignificant effect on entrepreneurship in the US, Hungary and China.

The effect of education on the probability of starting a business varies. The literature suggests that a higher level of education is likely to increase entry, survival and post- entry performance as, theoretically at least, individuals with higher educational levels have more managerial capacity and better employment and entrepreneurial opportunities (Calvo and Wellisz 1980). Similarly, empirical evidence suggests a positive effect of education on the probability of becoming an entrepreneur (Bates 1995; Goedhuys and Sleuwaegen 2000; Zissimopoulos, Karoly, and Gu 2010; Fritsch, Kritikos, and Rusakova 2012). Conversely, education is demonstrated to have a negative impact on entrepreneurship in Kosovo (Krasniqi 2009). This could be due to the fact that there, a high level of education provides better opportunities for formal sector employment than for entrepreneurship, thereby increasing the opportunity costs of becoming an entrepreneur. Moreover, Pisani and Pagan (2004), investigating the Nicaraguan context, found an insignificant relationship between education and the likelihood of becoming self- employed. However, recent empirical work in the US shows a U-shaped relationship between education and entry into entrepreneurship (Poschke 2013), with those who withdraw from education more likely to become entrepreneurs out of necessity, while highly educated individuals tend to capitalise on a latent opportunity to maximise profit.

Previous business experience is also found to be an important factor in influencing the decision to enter entrepreneurship, with the literature demonstrating that it has a positive impact on start-ups (Evans and Leighton 1989; Pisani and Pagan 2004; Poschke 2013). Previous experience in the same sector and same area can offer advantages for start- ups, such as increased levels of skills, capacities and information (Roberts, Klepper, and Hayward 2011).

Health is an important variable in a post-conflict situation given that a large number of people are left physically and emotionally handicapped. For Sri Lanka, it is estimated that 10-15% of the northern population was physically handicapped, as reported by the Sri Lanka Foundation for Rehabilitation of the Disabled (SLFRD) in 2011. However, empirical evidence regarding the condition of an entrepreneur’s health at the time of starting a business is inconclusive. In developed countries, such as the United Kingdom (UK), health is more likely to have a positive impact on the propensity to become self- employed due to employer discrimination against disability in the workplace (Borjas 1986; Jones and Latreille 2011), although Parker and Rougier (2007) found a negative

78 relationship between health and self-employment in the UK. In the case of post-conflict countries, such as Bosnia and Herzegovina, there is a positive relationship between health and entry into self-employment (Demirguc-Kunt, Klapper, and Panos 2011).

Family background and number of children can determine the probability of starting a business (Chlosta et al. 2012; Baumann and Brandle 2012), with family background one of the important factors affecting entrepreneurship due to the transfer of parents’ managerial skills and knowledge, and providing their children with access to wealth and opportunity (Simoes, Moreira, and Crespo 2013). Although the available empirical evidence regarding entrepreneurs who have children shows mixed results (Demirguc- Kunt, Klapper, and Panos 2011; Block and Sandner 2009; Lin, Picot, and Compton 2000), a positive relationship between these variables is clear in the literature (Baumann and Brandle 2012).

Furthermore, in terms of previous labour market engagement, unemployment pushes individuals towards starting a business (Santarelli and Tran 2012) while, due to their more financially secure positions, employees in the formal sector are less likely to do so than those in the informal sector (Demirguc-Kunt, Klapper, and Panos 2011).

5.2.2 Wealth and Access to Finance

Wealth: as the decision to become an entrepreneur requires finance, personal wealth (savings and/or inheritance) and access to external finance are important. A positive relationship between wealth and start-ups is found in the literature (Demirguc-Kunt, Klapper, and Panos 2011) and reported to be due to two reasons: (i) wealth can be used to start a new enterprise; and (ii) having access to collateral permits access to finance from the formal sector (Paulson and Townsend 2004; Elston and Audretsch 2011; Kerr and Nanda 2011). Inheritances, gifts and/or unexpected financial gains, such as lottery wins or job bonuses, are included as a measure of wealth in the literature (Hurst and Lusardi 2004). While income is the most appropriate measure of wealth, it does have some drawbacks as it is highly volatile and people tend to over- or under-estimate their actual incomes. Furthermore, income is a ‘flow’ while wealth is a ‘stock’. As a result, home ownership is also used as a proxy for wealth. For example, Lu and Tao (2010) found that home ownership has a significant impact on start-ups in China, and Haapanen and Tervo (2009) and Tokila (2009) also provide evidence from Finland. However, the relationship between wealth and start-ups is found to be insignificant in Switzerland, the US and the UK (Falter 2001; Van Praag 2003; Taylor 2004). The current study uses home ownership as a measure of the wealth of the entrepreneurs surveyed. 79

As demonstrated above, the extant literature finds a significant relationship between wealth and entry into entrepreneurship, which implies that financial constraints are important (Evans and Jovanovic 1989; Parker 2004; Demirguc-Kunt, Klapper, and Panos 2011). However, Kerr and Nanda (2011a) argue that this relationship can be due to endogeneity problems, that is, highly skilled individuals are more likely to accumulate savings and, therefore, become self-employed. Millan, Congregado, and Roman (2012) give evidence in support of this argument. The current study attempts to provide insights into the relationship between wealth and start-ups in the post-conflict setting.

Access to finance: this is identified as an important factor influencing entrepreneurship as it determines the rate of entry of new businesses, their survival and post-entry growth (Xu 1998; Aghion, Fally, and Scarpetta 2007). Quatraro and Vivarelli (2013) explain that a lack of collateral, information asymmetries and an imperfect financial market can impinge on access to finance in developing countries, which would be particularly apparent in a post-conflict situation. Further, financial constraints impose greater restrictions on entrepreneurial activity in poorer regions than in relatively affluent regions, as evidenced in Thailand (Paulson and Townsend 2004). Likewise, Demirguc-Kunt, Klapper, and Panos (2011) conclude that having an existing affiliation with a bank is not related to self-employment entry in Bosnia and Herzegovina. Instead, a bank relationship improves survival for a new entrepreneur and helps to create employment within the economy because bank financing is associated with faster growth (Ayyagari, Demirguc- Kunt, and Maksimovic 2010). However, access to formal finance is only possible after property rights are well established (Johnson, McMillan, and Woodruff 2000, 2002) and, as they and the enforceability of contracts could be compromised due to a conflict, it could be doubly difficult for an entrepreneur in this situation to access finance from the formal sector. Rajan and Zingales (1998) and Pissarides, Singer, and Svejnar (2003) argue that a significant association between wealth and a start-up, and an insignificant one between formal finance and entry can reflect financial constraints. Under these conditions, it is found that small firms rely heavily on informal sources of finance instead of formal borrowing from banks (Bigsten et al. 2003). Recently, Allen et al. (2012) revealed that informal forms of finance, such as loans obtained from family or friends, provide affordable funds to Indian Small and Medium-sized Enterprises (SMEs) during their start-up and growth periods. Also, because of the risk of failure they entail, younger firms still depend on informal rather than bank financing (Chavis, Klapper, and Love 2011).

In addition, remittances have significant effects on financial development in developing countries (Aggarwal, Demirguc-Kunt, and Peria 2011) as they can help to reduce poverty, increase human capital and improve investment in infrastructure (Adams and Cuecuecha 2013; Brown, Connell, and Jimenez-Soto 2014). Also, as access to finance is gender biased (Arenius and Minniti 2005), women are more likely than men to suffer from credit constraints (Khandker 1998).

Despite some contradictory findings, in general, previous research finds that access to finance influences entry into entrepreneurship, and this study assists further understanding of the relationship between these factors in a post-conflict setting.

5.2.3 Business Environment

The effects of the business sector on the probability of starting a business are diverse (Millan, Congregado, and Roman 2012). Kelley, Singer, and Herrington (2012) show that retail trade is the most prominent start-up in developing countries. Regarding business environments in post-conflict countries, Stiglitz (2006) argues that their socio-economic, political and institutional environments can be highly unstable during transition from conflict to peace. In addition, the facilitation of trade among ex-enemies is important for improving entrepreneurship in post-conflict settings in order to avoid future inter-group conflicts. For example, there were increased trade links among former enemies in post- conflict Rwanda (Boudreaux 2007; Tobias and Boudreaux 2011). The data and empirical strategy on each of the abovementioned factors is discussed next.

5.3 Data and Empirical Strategy

5.3.1 Data

As described in Chapter 4, the data collected via a purpose-designed survey allows this study to identify employer entrepreneurs who employ at least one paid worker, and solo self-employed entrepreneurs who do not employ workers. The survey was conducted using a structured questionnaire to collect data on the individual and socio-economic characteristics of entrepreneurs, and their levels of access to finance and the costs of doing business during the immediate post-conflict phase in the cities of Jaffna and Kilinochchi in 2012. Table 5.1 presents summary statistics of the sample, which show that 84.36% are employer entrepreneurs and the remaining 15.64% are solo self- employed entrepreneurs.

Table 5.1: Entrepreneurial status and city distribution of total sample

City Solo self-employed Employer entrepreneurs Whole sample entrepreneurs (No.) (%) (No.) (%) (No.) (%) Jaffna 25 19.84 101 80.16 126 51.85 Kilinochchi 13 11.11 104 88.89 117 48.15 Total 38 15.64 205 84.36 243 100 Note: ‘solo self-employed entrepreneurs’ are sole owner-operated businesses while ‘employer entrepreneurs’ are businesses with at least one paid employee

Definitions of the variables are presented in Table 5.2. The correlation matrix of the key variables are demonstrated in Table 5.1A in Appendix C.

5.3.2 Summary Statistics

Table 5.3 presents summary statistics of all the explanatory variables and difference in the mean between employer entrepreneurs and solo self-employed. The statistics suggest that able-bodied wealthy men are more likely to become employer entrepreneurs. Individuals who became employer entrepreneurs tend to be better educated and have inter-ethnic trade relationships with the south. Tamils, the majority population in both cities, represent 93% of the whole sample, Muslims 5.34% and, Sinhalese nearly 1%, with Tamil and Muslim co-partnerships having marginal representation. This table demonstrates that Muslims are overly represented in entrepreneurial activity given that they make up only 1.8% of the total population in the

82 area. A reason for this might be that they have a strong business network, which facilitates the starting up of new businesses (Ram 1994; Kilby 1983). Further, both Tamils and Muslims use the same language which facilitates communication between two ethnicities and Muslims have been treated as tradesmen in the history.

Table 5.2: Definitions of variables and descriptive statistics

Variable Definition NEWENT Dummy variable (DV) equal to 1 if respondent started his/her own business with at least one paid employee, 0 for own-account business owner (self-employed) City dummy KILINOCHCHI DV equal to 1 if respondent conducts his/her business in (Kilinochchi) Kilinochchi, 0 for Jaffna Individual Characteristics TAMIL DV equal to 1 if respondent Tamil, 0 otherwise MALE DV equal to 1 if respondent male, 0 if female AGE Age of respondent at start-up CHILDEN DV equal to 1 if respondent has children, 0 otherwise PRIMARY DV equal to 1 if respondent has primary school education, 0 otherwise UPPER SECONDARY DV equal to 1 if respondent received upper secondary school education, 0 otherwise GRADUATE & POST DV equal to 1 if respondent obtained university degree or higher, 0 otherwise ABLE-BODIED DV equal to 1 if respondent considers him/herself has no physical disability, 0 otherwise PAST BUS EXP DV equal to 1 if respondent has previous business experience, 0 otherwise Sector TRADE DV equal to 1 if respondent conducts retail or wholesale business, 0 otherwise SERVICES DV equal to 1 if respondent has service-oriented business, such as hotel, restaurant, communication, transport or education, 0 otherwise CONST& MANU DV equal to 1 if respondent has construction and related manufacturing business, 0 otherwise Peace TRADESOUTH DV equal to 1 if respondent has inter-ethnic trade linkage with south after cessation of conflict, 0 otherwise Wealth HOME OWNERSHIP DV equal to 1 if respondent has his/her own house, 0 otherwise Financial Characteristics SAVINGS DV equal to 1 if respondent used his/her savings to finance business at start-up, 0 otherwise INFORMALLOAN DV equal to 1 if respondent received informal loan from family member, friend or other person, 0 otherwise MICROLOAN DV equal to 1 if respondent received loan from micro-loan institution, such as rural bank, co-operative or NGO, 0 otherwise BANKLOAN DV equal to 1 if respondent received formal bank loan from private or government bank, 0 otherwise REMITABROAD DV equal to 1 if respondent received money from family members or friends working abroad, 0 otherwise

Variable Definition PAWNING DV equal to 1 if respondent received money for keeping his/her jewellery as collateral at formal or informal pawning centres, 0 otherwise

Table 5.3: Summary statistics

Characteristics Entrepreneurial status Whole Solo self- Employer t- sample employed test/Chi2 test Number of observations 38 205 243 City Dummy KILINOCHCHI 34.21% 50.73% 3.5* 48.14% Ethnicity TAMIL 89.47% 93.65% 0.8 93.00% MUSLIM 5.26% 5.36% 0.007 5.34% SINHALESE 0 0.97% 0.3 0.82% Gender MALE 81.57% 92.68% 4.8** 90.94% AGE (years) 35 years 38 years -1.52 37 years Health Status ABLE-BODIED 81.57% 95.12% 9.0*** 93.00% CHILDREN (binary variable) 71.05% 78.04% 0.8 76.95% Education NO EDUCATION 2.63% 0.49% 1.8 0.82% PRIMARY 5.26% 13.65% 2.1 12.34% LESS THAN LOWER 7.89% 5.36% 0.3 5.76% LOWER SECONDARY 55.26% 41.47% 2.5 43.63% UPPER SECONDARY 18.43% 31.70% 2.7* 29.63% GRADUATE & POST 10.53% 7.32% 0.4 7.82% Entrepreneurial Background PAST BUS EXP 16.21% 23.15% 2.1 22.08% Sector TRADE 50.00% 40.00% 1.3 41.56% SERVICES 28.94% 35.12% 0.5 34.15% CONST& MANU 5.26% 9.75% 0.8 9.05% Wealth HOME OWNERSHIP 36.84% 70.73% 16.2*** 65.43% Finance ɫ SAVINGS 36.37% 29.62% 0.4 30.56% BANKLOAN 27.27% 33.73% 1.7 32.83% INFORMALLOAN 16.37% 21.70% 0.2 20.96% PAWNING 14.55% 9.09% 2.6 9.85% REMITABROAD 3.63% 3.22% 0.002 3.28% MICROLOAN 1.81% 2.64% 1.13 2.52% Peace TRADESOUTH 15.79% 41.46% 9.0*** 37.44% Note: ɫ the percentage of the total seed capital is presented. All variables are binary except the “AGE”; t-test was calculated for comparing group means and Pearson Chi2 test was calculated for comparing proportions; *, **, *** denote significant levels at 10%, 5% and 1% respectively.

The statistics show that males are more likely to be either employer or solo self-employed entrepreneurs than females, that is, males are more likely to start a business than females. It is important to note that more females are involved in solo self-employment

84 activities such as beauty therapy and tailoring. The average age of the respondents is 37 years and 63% belong to the 25-44 age group. Individuals who became employer entrepreneurs are significantly more likely to be able-bodied persons. The statistics indicate that 7% of respondents are disabled and are more likely to be solo self-employed entrepreneurs, with the majority three-wheeler drivers, barbers, fruit and vegetable vendors, and tailors.

Figure 5.1: Entrepreneurs by educational attainment

60%

50% Solo Self-employed 40% Entrepreneurs

30% Employer Entrepreneurs

20% Poly. (Solo Self- employed 10% Entrepreneurs) Poly. (Employer Entrepreneurs) 0%

Notes: NO EDU - no school education; PRIM - primary education; PRIM to LS - less than lower secondary; LS - lower secondary; US - upper secondary; GRAD&POST - graduate and post- graduate degree holders

Table 5.3 shows that individuals educated to primary or upper secondary level are more likely to become employer than solo self-employed entrepreneurs. As represented in the data shown in Figure 5.1, however, the probability of becoming an entrepreneur (employer or solo self-employed) is highest among those with intermediate education levels. Individuals with the highest levels of education are less likely to become an entrepreneur due to the likelihood of having formal sector employment and also individuals with the lowest levels of education are less likely to become an entrepreneur due to lack of start-up capital, thus providing inverse U-shaped relationship between education and entrepreneurship. This result contradicts the U-shaped relationship, that is, the highest entrepreneurship rates are among individuals with either very high or very low levels of education (Poschke 2013). This inverse U-shaped relationship could have

85 been triggered by unemployment in the post-conflict setting. This inference is further confirmed by DCS (2012b) which shows that people with upper secondary level of education in northern province recorded the highest unemployment (13.2%) rate.

The difference between employer and solo self-employed entrepreneurs with respect to individuals’ wealth are significant, suggesting a strong positive relationship between own wealth and start-up. Notably, the survey statistics show that employer entrepreneurs are nearly twice as likely as solo self-employed entrepreneurs to be wealthy (HOME OWNERSHIP). About 95% of homes used as collateral are permanent and have tiled roofs and tiled or concrete floors. Private home ownership is a good measure of wealth, particularly in developing countries (Lu and Tao 2010).

With regard to access to finance, the statistics show no significant difference between the groups, indicating the importance of personal wealth compared to loans obtained from formal or informal financial institutions. All respondents rely heavily on personal savings (30.5%) and bank loans (33%), with informal loans accounting for nearly 21% of the total capital, micro loans 2.5% and seed capital sought from remittances from abroad 3%. The other most important source of finance is pawning which constitutes nearly 10% of the total seed capital. Solo self-employed entrepreneurs (i.e., those seeking income as a necessity) are more likely to rely on pawning than employer entrepreneurs.

Table 5.4: Business activities by solo self-employed and employer entrepreneurs

Business type Entrepreneurial Status Whole Solo self- Employer Chi2 sample employed (%) -test (%) (%) Construction and Manufacturing 5.26 9.75 1.3 9.05 Construction and related manufacturing 3.16 5.85 5.43 Construction 2.10 3.90 3.62 Trade 50.00 40.00 1.3 41.56 Retail and wholesale 46.29 35.70 37.38 Jewellery shop 0.00 2.35 1.93 Agricultural equipment 3.71 1.95 2.25 Services 28.94 35.12 0.5 34.15 Hotels and restaurants 2.23 10.98 9.55 Three-wheeler drivers/bus operators 11.12 6.14 6.97 Communication 0.00 5.71 4.77 Education/haircuts/tailoring/mechanic/ 15.59 12.29 12.86 Repair shops Multi-businesses 15.78 15.12 0.01 15.22 Total 100 100 100 Note: Pearson Chi2 test was calculated for comparing proportions

In addition, qualitative data indicates that half the respondents attribute their inability to expand their enterprise to financial constraints. The major impediments to accessing 86 finance are described as: (i) the requirement for guarantees (32.74%); (ii) high interest rates (24.01%); (iii) the requirement for collateral, such as land or a house (23.83%); (iv) the complex application procedure (13.24%); and (v) the requirement for business registration (2.19%).

This study incorporates a number of sectors, as shown in Table 5.4. However, no statistically significant difference is found between employer and solo self-employed entrepreneurs in terms of business sector engagement. Employer entrepreneurs are more likely to engage in the construction and related manufacturing, and services sectors than solo self-employed entrepreneurs because these sectors require more workers and capital. Solo self-employed entrepreneurs are more likely to conduct small-scale businesses in retail trade, trade in agricultural items, provision of three-wheeler transport, tailoring and hairdressing. Thus, a clear distinction is observed between industries that have sole owner-operated enterprises and those with paid employees.

As demonstrated in Table 5.5, this chapter also presents the role of previous labour market experience in starting a business, which shows there is no significant difference between the two groups. However, ex-entrepreneurs and formal sector employees are more likely to become employer and solo self-employed entrepreneurs respectively. Notably, 30.41% of entrepreneurs were unemployed before starting a business. It is important to note that about 2.5% of both types of entrepreneurs are ex-combatants.

Table 5.5: Previous labour market experience

Characteristics Entrepreneurial Status Whole sample Solo self- Employer Chi2 -test (%) employed (%) (%) Ex-entrepreneur 16.21 23.15 0.9 22.08 Formal sector employment 37.83 29.55 0.5 30.83 Informal sector employment 13.51 14.28 0.1 14.58 Unemployed 29.72 30.53 0.01 30.41 Ex-combatant 2.70 2.46 0.3 2.50 Note: Pearson Chi2 test was calculated for comparing proportions

5.3.3 Empirical Strategy

The following econometric model is based on the literature survey described in Section 5.2. This study examines the difference between emerging employer and solo self- employed entrepreneurs. It uses the probit model due to the binary nature of the dependent variable (Demirguc-Kunt, Klapper, and Panos 2011).

This econometric model is:

푁퐸푊퐸푁푇푖,푗 = 훽1푃퐸푅푖,푗 + 훽2퐹퐼푁푖,푗 + 훽3푇푅퐴퐷퐸푆푂푈푇퐻푖,푗+훽4퐾퐼퐿퐼푁푂퐶퐻퐶퐻퐼푖 + 휀푖,푗 (5.1)

where 푁퐸푊퐸푁푇푖,푗 is the dependent variable which takes the value ‘1’ if respondent 푖 started his/her own business in city 푗 with at least one paid employee and ‘0’ if he/she is a solo self-employed entrepreneur. The explanatory variables included are: 푃퐸푅푖,푗, a vector of variables depicting the respondent’s personal and socio-economic characteristics; 퐹퐼푁푖,푗 , a vector measuring wealth and access to finance;

푇푅퐴퐷퐸푆푂푈푇퐻푖,푗 , a vector measuring inter-ethnic trade links; 퐾퐼퐿퐼푁푂퐶퐻퐶퐻퐼푖 , a

Kilinochchi city dummy; 훽1, 훽2, 훽3 and 훽4, parameters to be estimated; and 휀푖,푗, noise which is assumed to be independent and identically distributed (i.i.d.).

The explanatory variables include ethnicity (Tamils), gender, age, a second-order polynomial of age, a binary variable for children, educational level (primary, upper secondary, and graduate and post-graduate), health status, entrepreneurial background and previous labour market experience. More specifically, this study uses the variable ABLE-BODIED to capture the health status of a business owner. The analysis includes PAST BUS EXP to capture the role of the skills and capacities of the respondents, and UNEMPLOYMENT and FORMAL SECTOR EMPLOYED to catch their labour market characteristics.

Three variables capture the business sectors of TRADE (retail and wholesale), SERVICES, such as communications, hotels, restaurants, education, and the more capital-intensive one of construction and related manufacturing (CONST& MANU).

HOME OWNERSHIP is used as a proxy for wealth in this study. The next set of characteristics is associated with access to both formal and informal finance. SAVINGS is employed as a measure of a respondent’s capacity to finance a start-up and to control his/her possible abilities. In addition, INFORMALLOAN is applied to capture loans received by respondents from a family member or friend. Respondents who receive a loan from micro-finance organisations, such as a co-operative bank, rural bank or NGO (MICROLOAN), are identified, with BANKLOAN included for loans received from a private or government bank at start-up and PAWNING for loans obtained using jewellery as collateral. In examining the impact of remittances received from friends or family working abroad to start a business, this study uses the dummy variable REMITABROAD.

TRADESOUTH, a proxy for peace, is included to identify the impact of inter-ethnic trade links between north and south Sri Lanka after the conflict, and KILINOCHCHI (Kilinochchi) to detect city-specific effects that impact business start-ups.

5.4 Estimation Results

5.4.1 Probit Models: the Characteristics of Employer and Solo Self-employed Entrepreneurs

Table 5.6 presents the probit estimation results for four specifications examining the entry of employer entrepreneurs, with the marginal effects and coefficients. The respective coefficients and their standard errors are presented in Table 5.2A, and marginal effects and their standard errors in Table 5.3A in Appendix C. In contrast to previous research, this study compares employer and solo self-employed entrepreneurs in terms of their personal characteristics, access to finance and inter-ethnic trade relationships using firm-level data. The specifications in columns (1) to (4) present the main personal characteristics, labour market experience prior to starting a business, sectoral characteristics of businesses and wealth, and inter-ethnic trade relationships between the north and south respectively.

The results indicate that emerging employer entrepreneurs constitute able-bodied older men with an upper secondary level of education. There is a significant relationship between upper secondary education and entry into an enterprise with the propensity to become employer entrepreneurs, while all other educational levels show insignificant results. Primary education has an insignificant positive effect and graduate-level education has both insignificant positive and negative effects. These findings are consistent with those in the literature (Vivarelli 2012).

Table 5.6 shows that the variables AGE and AGESQ are statistically insignificant in all specifications, although there is a positive relationship between entry and age in specifications (1) and (2) in Table 5.7. This implies that older people are slightly more likely to become employer entrepreneurs than younger people, because they may have more human and financial capital (Simoes, Moreira, and Crespo 2013).

Table 5.6: Estimated probability of being an employer entrepreneur Independent variable (1) (2) (2) (4) KILINOCHCHI 0.0579 0.0648 0.0615 0.0354 (0.2785) (0.3248) (0.3245) (0.1921) Ethnicity TAMIL 0.0913 0.1073 0.0850 0.0946 (0.4389) (0.5376) (0.4483) (0.5137) MALE 0.1144* 0.1254* 0.0808 0.0580 (0.5498) (0.6285) (0.4263) (0.3150) AGE 0.0235 0.0232 0.0172 0.0161 (0.1130) (0.1166) (0.0911) (0.0879) AGESQ/1000 -0.2601 -0.2558 -0.2035 -0.1849 (1.2479) (1.2819) (1.0729) (1.0036) CHILDREN 0.0067 0.0001 0.0042 0.0082 (0.0324) (0.0005) (0.0255) (0.0446) Education PRIMARY 0.1001 0.0740 0.1120 0.1156 (0.4811) (0.3712) (0.5906) (0.6279) UPPER SECONDARY 0.1504*** 0.1437*** 0.1556*** 0.1206** (0.7228) (0.7384) (0.8205) (0.6551) GRADUATE & POST 0.0287 0.0414 -0.0170 -0.0259 (0.1381) (0.2078) (0.0896) (0.1407) Health ABLE-BODIED 0.1896*** 0.2259*** 0.2177*** 0.1904*** (0.9110) (1.1320) (1.1480) (1.0339) Entrepreneurial Background ENTREPRENERIAL FAMILY 0.0220 -0.0111 0.0008 -0.0011 (0.1057) (0.0557) (0.0043) (0.0061) PAST BUS EXP 0.0190 0.0173 0.0089 0.0065 (0.0915) (0.0869) (0.0470) (0.0356) Prior labour engagements UNEMPLOYED --- -0.0821 ------(0.4115) FORMAL SECTOR EMPLOYED --- -0.0952* ------(0.4771) Sector TRADE ------0.0029 -0.0181 (0.0156) (0.0986) SERVICES ------0.0210 0.0238 (0.1108) (0.1295) CONST& MANU ------0.0529 0.0242 (0.2791) (0.1316) Wealth HOME OWNERSHIP ------0.1769*** 0.1768*** (0.9331) (0.9599) TRADESOUTH ------0.1212** (0.6582) Predicted probabilities 0.8436 0.8436 0.8436 0.8436 No. of observations 243 243 243 243 LR chi2 27.20 32.00 44.28 80.37 Prob > chi2 0.0072 0.0040 0.0002 0.0000 Pseudo 푅2 0.1291 0.1551 0.2101 0.2372 Log-likelihood -91.77 -87.16 -83.22 -80.37 Correctly classified 84.77% 84.17% 85.60% 87.24% Notes: *P < 0.10, **P < 0.05 and ***P < 0.01; dependent variable NEWENT (1/0) -employer /solo self- employed entrepreneurs – probit regressions; marginal effects and coefficients are presented in parentheses; the difference in the predicted values for discrete changes (0–1) are reported for the dummy variables 90

MALE has a positive impact in all specifications with significant results in specifications (1) and (2) in Table 5.6, which suggests that males are more likely to become employer entrepreneurs than females (Lu and Tao 2010; Demirguc-Kunt, Klapper, and Panos 2011). As employer entrepreneurs are more likely to be able-bodied individuals, this suggests that health is an important factor for starting a business and creating employment for others in Sri Lanka’s post-conflict setting.

This study obtained information of the previous labour market characteristics of respondents, such as unemployment and formal sector employment, as shown in specification (2) in Table 5.6. If all other factors remain constant, the results indicate that formal sector employees are less likely to become employer entrepreneurs than informal sector employees, which could be due to the former having less necessity to start a business. Also, having a formal job may be more attractive than entrepreneurial activity if it offers higher wages or other incentives, and more job security. Regarding the impact of unemployed individuals switching to employment-generated business, this study finds an insignificant negative effect.

Specifications (3) and (4) in Table 5.6 show that wealth, proxied by HOME OWNERSHIP, has a positive impact on start-ups. This suggests that the propensity to become an employer entrepreneur for those who own a house is nearly 18% higher than for those who do not. Combined with the qualitative information collected in the field, this suggests that access to finance is critical to business start-ups (Demirguc-Kunt, Klapper, and Panos 2011).

Furthermore, TRADESOUTH, as measured by inter-ethnic trade links between the north and south, has a significantly positive effect on start-ups. The probability of those who have inter-ethnic trade relationships with the south becoming employer entrepreneurs is about 12% higher than that of those who do not have these relationships. This implies that trade links between north and south stimulate employer entrepreneurship, as shown in specification (4) in Table 5.6 (Tobias and Boudreaux 2011).

In addition, sector- or industry-level variables show no significant results, with those such as previous business experience and CHILDREN having insignificant positive effects on entry into entrepreneurship. The models reveal the differences between two groups and all models classify about 84% of all entrepreneurs correctly.

5.4.2 Estimation Results – Probit Models: Access to Finance

The objective of this section is to understand the extent to which access to finance determines the beginning of entrepreneurship in the two post-conflict cities and how financial constraints impact an individual’s decision to become either an employer or solo self-employed entrepreneur. Table 5.7 presents a similar set of variables to Table 5.6, with the addition of financial variables for measuring access to finance in order to fund a start-up. Also specification (5) is added to identify the impact of other variables after the inclusion of both the wealth and savings of an entrepreneur, with their marginal effects and respective coefficients presented. The coefficients and their standard errors are reported in Table 5.4A, and the marginal effects and their standard errors in Table 5.5A in Appendix C. This study incorporates both an entrepreneur’s own resources (wealth and savings) and external finance. The results indicate that wealthier individuals, as determined by home ownership, are more likely to become employer than solo self- employed entrepreneurs, and their propensity to become entrepreneurs is nearly 18% higher than those who do not own a house. This indicates that financial constraints are critical to start-ups (Evans and Jovanovic 1989; Holtzeakin, Joulfaian, and Rosen 1994), which may imply that non-home owners are unable to borrow or can only borrow less than they require (Paulson and Townsend 2004), while those who have their own dwellings and/or wealth may be less dependent on external finance for their businesses. As the results are also robust after controlling for individual ability by using SAVINGS, the endogeneity problem noted earlier may not be serious for this case.

As Table 5.7 shows, ‘SAVINGS’ is not significantly related to start-ups, while none of the external finance, such as micro, informal or bank loans, seems to have a significant relationship with the entry decision. These results are consistent with prior empirical studies (Paulson and Townsend 2004; Demirguc-Kunt, Klapper, and Panos 2011; Johnson, McMillan, and Woodruff 2000, 2002; Pissarides, Singer, and Svejnar 2003; Rajan and Zingales 1998) and remain the same even after wealth measures are incorporated (Paulson and Townsend 2004; Demirguc-Kunt, Klapper, and Panos 2011). Interestingly, pawning has a negative and statistically significant effect on the probability of becoming an employer entrepreneur, which implies that those lacking wealth use pawning to access finance in ‘bad times’. This significant effect might be due to solo self- employed entrepreneurs, who are comparatively poor, using their own resources, such as jewellery, to obtain a loan from informal pawning centres or other individuals for their short-term cash requirements. On the other hand, remittances received from abroad appear to have an insignificant negative effect on the probability of becoming an

92 employer entrepreneur. This may be because 71.42% of the remittances sent to families in the two cities are spent on consumption rather than investment.

Table 5.7: Estimation results – access to finance

Independent variable (1) (2) (3) (4) (5) KILINOCHCHI 0.0624 0.0660 0.0589 0.0331 0.0551 (0.3205) (0.3521) (0.3342) (0.3090) (0.3146) Ethnicity TAMIL 0.0911 0.1117 0.0626 0.0695 0.0580 (0.4673) (0.5954) (0.3548) (0.4718) (0.3307) MALE 0.1374** 0.1422** 0.1107* 0.0854 0.1164 (0.7052) (0.7580) (0.6276) (0.5089) (0.6637) AGE 0.0277* 0.0262* 0.0221 0.0206 0.0229 (0.1422) (0.1399) (0.1253) (0.1229) (0.1307) AGESQ -0.3048 -0.2838 -0.2526 -0.2290 -0.2623 (1.5634) (1.5131) (1.4317) (1.3640) (1.4952) CHILDREN -0.0333 -0.0412 -0.0432 -0.0333 -0.0436 (0.1709) (0.2199) (0.2448) (0.1985) (0.2486) Education PRIMARY 0.0917 0.0726 0.0917 0.1048 0.0924 (0.4704) (0.3874) (0.5198) (0.6245) (0.5272) UPPER SECONDARY 0.1292** 0.1286** 0.1397** 0.1005* 0.1441*** (0.6631) (0.6859) (0.7922) (0.5986) (0.8217) GRADUATE & POST 0.0046 0.0204 -0.0341 -0.0411 -0.0331 (0.3960) (0.4087) (0.4323) (0.4471) (0.4359) Health ABLE-BODIED 0.1857*** 0.2236*** 0.2088*** 0.1764** 0.2093*** (0.9526) (1.1920) (1.1833) (1.0511) (1.1933) Entrepreneurial Background ENTREPRENERIAL FAMILY 0.0363 0.0005 0.0174 0.0181 0.0199 (0.1862) (0.0027) (0.0989) (0.1081) (0.1137) PAST BUS EXP -0.0055 -0.0074 -0.0141 -0.0155 -0.0147 (0.0283) (0.0398) (0.0801) (0.0925) (0.0841) Prior labour engagements UNEMPLOYED --- -0.0894 ------(0.4768) FORMAL SECTOR EMPLOYED --- -0.1083* ------(0.5773) Sector TRADE ------0.0218 0.0132 0.0210 (0.1235) (0.0791) (0.1197) SERVICES ------0.0580 0.0725 0.0615 (0.3292) (0.4320) (0.3505) CONST& MANU ------0.0593 0.0341 0.0474 (0.3365) (0.2035) (0.2707) Wealth HOME OWNERSHIP ------0.1736*** 0.1707*** 0.1750*** (0.9839) (1.0169) (0.9978) Peace TRADESOUTH ------0.1355*** --- (0.8071)

Finance Self-help Finance SAVINGS 0.0222 ------0.0365 93

Independent variable (1) (2) (3) (4) (5) (0.1143) (0.2081) External Finance INFORMALLOAN 0.0577 0.0649 0.0539 0.0767 0.0647 (0.2963) (0.3459) (0.3055) (0.4568) (0.3689) MICROLOAN 0.0487 0.0224 0.0947 0.0889 0.1047 (0.2501) (0.1196) (0.5371) (0.5296) (0.5972) BANKLOAN 0.0369 0.0260 0.0463 0.0475 0.0575 (0.1892) (0.1386) (0.2626) (0.2831) (0.3280) Remittances REMITABROAD -0.0515 -0.0589 -0.0450 -0.0439 -0.0526 (0.2642) (0.3144) (0.2554) (0.2620) (0.3000) Other funds PAWNING -0.1167* -0.1022* -0.0992* -0.1071* -0.0949** (0.5988) (0.5450) (0.5624) (0.6384) (0.5410) Predicted probabilities 0.8436 0.8436 0.8436 0.8436 0.8436 No. of observations 241 241 241 241 241 LR chi2 33.62 39.75 51.58 58.24 51.73 Prob > chi2 0.0140 0.0116 0.0003 0.0000 0.0003 Pseudo R2 0.1655 0.1956 0.2513 0.2866 0.2545 Log-likelihood -84.80 -81.73 -76.07 -72.49 -75.74 Correctly classified 86.72% 87.14% 86.31% 88.38% 87.14% Notes: *P < 0.10, **P < 0.05 and ***P < 0.01; dependent variable NEWENT (1/0)- Employer /Solo self- employed entrepreneurs – probit regressions; marginal effects and coefficients are presented in parentheses; the differences in the predicted values for discrete changes (0–1) are reported for the dummy variables

5.4.3 Robustness Checks

There is a concern about endogeneity arising from correlating the wealth and unobserved ability of an entrepreneur. Identifying the impact of wealth on the probability of being self-employed could be econometrically difficult, as espoused by Millan, Congregado, and Roman (2012). Therefore, the inability to control this type of variable may cast doubt on the findings. However, this study controlled this problem by using the savings deposits, upper secondary level of education and ABLE-BODIED variables which were found to have highly statistically significant effects in encouraging entrepreneurship. Moreover, it uses city dummies to control any possible endogeneity. More specifically, as this empirical chapter also examines the effect of inter-ethnic trade relationships on entrepreneurship, which correlates with city-level characteristics such as security conditions and level of economic development, endogeneity cannot be a major issue in this study.

5.5 Discussion

The results from this study are consistent for the two post-conflict cities in the northern province of Sri Lanka. They suggest that both have a poor investment climate and lack of access to finance from the formal sector, although many private and government- operated banks were established in those cities after the conflict. This study observed that loans are given to individuals at a comparatively low interest rate (although the formal banking system is still expensive) with fewer requirements for collateral. However, the loan amounts are insufficient to start a business due to the limited collateral.

Sri Lanka has recently shown some progress in terms of the World Bank’s indicators regarding the ease of doing business. However, it still lags when compared with other similar developing countries due to its weak institutional environment and lack of security for property rights. Resolving these issues has the potential to provide a boost to entrepreneurship; for example, in this sample, the average number of days required for business registration is 33. Furthermore, 2.41% of individuals claim they paid bribes to register their businesses – an impediment that could be addressed to reduce the costs of entry. This supports the argument that the external finance provided by formal financial institutions may impact the promotion of entrepreneurial activities in transition economies only after secure property rights are established (Johnson, McMillan, and Woodruff 2000, 2002).

5.6 Conclusions

This chapter identified three main differences between employer entrepreneurs and solo- self-employed entrepreneurs: access to finance, personal networks and an individual’s motivation to embark on a new enterprise. With regard to the last factor, individuals who were motivated to start a business due to their need for income, did so as solo self- employed entrepreneurs, with most of their enterprises small and self-funded, such as trade stores, barbershops and beauty salons. However, those who entered construction and related manufacturing businesses, created employment for others and often had the wealth to access finance from the formal sector, were drawn to running a business because they saw an opportunity to make a profit.

The survey results uncovered underlying differences between employer and solo self- employed entrepreneurs. Descriptive statistics found that emerging employer entrepreneurs were more likely to be between 25 and 44 years of age, and Muslims were over-represented in entrepreneurial activity compared with their demographic 95 proportions in the two cities. This may have been due to the networks this ethnic minority had formed across Sri Lanka. Further, this study found that variations in entrepreneurial activities among individuals could be attributed to gender, level of disability, educational level, wealth, business sector and informal financing arrangements (pawning).

The probit regression results suggested that males were more likely to become employer entrepreneurs than females, and that age had a positive and statistically significant relationship with entrepreneurship. However, this correlation was weak, which indicated that older people were only slightly more likely than younger ones to become employer rather than solo self-employed entrepreneurs. However, the squared age had a statistically insignificant negative effect on start-ups. The study also found a very strong relationship between able-bodied persons and entrepreneurship.

The results suggested that individuals who had completed upper secondary education were more likely to become employer entrepreneurs, but the relationship between other levels of education and start-ups remained insignificant. In contrast with a U-shaped relationship between entrepreneurship and education (Poschke 2013), this study found inverse U-shaped non-linear relationship. Individuals with an intermediate level of education were more likely to become employer or solo self-employed entrepreneurs than individuals with either high or low levels of education. This could be due to the fact that the majority of enterprises were small and business owners’ engagement in them was motivated more by necessity than opportunity.

The majority of entrepreneurs were wealthier and nearly 18% were more likely to become employer than solo self-employed entrepreneurs. At the same time, sources of finance, such as savings, bank loans, informal loans, micro loans and remittances received from abroad, had statistically insignificant impacts on their propensity to start a business. The exception was pawning which had a significant negative effect on employer entrepreneurs, suggesting that the very poor used pawning to access finance from the informal (non-bank) sector. These findings, coupled with the qualitative data gathered during fieldwork, indicates that financial constraints constituted a major obstacle to emerging entrepreneurial activity in this post-conflict setting. Although the Sri Lankan Government has taken some steps to re-build conflict-affected businesses, there is an urgent need to establish an appropriate long-term incentive mechanism for emerging entrepreneurs who face financial constraints. As with previous research, this study suggests that external finance provided by formal institutions such as ‘law and order’ can have a desirable effect on entrepreneurial development. The long-term strategy of

96 providing more security for property rights and enforceability of (debt) contracts has the potential to lower the costs of credit. At the time of this survey, institutional mechanisms were being developed. As argued by North (1990), and North et al. (2013), institutions are the critical determinant of economic growth in post-conflict countries. Their development may help the emergence of entrepreneurship that could then provide foundations for economic development in conflict-affected areas.

The results also suggest that increased inter-ethnic trade relationships encourage employment-generated entrepreneurial activity after a conflict, providing an avenue for conflict reduction. Therefore, better transportation and communication links with the rest of the nation could assist in building bridges across communities.

This chapter contributes to the existing literature by providing empirical evidence of the drivers of entrepreneurship in a post-conflict setting. The results support the view that the growth of an enterprise has the potential to generate employment and peace, both of which are necessary for transitioning out of conflict. These findings have relevance for policy-making in post-conflict settings beyond Sri Lanka. The next chapter will focus on this aspiration for further insights.

Chapter 6

Necessity and Opportunity Entrepreneurs

6.1 Introduction

The previous chapter investigated the underlying difference in motivation between employer entrepreneurs and solo self-employed entrepreneurs to start up. This chapter extends the understanding of entrepreneurs’ motivation and examines the dichotomy between necessity entrepreneurs, that is, individuals with survival motivations versus opportunity entrepreneurs, that is, individuals with profit-seeking motivations.

The cessation of fighting at the end of a civil war provides an opening for the commencement of enterprises that could not have emerged during the conflict. The establishment of businesses can be motivated by two considerations. On the one hand, the need for an income and livelihood attracts desperate and destitute people to the selling and hawking of goods and services as their sole means of earning a living (necessity motivated). On the other, a return to peace offers opportunities to commence businesses in order to make large profits and/or provide desperately needed services (opportunity motivated). With the former, some individuals are attracted to entrepreneurship as a means of escaping from unemployment and meeting family pressures and responsibilities that arise after a conflict. With the latter, the rebuilding of public infrastructure, such as roads and buildings, presents opportunities to earn large profits for latent construction companies, while those motivated to help the homeless and injured are also attracted to filling a void. In the main, the literature defines the dichotomy between these two groups as being that necessity entrepreneurs are pushed into entrepreneurship because of the absence of alternative employment, whereas opportunity entrepreneurs are pulled into entrepreneurship to exploit business opportunities (Bosma et al. 2009; Devins 2009; Reynolds et al. 2002). As such, the focus in this study is on business start-ups motivated by the need to generate income vis-à-vis those that take advantage of an opportunity to make a profit. This study uses survey data from two cities that were heavily affected by the conflict in Sri Lanka to provide further insights into these concepts which will enhance the broader literature on the forms of entrepreneurship that emerge following a civil conflict.

Unlike in developed countries, little is known about this necessity-opportunity dichotomy in developing countries (Brunjes and Diez 2012a) due to the assumption that the majority

99 of their entrepreneurs are motivated by necessity (Quatraro and Vivarelli 2013). However, empirical findings are lacking to support the above claim (Williams and Youssef 2014). Surprisingly, Rosa, Kodithuwakku, and Balunywa (2006) argue that in Uganda and Sri Lanka, the majority of entrepreneurs are opportunity motivated because the very poor are trapped in a cycle of needing to work long hours to subsist and not able to start a necessity-motivated enterprise. Similarly, Gurtoo and Williams (2009), and Brunjes and Diez (2012a) indicate that there are more opportunity than necessity entrepreneurs in India and rural Vietnam.

In contrast, this study finds that nearly 80% of entrepreneurs are attracted to business through necessity rather than opportunity in the two post-conflict Sri Lankan cities and are subject to financial constraints. Also, local residents with an entrepreneurial family background are less likely to be motivated by opportunity. However, opportunity- motivated entrepreneurial activities facilitate trade between the north and south. This study also finds that individuals with both the highest and lower levels of education are opportunity motivated, reflecting inverse U-shaped relationship between education and entrepreneurial decisions.

The remainder of the chapter is structured as follows: Section 6.2 discusses the theoretical perspective in relation to the different motivations for necessity and opportunity entrepreneurship; Section 6.3 reviews the relevant literature; Section 6.4 presents the data and summary statistics; Section 6.5 describes the econometric model used; Section 6.6 reports on the estimation results; Section 6.7 discusses the robustness of the findings; and, finally, Section 6.8 presents the conclusions.

6.2 Theoretical Perspective – Necessity versus Opportunity Entrepreneurship

This section discusses the theoretical perspective regarding necessity and opportunity entrepreneurship. An individual can start a business to either: (i) ‘escape from unemployment’; or (ii) pursue an opportunity (Audretsch and Thurik 2000), a distinction referred to in the extant literature as the ‘push-pull’ motivation (Amit and Muller 1995; Segal, Borgia, and Schoenfeld 2005; Cooper and Dunkelberg 1986; Moore and Mueller 2002; Christopher and Andrew 2012; Nabi, Walmsley, and Holden 2013). In the GEM 2001 Executive Report, Reynolds et al. (2002) reconceptualise this dichotomy as necessity entrepreneurs (push) and opportunity entrepreneurs (pull). Then, subsequent

100 studies apply this necessity and opportunity concept because of its simplicity and consistency (Acs, Desai, and Hessels 2008; Giacomin et al. 2011).

It is important to examine the factors affecting a decision to start a business as it helps to understand entrepreneurial dynamics. A testable, push-pull model was first developed by Johnson and Darnell (1976) based on the pioneering works of Knight (1921) and Oxenfeldt (1943). The model postulates that unemployed individuals or those with a low probability of acquiring salaried employment shift towards entrepreneurial activities to make a living. Therefore, unemployment in an economy can encourage start-ups (Evans and Leighton 1989; Ritsila and Tervo 2002; Storey and Jones 1987; Garba, Djafar, and Mansor 2013). In addition, other factors, such as family pressure (Giacomin et al. 2007b), job satisfaction (Hisrich and Brush 1986) and work-related insecurity (Mason 1989) may also push individuals to start new businesses.

In contrast to the push motivation, the pull one is provided by the perceived market opportunity, social status and expected profit (Giacomin et al. 2011). Empirical evidence offers different sets of pull motivations, including recognition, independence, innovation, financial success, self-realisation, improved welfare and wealth (Carter et al. 2003; Birley and Westhead 1994). However, as Block and Sandner (2009) note, as some individuals are driven by both push and pull motivations when starting a new business, it is important to examine the differences between them to understand entrepreneurial dynamics.

The GEM 2001 Executive Report distinguishes between two types of entrepreneurs on the basis of their levels of participation in entrepreneurial activities, that is, ‘necessity entrepreneurs’ and ‘opportunity entrepreneurs’ (Reynolds et al. 2002). The former are those motivated by the requirement for an income in the absence of other employment opportunities, while the latter are those motivated by the opportunity to make profits in the market (Reynolds et al. 2002; Block and Wagner 2010; Block and Sandner 2009). The literature identifies necessity entrepreneurs as being different from opportunity entrepreneurs in terms of taking risks, obtaining income from entrepreneurship, and having long periods of self-employment and job satisfaction, and also when considering their regional context and socio-economic factors (Block, Sandner, and Spiegel 2015; Verheul et al. 2010). Further, factors such as previous experience, social networks, search process and personal characteristics are identified as important for differentiating the groups (Block and Wagner 2010). Despite its importance, this dichotomy has been criticised by various authors based on its context and categorisation (Rosa,

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Kodithuwakku, and Balunywa 2006; Williams, 2008; Williams and Round 2009; Williams and Nadin 2010).

6.3 Factors Influencing Entrepreneurial Motivation – Brief Review of Literature

This section reviews the empirical evidence from differently motivated entrepreneurs as to the reasons driving entrepreneurs to start up businesses. The review is based largely on studies conducted in developed countries because of the limited evidence available from developing countries (Brunjes and Diez 2012a). This is due mainly to necessity entrepreneurs constituting larger proportions of entrepreneurial activities in those countries (Quatraro and Vivarelli 2013; Rosa, Kodithuwakku, and Balunywa 2006).

Personal characteristics, such as level of education, previous experience and age play an important role in entrepreneurial decisions regardless of whether entrepreneurs are motivated by necessity or opportunity (Reynolds et al. 2002; Amit and Muller 1995; Block and Wagner 2010). In addition, gender, entrepreneurial family, previous labour market engagements and access to finance are significant factors (Vivarelli 2013).

Education: the impact of educational level on necessity and opportunity entrepreneurial engagements appears to be inconclusive in the literature. Previous studies suggest that individuals who possess high human capital (formal education and experience) perceive entrepreneurial opportunity more favourably than those with less human capital (Davidsson and Honig 2003; Shane 2000). Empirical studies conducted in developed countries, such as Germany and Canada, reveal that opportunity entrepreneurs have higher levels of education than necessity entrepreneurs (Bhola et al. 2006; Robichaud, LeBrasseur, and Nagarajan 2010). Block and Wagner (2010) found that the education and experience of opportunity entrepreneurs have a positive effect on their earnings. Studying countries of the European Union (EU), Iceland, Norway and the US, Verheul et al. (2010) show that education has a positive effect on an individual’s engagement in opportunity entrepreneurial activity. Likewise, employing German data, Fossen and Buttner (2013) demonstrate that the returns from education are similar for opportunity entrepreneurs and paid employees, whereas the returns are lower for necessity entrepreneurs, while Bergmann and Sternberg (2007) reveal that educational level has an impact only on opportunity entrepreneurs. In contrast, Block and Sandner (2009) and Block and Wagner (2010) found that there is no educational difference between these

102 types of entrepreneurs in Germany. Given these different views, it is important to examine how education affects entrepreneurial decisions in a post-conflict setting.

Gender: although this is a key variable in terms of entrepreneurial decision-making, the relationship between gender and the necessity-opportunity dichotomy appears to be inconclusive in the extant literature (Verheul et al. 2010); for example, Wagner (2005) shows that in Germany males are more likely to become opportunity than necessity entrepreneurs, a finding supported by Bergmann and Sternberg (2007). In contrast, Hernandez, Nunn, and Warnecke (2012) determine that, in China females are more likely to be motivated by necessity than opportunity when engaging in entrepreneurial activities. This is because in such developing countries females are more likely to work in the informal sector than males. Although findings from previous research on the relationship between gender and the necessity-opportunity dichotomy are inconclusive, gender can have an impact on entrepreneurial decisions in post-conflict countries due to high unemployment and limited employment opportunities immediately after a conflict.

Age: although some previous studies show that age influences entrepreneurial decisions, their findings are inconclusive and ambiguous in relation to its impact. Using the GEM 2001 Report, Reynolds et al. (2002) demonstrate that opportunity entrepreneurs (aged 35-44) are older than necessity entrepreneurs (18-24). Bergmann and Sternberg (2007) argue that in Germany age does not influence the probability of becoming a necessity entrepreneur but has inverse U-shaped association with being an opportunity entrepreneur. In contrast, Wagner (2005) obtains the opposite results in the same country, that is, that age has no impact on opportunity entrepreneurs and inverse U-shaped relationship with necessity entrepreneurs. Therefore, it is important to explore the role of age in determining entrepreneurial motives in a post-conflict setting.

Family business: although being a member of a family with a business is an important determinant of engagement in entrepreneurial activity, previous empirical studies of the relationship between these factors appear to be indecisive; for example, Bhola et al. (2006) reveal that individuals in Germany with entrepreneurial parents have higher probabilities of becoming opportunity rather than necessity entrepreneurs. In contrast, Verheul et al. (2010) found that having self-employed parents increases the probability of becoming necessity and mixed-motivated rather than opportunity entrepreneurs in EU countries. However, using GEM data, Morales-Gualdrón and Roig (2005) determine that this factor has equal and significant positive effects on both opportunity and necessity entrepreneurship. Therefore, given the inconclusive evidence on the relationship

103 between belonging to a family with a business and entrepreneurial decisions in developed countries, it is important to examine this relationship in a post-conflict context.

Unemployment: although the impact of unemployment is more prominent for necessity entrepreneurs, Wagner (2005) found that unemployment has a positive effect on the probability of becoming either a necessity or opportunity entrepreneur. Also, Block and Wagner (2010) argue that previous unemployment is common among necessity entrepreneurs in Germany. In a post-conflict setting, unemployed individuals tend to undertake entrepreneurial activities through the necessity to earn income rather than because of opportunities. In other words, higher unemployment is expected to be correlated with necessity-motivated entrepreneurship, as explored in this study.

In addition to the above determinants, the literature identifies that the propensity for risk- taking is higher among opportunity than other types of entrepreneurs (Wennekers et al. 2005; Wong, Ho, and Autio 2005; Bergmann and Sternberg 2007; Block and Sandner 2009; Block and Wagner 2010; Block, Sandner, and Spiegel 2015), and that region is also a factor that influences start-ups (Verheul et al. 2010). The above brief review suggests that an individual’s socio-economic characteristics are important determinants of his/her necessity-opportunity motivation.

6.4 Data and Empirical Strategy

6.4.1 Data

This study uses the data collected through a purpose-designed survey conducted in the cities of Jaffna and Kilinochchi from June to October 2012 (explained in detail in Chapter 4). As Table 6.1 shows, the sample contains 243 respondents, of which 2 did not report their primary motivation for a start-up, while 192 (approximately 80%) are necessity entrepreneurs and 49 (approximately 20%) are opportunity entrepreneurs.

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Table 6 1: Entrepreneurial engagements of respondents

Category Number of respondents Per cent (%) Necessity entrepreneurs 192 79.67 Opportunity entrepreneurs 49 20.33 Total 241 100

Although classifying entrepreneurs into having necessity or opportunity motives is quite difficult, previous studies offer some insights (Evans and Jovanovic 1989; Carter et al. 2003; Cassar 2007; Giacomin et al. 2007a; Kautonen and Palmroos 2010). The primary motivations for start-ups provided by the majority of respondents at the time of the survey are shown in Table 6.2.

Table 6 2: Respondents’ motivations for engaging in entrepreneurial activities

Necessity No. of Opportunity No. of respondents respondents citing factor citing factor Escaping from (120) Benefiting from (21) unemployment peacetime opportunity Responding to family (104) Being a boss (43) pressure (to educate Providing services to (09) and feed their people children) and Continuing family tradition

Approximately 80% of entrepreneurs are motivated by necessity, as demonstrated in Table 6.2, generally driven to ‘escape from unemployment’ and earn income to provide a living (Block and Sandner 2009; Ritsila and Tervo 2002; Evans and Jovanovic 1989; Mason 1989; Fossen and Buttner 2013). For example, the unemployment rate in the northern province was 27.4% of the eligible workforce (15-59 years) in 2012 (Wickramasinghe 2014), which pushed individuals to start businesses due to the lack of employment opportunities after the conflict. This unemployment rate could have been much higher when the majority of businesses were seeded in May 2009. In addition, other factors, such as family pressure, that is, providing education and food for their children as well as family tradition, forced individuals to undertake necessity-motivated entrepreneurial activities (Bhola et al. 2006; Giacomin et al. 2007a; Amoros and Bosma 2014).

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Individuals who undertook entrepreneurial activity due to the opportunities offered by peacetime are considered opportunity entrepreneurs because they rigorously investigated entrepreneurial activities before starting businesses in the post-conflict situation. This type of entrepreneur may perceive the returns of entrepreneurial activity as exceeding paid employment in the formal sector. Additionally, individuals with motivations such as ‘being a boss’ or ‘providing services to society’ are grouped as opportunity entrepreneurs (Carter et al. 2003; Cassar 2007).

It is difficult to distinguish between the entrepreneurial motivations of necessity and opportunity because they can change over time (Williams and Williams 2014). As this chapter is limited to cross-sectional data, it considers only entrepreneurial motivation at the time of the survey given that the two cities’ macroeconomic environments and security conditions are similar.

6.4.2 Descriptive Statistics

Table 6.3 presents a comparison of necessity and opportunity entrepreneurs using descriptive statistics based on the five major factors of personal, labour market and business sectoral characteristics, wealth and access to finance, and inter-ethnic trade relationships (see Table 6.1A in Appendix D for summary statistics in details).

Table 6.3 demonstrates that there are significantly fewer opportunity than necessity entrepreneurs in Kilinochchi than in Jaffna which reflects the former entrepreneurs’ greater need for income. Also, the statistics show that necessity entrepreneurs are more likely to be Tamil and Muslim men. In both types of entrepreneurial activities, the proportion of men engaged is higher than that of women (88%), which agrees with the findings of Block and Sandner (2009), while there is no significant difference in terms of the ages of entrepreneurs. Able-bodied individuals are more likely to become necessity than opportunity entrepreneurs, which implies that a disabled individual has a higher probability of being an opportunity entrepreneur in a post-conflict environment. In addition, local residents and individuals who have links to an entrepreneurial family are significantly less likely to become opportunity than necessity entrepreneurs (with p<0.10). This suggests that local residents are more needs-based and individuals coming from entrepreneurial families continue their family obligation and tradition as a necessity rather than an opportunity.

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Table 6 3: Descriptive statistics

Variable Necessity Opportunity Total entrepreneurs entrepreneurs sample (n=192) (n=49) (n=241) Mean Mean t-test/Chi2- Mean test Ethnicity TAMIL 93.22% 91.83% 0.11 93.00% MUSLIM 5.72% 4.08% 0.20 5.34% MALE 91.67% 87.75% 0.72 90.94% AGE 37.44 37.08 0.21 37.41 CHILDREN (dummy 75.52% 81.63% 0.81 76.95% variable) Education NO EDUCATION 1.04% 0% 0.47 0.82% PRIMARY 12.50% 12.24% 0.96 12.34% PRIMARY to LOWER 5.20% 8.16% 0.62 5.76% SECONDARY LOWER SECONDARY 47.39% 30.61% 4.46** 43.62% UPPER SECONDARY 27.60% 34.69% 0.95 29.62% GRADUATE & POST 6.25% 14.28% 3.47* 7.81% Labour origins INFORMAL SECTOR 13.75% 18.36% 0.65 14.58% EMPLOYED UNEMPLOYED 24.33% 20.40% 0.33 23.33% PAST BUS EXP 72.3% 75.51% 0.19 72.83% Health status ABLE-BODIED 94.27% 87.75% 2.52 93.00% Wealth HOME OWNERSHIP 63.54% 73.46% 1.70 65.43% Residency LOCAL RESIDENT 91.66% 83.67% 2.78* 89.71% ENTREPRENEURIAL 36.97% 22.44% 3.67* 34.15% FAMILY Social networks TRADE MEMBERSHIP 56.25% 46.93% 1.36 53.90% Sector TRADE - retail and 42.18% 40.81% 0.03 41.56% wholesale SERVICES 33.85% 32.65% 0.02 34.15% CONST & MANU 8.85% 10.20% 0.08 9.05% Peace TRADESOUTH 35.41% 42.85% 0.92 37.44% Finance SAVINGS 51.05% 48.97% 0.06 50.20% INFORMALLOAN 35.78% 28.57% 0.90 34.43% MICROLOAN 2.63% 8.16% 3.28* 4.14% BANKLOAN 55.78% 48.97% 0.72 53.94% REMITABROAD 5.78% 4.08% 0.22 5.39% PAWNING 17.36% 12.24% 0.78 16.18% KILINOCHCHI (1=yes) 54.16% 24.48% 13.77*** 48.14% Note: two respondents did not provide reasons for starting a business; a T-test was calculated to compare group means and the Pearson Chi2 test to compare proportions; and *, ** and *** denote significance at the 10%, 5% and 1% levels respectively.

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Opportunity entrepreneurs exhibit higher levels of education than necessity entrepreneurs, with individuals who were educated to the upper secondary and above levels more likely to pursue opportunity entrepreneurial activities. This agrees with the results from previous studies (Bhola et al. 2006; Robichaud, LeBrasseur, and Nagarajan 2010). Table 6.3 shows that individuals who achieved lower secondary education are significantly more likely to become necessity than opportunity entrepreneurs (with p<0.05), whereas the opposite is true for graduates and post-graduates (with p<0.10). The relationship between levels of education and their respective percentages are illustrated in Figure 6.1. The probability of becoming an opportunity entrepreneur is highest among those with upper secondary level education and above. This inverse U- shaped relationship could be due to unemployment, particularly among individuals with intermediate levels of education, stemming from the lack of alternative employment opportunities in this post-conflict setting.

Figure 6 .1: Opportunity and necessity interactions with education

50% More Opportunity- driven 40% More Necessity- driven 30% Necessity-driven Entrepreneurs Opportunity-driven 20% Entrepreneurs Poly. (Necessity-driven 10% Entrepreneurs) Poly. (Opportunity- driven Entrepreneurs) 0%

Notes: NO EDU – no school education, PRIME – primary education, PRIM to LS – primary to lower secondary, LS – lower secondary, US – upper secondary, GRAD&POST – graduate and post-graduate

In addition, those unemployed before starting a business were more likely to become necessity entrepreneurs because they were pushed into entrepreneurship to earn income. However, informal sector workers were more likely to become opportunity entrepreneurs. The statistics report that opportunity entrepreneurs are more likely to

108 engage in the construction sector due to its increased demand resulting from reconstruction activities in a post-conflict phase (Collier 2009).

Opportunity entrepreneurs are more likely to be homeowners (wealthier) than necessity entrepreneurs (73% versus 63%), while access to micro loans is significantly higher for opportunity than necessity entrepreneurs (with p<0.10). However, access to bank loans at the time of starting up is greater among necessity than opportunity entrepreneurs. Furthermore, bank finance is the major source of external finance in this post-conflict setting, a result in line with findings from studies of developing countries (Beck, Demirguc-Kunt, and Maksimovic 2008). Conversely, necessity entrepreneurs are more likely to rely on pawning than opportunity entrepreneurs, which suggests that they are very poor and unable to borrow from formal sources. All these results suggest that financial constraints are a critical factor in terms of entrepreneurial decisions.

Opportunity entrepreneurs are more likely to have inter-ethnic trade relationships with the south than necessity entrepreneurs, which could be the reason opportunity entrepreneurs engage in construction activities. As this study observes, carpenters, welders, plumbers, electricians, bricklayers and some professionals, such as architects and managers, and even labourers coming from the south, work in both public and private construction in the north due to the absence of skilled workers there after the conflict. These activities have created an environment for establishing inter-ethnic trade relationships since the end of the conflict.

6.4.3 Empirical Strategy

In this section, a detailed discussion of the estimation techniques is provided, with the modelling strategy involving the probit model (Block and Sandner 2009; Ardagna and Lusardi 2008).

The survey respondents were divided into two groups in terms of their different ‘push’ and ‘pull’ motives for starting businesses as necessity and opportunity entrepreneurs (Reynolds et al. 2002). Thus, the dependent variable ( 푦) is binary and represents ‘being an opportunity entrepreneur’.

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Based on the literature reviewed, the model is specified as:

푦푖,푗 =∝푗+ 푥푖,푗 + 퐹퐼푁푖,푗 + 푇푅퐴퐷퐸푆푂푈푇퐻푖,푗 + 휀푖,푗 (7.1)

where ∝푗 is a vector of city dummies to correct for city-specific effects; 푥푖,푗 a vector of personal characteristics, including gender, ethnicity, age, able-bodied person, levels of education, business networks and residency status; 퐹퐼푁푖,푗 a vector of variables measuring access to finance; 푇푅퐴퐷퐸푆푂푈푇퐻푖,푗 a vector of variable measuring the inter- ethnic trade relationship with the south of individual 푖 in city 푗; and 휀푖,푗 the iid as an extreme value distribution (Train 2009). The city dummy (∝푗 ) is included to capture the macroeconomic variables in order to reduce omitted variable bias.

The study includes variables, such as human and social capital, labour origins, business sectors, access to finance and inter-ethnic trade links with the south, in the empirical analysis. The personal characteristics considered consist of gender, ethnicity (Tamils), age, able-bodied person, educational levels (primary, upper secondary, graduate and post-graduate), previous labour origins (unemployed and informal worker), involvement in family business and residency status (Williams, CC 2008; Block and Sandner 2009; Block and Wagner 2010; Demirguc-Kunt, Klapper, and Panos 2011). The social networks of the respondents, such as business relationships between entrepreneurs (Aldrich and Zimmer 1986) are also added. Business sectors, such as trade (retail and wholesale), services and construction and related manufacturing, are included in the analysis.

In terms of access to finance, this study incorporates both external and internal sources, such as bank, micro and informal loans, as well as pawning and remittances from abroad (Demirguc-Kunt, Klapper, and Panos 2011), and includes a respondent’s home ownership as a proxy for wealth (Lu and Tao 2010). More importantly, the study adds the inter-ethnic trade relationship with the south in the model as a proxy for peace to investigate the impact of peace on entrepreneurial motivation after the conflict (Tobias and Boudreaux 2011).

6.5 Estimation Results

Table 6.4 presents the results from the probit regressions estimated to compare the differences between necessity and opportunity entrepreneurs in terms of personal characteristics, access to finance and inter-ethnic trade relationships. It consists of five

110 models and their marginal effects and coefficients (in parentheses), with details of their standard errors reported in Table 6.2A and Table 6.3A in Appendix D. The first model includes an individual’s personal characteristics, wealth and social networks; and then the second adds previous labour market engagements; the third the sectors of business in which the entrepreneurs were engaged; the fourth inter-ethnic trade links with the south as a proxy for peace; and the fifth access to finance. The estimation results distinguish between the impacts of personal characteristics, access to finance and inter- ethnic trade relationships on the motives of opportunity entrepreneurs relative to those of necessity entrepreneurs.

6.5.1 Personal Characteristics

The results show that the propensity to belong to the category of opportunity entrepreneurs decreases for those who conducted their businesses in Kilinochchi, in all specifications. The coefficient of this variable is negative and statistically significant at the 1% level. This indicates that entrepreneurs in Kilinochchi may have chosen entrepreneurship ‘out of necessity’ and have a greater need for livelihood income than those in Jaffna. Kilinochchi was one of the most severely affected cities, particularly at the end of the conflict in 2008/09, as its physical and social infrastructure, such as education, health and other economic activities, were completely destroyed; for example, its students were well below the provincial average pass rate in the lower secondary level examinations conducted in 1998 and 2002 (Sarvananthan 2007).

Table 6.4 shows that individuals with family businesses are less likely to pursue opportunity entrepreneurial activities which suggests that they may be forced into entrepreneurial activity due to family pressure, such as educating and feeding their children and/or family obligation (Bhola et al. 2006; Giacomin et al. 2007a). Although this result is consistent with those from the study conducted by Verheul et al. (2010), it contradicts the findings of Wagner (2005) study in Germany.

This study finds important results with regard to local residents. They are less likely to pursue opportunity-motivated entrepreneurial activity, which suggests that they need a livelihood income. The coefficient of this variable is negative and statistically significant at the 1% level in all specifications, as shown in Table 6.4. On the other hand, it implies that non-local residents were attracted to business in these conflict-affected areas because of the opportunities created for business after cessation of the conflict.

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Table 6.4: Estimated probability of being an opportunity entrepreneur Variable Model 1 Model 2 Model 3 Model 4 Model 5 KILINOCHCHI -0.2685*** -0.2707*** -0.2741*** -0.2871*** -0.2846*** (1.1310) (1.1381) (1.1645) (1.2431) (1.2349) TAMIL 0.0690 0.0819 0.0571 0.0405 0.0296 (0.2907) (0.3445) (0.2426) (0.1755) (0.1288) MALE -0.0120 -0.0048 -0.0218 -0.0389 -0.0078 (0.0505) (0.0202) (0.0926) (0.1687) (0.0339) AGE -0.0180 -0.0158 -0.0187 -0.0206 -0.0147 (0.0758) (0.0668) (0.0795) (0.0891) (0.0638) AGESQ/1000 0.1952 0.1768 0.2022 0.2336 0.1410 (0.8224) (0.7432) (0.8592) (1.0114) (0.6118) CHILDREN 0.0737 0.0630 0.0762 0.0760 0.0666 (0.3105) (0.2651) (0.3237) (0.3294) (0.2890) PRIMARY 0.1567* 0.1507* 0.1609** 0.1835** 0.1572* (0.6599) (0.6335) (0.6834) (0.7948) (0.6823) UPPER SECONDARY 0.0537 0.0670 0.0543 0.0377 0.0720 (0.2263) (0.2817) (0.2310) (0.1636) (0.3125) GRADUATE & POST 0.1522* 0.1606* 0.1537* 0.1451* 0.1410 (0.6410) (0.6752) (0.6531) (0.6285) (0.6120) INFORMAL SECTOR EMPLOYED --- 0.0709 ------(0.2980) UNEMPLOYED --- 0.0242 ------(0.1017) ABLE-BODIED -0.1216 -0.1119 -0.1232 -0.1409 -0.1217 (0.5121) (0.4704) (0.5235) (0.6103) (0.5281) HOME OWNERSHIP 0.0847 0.0863 0.0848 0.0875* 0.0967* (0.3567) (0.3628) (0.3605) (0.3791) (0.4197) ENTREPRENEURIAL FAMILY -0.1230** -0.1296** -0.1275** -0.1315** -0.1425*** (0.5181) (0.5448) (0.5416) (0.5696) (0.6184) LOCAL RESIDENT -0.2790*** -0.2862*** -0.2862*** -0.2605*** -0.2770*** (1.1752) (1.2030) (1.2156) (1.1281) (1.2018) TRADE MEMBERSHIP 0.0163 0.0152 0.0151 0.0010 0.0323 (0.0686) (0.0640) (0.0642) (0.0043) (0.1404) PAST BUS EXP 0.1301** 0.1300** 0.1301** 0.1223** 0.1273** (0.5482) (0.5465) (0.5527) (0.5295) (0.5525) TRADE ------0.0083 0.0089 0.0097 (0.0355) (0.0386) (0.0422) SERVICES ------0.0392 0.0542 0.0412 (0.1668) (0.2347) (0.1788) CONST & MANU ------0.0945 0.0877 0.0965 (0.4014) (0.3797) (0.4189) TRADESOUTH ------0.0968* --- (0.4193) INFORMALLOAN ------0.0250 (0.1085)

MICROLOAN ------0.2414** (1.0475) BANKLOAN ------0.0021 (0.0093) REMITABROAD ------0.0423 (0.1839) PAWNING ------0.0200 (0.0869) Predicted probabilities 0.8436 0.8436 0.8436 0.8436 0.8436 No. of observations 241 238 241 241 239 LR chi2 41.36 42.35 42.61 45.68 47.19 Prob > chi2 0.0003 0.0006 0.0009 0.0006 0.0021 Pseudo 푅2 0.1699 0.1750 0.1751 0.1877 0.1946 Log-likelihood -101.01 -99.83 -100.39 -98.85 -97.64 Correctly classified 82.16% 81.09% 82.16% 81.33% 82.43% *p<0.10, **p<0.05, ***p<0.01 denote respective significant levels; dependent variable: opportunity entrepreneur; probit regressions; marginal effects and numbers in parenthesis are respective coefficients 112

Turning to the impact of the variable measuring previous business experience, the results show that individuals with past business experience are more likely to become opportunity than necessity entrepreneurs, with the coefficient positive and statistically significant at the 5% level in all specifications. This result indicates that individuals with previous business experience seem to have an ability to perceive profitable business opportunities in the post-conflict phase vis-à-vis those without experience.

The other important variable is the effect of education on entrepreneurship. This study finds a non-linear (inverse U-shaped) relationship between education and opportunity entrepreneurship, as depicted in Figure 6.1. The probability of starting opportunity- motivated entrepreneurship is higher for the lowest and highest levels of educated individuals as both the PRIMARY and GRADUATE & POST variables are positive and statistically significant at the 10% level, with the significance level of PRIMARY 5% in specifications (3) and (4). In addition, although the coefficient of the UPPER SECONDARY variable is positive, it is statistically insignificant. This appears to be inverse U-shaped relationship between education and opportunity entrepreneurs, that is, highly educated individuals are more likely to capitalise on a peacetime opportunity to maximise their profits, while entrepreneurs who secured only primary levels of education are forced into entrepreneurship for their survival. Thus, these results are in line with the findings discussed in Chapter 5 and suggest that individuals with intermediate levels of education are more likely to become entrepreneurs due to the necessity to escape unemployment. Moreover, estimates of all variables do not change significantly when adding finance and peace variables which prove the robustness of this study’s findings.

Other factors affecting entrepreneurial decisions found to be significant by previous studies, including age, gender, previous labour market engagements, business networks and the business sectors in which the entrepreneurs are engaged, show insignificant effects on opportunity entrepreneurial activity in this post-conflict context.

6.5.2 Inter-ethnic Trade Relationship

This study examines the effect of inter-ethnic trade relationships (a proxy for peace) on entrepreneurial decisions, as shown in specification (4) in Table 6.4. The study finds that individuals who have inter-ethnic trade relationships with the south are significantly more likely to pursue opportunity-motivated entrepreneurial activities and vice versa. The effect of this factor is positive and statistically significant at the 10% level. This shows that entrepreneurs may contribute to peace-building activities through trade (Bruck, 113

Naude, and Verwimp 2013) by developing inter-ethnic relationships between north and south.

6.5.3 Wealth and Access to Finance

Regarding an individual’s wealth, HOME OWNERSHIP is used as a proxy and is positive and statistically significant at the 10% level, as shown in specifications (4) and (5). However, it shows a significant effect only after adding other financial and peace variables. These results demonstrate that wealth seems to be an important factor for starting a business, which suggests that financial constraints play a critical role.

In terms of the impact of access to finance on entrepreneurial decisions, this study finds that no external finance sources, such as informal and bank loans as well as remittances from abroad and pawning, have a significant effect on opportunity-motivated entrepreneurial activity, as shown in specification (5), which also indicates the importance of financial constraints on entrepreneurial decisions (Rajan and Zingales 1998; Pissarides, Singer, and Svejnar 2003; Demirguc-Kunt, Klapper, and Panos 2011).

The only exception is the micro loan received from micro-finance organisations, such as a co-operative or rural bank, or NGO, as the variable MICROLOAN is positive and significant at the 5% level although its effect is small given the sample size. This result supports the argument that the impact of micro finance on opportunity entrepreneurs depends on the socio-economic environment of the country (Lahimer, Dash, and Zaiter 2013). The literature also argues that opportunity entrepreneurship contributes to poverty reduction under the three conditions that the poor have: (i) the required skills and abilities; (ii) links to exploit market opportunities; and (iii) ability to repay loans at market interest rates (Kiiru 2007). This is further supported by the results from studies in India (Imai, Arun, and Annim 2010) and Bangladesh (Imai and Azam 2012). This study suggests that micro finance provides a solution for emerging entrepreneurs in post- conflict settings due to the lack of financial support and under-developed financial systems in these areas.

6.5.4 Robustness Checks

Concerns regarding the approach adopted in this study are the endogeneity of peace (inter-ethnic trade relationships with the south) and bias in the parameter estimates emanating from the omission of relevant variables from the models, for example, macroeconomic conditions and government institutions which can encourage peace and

114 entrepreneurship. As this study examines individual behaviour at a point in time, these factors cannot directly affect the level of security, and macroeconomic and institutional quality in cross-sectional data. Also, as controlling possible endogeneity problems by using the city variable, this study consistently finds a robust positive association between peace and opportunity-motivated entrepreneurs.

6.6 Conclusions

This study used purpose-designed survey data from two cities to estimate the dichotomy between necessity and opportunity entrepreneurs in terms of their individual characteristics, such as age, gender, education, access to finance and inter-ethnic trade relationships. The survey allowed differentiation between two types of entrepreneurs, those who started enterprises to pursue peacetime business opportunities versus those who set them up to earn livelihood income.

The study found that approximately 80% of entrepreneurs, particularly in Kilinochchi, were necessity motivated due mainly to the level of unemployment. Conversely, opportunity entrepreneurs were largely engaged in the construction and related manufacturing sector to exploit new market opportunities. In addition, both lower and higher levels of education and past business experience had positive effects on pursuing opportunity-motivated entrepreneurial activities, whereas people who belonged to an entrepreneurial family were less likely to do so. None of the sources of external finance had a significant effect in terms of the pursuit of opportunity entrepreneurship, except micro loans provided by micro-finance organisations such as co-operative and rural banks and NGOs. These results suggested that financial constraints played a key role in entrepreneurship decisions and hindered private sector development following the conflict, but that opportunity entrepreneurs contributed to creating bridges between north and south through trade.

This study found that post-conflict entrepreneurs were primarily necessity motivated, which contradicts most studies of the informal sectors in developing countries (Brunjes and Diez 2012a; Williams and Williams 2014; Rosa, Kodithuwakku, and Balunywa 2006). They were largely pushed to entrepreneurship as a means of escaping unemployment, and entrepreneurs in Kilinochchi were less likely to pursue opportunity-motivated entrepreneurial activities because they needed income for subsistence more than those in Jaffna.

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Regarding the effect of education on the probability of being an opportunity entrepreneur, this study revealed that those with the highest and lowest levels of education were more likely to become opportunity entrepreneurs, with the insignificant effect of upper secondary level suggesting inverse U-shaped relationship between education and entrepreneurial decisions. Moreover, this study revealed that local residents were less likely to pursue opportunity-motivated entrepreneurial activities, indicating that they are ‘survival-driven’ to earn a livelihood.

This study found that financial constraints played a critical role in entrepreneurial decisions. External sources of finance, such as bank and informal loans, did not have a significant effect, except for micro loans which had a positive effect, while wealth was found to have a significant positive effect on opportunity entrepreneurship. These results demonstrate the importance of targeting micro loans to opportunity entrepreneurs as this reduces entry barriers to business in the post-conflict setting. Moreover, those who had inter-ethnic trade relationships with the south were more likely to pursue opportunity- motivated entrepreneurial activity. Such entrepreneurs played a key role in economic revival and peace-building activities following cessation of conflict (e.g. Naude 2011a; Tobias, Mair, and Barbosa-Leiker 2013).

Opportunity entrepreneurs seemed to have more potential than necessity entrepreneurs to revitalise the two conflict-affected Sri Lankan cities because of their entrepreneurial skills, particularly in the construction and related manufacturing sector. Conversely, 80% of entrepreneurs were largely motivated by necessity, which reflects the importance of support mechanisms in helping them improve their businesses while developing infrastructure facilities. However, their proportion in terms of total entrepreneurial activity would decline with improvements in economic activities (Wennekers et al. 2005; Koster and Rai 2008); for example, Brunjes and Diez (2012b) found that the probability of becoming an opportunity entrepreneur is higher in rural areas of Vietnam due to better access to non-farm wage employment. Thus, the findings of this study reflect that, once economic development is rejuvenated, there should be an increase in the number of opportunity-motivated entrepreneurs, which has implications for the risk of conflict recidivism.

The previous chapter and this chapter explain the two main research questions. They found that the majority of entrepreneurs are necessity driven and they face financial constraints. Conversely, employer entrepreneurs as well as opportunity entrepreneurs contribute to building bridges between the north and south. They also contribute to

116 employment generation. Additionally, employer entrepreneurs are able-bodied older men with upper secondary level education. Also, individuals with intermediate levels of education are more likely to become entrepreneurs. The next chapter examines the factors affecting the growth of these embryonic enterprises.

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Chapter 7

Determinants of Small Firm Growth in Post-conflict Sri Lanka

7.1 Introduction

Following the analysis of the opportunity and necessity motives involved in entrepreneurial decision-making discussed in the previous chapter, this chapter empirically investigates the determinants of emerging small firm growth. This has become a popular topic in recent years as it is considered that these businesses contribute to employment generation, industrial development and wealth creation (Acs and Audretsch 1990; Wiklund, Patzelt, and Shepherd 2009; Coad and Tamvada 2012; Haltiwanger, Jarmin, and Miranda 2013; Ayyagari, Demirguc-Kunt, and Maksimovic 2011), despite their small sizes and production levels (Tybout 2000a).

Unlike in developed countries, MSEs in developing countries are set up to provide individuals with a source of employment and income (Vivarelli 2013). Further, many new firms are created by entrepreneurs as a ‘last resort’ to provide a means of livelihood rather than as a ‘first choice’ (Coad and Tamvada 2012; Beck, Demirguc-Kunt, and Levine 2005). As stated in the previous chapter, most respondents (80%) are motivated by the ‘necessity’ to earn a living. This is because the lack of formal employment opportunities and poverty in developing countries push people towards ‘entrepreneurial’ activities (Sonobe, Akoten, and Otsuka 2011). Many studies of small firm growth have focused on developed or transitional countries (Hoxha and Capelleras 2010), although there is an increasing interest in developing countries (Doern and Goss 2013). However, the role of small firms in the economic revival of a post-conflict environment is under- studied in the extant literature. Despite its significance, little is known about this phenomenon in Sri Lanka, an emerging post-conflict developing country in South Asia.

The aim of this chapter is to provide new empirical evidence of the nature and dynamics of small firms in post-conflict Sri Lanka. The analysis is based on survey data and produces results in a field where data is sparse. The findings can be compared with previous studies of small business growth in developing countries which have relied on small samples (Coad and Tamvada 2012). Thus, they may have relevance to understanding the performance of small businesses in similar environments.

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Previous studies identify many factors associated with small firm growth (Nichter and Goldmark 2009). The literature claims that, although several theories of firm growth have been conceptualised, there is no ‘integrative theory’ for understanding it (Wiklund, Patzelt, and Shepherd 2009; Davidsson and Wiklund 2000; Leitch, Hill, and Neergaard 2010) and the theories need empirical exploration (Storey 2011). Recent empirical studies (Baptista, Lima, and Mendonca 2012; Efendic, Mickiewicz, and Rebmann 2014; Nichter and Goldmark 2009) present some robust factors which affect small firm growth from the perspective of a number of disciplines, such as economics, management, sociology and anthropology. A firm’s growth is determined by a mix of its characteristics, entrepreneur’s attributes, relational (social networks) and contextual factors, and of its strategy (Storey 1994a; Nichter and Goldmark 2009). This chapter provides empirical evidence from two post-conflict cities in Sri Lanka using data gathered in 2012 through a purpose-designed survey of 243 emerging enterprises (see Chapter 4). It investigates the association between firm growth and the above factors by applying LAD and OLS regressions while employing two measures of growth: income and employment. Thus, the chapter contributes to the current debate about explanatory variables that affect firm growth in a post-conflict context.

Consistent with previous research in developed countries, the present study suggests that firm size has a negative impact on firm growth but, in contrast to the findings from most studies, it indicates that the effect of firm age is positive. Regarding individual characteristics, its results suggest that lower and upper secondary levels of education have a negative effect on firm growth, which is opposite to previous studies. It indicates that entrepreneurs are largely driven by the necessity for an income due to the lack of alternative employment opportunities in the immediate aftermath of a conflict. Construction and related manufacturing enterprises were the first to emerge after peace was declared in 2009 and have shown faster rates of growth than businesses in other sectors. Furthermore, this study’s results suggest that social networks play an important role in the performance of small firms in this post-conflict setting, which highlights the need for policy initiatives to create profitable business opportunities for entrepreneurs if the full potential for the growth of private enterprise is to be realised.

The remainder of this chapter is structured as follows: Section 7.2 defines MSEs; Section 7.3 focuses on the theoretical background to small firm growth in developing countries; Section 7.4 provides a brief overview of related literature; Section 7.5 describes the empirical analysis undertaken using gross income, including its data and summary statistics, methodology, empirical strategy and results; Section 7.6 presents an empirical

120 analysis using employment growth; the robustness of the study’s findings are presented in Section 7.7; and Section 7.8 discusses the conclusions drawn.

7.2 What are Micro and Small Enterprises?

The existing literature does not indicate a widely accepted definition of MSEs, often referring to them as ‘small firms’. Researchers have used different definitions and different indicators, such as number of employees, capital assets, turnover level, legal status and method of production, in their contextual settings. In line with previous studies, Nichter and Goldmark (2009, p.1453) define MSEs as “firms with up to 50 workers that engage in non-primary activities and sell at least half of their output”, a definition this paper adopts.

The European Commission (EC) defines enterprises based on the number of employees and either annual turnover or balance sheet total, as shown in Table 7.1. Micro, small and medium enterprises have fewer than 10, 50 and 250 employees respectively, and annual turnovers of less than EUR 2 million, EUR 10 million and EUR 50 million respectively, or annual balance sheet totals of less than EUR 2 million, EUR 10 million and EUR 43 million respectively. Therefore, the MSE sector is comprised of firms that employ fewer than 50 workers and with an annual turnover or balance sheet total of less than EUR 10 million.

Table 7.1: European Commission definitions of micro, small and medium enterprises

Category Number Turnover Balance sheet total of employees Micro < 10 ≤ € 2 m ≤ € 2 m Small < 50 ≤ € 10 m ≤ € 10 m Medium < 250 ≤ € 50 m ≤ € 43 m Source: adapted from Enterprise and Industry, the European Commission (http://ec.europa.eu/enterprise/policies/sme/facts-figures-analysis/sme-definition/index_en.htm)

However, this EC definition is difficult to apply in the context of developing countries. Moreover, the United Nations Industrial Development Organisation (UNIDO) classifies micro, small, medium and large enterprises in developing countries as:

 micro – firms with < 5 workers;  small – firms with 5-19 workers;  medium – firms with 20-99 workers; and 121

 large – firms with 100 or more workers (Abor and Quartey 2010).

In Sri Lanka, various institutions also use different definitions. For example, the Department of Census and Statistics (DCS) defines small enterprises as firms that employ 5-29 workers, medium enterprises as 30-149 and large enterprises as 150 or more. The Industrial Development Board of Sri Lanka (IDB) defines SMEs based on the amount of capital investment and number of employees as those with less than a LKR 4 million (USD 40,000) investment in plant and machinery and fewer than 50 employees; and the Department of Small Industries (DSI) defines SMEs as those with less than LKR 5 million (USD 50,000) and fewer than 50 employees.

7.3 Small Firm Growth – Theoretical Perspective

Theories of firm growth are many, with each theoretical perspective having unique understandings of the concept and the shortcoming of adopting a ‘theoretical firm’ aspect to empirical studies (Davidsson, Delmar, and Wiklund 2006). Also, as these theories have been formulated based on mature firms in developed country settings, their relevance to incipient firms, particularly in post-conflict developing economies, might be limited. While theories of small firm growth have been discussed at length by Coad (2009) in his recent literature survey, this section briefly discusses the following: (i) the neoclassical theory of ‘optimal size’; (ii) Penrose’s resource-based view; (iii) the managerial approach; (iv) evolutionary economics – ‘growth of the fitter’; and (v) the population ecology approach.

7.3.1 Neoclassical Theory of ‘Optimal Size’

This theory recognises that firms are attracted to a profit-maximising level of production, that is, an ‘optimal size’ assuming perfect market competition and the rational behaviour of producers (Viner 1932). According to this perspective, once a firm reaches its ‘optimal size’, it is assumed to be unable to achieve more growth, which implies that small firms grow faster than larger ones until they reach their ‘optimal size’. One variation of this is Coase’s (1937) transactions costs theory, refined by Williamson (1975), which argues that firms can use the price mechanism to pool resources and coordinate activities within a firm.

However, noting that the ‘optimal size’ theory lacks empirical support, Coad (2007) questions its practical value. Criticisms of the theory highlight that it: (i) fails to explain why firms differ from each other and why people start sub-optimal firms that may be too 122 small to be viable in the long run (Audretsch, Houweling, and Thurik 2004); (ii) does not indicate why some firms, particularly in developing countries, choose to remain small to avoid taxes (Tybout 2000); and (iii) does not assume perfect competition in predicting firm growth (O’Farrell and Hitchens 1988). The above discussion suggests that, despite its importance, the neoclassical theory is insufficient for understanding firm growth, particularly in the context of this study.

7.3.2 Penrose’s Resource-based View

The ‘resource-based’ view was first introduced by Penrose (1959) and then refined by Wernerfelt (1984), Barney (1986), Winter (2003) and others. It stipulates that the growth of a firm is determined by the ‘resources of firms’, including brand names, skilled employees, in-house knowledge of technology, trade contracts, machinery and efficient procedures (Wernerfelt 1984). This growth can be achieved by accumulating knowledge through learning to use existing knowledge within a firm and/or adopting new knowledge from external sources, depending on the firm’s internal absorptive capacity. Penrose’s view has an implication for larger firms, as they tend to be resource-based and grow more rapidly than smaller firms. On the other hand, it can have implications for this study because managers of small firms are more directly involved in decision-making and the control of resources than those of larger firms. Kraaijenbrink, Spender, and Groen (2010) recently reviewed the literature related to this theory and its critiques.

7.3.3 Managerial Approach

This theory was pioneered by Marris (1963, 1964) and further developed by Baumol (1967), Williamson (1967), Jensen and Meckling (1976), and Jensen (1986). Its perspective of firm growth is that a manager’s utility, such as compensation, bonuses, other financial incentives and non-pecuniary incentives (prestige, social status), are associated with the size of the firm. Therefore, a ‘firm’s size’ is an important factor in a manager’s utility.

Marris (1964) hypothesises that there is a quadratic relationship between the profit and growth of a firm. This means that, above a certain level of growth, additional diversification has a lower expected profitability because of the constraints on a manager’s time and attention to the efficiency of existing operations, and the development of new activities. Deviating from this theory of internal firm growth through diversification, Mueller (1969) argues that faster growth is achieved through the merging of firms. However, recent studies criticise this approach on two grounds: (i) the difficulty 123 of testing the quadratic relationship between profits and growth rates; and (ii) contradictory empirical evidence (Coad 2009). Due to my focus on incipient emerging firms immediately after the conflict, application of this theory is minimal in this study.

7.3.4 Evolutionary Economics – ‘Growth of the Fitter’

Evolutionary economics is based on Schumpeter’s vision of ‘creative destruction’ – the incessant product and process innovation, diversity creation and dynamics of economic development. This theory is more relevant to a modern context in which economies are characterised by competition and rapid technical and technological changes. Based on evolutionary economics, Alchian (1950) argues that fitter firms survive and grow, while weaker firms exit from the market. The ‘growth of the fitter’ argument has not received much support from empirical evidence which, as identified by Coad (2009), can be attributed mainly to: (i) its high interdependency between profitability and sales growth; and (ii) the difficulty of observing the quality of products and costs as indicators of ‘fitness’ in an empirical analysis. Furthermore, studying surviving firms poses problems of selection for a statistical analysis.

7.3.5 Population Ecology Approach

‘Population ecology’ was drawn from sociology following the contribution of Hannan and Freeman (1977). This theory postulates that the growth of organisations requires resources and the ability to grow without disturbances, if a firm finds a rich resource pool. However, the attraction of new players to a new niche increases competition for resources and will limit the growth rates of firms if the population increases to a resources-saturated level. Details of this approach can be found in the surveys conducted by Geroski (2001) and Hannan (2005).

In conclusion, as firm growth is a multifaceted phenomenon with firm-specific characteristics that influence growth, the above theories cannot provide a comprehensive explanation of it (Coad 2009). Similarly, as they focus on mature firms in the context of developed country settings, their applicability to developing countries, particularly emerging firms in a post-conflict setting can be limited. The next section provides a brief review of the factors affecting small business growth.

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7.4 Determinants of Small Firm Growth – Brief Review of the Literature

Section 7.3 highlighted that the framework for studying small firm growth is multifaceted (Coad 2009). Also, as the above theories are based on the ‘theorised firm’, their application to the practical world is limited (Wiklund, Patzelt, and Shepherd 2009; Davidsson and Wiklund 2000; Leitch, Hill, and Neergaard 2010). Therefore, these theories need empirical testing (Storey 2011). The objective of this section is to provide a brief overview of empirical research on the growth of firms based on evidence from multidisciplinary studies. Various measures have been used to evaluate firm growth: total sales; value-adding; total assets; total profits; and total employment. However, in this literature review, the emphasis is on economic variables, such as total sales and employment. Likewise, growth rates have been measured in different ways: (i) relative growth (percentage); (ii) absolute growth (absolute increase in the number of employees); and (iii) weighted average of both relative and absolute growth rates (Birch Index).

7.4.1 Measuring Size and Growth

This section discusses various measures of size and growth employed in previous empirical studies.

Firm size: a number of different indicators are used to measure this, with empirical evidence suggesting that employment and total sales are the most common (Delmar 2012). However, because of the difficulty of obtaining reliable financial data (Robson and Obeng 2008) and a lack of account records for small firms (Nichter and Goldmark 2009), most studies of developing countries use employment.

Firm growth: in most empirical studies, this has been measured by the logarithmic differences in firm size:

퐺푟표푤푡ℎ푖,푡 = 푙표푔(푋푖,푡) − 푙표푔 (푋푖,푡−1) (7.1)

where 푋푖,푡 represents the size of firm 푖 at time 푡.

Other indicators have also been used: size (by taking a zero size in the initial year) (Storey 1994b); relative growth (growth rate in percentage terms); and absolute growth (increase in the number of employees) (Delmar, Davidsson, and Gartner 2003). Although Almus (2002) claims that relative and absolute growth measures can give different

125 results, this can be overcome by using the Birch Index which is a weighted average of both relative and absolute growth rates, that is:

퐸푖,푡 퐵푖푟푐ℎ 퐼푛푑푒푥푖,푡 = (퐸푖,푡 − 퐸푖,푡−1). (7.2) 퐸푖,푡−1 where 퐸 represents the total employment of firm 푖 at time 푡.

However, the initial size can be a poor indicator, since if growth is low due to temporary shocks, the growth achieved at the end of a considered period will be abnormally high. To avoid this drawback, the DHS Index developed by Davis, Haltiwanger, and Schuh (1996) can be used as:

퐸푖,푡−퐸푖,푡−1 퐷퐻푆 퐼푛푑푒푥푖,푡 = (7.3) 1/2(퐸푖,푡−1−퐸푖,푡 )

where the increase in growth ( 퐸푖,푡 − 퐸푖,푡−1) is scaled down by the average size over the period.

Regarding the firms growth, sections 7.4.2 and 7.4.3 describe individual and firm characteristics respectively, and Section 7.4.4 discusses the business environment.

7.4.2 Individual Characteristics of Entrepreneurs

This section explores the individual characteristics of entrepreneurs associated with firm growth, particularly in developing countries. Although they consist mainly of gender, education and work experience, others examined in the context of developed countries are entrepreneurial motivation and team composition (Nichter and Goldmark 2009).

Gender: is one of the important factors affecting firm growth. Although the literature on small firms in developed countries shows that it is not significantly associated with growth (Storey 1994a), Swedish data suggests that female-owned firms seem to perform well in terms of increasing profitability, employment and orders (Du Rietz and Henrekson 2000). In contrast, female entrepreneurs under-perform relative to their male counterparts in developing countries (Rubio 1991; Mead and Liedholm 1998). Nichter and Goldmark (2009) observe that females are confronted with many challenges in relation to firm growth as they: (i) are focused on a narrow range of business activities due to their asymmetrical rights and obligations; (ii) suffer from problems of innumeracy, illiteracy and lack of business skills; and (iii) have unequal access to markets. Therefore, this

126 study expects that gender will be one of the significant factors affecting firm growth in the post-conflict areas in Sri Lanka.

Education: although the empirical results for the relationship between an entrepreneur’s education and firm growth are mixed, higher levels of education may exercise significant influence. In terms of developed countries, the positive effects of education on firm growth have been identified for German firms (Almus 2002). Alternatively, a negative relationship between education and small firm growth has been determined in studies conducted in New York/the New England area and Singapore (Tan and Tay 1995; Lee and Tsang 2001; Stuart and Abetti 1990). Further, an insignificant association between education and growth has been found in studies conducted in Britain (Robson and Bennett 2000; Barkham, Gudgin, and Hart 2012).

Entrepreneurs in developing countries have relatively low levels of education (Nichter and Goldmark 2009); for example, Ghana data suggests that small firms may have fewer educated owners and workers than larger ones (Soderbom and Teal 2001). A study conducted in Pakistan shows that firms owned by better-educated entrepreneurs are more efficient (Burki and Terrell 1998). Supporting this result, in Ghana, better education offers an advantage in terms of facing business barriers and expanding business activities (Robson and Obeng 2008). Despite the low levels of education in developing countries, the literature has found a positive relationship between education and firm growth in five southern African countries (McPherson 1996), with other studies in African countries showing that secondary school attainment has a positive effect on firm growth (Mead and Liedholm 1998; McPherson 1991). Therefore, this study expects a positive effect of education on firm growth.

Work experience: an entrepreneur’s work experience and resultant abilities are important for the growth of a firm. Work experience may contribute to small firm growth in two ways: (i) by improving the capabilities (skills and knowledge) of owners and employees; and (ii) by expanding entrepreneurs’ social networks (Nichter and Goldmark 2009). However, the literature related to developed countries shows mixed results. A recent study in the Netherlands concludes that work experience in the same industry expands firm growth, profits and survival (Bosma et al. 2004), while empirical evidence provides a negative and insignificant association between work experience and growth (Storey 1994a), with Jovanovic (1982) finding that entrepreneurs learn their abilities as they operate their businesses. However, in terms of developing countries, entrepreneurs

127 with more work experience have faster-growing firms (Nichter and Goldmark 2009). Therefore, this study anticipates a positive effect of work experience on firm growth.

According to Storey’s (1994a) review, unemployment prior to starting up a business has a negative effect on firm growth, while evidence of previous self-employment shows mixed results, with most studies suggesting it has an insignificant impact. Others factors associated with growth rates include the role of training as a mechanism for expanding the capabilities of managers and employees (Cosh, Hughes, and Weeks 2000), an entrepreneur’s age (Storey 1994a; Lee and Tsang 2001), businesses owned by teams (Storey 1994a) and an entrepreneur’s psychological perspective (Wiklund, Patzelt, and Shepherd 2009).

7.4.3 Characteristics of Firm

This section reviews firm characteristics associated with firm growth. It discusses firm size and age, and then summarises other factors, such as legal status, management style and social networks. Although factors such as age, formality of business and access to finance have been extensively studied in relation to small firm growth in developing countries (Nichter and Goldmark 2009), few studies have focused on emerging firms in a post-conflict environment.

A common finding is that young small firms might grow faster than older firms. Nichter and Goldmark (2009) provide explanations for this: (i) a firm grows rapidly in its very first stage and then its growth rate slows as the firm reaches its optimal size but, even with slow growth, firm productivity is expected to increase as the firm ages (Jovanovic 1982); and (ii) firms experience productivity losses as they become older, particularly in developing countries (Burki and Terrell 1998). Although young firms grow faster than older ones, they face difficulties such as a lack of experience and knowledge because of their ‘newness’ and a lack of recognition and legitimacy in the market (Coad and Tamvada 2012).

Firm size: it is well documented that there is a negative relationship between firm size and growth, particularly for small firms, which means that smaller firms grow faster than larger ones. This relationship has attracted much attention in recent years based on Gibrat’s ‘Law of Proportionate Effect’ (Storey 2011), which states that a firm’s growth is independent of its size (formulated by Robert Gibrat in 1931). The literature related to Gibrat’s law has been widely reviewed (Lotti, Santarelli, and Vivarelli 2003; Audretsch et

128 al. 2004; Storey 1994a; Coad 2009). However, this study hypothesises that smaller firms will grow faster in the two post-conflict Sri Lankan cities investigated.

Firm age: in the literature, the relationship between firm age and growth rate shows mixed results, with studies conducted in developed countries, such as the US, Japan and Taiwan, demonstrating a negative association (Dunne, Roberts, and Samuelson 1989; Yasuda 2005; Liu, Tsou, and Hammitt 1999). On the contrary, from a study conducted in Sweden, Heshmati (2001) found that: (i) younger firms grow faster in terms of employment; whereas (ii) older ones grow faster in terms of assets and sales. A negative relationship between age and growth has been observed in developing countries, such as Africa and Latin America (Mead and Liedholm 1998), but a positive one from two studies conducted in India. Examining the young, computer hardware industry in India, Das (1995) found that age has a significant positive effect on firm growth. Focusing on 392 Indian manufacturing firms, Shanmugam and Bhaduri (2002) arrive at the same conclusion. Therefore, it is interesting to see how age affects the growth of emerging firms in this study.

Formality: while informal businesses (unregistered firms) are more common in developing countries, they face difficulties in conducting business because they: (i) are vulnerable in terms of expansion; (ii) are in danger of having limited contracts with government and/or other formal buyers because of legitimacy problems; and (iii) encounter problems of accessing formal credit (Nichter and Goldmark 2009). Consequently, a formal business has a more positive effect on firm growth than its informal counterparts, for example, in the Cote d’Ivoire (Sleuwaegen and Goedhuys 2002). Therefore, this study anticipates that a firm’s formality will a have positive effect on growth rates.

Social networks: this section briefly reviews the association between the entrepreneurs’ social networks and firm growth. Social networks bring benefits to an entrepreneur in terms of access to market opportunities and resources, such as credit, increased productivity and the ability to overcome problems such as regulation and contract enforcement (Nichter and Goldmark 2009). However, they have some implications for firm growth as, according to Nichter and Goldmark’s review, they can: be expensive; exclude the poorest entrepreneurs, women and/or outsiders; and create unequal access to resources and a lack of stability. Therefore, this study will contribute to knowledge on how social networks affect firm growth in post-conflict settings.

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7.4.4 Business Environment

The business environment is an important factor for firm growth. In developing countries, the number of firms expands during economic recessions due to an increase in ‘livelihood’ types of businesses (Coad 2009). Financial conditions, the macroeconomic, regulatory and institutional environment, the flexibility of the labour market and access to information are some of the factors affecting firm growth, particularly in developing countries (Nichter and Goldmark 2009). In addition, the regional effect (urban or rural) is important (Coad 2009).

Moreover, Coad (2009) highlights that firm growth is linked to industry sectors. To understand the business environment in two post-conflict Sri Lanka cities, this study employs three broad categories of business sectors, namely trade, services, and construction and related manufacturing.

7.5 Determinants of Firm Growth – Empirical Evidence from Gross Income

In this study, small firm growth is evaluated using two economic measures: (i) gross income (this section); and (ii) employment (Section 7.6).

The remainder of this section is organised as follows: 7.5.1 briefly discusses the data, 7.5.2 the distributions of some of the key variables, 7.5.3 the summary statistics, 7.5.4 the methodology, 7.5.5 the empirical strategy, 7.5.6 the empirical findings from the LAD regression, and 7.5.7 those from the OLS regression.

7.5.1 Data

This study is based on a purpose-designed survey as described in detail in Chapter 4. The analysis is based on a sample of 205 entrepreneurs who employed at least one paid worker when they started their businesses. As the remaining 38 are solo self-employed entrepreneurs and have no employees, they are excluded from this analysis because they are engaged in survival-based activities, such as being street vendors, barbers, three-wheeler drivers and tailors, and have no record of income from past years. In line with Obeng, Robson, and Haugh (2012) who conducted a similar study in Ghana, we also focus on firms that employ at least one paid worker.

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7.5.2 Univariate Distributions of Firm Size, Age and Growth

This section reports the univariate distributions of firm size, age and growth in relation to gross income.

Figure 7.1 Kernel densities of (a) size distribution (gross income 2009/10) and (b) age distribution

(a) (b)

Density

0 8 10 12 14 16 Log Size 0 5 10 15 20 Firm Age kernel = epanechnikov, bandwidth = 0.4597 kernel = epanechnikov, bandwidth = 0.2359

Notes: the y-axes present density probabilities with kernel densities computed using Epanenchbikov kernel

Figures 7.1 (a) and (b) depict the firm size and age distributions respectively for start-up firms. Although the former shows a unimodal shape, in keeping with previous empirical studies, it is slightly skewed to the right on the upper tail, which indicates that the larger firms are older enterprises that recommenced operations after the conflict.

Figure 7.1 (b) shows the firm age distribution, which is scarce in the literature, as pointed out by Coad (2009), because of the difficulty of finding relevant data (Coad and Tamvada 2012). According to this graph, most of the firms were embryonic enterprises with an average age of 3 years. About 86% are 3 years or younger, approximately 6% are between 4 and 8 years, and about 4% between 11 and 12 years, with the remaining 4% between 13 and 17 years.

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Figure 7.2: Growth rate distribution (log growth of gross income from 2009/10 to 2011/12)

Density

-2 -1 0 1 2 Firm Growth kernel = epanechnikov, bandwidth = 0.1594

Note: the y-axis presents density probabilities, with kernel densities computed using the Epanenchbikov kernel

Figure 7.2 illustrates the growth rate distribution using kernel densities which shows a unimodal shape.

The dependent variable, firm growth, was calculated by taking the differences between the logarithms of size (gross income) over a two-year period to reduce the burden of possibly unreliable data and smooth out short-term spikes, a similar method to that used by Liu, Tsou, and Hammitt (1999) and Coad and Tamvada (2012), as:

퐹푖푟푚 퐺푟표푤푡ℎ푖,푗 = log(푖푛푐표푚푒푖,푗,푡) − 푙표푔(푖푛푐표푚푒푖,푗,푡−2) (7.4)

The following section discusses the summary statistics.

7.5.3 Summary Statistics

Before presenting the summary statistics, definitions of the variables are provided in Table 7.2.

Table 7.3 presents the summary statistics. In the initial sample, there were 205 entrepreneurs who employed at least one paid worker. However, after removing non- respondents of income, as there was an extreme observation of a more than 300% growth in income and outliers from this sample, the analysis includes only 181 firms

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(details of this process are provided in 7.5.4). Table 7.3 illustrates that the average age of firms was 2.77 years, with a minimum of 0.5 and maximum of 17 years, and the average annual growth rate 4.5%. About 90% of firms were formally registered businesses, with single ownership the most prevalent type.

Table 7.2: Definitions of variables

Variable Definition Gross Income Growth 2012-09 Differences between logarithms of income 2011/12 and income 2009/10 Firm Size Logarithm (income 2009/10) Firm Age Logarithm of age Employment Growth Differences between logarithms of employment levels between 2011/12 and 2009/10 Ln (Firm Size) Logarithm (employment level 2009/10) Firm Size Level of employment in 2009 Formal 1 if respondent registered his/her business, 0 otherwise Male 1 if respondent male, 0 if female Primary 1 if respondent received primary level education, 0 otherwise Lower Secondary 1 if respondent received lower secondary level education, 0 otherwise Upper Secondary 1 if respondent received upper secondary level education, 0 otherwise Graduate and Post-graduate 1 if respondent received university degree or post-graduate level education, 0 otherwise Unemployed 1 if respondent claimed as inactive, unemployed or student prior to establishment of business, 0 otherwise Employed 1 if respondent reported as employed prior to start-up of business, 0 otherwise Ex-combatant 1 if respondent claimed he/she joined rebel group prior to establishment of business, 0 otherwise Past Business Experience 1 if respondent had prior experience in business operation before start-up of present business, 0 otherwise Sole Proprietorship 1 if business owned by one person, 0 otherwise Trade Membership 1 if respondent member of any trade link or association, 0 otherwise Trade 1 if respondent has retail or wholesale business, 0 otherwise Services 1 if respondent has service business, such as hotel, restaurant, telecommunication or education, 0 otherwise Construction and Related 1 if respondent has construction and related manufacturing Manufacturing business, 0 otherwise Rented Premises 1 if respondent rents his/her current business place, 0 otherwise Kilinochchi 1 if respondent operates his/her business in Kilinochchi, 0 if in Jaffna

Of the total number of firms, 92.2% were owned by individuals and the remainder (7.8%) by partnerships, with one half of the entrepreneurs members of their respective trade associations. While 42.5% were engaged in wholesale and retail trade activities, 37% were in the services sector and the rest (8%) in the construction and related

133 manufacturing sector. Males dominated in business, owning 92.2% of firms, while only 7.7% were owned by females. About 75% of entrepreneurs had previous business experience, while the majority acquired upper secondary level education (42%), 30.3% lower secondary education and 14.3% primary education, with only 7.7% graduates or higher degree holders. Considering their previous labour market engagements, nearly 42% were employed in either the formal or informal sectors, nearly 30% were unemployed and 2.7% were ex-combatants.

Table 7.3: Summary statistics

All firms New firms (established only after 2009) N=181 N=156 Variable Mean SD Min Max Mean SD Min Max Firm Growth 2012/2011- 0.08 0.50 -1.38 1.38 0.01 0.47 -1.38 1.20 2010/2009 Ln (Firm Size) 11.07 1.44 8.51 15.83 11.18 1.46 8.52 15.83 Ln (Income 2012) 11.16 1.27 8.01 16.11 11.19 1.31 8.01 16.12 Ln (Age) 0.66 0.80 -0.69 2.83 0.43 0.55 -0.69 1.09 Age 2.77 2.93 0.5 17 1.73 0.73 0.5 3

Formal 0.90 0.30 0 1 0.90 0.29 0 1 Entrepreneur’s Characteristics Male 0.92 0.27 0 1 0.92 0.26 0 1 Primary 0.14 0.35 0 1 0.16 0.37 0 1 Lower Secondary 0.42 0.49 0 1 0.43 0.49 0 1 Upper Secondary 0.30 0.46 0 1 0.27 0.45 0 1 Graduate and Post- 0.07 0.27 0 1 0.06 0.24 0 1 graduate Unemployed 0.29 0.46 0 1 0.26 0.44 0 1 Employed 0.42 0.49 0 1 0.42 0.49 0 1 Ex-combatant 0.03 0.16 0 1 0.02 0.16 0 1 Past Business 0.76 0.43 0 1 0.74 0.44 0 1 Experience Ownership Sole Proprietorship 0.92 0.27 0 1 0.93 0.26 0 1 Social Networks Trade Membership 0.56 0.49 0 1 0.58 0.49 0 1 Sector Trade 0.42 0.49 0 1 0.41 0.49 0 1 Services 0.37 0.48 0 1 0.37 0.48 0 1 Construction and 0.08 0.27 0 1 0.08 0.28 0 1 Related Manufacturing Rented Premises 0.76 0.43 0 1 0.78 0.41 0 1 Kilinochchi 0.52 0.50 0 1 0.61 0.49 0 1

The summary statistics for the sub-sample of new firms established after 2009 shows similar characteristics except for the variables of growth and age. Their average annual

134 growth was 0.36% and average age 1.73 years. The correlation matrix is presented in Table 7.1A in Appendix E.

7.5.4 Methodology

The analysis used both LAD and OLS regressions. The former is a good estimator and preferable when outliers are present or the residuals are not normally distributed (Bottazzi and Duenas 2012; Bottazzi et al. 2011). The methodology applied is that used by Coad and Tamvada (2012) in their empirical work conducted in India.

This study employed gross income as a measure of growth. However, this might generate several measurement errors. Although due attention was paid to the accuracy of data obtained from the survey, the income declared by the respondents may not have been completely accurate. For example, firstly, of 205 total entrepreneurs, seven did not report their income and, secondly, there could have been data-recording mistakes when completing the questionnaires (i.e. adding or omitting a zero in the gross income), which would have created outliers in the sample.

This study applied several strategies to correct for any measurement errors. First, it omitted businesses that show extreme growth rates of more than 300%, of which there are seven. Second, it used LAD, or the 퐿1 method, which performs better than OLS in the case of a non-normal distribution of the dependent variable (Coad 2007). The LAD minimises the sum of the absolute values of the residuals whereas OLS minimises the sum of the squared residuals (Wang, Yu, and Liu 2013). Third, as a robustness test, it also presented OLS estimates. Normally, the results obtained from both methods appear to be similar. Fourth, it employed the growth rate measured over a two-year period to reduce the burden of possible unreliable data and smooth out short-term spikes, a similar method to that used by Coad and Tamvada (2012), and Liu, Tsou, and Hammitt (1999). Fifth, it checked for outliers using studentised residuals, that is, in keeping with the cut- off point suggested in Belsley, Kuh, and Welsch (1980). Observations containing studentised residuals exceeding +2 or -2 were excluded from the initial sample (10 cases) and LAD regression is applied on the remaining 181.

The following sub-section discusses the empirical strategy adopted to estimate the effects of factors that influence firm growth.

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7.5.5 Empirical Strategy

Based on the literature review and cross-sectional survey, the following empirical model is used.

퐹푖푟푚 퐺푟표푤푡ℎ푖,푗 = 훼 + 훽1퐹푖푟푚푠푖푧푒푖,푗 + 훽2퐹푖푟푚푎푔푒푖,푗 +훽3퐹표푟푚푎푙푖,푗 + 훽4푀푎푙푒푖,푗 + 훽5퐸푑푢푐푎푡푖표푛푖,푗 + 훽6푃푟푒푣푖표푢푠퐸푛푔푎푔푒푚푒푛푡푖,푗 + 훽7퐵푢푠푖푛푒푠푠퐸푥푝푒푟푖푒푛푐푒푖,푗 + 훽8푆표푙푒푃푟표푝푟푖푒푡표푟푠ℎ푖푝 푖,푗 + 훽9푇푟푎푑푒푀푒푚푏푒푟푖,푗 + 훽10푆푒푐푡표푟푖,푗 + 훽11푅푒푛푡푒푑푃푟푒푚푖푠푒푠푖,푗 + 훽12퐶푖푡푦퐷푢푚푚푦 + 휀푖,푗 (7.5)

The main explanatory variables are firm size (log of firm income in 2009/10), firm age (log age in years), dummy variables for formality of business, male-ownership firm, the education levels of primary, lower secondary, upper secondary or graduate and post- graduate, previous work status (unemployed, employed or ex-combatant), past business experience, sole proprietorship (privately owned firms), trade membership, sector (trade, services or construction and related manufacturing) and ownership of business premises

th (rented or owned) of the 푖 individual in the 푗 city, with 휀푖,푗 the error term. The model includes the Kilinochchi city dummy as a control variable. The next sub-section presents the findings obtained based on the above empirical strategy.

7.5.6 Empirical Results – LAD Regression

Table 7.4 and Table 7.5 present the estimation results from the LAD and OLS regressions respectively, with the dependent variable the gross income growth rate. In these tables, specification (1) is the basic one and indicates the effect of firm size and age on growth, specification (2) consists of characteristics of the entrepreneur, sole proprietorship, social networks, business sector and ownership of business premises (rented or owned) and, in specification (3), the sample is restricted to young firms established only after the cessation of the conflict in May 2009.

Firm size: firm size and age provide similar results for all specifications despite the regression method used, as shown in Table 7.4 and Table 7.5. Specifications (1) and (2) show that the estimated coefficient of the log firm size variable is negative, whereas that of the log firm age variable is positive, while both are statistically significant at the 1% level.

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Table 7.4: Determinants of firm growth (gross income) – LAD regression

Variable (1) All firms (2) All firms (3)Young firms (age≤ 3 years) Firm Log (Income 2009) - Firm Size -0.1382*** -0.1419*** -0.1421** (0.0280) (0.0209) (0.0570) Log (Age) 0.0932** 0.1479*** 0.0869 (0.0465) (0.0339) (0.1460) Formal -0.3294*** -0.2808 (0.0764) (0.2469) Entrepreneur’s Characteristics Male 0.1021 0.1382 (0.0935) (0.2527) Education: Primary -0.1877 -0.1699 (0.1167) (0.3170) Lower Secondary -0.1986* -0.1157 (0.1023) (0.2890) Upper Secondary -0.2209** -0.2119 (0.1079) (0.3002) Graduate and Post-graduate -0.0133 -0.0526 (0.0761) (0.3942) Previous Engagement: Unemployed -0.0616 -0.0815 (0.0672) (0.1862) Employed -0.0339 -0.0144 (0.0620) (0.1708) Ex-combatant -0.2979** -0.6465 (0.1448) (0.4452) Past Business Experience 0.1243** 0.1349 (0.0586) (0.1730) Ownership -0.3410*** -0.2677 (0.0859) (0.2739) Sole Proprietorship Relational Factor (Social Networks) Trade Membership 0.0971* 0.0817 (0.0539) (0.1506) Business Environment Trade -0.0332 -0.0744 (0.0773) (0.2159) Services -0.0768 -0.1557 (0.0797) (0.2269) Construction and Related Manufacturing 0.3101*** 0.2356 (0.1044) (0.3037) Proprietary Ownership Rented Premises 0.1830*** 0.0753 (0.0588) (0.1709) City Dummy Kilinochchi -0.2231** -0.3251*** -0.2577 (0.0861) (0.0684) (0.1939) Constant 1.6785*** 2.1890*** 2.1364** (0.2955) (0.2941) (0.8169) Regression Statistics Observations 181 181 156 푃푠푒푢푑표 푅2 0.2066 0.2883 0.2224 Notes: standard errors are presented in parentheses; *** P < 0.01, ** p < 0.05 and * p < 0.1; the dependent variable is growth rate measured in gross income over 2009/2010 to 2011/2012.; firms reporting more than threefold growth are excluded from the regressions.

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Firm size was found to have a strong negative effect on small firm growth which means that, ceteris paribus, smaller firms grow faster. This finding is not surprising as many empirical studies accept it as a ‘stylised fact’ (Coad 2009). The coefficient of the firm size variable in specification (2) in Table 7.4 is -0.1419 which indicates that a 1% increase in firm size decreases the estimated growth rate by 0.1419%. The estimates further suggest that a one standard deviation increase in size results in a decrease of 18.53 percentage points in growth.8 The reason for this negative relationship might be due to larger firms responding more slowly to new information in the market than small ones (Das 1995). Also, this relationship holds even for the sub-sample of young firms defined equal or less than three years old.

Firm age: the study found that there is a strong positive dependency of growth on age, reflecting that older firms have higher anticipated growth rates. The coefficient of the ‘Age’ variable is 0.1479, as depicted in Table 7.4, the elasticity value of which implies that a 1% increase in age leads to a 0.1479% increase in the expected growth rate. This may be due to various reasons, such as: (i) most nascent firms keep learning about their own efficiencies over time and may increasingly return to such learning; (ii) consumer awareness of a firm/product may increase over time; (iii) a firm’s reputation may increase with its age (Das 1995). This finding contradicts the results from studies of developed countries that focus mainly on mature firms. However, it is consistent with those from studies that analysed the patterns of growth of infant firms in developing countries which concentrated particularly on young and fast-growing computer firms in India and Indian manufacturing firms (Shanmugam and Bhaduri 2002; Das 1995). However, from the sub- sample of young firms defined equal or less than three years old, the positive effect of age on growth is insignificant because these nascent firms are still in their early stages. This result suggests that new firms first attempt and survive through their initial years of establishment and then, having gained their feet, grow rapidly in a post-conflict situation.

Formal: the impact of formal status on growth shows a strong negative sign, with the coefficient estimate significant at the 1% level in specification (2) in Table 7.4. Its coefficient is -0.3294 which corresponds to a decrease in estimated growth of 32.90

8 Calculations are based on the method applied by Coad and Tamvada (2012), as follows. According to Table 7.3 (Summary Statistics), the standard deviation of the log (gross income) is 1.4446 and specification (2) in Table 7.4 presents the coefficient of the log (gross income) as -0. 1419. If, ceteris paribus, changing the log (gross income) by one standard deviation changes the dependent variable by 1.4446 X -0.1419 = -0.20498874, the dependent variable is 푙표푔푒(푔푟표푤푡ℎ 푟푎푡푒) and the change in it =-0.20498874. A log (growth rate) of -0.20498874 equals a (conventionally measured) growth rate of -0.18534351 (provided that 푒(−0.20498874) -1 = - 0.18534351) which is about -18.53%. 138 percentage points (not exactly 32.94 percentage points due to the log form of the dependent variable). This finding is opposite to those from most empirical studies; for example, Sleuwaegen and Goedhuys (2002) found that formality has a positive effect on firm growth in the Cote d’Ivoire. This is not surprising as informality is common in an economy after a long and severe conflict. In the study areas, both formal and informal businesses faced difficulties in accessing formal credit and joining trade associations, which could suggest that formalisation takes place after firms have established themselves, because nascent firms may need to develop their credentials before registering if registration is costly and enforcement of regulations lax. Also, as young firms grow fast, this is seen as formal firms growing slowly.

Characteristics of entrepreneurs: the effect of an entrepreneur’s education on firm growth was found to be negative (Table 7.4), also evidenced in a study conducted in post-conflict Kosovo (Krasniqi 2012). However, this result contradicts most empirical findings (Robinson and Sexton 1994; Kangasharju and Pekkala 2002; Burki and Terrell 1998; Tan and Tay 1995). Although primary, lower secondary, upper secondary and graduate levels of education show negative effects on the growth rate, only those of lower and upper secondary levels are significant. This is not surprising because owners and employees of small businesses in developing country settings have relatively low levels of education (Nichter and Goldmark 2009). Also, owners of small businesses are less educated than those of larger ones (Soderbom and Teal 2001). Another reason for the creation of survival firms in fragile and post-conflict environments is the lack of alternative formal employment opportunities (Nichter and Goldmark 2009; Krasniqi 2012), particularly for the least educated given that the public service provide jobs to the better educated; that is, it is the ‘necessity’ motive to establish a small business to escape unemployment that drives the creation of businesses in post-conflict environments, as discussed in Chapter 5. As the need for a livelihood is greatest for those without skills and professional qualifications, this may explain why education is negatively correlated with starting a new enterprise.

Past business experience: this has a positive effect on the rate of firm growth, showing a 5% significant level, a result in line with previous findings (Nichter and Goldmark 2009). Specification (2) in Table 7.4 indicates that the coefficient of past business experience is 0.1243 which corresponds to an increase in the estimated growth rate of 12.40 percentage points (not exactly 12.41 percentage points because of the log form of the dependent variable). As work experience helps to expand the capabilities and social networks of entrepreneurs, it contributes to small business growth. Nichter and Goldmark

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(2009) argue that entrepreneurs with more work experience have faster-growing small businesses and also benefit from being able to identify business opportunities and to access finance.

Previous labour market engagement: for the unemployed, employed and ex- combatants, this factor shows a negative effect on growth, with the ex-combatants’ variable indicating a 5% significance level, as illustrated in Table 7.4. This means that businesses of entrepreneurs who are ex-combatants seem to have less growth, which could be due to them lacking the necessary skills or their enterprises being discriminated against by customers. Despite the significance of this finding, the representation of ex- combatants in the whole sample is small (2.76%) which reduces the importance of this impact. However, their presence as entrepreneurs in a post-conflict situation can be important for reconciliation activities.

Male: although this variable shows a positive effect on growth, it is not statistically significant, which is inconsistent with some findings that argue that male entrepreneurs perform better in growth of their businesses than female entrepreneurs.

City effects: the Kilinochchi city dummy shows a negative impact on growth at the 1% significance level, which suggests that, on average, firms in Kilinochchi have lower growth rates. This might be due to Kilinochchi entrepreneurs lacking business knowledge and skills and the ability to perceive business opportunities, as well as encountering barriers to business after the conflict. Further, firms in Kilinochchi have small market size and low per capita income than firms in Jaffna. Also, Kilnochchi may lack the infrastructure – law and order, roads, etc., – required for business. Unlike the Jaffna enterprises, all the firms in Kilinochchi were established after the cessation of hostilities, as Kilinochchi city remained a rebel-controlled area for nearly 10 years and was severely damaged by the end of the conflict. As a result, on average, these firms show llower growth due to their loss of human capital, which is referred to as ‘forgetting by not doing’ (Collier and Duponchel 2013). This finding can be further supported by the Kilinochchi entrepreneurs having lower levels of education than those in Jaffna.

Sole proprietorship: this factor has a significant negative impact on growth. The coefficient of sole proprietorship, as shown in Table 7.4, is -0.3410 which indicates a decrease in the estimated growth rate of 34.10 percentage points and one which is statistically significant at the 1% level. This result suggests that financial constraints constitute a major obstacle to conducting businesses in post-conflict Sri Lanka, as mentioned in Chapter 5. The result is consistent with the findings from a study conducted 140 in India which showed that small firms, particularly those under sole proprietorship, seem to have problems in obtaining working capital (Coad and Tamvada 2012).

Social networks: the relationship between social networks (trade membership), which refer to relationships among individual entrepreneurs, and small firm growth is positive and significant at the 10% level, as depicted in Table 7.4. Studies conducted in Morocco and Ghana highlight the importance of social networks in overcoming barriers, such as transaction costs and regulation (Geertz 1978; Fafchamps 2000; Portes 1998). This suggests that they help entrepreneurs to enjoy the competitive advantages of business opportunities and resources, such as credit, leading to higher firm growth in both the post-conflict cities studied. As, in the case of a post-conflict situation, the lack of trust and absence of a strong state that can enforce contracts is important, social networks may provide the basis for business deals.

Business sector: Table 7.4 demonstrates that the construction and related manufacturing sector appears to have a strong positive impact on growth, with a statistical significance at the 1% level. The corresponding coefficient is 0.3101 which demonstrates an increase in the estimated growth rate of 31.00 percentage points. This indicates that this sector was the first in which businesses were set up after peace was declared due to the rapid resumption of construction activities. These activities include building permanent business premises, houses and other infrastructure, in conflict- affected areas, which produced high demand for construction and related products. Although the trade and services sectors have negative effects, these are statistically insignificant.

Rented premises: this is found to have a positive effect on growth, with significance at the 1% level and a related coefficient of 0.1830, as demonstrated in Table 7.4, which indicates an increase of 18.00 percentage points in the estimated growth rate. These results could be interpreted as demonstrating that entrepreneurs who rent their business premises have additional aspirations, and a commitment and risk-taking attitude towards developing their businesses. It could also suggest that those who rent enterprises have the cash flow and knowledge to start a business but lack the buildings to commence operations. Furthermore, renting reduces the risks of large outlays on construction while using an idle asset that may be cheaply acquired following the resumption of peace.

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7.5.7 Empirical Results – OLS Regression

As a robustness analysis of the results from the LAD regression, the study performs OLS estimates. Table 7.5 shows the empirical findings based on the OLS regression with robust standard errors which mostly confirm the LAD regression estimates, particularly the variables of firm size, firm age, formality, sole proprietorship, construction and related manufacturing, and the Kilinochchi city dummy in terms of the magnitudes of their regression coefficients.

However, the variables of lower secondary and upper secondary levels of education, past business experience, social networks (trade membership) and rented premises turn out to be insignificant when OLS estimations are used. Conversely, the OLS regression employed to measure gross income demonstrates heteroskedasticity, that is, its estimator is no longer unbiased, a result which suggests that the LAD is more efficient than the OLS regression. However, as there is insufficient evidence to conclude either way, it may be safer to use LAD estimations (Foss, Myrtveit, and Stensrud 2001; Wang, Li, and Jiang 2007; Khan and Ferdous 2012).

With regard to the sub-sample of young firms defined as equal or less than three years of age, the OLS regression with robust standard errors, as in specification (3) in Table 7.5, reports that formal businesses, sole proprietorship and ex-combatants are negatively associated with small firm growth. In addition, firm age is insignificant which suggests that the variation in age data is reduced by omitting all firms that are more than three years old. This is because nascent firms try to survive through their years of establishment but, once gaining their feet, grow rapidly in a post-conflict situation. Also, the variable of construction and related manufacturing shows an insignificant result, which indicates that it is the domain of mature firms.

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Table 7.5: Determinants of firm growth (income) – OLS regression with robust standard errors

Variable (1) (2) (3) All firms All firms Young firms (age≤ 3 years) Firm Log (Income 2009) - Firm size -0.1094*** -0.1159*** -0.1172*** (0.2905) (0.0314) (0.0892) Log (Age) 0.0751** 0.1000** 0.0122 (0.0359) (0.0383) (0.0697) Formal -0.2162** -0.1599* (0.0827) (0.0957) Entrepreneur’s Characteristics Male 0.0217 0.0298 (0.0991) (0.1075) Education: Primary -0.0710 -0.1355 (0.1665) (0.1859) Lower Secondary -0.1618 -0.2119 (0.1538) (0.1779) Upper Secondary -0.1245 -0.2527 (0.1584) (0.1794) Graduate and Post-graduate 0.0535 -0.1075 (0.1864) (0.2229) Previous Engagement: Unemployed -0.0418 -0.0807 (0.0868) (0.0917) Employed -0.0928 -0.0889 (0.0823) (0.0858) Ex-combatant -0.3986* -0.4830* (0.2125) (0.2626) Past Business Experience 0.1392 0.0698 (0.0762) (0.0849) Ownership -0.2655*** -0.2823*** (0.0825) (0.1040) Sole Proprietorship Relational Factor (Social Networks) Trade Membership 0.0831 0.0918 (0.0616) (0.0662) Sector Trade 0.0312 0.0262 (0.1033) (0.1158) Services -0.0807 -0.0900 (0.1021) (0.1147) Construction and Related Manufacturing 0.3011** 0.2569 (0.1397) (0.1633) Proprietary Ownership Rented Premises --- 0.0846 0.0405 (0.0618) (0.0702) City Dummy Kilinochchi -0.2968*** -0.3450*** -0.3056*** (0.0771) (0.0797) (0.0892) Constant 1.4028*** 1.8524*** 2.0062*** (0.2966) (0.4301) (0.4648) Regression Statistics Observations 181 181 156 F Statistics 27.52 8.19 5.23 푅2 0.3376 0.4664 0.3960 Notes: the results are drawn from the OLS regression; standard errors are in parentheses; *** 푝 <0.01** 푝 <0.05* and 푝 <0.1; the dependent variable, gross income growth, is the log income difference between 2009/2010 and 2011/2012; firms experiencing more than threefold growth are excluded from the regression.

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7.6 Determinants of Firm Growth – Empirical Evidence from Employment

This section examines the impacts of various variables discussed in 7.5.6 and 7.5.7, rather than gross income, or employment growth. The advantage of this analysis is that entrepreneurs can better recall their numbers of employees over time than their financial information (Nichter and Goldmark 2009).

This section is structured as follows: 7.6.1 discusses the summary statistics; 7.6.2 examines the methodology and empirical strategy; and 7.6.3 reviews the findings.

7.6.1 Summary Statistics – Employment Growth

Table 7.6 presents the summary statistics.

Table 7.6: Summary statistics – employment growth

All firms New firms (established only after 2009) N=187 N=158 Variable Mean SD Min Max Mean SD Min Max Firm Growth-log (2009/2010 – 0.15 0.37 -1.09 1.38 0.13 0.35 -1.09 1.09 2011/2012) Ln (Age) 0.68 0.84 -0.69 3.58 0.41 0.55 -0.69 1.09 Firm Age 3.02 4.01 0.50 36 1.71 0.72 0.5 3 Ln (Firm Size) 0.84 0.63 0 3.04 0.82 0.64 0 3.04 Firm Size 2.90 2.52 1 21 2.88 2.63 1 21 Formal 0.89 0.31 0 1 0.89 0.31 0 1 Entrepreneur’s Characteristics Male 0.92 0.27 0 1 0.92 0.27 0 1 Education: 0.13 0.34 0 1 0.15 0.36 0 1 Primary Lower Secondary 0.42 0.49 0 1 0.43 0.49 0 1 Upper Secondary 0.31 0.46 0 1 0.28 0.45 0 1 Graduate and Post-graduate 0.07 0.25 0. 1 0.05 0.23 0 1 Previous engagement: 0.27 0.44 0 1 0.24 0.43 0 1 Unemployed Employed 0.44 0.49 0 1 0.45 0.49 0 1 Ex-combatant 0.03 0.16 0 1 0.02 0.15 0 1 Past Business Experience 0.75 0.43 0 1 0.73 0.44 0 1 Ownership Sole Proprietorship 0.91 0.29 0 1 0.92 0.26 0 1 Social Networks Trade Membership 0.55 0.49 0 1 0.57 0.49 0 1 Sector Trade 0.40 0.49 0 1 0.38 0.49 0 1 Services 0.36 0.48 0 1 0.38 0.49 0 1 Construction and Related 0.08 0.27 0 1 0.07 0.26 0 1 Manufacturing City Dummy Kilinochchi 0.49 0.50 0 1 0.59 0.49 0 1

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The average age of firms was three years, the average firm size nearly three employees and average annual employment growth rate 6.5%. The majority of entrepreneurs were sole male proprietors, 75% of whom had past business experience. Half of the entrepreneurs were members of a trade association or trade link.

This section employs the same empirical strategy followed in 6.4.5 except that the dependent variable is employment instead of gross income. Firm size is the logarithm of the number of employees instead of gross income.

7.6.2 Methodology and Empirical Strategy

The following model is used to estimate firm growth for the cross-sectional survey data.

퐹푖푟푚 퐺푟표푤푡ℎ푖,푗 = 훼 + 훽1퐹푖푟푚푠푖푧푒푖,푗 + 훽2퐹푖푟푚푎푔푒푖,푗 + 훽3퐹표푟푚푎푙푖,푗 + 훽4푀푎푙푒푖,푗 +

훽5퐸푑푢푐푎푡푖표푛푖,푗 + 훽6푃푟푒푣푖표푢푠 퐸푛푔푎푔푒푚푒푛푡푖,푗 + 훽7퐵푢푠푖푛푒푠푠퐸푥푝푒푟푖푒푛푐푒푖,푗 +

훽8푆표푙푒푃푟표푝푟푖푒푡표푟푠ℎ푖푝푖,푗 + 훽9푇푟푎푑푒푀푒푚푏푒푟푖,푗 + 훽10푆푒푐푡표푟푖,푗 + 훽11푅푒푛푡푖,푗 +

훽12퐶푖푡푦푑푢푚푚푦 + 휀푖,푗 (7.6)

The main variables in this model are firm size (log of employment level at 2009); firm age (log age), dummy variables for male-owned firms; education (primary, lower secondary, upper secondary or graduate and post-graduate levels); previous status of engagement (unemployed, employed or ex-combatant); past business experience; sole proprietorship (privately owned or partnership); trade membership; sectors (trade, services or construction and related manufacturing); and ownership of a business premise (rented or owned) of 푖 individual in 푗 th city, with a city dummy included as a control variable. Detailed information of each variable can be found in the summary statistics shown in Table 7.6 and their definitions are presented in Table 7.2.

The dependent variable, firm growth, is calculated by taking the differences in the logarithm of size (number of employees) and applying the growth rate over a two-year period to smooth out short-term growth shocks as:

퐺푟표푤푡ℎ푖,푗 = log(푁푢푚푏푒푟 표푓 퐸푚푝푙표푦푒푒푠 푖,푗,푡) −

푙표푔(푁푢푚푏푒푟 표푓 퐸푚푝푙표푦푒푒푠 푖,푗,푡−2) (7.7)

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7.6.3 Empirical Results – Employment Growth

As presented in Table 7.7, the variables firm size and age, ex-combatant, rented premises and city dummy show similar results to those obtained from the LAD regression: that is, firm size has a negative association with firm growth at a 1% significance level; firm age has a positive relationship at a 5% significance level; ex- combatants variable has a negative effect at a 10% significance level; entrepreneurs who rent their business premises a positive effect; and the Kilinochchi city dummy a negative effect.

There are some differences between the results in terms of the statistical significance of the variables formal, education, past business experience, sole proprietorship, social networks (trade membership), and construction and related manufacturing. In contrast to the LAD regression, enterprises started by individuals who had previously been employed show a faster growth in their numbers of employees.

Regarding young firms defined as equal or less than three years old, specification (3) in Table 7.7 shows that younger firms grow faster. However, the main variables of firm size and age are consistent with different types of specifications and regressions, with formality of business having a positive effect on growth, even for a young business.

Despite some contradictory findings from different measures and regressions, the study provides consistent results, particularly in terms of the important variables. Considering the overall results, both regressions give similar qualitative findings despite the fact that the proxies used to measure size are different, that is, turnover in the first case and employment in the latter. These results indicate that past business experience, education, social networks (trade membership), and construction and related manufacturing are important variables for the growth of firms in this post-conflict setting.

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Table 7.7: Determinants of firm growth (employment) – OLS regression

Variable (1) (2) (3) All firms All firms Young firms (age≤ 3 years) Firm Log (Employment Level 2009)- Firm Size -0.1974*** -0.1748*** -0.1699*** (0.0399) (0.0428) (0.0448) Log (Age) 0.0691** 0.0588* 0.0095 (0.0324) (0.0348) (0.0549) Formal 0.1286 0.1889* (0.0876) (0.0981) Entrepreneur’s Characteristics Male -0.0917 -0.0594 (0.0976) (0.1010) Education: Primary 0.0047 -0.0473 (0.1230) (0.1261) Lower Secondary -0.1720 -0.1969* (0.1085) (0.1138) Upper Secondary -0.0452 -0.1026 (0.1115) (0.1162) Graduate and Post-graduate -0.1655 -0.2197 (0.1444) (0.1595) Previous Engagement: Unemployed 0.0106 -0.0012 (0.0700) (0.0750) Employed -0.1079* -0.0811 (0.0620) (0.0651) Ex-combatant -0.4562*** -0.4873*** (0.1631) (0.1818) Past Business Experience -0.0652 -0.1147 (0.0630) (0.0701) Ownership 0.0054 0.0192 (0.0881) (0.1039) Sole Proprietorship Relational factors (social networks) Trade membership 0.0304 0.0085 (0.0573) (0.0617) Industry/Business Environment Trade -0.0975 -0.0312 (0.0753) (0.0811) Services -0.0211 -0.0250 (0.0776) (0.0816) Construction and Related Manufacturing 0.1199 0.0811 (0.1120) (0.1208) Proprietary Ownership Rented Premises --- 0.1668** 0.0997 (0.0644) (0.0691) City Dummy Kilinochchi -0.0779 -0.1222* -0.0864 (0.0544) (0.0686) (0.0723) Constant 0.3060*** 0.3762** 0.3815* (0.0581) (0.1955) (0.2108) Regression Statistics Observations 187 187 158 F Statistics 10.71 3.66 2.59 푅2 0.1493 0.2941 0.2626 Adjusted 푅2 0.1354 0.2138 0.1611 Notes: the results are drawn from the OLS regression; standard errors are in parentheses; *** 푝 <0.01** 푝 <0.05* and 푝 <0.1; the dependent variable, employment growth, is the log number of employees difference between 2009/2010 and 2011/2012

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7.7 Robustness of Findings

This section explores the robustness of the findings from the LAD and OLS regressions employed to measure income (specifications (1) and (2)) and employment growth (specification (3) respectively, as shown in Table 7.8. Robustness checking of the LAD regression is discussed in detail in 7.5.4.

In summary, the study removes outliers from the dataset, omits firms experiencing more than 300% growth rates, employs the OLS regression as a robustness analysis of the LAD regression, and eliminates short-term growth shocks by measuring growth over a two-year period. However, it is observed that, as the OLS regression employed to measure gross income exhibits heteroskedasticity, its estimator is no longer unbiased whereas, although that used to measure employment growth does not show heteroskedasticity, this is detected by employing the Breusch-Pagan test (BP test). As the p-value of this test is 0.0145, the null hypothesis is rejected and it is accepted that the variance is not homogeneous. Therefore, robust standard errors, referred to as Huber/White estimators or sandwich estimators of variance, which do not assume independent or identically distributed errors, are used.

The study also tests multicollinearity between independent variables, the degree of which increases, with the regression model estimates of the coefficients becoming unstable and the standard errors for the coefficients turning out to be inflated. This problem is common when there are many independent variables in a small sample (Gujarati 2003). To check for multicollinearity, the variance-inflated factor (VIF) is used, with the most common rule that, if its values are greater than 10, there is a multicollinearity (O’Brien 2007). However, this is not indicated. The study further checks the magnitude of correlation using the correlation matrix of explanatory variables presented in Table 7.3A in Appendix E. The highest correlation coefficients are between lower secondary and upper secondary (-0.5621), unemployed and employed (-0.5548), Kilinochchi and logsize (0.5203), Kilinochchi and growth (-0.5043), and growth and logsize (-0.4895), while the other correlation coefficients remain generally low. As a multicollinearity problem between correlation coefficients is detected if the absolute value is smaller than 0.7 (Dormann et al. 2013), this study may suggest that multicollinearity in the data is not a serious issue.

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Table 7.8: Determinants of small firm growth – regression results from LAD and OLS – gross income (1) and (2), and employment (3)

Variable (1) (2) (3) Firm Log (Employment Level 2009) - Firm Size -0.1419*** -0.1159*** -0.1748*** (0.0209) (0.0314) (0.0428) Log (Age) 0.1479*** 0.1000** 0.0588* (0.0339) (0.0383) (0.0348) Formal -0.3294*** -0.2162** 0.1286 (0.0764) (0.0827) (0.0876) Entrepreneur’s Characteristics Male 0.1021 0.0217 -0.0917 (0.0935) (0.0991) (0.0976) Education: Primary -0.1877 -0.0710 0.0047 (0.1167) (0.1665) (0.1230) Lower Secondary -0.1986* -0.1618 -0.1720 (0.1023) (0.1538) (0.1085) Upper Secondary -0.2209** -0.1245 -0.0452 (0.1079) (0.1584) (0.1115) Graduate and Post-graduate -0.0133 0.0535 -0.1655 (0.0761) (0.1864) (0.1444) Previous Engagement: Unemployed -0.0616 -0.0418 0.0106 (0.0672) (0.0868) (0.0700) Employed -0.0339 -0.0928 -0.1079* (0.0620) (0.0823) (0.0620) Ex-combatant -0.2979** -0.3986* -0.4562*** (0.1448) (0.2125) (0.1631) Past Business Experience 0.1243** 0.1392 -0.0652 (0.0586) (0.0762) (0.0630) Ownership Sole Proprietorship -0.3410*** -0.2655*** 0.0054 (0.0859) (0.0825) (0.0881) Relational factor (social networks): Trade Membership 0.0971* 0.0831 0.0304 (0.0539) (0.0616) (0.0573) Industry/Business Environment Trade -0.0332 0.0312 -0.0975 (0.0773) (0.1033) (0.0753) Services -0.0768 -0.0807 -0.0211 (0.0797) (0.1021) (0.0776) Construction and Related Manufacturing 0.3101*** 0.3011** 0.1199 (0.1044) (0.1397) (0.1120) Proprietary Ownership Rented Premises 0.1830*** 0.0846 0.1668** (0.0588) (0.0618) (0.0644) City dummy Kilinochchi -0.3251*** -0.3450*** -0.1222* (0.0684) (0.0797) (0.0686) Constant 2.1890*** 1.8524*** 0.3762** (0.2941) (0.4301) (0.1955) Regression Statistics Observations 181 181 187 F Statistics - 8.19 3.66 푅2/Pseudo 0.2883 0.4664 0.2941 Adjusted 푅2 - - 0.2138

Notes: the results are drawn from the LAD and OLS regressions; standard errors are in parentheses; *** 푝 <0.01** 푝 <0.05* and 푝 <0.1; in specifications (1) and (2), the dependent variable is gross income calculated from the log gross income differences between 2009/2010 and 2011/2012; more than threefold income observations are excluded from the regressions; in specification (3), the dependent variable, employment growth, is calculated from the log number of employees difference between 2009/2010 and 2011/2012 149

In addition, the study performs model/models specification errors to determine whether one or more relevant variables are omitted from, or one or more irrelevant variables are included in, the model. The study uses Linktest and creates two new variables, the variable of prediction (_hat) and variable of squared prediction (_hatsq). The model then refits using these two variables as predictors whereby _hat should be significant and _hatsq insignificant because the latter should not have much explanatory power if the model is correctly specified. In Linktest, _hatsq is not significant which means that it fails to reject the assumption that the model is correctly specified. In this test, this study found that ex-combatant is an important variable to explain small firm growth. Omitting this variable, the growth measured by employment, shows omitted variable bias. Thus, this study included the ex-combatant variable into the model despite its small sample size.

Further, the study performs the regression specification error test (RESET) for omitted variables which indicates that there is no specification error in the model, with the F statistics 0.45 and insignificant for the given degrees of freedom. In addition, as the study tracks for outliers, as described in the LAD regression, it tends to have a normal distribution dataset. Both these tests suggest that the model is correctly specified.

For methodological robustness checks, the study employs different growth measures (income and employment) and different regression approaches (LAD and OLS), and concludes that their main findings tend to be similar.

7.8 Conclusions

The objective of this study was to contribute to the literature on the nature and dynamics of small businesses in post-conflict settings. It examined the factors affecting embryonic small firm growth in the two post-conflict cities of Jaffna and Kilinochchi, Sri Lanka, based on a purpose-designed survey conducted in these cities in 2012. The findings could provide insights for understanding small firms in other post-conflict countries.

This study highlighted a number of factors associated with small firm growth in a post- conflict setting, with three key findings being that: (i) construction and related manufacturing enterprises were the first to emerge after peace was declared in 2009 and showed the fastest rate of growth compared with businesses created in other sectors; (ii) most individuals were pushed into entrepreneurial activities due to the ‘necessity’ to earn an income to provide a livelihood for themselves and their families and survive in this post-conflict environment; and (iii) social networks were critical to the growth of private enterprise. 150

The results determined five main factors associated with firm growth: firm-specific, entrepreneurs’ characteristics, relational, business sector and ownership. Although the study found that smaller firms appeared to grow faster than larger ones, it also revealed that there was a strong positive dependency of a firm’s growth on its age, with growth increasing with age. Although this finding contradicted the results obtained from studies of developed countries that focused on relatively mature firms, it was consistent with those from previous research that analysed the patterns of growth of infant firms in India. This study also explored the factors associated with the growth of a sub-sample of young firms defined as equal or three years old and found that there was an insignificant effect of firm age on growth rate. This indicated that these nascent firms tried hard and survived through their initial stages and, once they became established, grew rapidly, which could have policy relevance, particularly in a post-conflict situation.

Although the literature shows that the formality of business has a positive effect on growth, this study found a strong negative effect, which is not surprising given the numerous barriers encountered in this post-conflict setting. Firms that registered saw that the benefits of doing so outweighed the costs, but the bulk of newly created firms reached this decision only after having established themselves in the marketplace.

Regarding the effect of education on firm growth, this study suggested that it was negative. Also, it found that, although the circumstances in a post-conflict environment where the unemployment rate was high created a ‘necessity’ motive to establish small businesses to escape unemployment, past business experience had a positive effect, with entrepreneurial capital very important Social networks had a positive effect on growth, which indicated their importance in the two cities after the conflict where institutions were less developed than elsewhere.

All these findings contribute to a larger body of literature by providing evidence from post- conflict settings for which data is sparse. They highlight the need for policy initiatives to create profitable business opportunities for entrepreneurs if the full potential for the growth of private enterprise in post-conflict settings is to be realised, and also demonstrate the importance of developing the infrastructure necessary for entrepreneurship. The analysis demonstrates that the potential for profitable opportunities are in construction activities and small informal businesses which could have policy relevance beyond Sri Lanka. The key issues pertaining to this chapter and the previous two empirical chapters are summarised with the contributions, policy implication, limitations of the study in the next chapter, which concludes the thesis.

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Chapter 8

Conclusions

8.1 Introduction

The aim of this thesis was, primarily, to investigate the entrepreneurs’ motivations to start an enterprise and, secondarily, to decipher the contributions such enterprises make to the socio-economic development. As my case study I used Sri Lanka following the cessation of conflict in 2009. A decisive military victory provided the window for peace, but has this led to economic development? And if so, then what are the factors underscoring the economic revival?

The two overarching questions addressed are: (i) “Who are the emerging entrepreneurs in post-conflict Sri Lanka?”; and, (ii) “How do they contribute to post-conflict economic recovery?”. The focus has been very much on the former question, with comments regarding the later question only being made when the evidence and arguments have permitted it. The specific objectives of this study were to examine: (i) the factors that affect incipient entrepreneurship, namely employer and solo self-employed entrepreneurs; (ii) the motivations for individuals to start a business; and (iii) the determinants of the growth of small firms. This chapter brings together the empirical findings and draws some policy implications from the findings.

The analyses in Chapters 5, 6, and 7 were based on data collected in 2012 through a purpose-designed survey administered to 243 embryonic enterprises in two severely conflict-affected Sri Lankan cities, 126 in Jaffna and 117 in Kilinochchi. The survey included the basic demographic and personal characteristics of entrepreneurs, firm characteristics, inter-ethnic trade relationships established after the conflict, barriers to doing business and diaspora involvement in business.

The rest of this chapter is organised as follows. Section 8.2 presents the main empirical findings from the study. Section 8.3 discusses policy implications arising from the three empirical chapters. Section 8.4 provides the contributions of this study to the existing body of literature. Section 8.5 discusses the major limitations of this research and identifies avenues for future research, while Section 8.6 provides concluding statements.

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8.2 Main Empirical Findings

8.2.1 Employer Entrepreneurs versus Solo Self-employed Entrepreneurs and Necessity-motivated versus Opportunity-motivated Entrepreneurs

With reference to the first research question, this study identified two types of entrepreneurs, employer entrepreneurs and solo self-employed entrepreneurs (Van Stel, Wennekers, and Scholman 2014). It empirically showed that factors affecting both types of entrepreneurs are different (see Chapter 5). Compared to solo self-employed entrepreneurs, employer entrepreneurs are more likely to be able-bodied older men with an upper secondary level of education. These findings are consistent with a study conducted in post-conflict Bosnia and Herzegovina (Demirguc-Kunt, Klapper, and Panos 2011).

Most importantly, the motivations for starting a business differed between employer and solo self-employed entrepreneurs. The latter were motivated by the need for income and most of their enterprises, such as trade stores, barbershops and beauty salons, were small and self-funded. These entrepreneurs are also called “freelancers” (Burke 2011, p. 25), that is, “the enablers of entrepreneurship”. However, the former were motivated by the opportunity to make profits. They entered construction and related manufacturing businesses, created employment for others and often had the wealth to access finance from the formal sector.

Regarding the second research question, this study investigated the entrepreneurial motivation (see Chapter 6). It identified two types of entrepreneurs in terms of their primary motivations for starting a business: necessity-motivated entrepreneurs and opportunity-motivated entrepreneurs (Reynolds et al. 2002; Reynolds et al. 2005; Vivarelli 2013). The former starts a business “because they cannot find a suitable role in the world of work, creating a new business is their best available option” (Reynolds et al. 2005, p. 217). Approximately 80% of the entrepreneurs were necessity motivated. This observation contradicts most findings from studies of informal sectors in developing countries (Brunjes and Diez 2012a; Williams and Williams 2014; Rosa, Kodithuwakku, and Balunywa 2006). Self-employed entrepreneurs were mostly pushed towards entrepreneurship through necessity of finding a source of livelihood, from family pressure and family obligations. Conversely, employer entrepreneurs were mostly attracted to entrepreneurial activities to exploit new market opportunities emerging after the

154 cessation of the conflict, and to feed their self-esteem such as ‘being a boss’ and make ‘a service to the society’.

8.2.2 The Non-linear Relationship between Education and Entrepreneurship

The relationship between levels of education and the probability of becoming an entrepreneur (including all four types of entrepreneurs identified in this study) is non- linear. Contrary to the ‘U-shaped’ relationship between entrepreneurship and education in the US (Poschke 2013), this study found inverse U-shaped association in this post- conflict setting. Those who received the highest and lowest levels of education were less likely to start a business compared to those with intermediate levels of education. That is, individuals with an intermediate level of education were more likely to become entrepreneurs (Chapters 5 and 6). Individuals with low levels of education were less likely to become an entrepreneur due to lack of start-up capital, and also individuals with higher levels of education were less likely to become an entrepreneur possibly because they may have formal employment opportunities, thus the relationship is inverse U-shaped. Conversely, entrepreneurs’ intermediate levels of education have a negative effect on the growth of their enterprises (Krasniqi 2012). These results support the finding that most entrepreneurs are necessity motivated in this context, as discussed above. These findings may imply that there could be high unemployment (27.4% among the eligible workforce in 2012 in the north), particularly among youths who are pushed to necessity- motivated entrepreneurial activity due to lack of other formal employment opportunities. Furthermore, these results indicate that, once individuals with better levels of education become solo self-employed, they become employer entrepreneurs with ambitions to grow their businesses. This lends support to the proposition that employer entrepreneurs are wealthier due to their human capital.

These findings strongly support the argument that the bulk of entrepreneurs are necessity motivated. The presence of more necessity entrepreneurs reflects the importance of support mechanisms helping them improve their businesses while infrastructure facilities are developed. This finding may be due to lower economic development in the initial post-conflict phase. However, their proportion in terms of total entrepreneurial activity would decline with improvements in economic activities (Wennekers et al. 2005; Koster and Rai 2008). Thus, the findings from the current study reflect that, once economic development is rejuvenated, there could be an increase in the number of opportunity-motivated entrepreneurial activities (Amoros 2009; Amoros and Cristi 2008).

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8.2.3 Entrepreneurs in Kilinochchi are ‘Out of Necessity’ Compared to Jaffna Entrepreneurs

Entrepreneurs in Kilinochchi were less likely to pursue opportunity-motivated entrepreneurial activities because they needed income for subsistence more than those in Jaffna (Chapter 6). In addition, on average, firms in Kilinochchi have lower growth rates. This might be due to Kilinochchi entrepreneurs lacking business knowledge and skills and the ability to perceive business opportunities as well as encountering more barriers to business. Kilinochchi was one of the most severely conflict-affected cities, particularly at the end of the conflict in 2008/09, as its physical and social infrastructure, such as education, health and other economic activities, was completely destroyed. For example, its students were well below the provincial average pass rate in the lower secondary level examinations conducted in 1998 and 2002 (Sarvananthan 2007). Conversely, Kilinochchi firms show lower growth, perhaps, due to their loss of human capital, which is referred to as ‘forgetting by not doing’ (Collier and Duponchel 2013). In addition, market size and per capita income in Kilinochchi are lower than that in Jaffna.

8.2.4 Financial Constraints Play a Major Role at the Start-ups

Sources of finance, such as savings deposits, bank loans, informal loans, micro loans and remittances received from abroad, had statistically insignificant impacts on the propensity to start a business (Chapter 5). One exception was pawning which had a significant negative effect on employer entrepreneurs. This may suggest the poor using pawning to access finance from the informal (non-bank) sector. Conversely, individuals’ own wealth has a significant effect on the probability of becoming an employer entrepreneur. These findings, coupled with the qualitative data gathered during fieldwork, indicate that financial constraints constitute a major obstacle to emerging entrepreneurial activity. As in previous research, external finance provided by formal institutions could have a desirable effect on entrepreneurial development and that a long- term strategy to provide more security for property rights and enforceability of (debt) contracts has the potential to lower the costs of credit. At the time of the survey, these institutional mechanisms, such as property rights, were being developed by the Sri Lankan Government. For such institutions, ‘formal legal rules and informal social norms’ are critical determinants of economic growth in post-conflict countries (North, Wallis and Weingast 2009; North et al. 2013). Therefore, the development of these institutions could provide foundations for economic development in conflict-affected areas.

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Additionally, micro loans and the individual’s level of wealth had a significant positive effect on the probability of becoming an opportunity-motivated entrepreneur (Chapter 6). These results demonstrate the importance of targeting micro loans to opportunity entrepreneurs, as this would lower barriers to business entry. This result supports the argument that the impact of micro finance on opportunity entrepreneurs depends on the socio-economic environment (Lahimer, Dash, and Zaiter 2013). This is supported by results from studies in India (Imai, Arun, and Annim 2010) and Bangladesh (Imai and Azam 2012). This study suggests that micro finance provides a solution for emerging entrepreneurs in post-conflict settings due to the lack of financial support and underdeveloped financial systems in these areas.

8.2.5 Increased Inter-ethnic Relationships via Trade for Building Bridges Across Communities

Differences in the extent of inter-ethnic trade relationships were revealed between employer and solo self-employed entrepreneurs as well as necessity- and opportunity- motivated entrepreneurs (Chapters 5 and 6). Those who had inter-ethnic trade relationships with the south were more likely to become employer entrepreneurs and they pursued opportunity-motivated entrepreneurial activity. These findings indicate that trade relationships between north and south provided a platform for economic revival and peace-building following the cessation of conflict (Naude 2011a; Tobias, Mair, and Barbosa-Leiker 2013; Tobias and Boudreaux 2011). Therefore, better transportation and communication links with the rest of the nation could assist in building bridges across communities. The results support the view that the growth of an enterprise has the potential to generate employment and peace, both of which are necessary for transitioning out of conflict (Tobias, Mair, and Barbosa-Leiker 2013).

8.2.6 Determinants of Small Firm Growth

Chapter 7 shifted the study’s focus from individual entrepreneurs to the growth of their small enterprises, with the objective of contributing to the literature on the nature and dynamics of small businesses in post-conflict settings. Five main factors associated with firm growth were taken into account: firm-specific attributes; entrepreneurs’ characteristics; relational trade links across entrepreneurs; business sector; and ownership type.

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The summary statistics showed that all firms were MSEs, with their average age nearly three years, and average size nearly three employees. About 90% were formally registered businesses, with single ownership the most prevalent type. The most common finding is that there is a negative relationship between firm size and growth, which means that smaller firms grow faster than their counterparts. Regardless of the model and measurements used, all specifications supported the common finding which has two major implications. Firstly, from a theoretical and methodological perspective, the results indicate that firm size has a negative effect on growth. Secondly, the results indicate that the role of small firms is important for growth, since 80% of entrepreneurs were engaged in generating employment for others (Chapter 5).

The results also reveal a strong positive dependency of a firm’s growth on its age, that is, older firms grow faster than younger firms. Although this finding contradicts the results from studies of developed countries that focus on relatively mature firms, it is consistent with previous research that analyses the patterns of growth of infant firms in India (Das 1995; Shanmugam and Bhaduri 2002). This study also explored the factors associated with the growth of a sub-sample of ‘embryonic firms’, defined as those less than three years old, and found an insignificant effect of firm age on growth rate. This indicates that these nascent firms strived and survived through their initial stages and, once they became established, grew rapidly.

In contrast to the extant literature, the research reveals that being a registered businesses has a negative effect on firm growth. For example, Sleuwaegen and Goedhuys (2002) found that formality has a positive effect on firm growth in the Cote d’Ivoire. For firms that registered the benefits of doing so outweighed the costs, but the bulk of new firms reached this decision only after having established themselves in the marketplace. In the study areas, both formal and informal businesses face difficulties in accessing formal credit and joining trade associations. This result could suggest that formalisation takes place after firms have established themselves, because nascent firms may need to develop their credentials before registering if registration is costly and enforcement of regulations lax.

8.2.7 Faster Growth in Construction and Related Manufacturing Sector

Businesses in the construction and related manufacturing sector, and enterprises having trade networks, grew faster. Construction was the first sector to take hold after peace was declared. Also, this relationship was apparent for younger enterprises defined as equal or less than three years old. This result lends support to the argument of the 158

‘construction boom’, that is, a sudden increase in demand in capital goods such as equipment and structures in post-conflict countries (Collier 2009). The construction sector expands in the post-conflict setting for many reasons including: (i) a surge of construction activities by the government; (ii) private demand for domestic consumption such as construction of houses and business premises; and (iii) construction of housing schemes by donors, such as the Indian government sponsoring housing projects in the north.

All these findings contribute to expanding the literature by providing evidence from post- conflict settings for which data was sparse. They highlight the need for policy initiatives to create profitable business opportunities for entrepreneurs if the full potential for the growth of private enterprise in post-conflict settings is to be realised. The analysis in Chapter 7 showed that the potential for profitable opportunities was in construction activities and small informal businesses, which could have policy relevance beyond Sri Lanka.

8.3 Theoretical and Policy Implications

8.3.1 Theoretical Implications

Chapters 5 and 6 found that the relationship between education and entrepreneurship has ‘inverse U-shaped’ (explained in 8.2.2). Given the importance of this contribution to the existing literature, its implication is that education is important for explaining entrepreneurship in post-conflict countries. Furthermore, this study obtained a theoretically contradictory finding in terms of the association between firm age and growth as it seemed that, in the context of post-conflict Sri Lanka, older firms tended to grow faster than younger ones.

8.3.2 Implications for Policies

Post-conflict countries require ‘distinctive policies’ in their economic revival due to the high risk of reverting to conflict (Collier 2009). Economic policies are significant because economic recovery can contribute to peace (Collier, Hoeffler, and Soderbom 2008). Although the Sri Lankan economy has growth potential, there is an absence of strategy and policy framework to exploit that potential (Sarvananthan 2009; Sarvananthan 2010; Athukorala and Jayasuriya 2013).

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While development of a policy framework for Sri Lanka is beyond the scope of this thesis, the lessons drawn from the findings would benefit the development of entrepreneurship in conflict-affected areas of Sri Lanka. Five main major policies were identified.

First, despite the finding that there was heterogeneity among different types of entrepreneurs, the study emphasises the importance of improving the overall business environment to stimulate the private sector. This could be conducted through coordination of all stakeholders and partners, such as entrepreneurs, the Sri Lankan Government, communities, local municipal councils and the Northern Provincial Council, donor agencies, NGOs and the diaspora. However, as this would be difficult in practise because of individual priorities, it would be necessary to establish a coordination authority with stakeholder participation to drive a common development agenda in a post-conflict phase (Collier 2009). In this regard, there should be specific government policies for developing possible projects and programs, as this would help reduce the possible duplication of projects by multiple organisations, result in development projects being applied to the targeted beneficiaries, reduce conflicts of interest among different groups and empower communities. The policies could be directly aimed at improving the business environment while stimulating the overall economy. As this study observed, both interventions would be equally important because of conflict-damaged or destroyed personal properties, as well as infrastructure such as roads and electricity supplies. Also, the government could tailor specific policies to support small and older firms with high growth potential to make the MSE sector more vibrant.

Second, the empirical results in Chapter 5 highlighted the importance of financial constraints, while Chapter 6 pointed out that access to the micro finance provided by cooperative banks, rural banks and NGOs increased the probability of becoming an opportunity entrepreneur. All these results demonstrate the need for policies and financial regulations to promote the growth of institutions, which can be defined as the ‘rule of law’ and ‘social norms’, particularly with regard to property rights and enforceability contracts, to lower the costs of credit. Nearly 3% of entrepreneurs paid bribes to register their businesses, which was a hurdle because business registration was a requirement for obtaining a loan. As pointed out by North (2009), and North et al. (2013), formal institutions such as ‘law and order’ are the critical determinant of economic growth in post-conflict countries, because the external finance they provide can have a desirable effect on entrepreneurial development. The destruction of collateral during a conflict can reduce access to finance from banks (Rubin and Wagner 2014). In Sri Lanka, the government can assist in providing loans. The government could become involved

160 in proposing guaranteed loan schemes for MSEs. The government could also organise programs to empower communities to be involved in development activities by linking banks and entrepreneurs.

Third, creation of employment for unskilled youth is important in the post-conflict setting, because the risk of reverting to conflict during the first 10 years is estimated at about 40% (Collier, Hoeffler, and Soderbom 2008). Therefore, employment generation, particularly for the less educated or unskilled youths, could be a government policy priority. As found in this study, expansion of private sector employment, particularly in the construction and related manufacturing sector, can provide ground to attract unskilled labours to the production sector. As this study observed, some labourers who worked on construction sites in the north were from the south, because of the lack of skilled labourers in the north. Therefore, it is important to provide training. Government policy could be directed to: (i) training local workers in skills needed for the construction sector; and (ii) promoting north and south participation in construction activities to improve skills of local entrepreneurs and workers.

Fourth, although it is important to highlight the involvement of diaspora investment in entrepreneurship in a post-conflict setting, this research found that two-thirds of remittances from abroad were used for consumption rather than investment. However, the diaspora could be a good source of finance for future investment if there was a government policy to attract them.

Fifth, there is an opportunity to further expand the construction sector while relaxing regulations such as construction permits. Conversely, the sector requires skilled workers such as carpenters, welders and plumbers. However, the conflict has made it difficult to obtain these skills. Therefore, it is important to establish apprenticeship institutions in Kilinochchi and start vocational training to improve youths’ skills and employability. In addition, the institutions already established in the Jaffna could be improved. It is also important to remove possible bottlenecks for the expansion of construction activities. For example, increased prices of construction-related products and services and increased prices of rented business premises.

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8.4 Contributions of the Present Study

This study contributes to the body of knowledge in many ways. Firstly, it contributes to the literature by providing the factors affecting entrepreneurship and determinants of small firm growth in a post-conflict context. As a result, this study will assist in the understanding of entrepreneurial behaviour and small firm growth. Previous research focused on entrepreneurship in developed countries and there is growing interest in developing countries (Bruck, Naude, and Verwimp 2011; Acs and Naude 2013; Desai 2011). However, few studies have been undertaken on entrepreneurship in post-conflict countries (Acs, Desai, and Hessels 2008; Bennett 2010; Bruck, Naude, and Verwimp 2011; Demirguc-Kunt, Klapper, and Panos 2011; Gries and Naude 2010; Naude 2010, 2011b).

Secondly, this study’s purpose-designed survey provides a snapshot of the emerging entrepreneurs immediately after the cessation of the conflict, which provides new insights for the existing literature.

Thirdly, the bulk of previous research on the determinants of entrepreneurship has distinguished between entrepreneurs and non-entrepreneurs. This study distinguishes the factors related to ‘employer entrepreneurs’, who generate employment for others, and ‘solo self-employed entrepreneurs’ in a post-conflict context.

Fourthly, Chapter 7 found that ex-combatants are an important variable in examining small firm growth. However, this study encountered a drawback in the small sample size. It would be beneficial to examine their transformation to entrepreneurial activities as a separate study.

Another contribution of the thesis relates to the focus on emerging entrepreneurs in the Sri Lankan post-conflict context. The study is first of its kind examining entrepreneurs’ motivations and their contributions in post-conflict Sri Lanka. Although, promotion of entrepreneurship is one of the main policy thrusts in Sri Lanka, little research has been undertaken on the regional aspect of entrepreneurship. Therefore, the current study contributes to these areas for the development of conflict-affected regions to sustain economic development and peace. This allows for further research, which in turn may help the conflict-affected economy. The application of this study in a small, open and developing economy can be a useful empirical lesson for economies with similar characteristics.

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8.5 Limitations and Areas for Future Research

The following limitations of this research provide opportunities for further studies in Sri Lanka and similar contexts.

The first limitation concerns the cross-sectional survey in this research which neglects longitudinal data. In future research, it would be beneficial to collect longitudinal data employing a larger sample to ensure more accurate findings and provide an avenue for assessing policy changes and development, for example, the growth of enterprises over years.

The second limitation pertains to the external validity of the findings using Jaffna and Kilinochchi as a case study, which may provide only limited lessons for the whole country. Due to the selection of city areas, the results also cannot be generalised to village-level entrepreneurs, which require a separate analysis; this could be an interesting area for future research. New research, such as counterfactual analyses of severely conflict-affected and non-conflict-affected areas, could be conducted.

Additionally, confining the analysis to the cities of Jaffna and Kilinochchi, combined with the employment of random-sampling techniques, has meant that the role of armed forces-run enterprises in the economy has not been investigated. Arguably, such enterprises may act in ways detrimental to building bridges across ethnicities. This remains an area of future research.

The third study limitation concerns the cross-sectional method of analysis which could not control for unobserved heterogeneity among cities, although this research used city effects to control for bias. Therefore, employing panel or longitudinal data in future research would be beneficial.

The fourth limitation is that, as the research examined the motivations of entrepreneurs who had already established their businesses, it would be beneficial for future research to look at the motivational difference between entrepreneurs and non-entrepreneurs. Also as, in terms of necessity- and opportunity-driven entrepreneurship, this study was based on entrepreneurs’ primary motives, which can change over time; it could be extended to using longitudinal and household surveys.

The fifth limitation is that, in terms of small firm growth, this study used gross income and employment as indicators of growth, whereas future research could add profit and/or

163 value-added because using multiple indicators of growth would increase the robustness of the results. A possible area of research in this context could be using panel rather than cross-sectional data to examine the determinants of growth. Also, it could be beneficial to investigate barriers to small firm growth.

8.6 Concluding Statements

Entrepreneurship emerges after the installation of peace, which then provides preconditions for prosperity necessary for sustaining peace. Given this finding, entrepreneurship development, particularly at the regional level, to establish bridges between communities via trade and commerce and to create employment opportunities is essential as it could play a pivotal role in avoiding conflict. The above is encapsulated in the quote from the World Bank: “If we are to break the cycles of violence and lessen the stresses that drive them, countries must develop more legitimate, accountable and capable national institutions that provide for citizen security, justice and jobs.” (Robert B. Zoellick, President, World Bank Development Group, 2011). The case of Sri Lanka reported in this thesis provides evidence in support of the above proposition.

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Appendices

Appendix A: Participant Information Statement and Consent Form

School of Business, UNSW, Canberra

Approval No. A-12-05

THE UNIVERSITY OF NEW SOUTH WALES

PARTICIPANT INFORMATION STATEMENT AND CONSENT FORM

Cost of Doing Business in Post-conflict Jaffna and Kilinochchi, Sri Lanka

Participant selection and purpose of study You are invited to participate in a study of “Cost of Doing Business in Post-conflict Jaffna and Kilinochchi, Sri Lanka” Scheduled be held from June to September 2012. We hope to learn directly from entrepreneurs about the costs associated with doing business such as time taken to obtain statutory approvals, access to finance and other infrastructure obstacles that you may face now and personal characteristics and business relationship with the South. You are selected as a possible participant in this study because, being the owner of enterprise, you would have first-hand information and knowledge regarding the topic under investigation.

Description of your participation in the study If you decide to participate, we will provide a questionnaire and will also contact you to arrange an appointment for an interview during which the questionnaire will be completed. Responses will all be written down and not recorded via audio or visual means. There will be no cost incurred for participation except for your time. The interview is expected to last 30-45 minutes. While no monetary inducement is being offered to encourage your involvement, the key benefit to participants would be the opportunity to contribute to a study, the outcomes of which are anticipated to influence the development of policy for the improvement of the business condition in Jaffna and Kilinochchi after the civil war. In addition, a summary of the research findings will be offered to research participants following the completion of the study. Since the final decision on the adoption and implementation of policy recommendations resulting from the study rests with the relevant authorities in Sri Lanka, we cannot guarantee or promise that your concerns will be sufficiently addressed. 165

Risks of participation, confidentiality and disclosure of information Such research can cause discomfort as participants may not wish to disclose their identity or to have their views revealed to others. Hence, your name or the name of your company will not be recorded in the questionnaire. Any information that is obtained in connection with this study and that can be identified with you will remain confidential and will be disclosed only with your permission, except as required by law. If you give us your permission by signing this document, we plan to analyse and discuss the results of this study with the academic community of the University of New South Wales in support of its business research programme. In any publication or discussion, information will be provided in such a way that you cannot be identified.

Your consent Your decision whether or not to participate will not prejudice your future relations with the University of New South Wales or with any institution or individual involved in or referred to in the study. If you decide to participate, you are free to withdraw your consent and to discontinue participation at any time without prejudice (see revocation of consent from below).

Complaints Complaints regarding the conduct of this research may be directed to the Ethics Secretariat, The University of New South Wales, Sydney 2052 AUSTRALIA (phone +612 9385 4234, fax +612 9385 6648, email [email protected]).

Any complaint you make will be investigated promptly and you will be informed about the outcome.

Should you have any complaints or concerns about the manner in which this research is conducted, please do not hesitate to Mr. Kapila Maddumage ( Phone +612 6268 8834, mobile +612 0410 724 081, email: [email protected]).

You will be given a copy of this form to keep.

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THE UNIVERSITY OF NEW SOUTH WALES

PARTICIPANT INFORMATION STATEMENT AND CONSENT FORM (continued) Cost of Doing Business in Post-conflict Jaffna and Kilinochchi, Sri Lanka You are making a decision whether or not to participate. Your signature indicates that, having read the information provided above, you have decided to participate. ………………………………………………. ………………………………………………… Signature of Research Participant Signature of Witness ……………………………………………… .…………………………………..…………… (Please PRINT name) (Please PRINT name) …………………………………………… ………………………………….…………… Date Nature of Witness

REVOCATION OF CONSENT Cost of Doing Business in Post-conflict Jaffna, Sri Lanka

I hereby wish to WITHDRAW my consent to participate in the research proposal described above and understand that such withdrawal WILL NOT jeopardise any treatment or my relationship with The University of New South Wales, or with any institution or individual involved in or referred to in the study.

…………………………………………………… ………………….. .……… Signature Date

…………………………………………………… Please PRINT Name

The section for Revocation of Consent should be forwarded to: Mr Kapila Maddumage, Room No. 117, School of Business, UNSW@ADFA, Northcott Drive, Canberra, ACT 2600, Australia. Phone +612 6268 8834, mobile +612 0410 724 081, email: [email protected].

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Appendix B: Survey Questionnaire Serial No.

Entrepreneur Survey Questionnaire for Start-up Entrepreneurs in Post-conflict Jaffna and Kilinochchi, Sri Lanka

June 2012 University of New South Wales Canberra

168

1: General Information

Code T S M

1:1 Your business is in:

Major product/category Manufacturing of Construction of Agro activities of Retail/wholesale trade of Hotel & restaurant of Transport of Other – please specify…......

1:2 Who is the owner of the business?

Privately owned Partnership Government Cooperative Other – please specify…...... 1:3 In what year was this firm established?

Year Years operated during conflict (before 2009) Year began operations

1:4 Why did you start up this firm?

Peace-time opportunity To be my own boss Family responsibility Good way of finding employment Other – please specify…......

1:5 No. of workers employed by this firm

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Year started Now Full-time Temporary

How many male employees do you have? Year started Now Male

1:6 What is the approximate total income per month from your business?

At start-up Now Monthly income (Rs.)

1:7 What is the highest level of education completed by the owner?

No schooling Primary G.C.E. (O/L) G.C.E. (A/L) Graduate Higher

1:8 Has any member of your immediate or extended family ever run a business?

Yes No

1:9 What was your major income prior to establishing this business?

Business in non-conflict area Formal paid employee Recently Rehabilitated Inactive Other – please specify......

1:10 Do you have membership in the following organisations?

Chamber of Commerce and Industries Yarlpanam (CCIY) Jaffna Chamber of Commerce Local Business Network Other – please specify......

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2: Entrepreneurial Training

2:1 Have you undergone any entrepreneurial training since May 2009?

Yes No Please go to Question 2:3

2:2 If yes, who conducted the training?

Government Private sector NGOs International aid agency Other –please specify......

2:3 Are you willing to invest in training for your employees?

Yes No

2:4 Did you acquire new assets during the last 6 months?

Yes No

Cost of Doing Business

3: Finance

3:1 From where did you obtain funds to start this business?

Start-up capital (Rs.) Savings Informal loan Micro loan (Samurdhi/Sanasa, etc.) Bank loan Remittances NGO support Government Pawning Other - please specify ......

3:2 If external capital was needed, did you have any difficulties obtaining finance? 171

Yes No Please go to Question 4:1

3:3 If yes, what kinds of problems did you face in obtaining any recent loan? (If you have more than one answer, please prioritise them.)

Complex application procedure Too high interest rates Required guarantees Required collateral, such as jewellery Required business registration Other – please specify …….. ……….

3:4 What is the maximum loan amount you have received to date?

Rs.

3:5 Regarding recent loan/s obtained, what type of collateral was required?

Land, building Personal assets of owner, e.g., houses, jewellery, etc. Machinery Other – please specify…………......

4: Business Registration

4:1 Have you registered your establishment at any authority?

Yes No Please go to Question 4:3 4:2 If yes, did you have to give the authority/person extra money or a gift to register your establishment?

Yes No

If yes, please provide details......

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4:3 How long did it take to register your establishment?

Days

No. of days taken

5: Payment of Taxes

5:1 After May 2009, did tax officials visit your establishment?

Yes No Please go to Question 6:1

5:2 If yes, were you expected to give a gift or other informal payment at any inspection or meeting?

Yes No

If yes, please provide details...... 6: Trading with South (Colombo)/Abroad

6:1 Do you have any trade relationship with the south (with other ethnicities)?

Yes No

6:2 What is your main market?

Jaffna/Kilinochchi Colombo Other – please specify......

6:3 Do you have any overseas market linkages through any member of your immediate or extended family, or friends?

Yes No If yes, please provide details...... 6:4 Do you pay security or other extra charges when trading goods in your main market?

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Yes No

If yes, please provide details...... What obstacles do you encounter when doing business with the south? 1...... 2...... 3...... 4...... 5...... 7: Infrastructure

7:1 Do you have your own transportation system?

Yes No

If no, how do you transport your items? ...... Electricity 7:2 After May 2009, did you submit an application for an electrical connection?

Yes No Please go to Question 7:4

7:3 If yes, how many days did you wait for the connection?

Days waited

7:4 Did you experience any power outages during the first half of 2012?

Yes No Please go to Question 7:6 If yes, how many power outages did you experience during the first half of 2012?

No. of power outages

7:5 On average, how long did the power outages last?

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Hours lasted

Water 7:6 After May 2009, did you submit an application for a water connection?

Yes No Please go to Question 7:8

7:7 If yes, how many days did you wait for the connection?

Days waited

Telecommunications 7:8 After May 2009, did you submit an application for a telephone connection?

Yes No Please go to Question 7:10

7:9 If yes, how many days did you wait for the connection?

Days waited

Land 7:10 Who owns the land occupied by the firm?

Owned by firm Leased by firm

7:11 If you own the land, does it have a clear title?

Yes No

Security 7:12 Have you experienced any losses due to robbery, theft or arson?

Yes No

If yes, please provide details......

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General Overview of Infrastructure

7:13 Do the following requirements affect you in the conduct of your business?

No Minor Moderate Major Severe Obstacle Obstacle Obstacle Obstacle Obstacle (1) (2) (3) (4) (5) Business registration Payment of taxes Electricity Water Telecommunications Trading with south Access to land Access to finance Crime, theft and Giftsdisorder and/or other Securityinformal payments Educated work force Demand for products Accessand/or services to government services 7:14 What are the main obstacles you currently encounter when conducting business?

1. 2. 3. 4. 5.

7:15 How would you describe the peace-time (after May 2009) business situation in Jaffna/Kilinochchi? …………………………………………………………………………………………………….. 7:16 Do you have any suggestions for improving the present business conditions in Jaffna/Kilinochchi?

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

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8: Remittances 8:1 Do any of your family members or friends live overseas?

Yes No Please go to Section 9

8:2 Does your household receive remittances from abroad?

Yes No

8:3 How often does your household receive remittances from abroad?

Once a month Once a year Once in 2-3 months Less often Once in 4-6 months As and when required Once in 7-11 months Other......

8:4 What is the amount of remittance your household received from abroad during the past year?

None Rs.

8:5 How many family members or friends whom you personally know live abroad?

Numbers staying abroad Members of immediate family Members of extended family Friends

8:6 Please provide the following details of the member/s of your immediate family who lives abroad? For ‘Likelihood of return’, code, please see grid below.

Relationship Age Gender Education Occupation Years Country Remittances Likeli abroad received last year -hood of return

Member 1

Member 2

Member 3

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Likelihood of return grid Likelihood of return Very likely 01 Likely 02 Unlikely 03 Very unlikely 04 Do not know 05

8:7 What services did you use to receive money from abroad?

Demand draft Family member brought NRI account Through friends Internet-based transfer Through relatives Other money transfer Other……………………………….. services

8:8 For what purpose did you spend the money received from abroad?

Household expenses Medical emergencies Festivals Real estate Bank deposits, Special occasions (marriage, investments in stocks, travel, etc.) mutual funds Business Other (please specify)......

8:9 Has anyone in your household started a business in the last 12 months?

Yes No

8:10 What type/s of business-related investments did you make in the last 12 months?

Buildings Land Machinery Animals Office equipment Other (please specify)...... Transport equipment No investment (vehicles, etc.)

8:11 Do you have any market linkage with family members/relatives/friends who live abroad? Yes No

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8:12 If yes, please provide details?

9: Owner’s Personal Characteristics

Gender Year born Male

Female

Marital status No. of children Single

Married Divorced Widowed

Physical health Prior experience (before 2009) in business (years) Able-bodied Differently -abled

House owner Any other properties apart from family home Yes No Yes No Religion Residency status

Hindu Local resident Christian Returned migrant from overseas Muslim Returned migrant from the south Buddhist Other- SPECIFY...... No religion

End of Survey Thank you very much for your participation

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Appendix C : Correlation Matrix and Additional Results Tables

Table 5.1A: Correlation matrix of key variables Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (1) NEWENT 1.00 (2) KILI 0.10 1.00 (3) TAMIL 0.06 -0.08 1.00 (4) MALE 0.15 0.19 -0.08 1.00 (5) AGE 0.10 -0.01 0.12 -0.03 1.00 (6) UPPER 0.09 -0.10 0.002 -0.06 -0.09 1.00 SECONDARY (7) ABLE- 0.20 0.008 -0.07 0.07 0.11 -0.10 1.00 BODIED (8) HOME 0.25 -0.01 0.03 0.10 0.17 -0.04 -0.02 1.00 OWNERSHIP (9) SAVINGS -0.04 -0.07 -0.01 -0.04 -0.02 0.01 -0.04 0.01 1.00 (10) INFORMAL 0.08 0.11 -0.07 -0.17 -0.02 -0.07 -0.005 0.06 -0.18 1.00 LOAN (11) MICRO 0.02 -0.07 0.05 0.08 0.19 -0.09 -0.02 -0.06 -0.12 0.02 1.00 LOAN (12) BANK 0.10 0.26 0.07 0.04 -0.04 0.07 0.07 -0.03 -0.17 -0.24 -0.01 1.00 LOAN (13) -0.003 0.02 -0.14 -0.08 -0.06 0.12 -0.006 -0.05 0.09 0.05 -0.04 0.03 1.00 REMITABROAD (14) PAWNING -0.06 0.31 0.03 0.01 0.006 -0.11 0.07 -0.10 -0.05 -0.10 -0.03 0.06 -0.10 1.00 (15) 0.18 0.20 -0.08 -0.15 -0.07 0.18 0.08 -0.06 -0.09 -0.06 -0.07 0.18 0.07 0.02 1.00 TRADESOUTH Notes: the highest correlation coefficient is between NEWENT and HOME OWNERSHIP (0.25) while other correlation coefficients are generally low; see Table A1 for definitions of variable.

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Table 5.2A: Coefficients: estimated probability of being an employer entrepreneur

Independent variable (1) (2) (3) (4) KILINOCHCHI 0.2785 0.3248 0.3245 0.1921 (0.2329) (0.2421) (0.2495) (0.2592) TAMIL 0.4389 0.5376 0.4483 0.5137 (0.3755) (0.3844) (0.3605) (0.4360) MALE 0.5498 0.6285 0.4263 0.3150 (0.3372) (0.3427) (0.3605) (0.3685) AGE 0.1130 0.1166 0.0911 0.0879 (0.0750) (0.0767) (0.0795) (0.0810) AGESQ/1000 -1.2479 -1.2819 -1.0729 -1.0036 (0.8960) (0.9165) (0.9512) (0.9677) CHILDREN 0.0324 0.0005 0.0225 0.0446 (0.2807) (0.2893) (0.2921) (0.2967) PRIMARY 0.4811 0.3712 0.5906 0.6279 (0.4114) (0.4161) (0.4619) (0.4662) UPPER SECONDARY 0.7228*** 0.7384*** 0.8205*** 0.6551** (0.2713) (0.2823) (0.2912) (0.2984) GRADUATE & POST 0.1381 0.2078 -0.0896 -0.1407 (0.3813) (0.3940) (0.4114) (0.4225) Health ABLE-BODIED 0.9110** 1.1320*** 1.1480*** 1.0339*** (0.3541) (0.3803) (0.3843) (0.3872) ENTREPRENERIAL FAMILY 0.1057 -0.0557 0.0043 -0.0061 (0.2281) (0.2445) (0.2389) (0.2435) PAST BUS EXP 0.0915 0.0869 0.0470 0.0356 (0.2510) (0.2567) (0.2712) (0.2763) UNEMPLOYED --- -0.4115 ------(0.2926) FORMAL SECTOR EMPLOYED --- -0.4771* ------(0.2805) TRADE ------0.0156 -0.0986 (0.3532) (0.3611) SERVICES ------0.1108 0.1295 (0.3609) (0.3640) CONST & MANU ------0.2791 0.1316 (0.5021) (0.5152) HOME OWNERSHIP ------0.9331*** 0.9599*** (0.2363) (0.2384) TRADESOUTH 0.6582** (0.2848) Constant -3.5002** -3.6029** -3.5938** -3.5124** (1.4385) (1.4800) (1.5392) (1.5722) Predicted probabilities 0.8436 0.8436 0.8436 0.8436 No. of observations 243 243 243 243 LR chi2 27.20 32.00 44.28 80.37 Prob > chi2 0.0072 0.0040 0.0002 0.0000 Pseudo 푅2 0.1291 0.1551 0.2101 0.2372 Log-likelihood -91.77 -87.16 -83.22 -80.37 Correctly classified 84.77% 84.17% 85.60% 87.24% Notes: *P < 0.10, **P < 0.05 and ***P < 0.01; dependent variable NEWENT (1/0) employer /solo self-employed entrepreneurs – probit regressions; coefficients are presented and standard errors are in parentheses; the differences in the predicted values for discrete changes (0–1) are reported for the dummy variables

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Table 5.3A: Marginal effects: estimated probability of being an employer entrepreneur

Independent variable (1) (2) (2) (4) KILINOCHCHI 0.0579 0.0648 0.0615 0.0354 (0.0483) (0.0480) (0.0470) (0.0476) TAMIL 0.0913 0.1073 0.0850 0.0946 (0.0778) (0.0760) (0.0800) (0.0798) MALE 0.1144* 0.1254* 0.0808 0.0580 (0.0691) (0.0670) (0.0673) (0.0672) AGE 0.0235 0.0232 0.0172 0.0161 (0.0154) (0.0151) (0.0150) (0.0148) AGESQ -0.2601 -0.2558 -0.2035 -0.1849 (0.1851) (0.1813) (0.1797) (0.1776) CHILDREN 0.0067 0.0001 0.0042 0.0082 (0.0584) (0.0577) (0.0554) (0.0546) PRIMARY 0.1001 0.0740 0.1120 0.1156 (0.0853) (0.0829) (0.0869) (0.0850) UPPER SECONDARY 0.1504*** 0.1437*** 0.1556*** 0.1206** (0.0553) (0.0552) (0.0537) (0.0537) GRADUATE & POST 0.0287 0.0414 -0.0170 -0.0259 (0.0793) (0.0784) (0.0781) (0.0779) ABLE-BODIED 0.1896*** 0.2259*** 0.2177*** 0.1904*** (0.0717) (0.0730) (0.0700) (0.0689) ENTREPRENERIAL FAMILY 0.0220 -0.0111 0.0008 -0.0011 (0.0474) (0.0487) (0.0453) (0.0448) PAST BUS EXP 0.0190 0.0173 0.0089 0.0065 (0.0522) (0.0512) (0.0514) (0.0509) UNEMPLOYED --- -0.0821 ------(0.0579) FORMAL SECTOR EMPLOYED --- -0.0952* ------(0.0553) TRADE --- -0.0029 -0.0181 (0.0669) (0.0665) SERVICES --- 0.0210 0.0238 (0.0684) (0.0670) CONST & MANU --- 0.0529 0.0242 (0.0952) (0.0949) HOME OWNERSHIP --- 0.1769*** 0.1768*** (0.0421) (0.0410) TRADESOUTH ------0.1212** (0.0518) No. of observations 243 243 243 Notes: *P < 0.10, **P < 0.05 and ***P < 0.01; dependent variable NEWENT (1/0) employer /solo self-employed entrepreneurs – probit regressions; marginal effects are presented and standard errors are in parentheses; the differences in the predicted values for discrete changes (0–1) are reported for the dummy variables

182

Table 5.4A: Coefficients: estimated probability of being an employer entrepreneur and access to finance

Independent variable (1) (2) (3) (4) (5) KILINOCHCHI 0.3205 0.3521 0.3342 0.1971 0.3146 (0.2765) (0.2841) (0.2988) (0.3090) (0.2989) TAMIL 0.4673 0.5954 0.3548 0.4142 0.3307 (0.3991) (0.4093) (0.4512) (0.4718) (0.4538) MALE 0.7052** 0.7580** 0.6276* 0.5089 0.6637* (0.3530) (0.3553) (0.3807) (0.3914) (0.3842) AGE 0.1422* 0.1399* 0.1253 0.1229 0.1307 (0.0804) (0.0817) (0.0862) (0.0891) (0.0869) AGESQ -1.5634 -1.5131 -1.4317 -1.3640 -1.4952 (0.9620) (0.9781) (1.0334) (1.0663) (1.0417) CHILDREN -0.1709 -0.2199 -0.2448 -0.1985 -0.2486 (0.3052) (0.3142) (0.3256) (0.3324) (0.3261) PRIMARY 0.4704 0.3874 0.5198 0.6245 0.5272 (0.4142) (0.4243) (0.4615) (0.4775) (0.4617) UPPER SECONDARY 0.6631** 0.6859** 0.7922** 0.5986* 0.8217*** (0.2856) (0.2977) (0.3112) (0.3231) (0.3150) GRADUATE & POST 0.0237 0.1091 -0.1932 -0.2451 -0.1887 (0.3960) (0.4087) (0.4323) (0.4471) (0.4359) ABLE-BODIED 0.9526** 1.1920*** 1.1833*** 1.0511** 1.1933*** (0.3730) (0.4028) (0.4097) (0.4199) (0.4103) ENTREPRENERIAL FAMILY 0.1862 0.0027 0.0989 0.1081 0.1137 (0.2448) (0.2601) (0.2565) (0.2663) (0.2587) PAST BUS EXP -0.0283 -0.0398 -0.0801 -0.0925 0.0841 (0.2719) (0.2776) (0.3011) (0.3082) (0.3025) UNEMPLOYED --- -0.4768 ------(0.3067) FORMAL SECTOR EMPLOYED --- -0.5773* ------(0.2998) TRADE ------0.1235 0.0791 0.1197 (0.3757) (0.3889) (0.3780) SERVICES ------0.3292 0.4320 0.3505 (0.3798) (0.3941) (0.3826) CONST & MANU ------0.3365 0.2035 0.2707 (0.5246) (0.5466) (0.5295) Wealth HOME OWNERSHIP ------0.9839*** 1.0169*** 0.9978*** (0.2498) (0.2553) (0.2519) TRADESOUTH ------0.8071** --- (0.3186) SAVINGS 0.1143 ------0.2081 (0.2371) (0.2594) INFORMALLOAN 0.2963 0.3459 0.3055 0.4568 0.3689 (0.2615) (0.2600) (0.2759) (0.2928) (0.2902) MICROLOAN 0.2501 0.1196 0.5371 0.5296 0.5972 (0.5449) (0.5540) (0.6055) (0.6299) (0.6124) BANKLOAN 0.1892 0.1386 0.2626 0.2831 0.3280 (0.2583) (0.2561) (0.2681) (0.2720) (0.2827) REMITABROAD -0.2642 -0.3144 -0.2554 -0.2620 -0.3000 (0.5120) (0.5204) (0.5275) (0.5373) (0.5355) PAWNING -0.5988* -0.5450** -0.5624* -0.6384* -0.5410* (0.3145) (0.3265) (0.3234) (0.3363) (0.3256) Constant -4.1998*** -4.0837*** -4.4200*** - - (1.5568) (1.5658) (1.6502) 4.4778*** 4.7092*** (1.7083) (1.6906) Predicted probabilities 0.8436 0.8436 0.8436 0.8436 0.8436 No. of observations 241 241 241 241 241 LR chi2 33.62 39.75 51.58 58.24 51.73 Prob > chi2 0.0140 0.0116 0.0003 0.0000 0.0003 Pseudo 푅2 0.1655 0.1956 0.2513 0.2866 0.2545 Log-likelihood -84.80 -81.73 -76.07 -72.49 -75.74 Correctly classified 86.72% 87.14% 86.31% 88.38% 87.14% Notes: *P < 0.10, **P < 0.05 and ***P < 0.01; dependent variable: NEWENT (1/0) employer /solo self- employed entrepreneurs – probit regressions; coefficients are presented and standard errors are in parentheses; the differences in the predicted values for discrete changes (0–1) are reported for the dummy variables

183

Table 5.5A: Marginal effects: estimated probability of being an employer entrepreneur and access to finance

Independent Variable (1) (2) (3) (4) (5) KILINOCHCHI 0.0624 0.0660 0.0589 0.0331 0.0551 (0.0536) (0.0529) (0.0525) (0.0518) (0.0522) TAMIL 0.0911 0.1117 0.0626 0.0695 0.0580 (0.0776) (0.0764) (0.0793) (0.0789) (0.0794) MALE 0.1374** 0.1422** 0.1107* 0.0854 0.1164 (0.0675) (0.0651) (0.0654) (0.0645) (0.0656) AGE 0.0277* 0.0262* 0.0221 0.0206 0.0229 (0.0154) (0.0151) (0.0151) (0.0148) (0.0181) AGESQ -0.3048 -0.2838 -0.2526 -0.2290 -0.2623 (0.1857) (0.1819) (0.1812) (0.1779) (0.1813) CHILDREN -0.0333 -0.0412 -0.0432 -0.0333 -0.0436 (0.0594) (0.0587) (0.0574) (0.0557) (0.0572) PRIMARY 0.0917 0.0726 0.0917 0.1048 0.0924 (0.0805) (0.0795) (0.0810) (0.0795) (0.0806) UPPER SECONDARY 0.1292** 0.1286** 0.1397** 0.1005* 0.1441*** (0.0551) (0.0553) (0.0541) (0.0537) (0.0543) GRADUATE & POST 0.0046 0.0204 -0.0341 -0.0411 -0.0331 (0.0772) (0.0766) (0.0763) (0.0750) (0.0765) ABLE-BODIED 0.1857*** 0.2236*** 0.2088*** 0.1764** 0.2093*** (0.0710) (0.0732) (0.0698) (0.0684) (0.0694) ENTREPRENERIAL FAMILY 0.0363 0.0005 0.0174 0.0181 0.0199 (0.0476) (0.0488) (0.0453) (0.0447) (0.0454) PAST BUS EXP -0.0055 -0.0074 -0.0141 -0.0155 -0.0147 (0.0529) (0.0520) (0.0530) (0.0516) (0.0529) UNEMPLOYED --- -0.0894 ------(0.0569) FORMAL SECTOR EMPLOYED --- -0.1083* ------(0.0556) TRADE ------0.0218 0.0132 0.0210 (0.0662) (0.0653) (0.0663) SERVICES ------0.0580 0.0725 0.0615 (0.0668) (0.0658) (0.0668) CONST & MANU ------0.0593 0.0341 0.0474 (0.0924) (0.0917) (0.0928) HOME OWNERSHIP ------0.1736*** 0.1707*** 0.1750*** (0.0413) (0.0397) (0.0413) TRADESOUTH ------0.1355*** --- (0.0518) SAVINGS 0.0222 ------0.0365 (0.0461) (0.0452) INFORMALLOAN 0.0577 0.0649 0.0539 0.0767 0.0647 (0.0508) (0.0486) (0.0486) (0.0487) (0.0507) MICROLOAN 0.0487 0.0224 0.0947 0.0889 0.1047 (0.1063) (0.1040) (0.1065) (0.1054) (0.1070) BANKLOAN 0.0369 0.0260 0.0463 0.0475 0.0575 (0.0502) (0.0479) (0.0472) (0.0455) (0.0494) REMITABROAD -0.0515 -0.0589 -0.0450 -0.0439 -0.0526 (0.0998) (0.0977) (0.0931) (0.0902) (0.0939) PAWNING -0.1167* -0.1022* -0.0992* -0.1071* -0.0949** (0.0609) (0.0609) (0.0566) (0.0556) (0.0568) No. of observations 241 241 241 241 241 Notes: *P < 0.10, **P < 0.05 and ***P < 0.01; dependent variable: NEWENT (1/0) employer /solo self-employed entrepreneurs – probit regressions; marginal effects are presented and standard errors are in parentheses; the differences in the predicted values for discrete changes (0–1) are reported for the dummy variables.

184

Appendix D: Detailed Summary Statistics and Additional Results Tables

Table 6.1A: Summary statistics

Variable Necessity Opportunity Total entrepreneurs entrepreneurs sample(n=241)* (n=192) (n=49) Mean Std. Mean Std. Mean Std. Dev. Dev. Dev. TAMIL 93.22% 0.25 91.83% 0.27 93.00% 0.25 MUSLIM 5.72% 0.23 4.08% 0.19 5.34% 0.22 SINHALESE 0.52% 0.07 2.04% 0.14 0.82% 0.09 TAMIL-MUSLIM PARTNERSHIPS 0.52% 0.07 0% 0 0.41% 0.06 MALE 91.67% 0.28 87.75% 0.33 90.94% 0.25 AGE 37.44 10.59 37.08 10.45 37.41 10.61 CHILDREN (dummy variable) 75.52% 0.43 81.63% 0.39 76.95% 0.42 NO EDUCATION 1.04% 0.10 0% 0 0.82% 0.09 PRIMARY 12.50% 0.33 12.24% 0.33 12.34% 0.32 LESS THAN LOWER SECONDARY 5.20% 0.22 8.16% 0.27 5.76% 0.23 LOWER SECONDARY 47.39% 0.50 30.61% 0.46 43.62% 0.49 UPPER SECONDARY 27.60% 0.44 34.69% 0.48 29.62% 0.45 GRADUATE & POST 6.25% 0.24 14.28% 0.35 7.81% 0.26 Labour origins FORMAL SECTOR EMPLOYED 29.10% 0.45 34.69% 0.48 30.41% 0.46 INFORMAL SECTOR EMPLOYED 13.75% 0.34 18.36% 0.39 14.58% 0.35 EXCOMBATANT 3.17% 0.17 0% 0 2.50% 0.15 UNEMPLOYED 24.33% 0.43 20.40% 0.40 23.33% 0.42 PAST BUS EXP 72.3% 0.44 75.51% 0.43 72.83% 0.44 Health status ABLE-BODIED 94.27% 0.23 87.75% 0.33 93.00% 0.25 HOME OWNERSHIP 63.54% 0.48 73.46% 0.44 65.43% 0.47 LOCAL RESIDENT 91.66% 0.27 83.67% 0.37 89.71% 0.30 ENTREPRENEURIAL FAMILY 36.97% 0.48 22.44% 0.42 34.15% 0.47 TRADE MEMBERSHIP 56.25% 0.49 46.93% 0.50 53.90% 0.49 Sector TRADE - retail and wholesale 42.18% 0.49 40.81% 0.49 41.56% 0.49 SERVICES 33.85% 0.47 32.65% 0.47 34.15% 0.47 CONST & MANU 8.85% 0.28 10.20% 0.30 9.05% 0.28 TRADESOUTH 35.41% 0.47 42.85% 0.50 37.44% 0.48 Finance SAVINGS 51.05% 0.50 48.97% 0.50 50.20% 0.50 INFORMALLOAN 35.78% 0.48 28.57% 0.45 34.43% 0.47 MICROLOAN 2.63% 0.16 8.16% 0.27 4.14% 0.19 BANKLOAN 55.78% 0.49 48.97% 0.50 53.94% 0.49 REMITABROAD 5.78% 0.23 4.08% 0.19 5.39% 0.22 PAWNING 17.36% 0.37 12.24% 0.33 16.18% 0.36 KILINOCHCHI (1=yes) 54.16% 0.49 24.48% 0.43 48.14% 0.50 *Note: two respondents did not give reasons for starting a business. 185

Table 2.2A: Marginal effects: estimated probability of being opportunity entrepreneur

Variable Model 1 Model 2 Model 3 Model 4 Model 5 KILINOCHCHI - - - - -0.2846*** 0.2685***[0.0574] 0.2707***[0.0586] 0.2741***[0.0576] 0.2871***[0.0571] [0.0692] TAMIL 0.0690 [0.1033] 0.0819 [0.1050] 0.0571 [0.1078] 0.0405 [0.1053] 0.0296 [0.1073] MALE -0.0120 [0.0859] -0.0048 [0.0881] -0.0218 [0.0861] -0.0389 [0.0852] -0.0078 [0.0842] AGE -0.0180 [0.0178] -0.0158 [0.0181] -0.0187 [0.0187] -0.0206 [0.0176] -0.0147 [0.0178] AGESQ/1000 0.1952 0.1768 0.2022 0.2336 0.1410 [0.2130] [0.2164] [0.2129] [0.2113] [0.1410] CHILDREN 0.0737[0.0690] 0.0630 [0.0707] 0.0762 [0.0694] 0.0760 [0.0688] 0.0666 [0.0699] PRIMARY 0.1567*[0.0817] 0.1507*[0.0831] 0.1609**[0.0816] 0.1835**[0.0808] 0.1572*[0.0821] UPPER 0.0537 [0.0564] 0.0670 [0.0577] 0.0543 [0.0562] 0.0377 [0.0567] 0.0720 [0.0590] SECONDARY GRADUATE & 0.1522* [0.0843] 0.1606* [0.0856] 0.1537* [0.0848] 0.1451* [0.0849] 0.1410 [0.0865] POST INFORMAL --- 0.0709[0.0760] UNEMPLOYED --- 0.0242[0.0611] ABLE-BODIED -0.1216 [0.0904] -0.1119 [0.0919] -0.1232 [0.0896] -0.1409 [0.0886] -0.1217 [0.0879] HOME 0.0847 [0.0542] 0.0863 [0.0549] 0.0848 [0.0538] 0.0875* [0.0530] 0.0967* [0.0542] OWNERSHIP ENTREPRENEU -0.1230** -0.1296** -0.1275** -0.1315** -0.1425*** RIAL FAMILY [0.0538] [0.0544] [0.0537] [0.0530] [0.0543] LOCAL -0.2790*** -0.2862*** -0.2862*** -0.2605*** -0.2770*** RESIDENT [0.0849] [0.0864] [0.0842] [0.0845] [0.0854] TRADE 0.0163 [0.0548] 0.0152 [0.0559] 0.0151 [0.0642] 0.0010 [0.0761] 0.0323 [0.0792] MEMBERSHIP PREVIOUS BUS 0.1301**[0.0592] 0.1300**[0.0599] 0.1301**[0.0588] 0.1223**[0.0586] 0.1273**[0.0587] EXP TRADE 0.0083 [0.0771] 0.0089 [0.0761] 0.0097 [0.0792] SERVICES 0.0392 [0.0766] 0.0542 [0.0763] 0.0412 [0.776] CONST & MANU 0.0945 [0.1015] 0.0877 [0.0997] 0.0965 [0.1016] TRADESOUTH 0.0968*[0.0544] --- INFORMALLOA -0.0250 [0.0567] N MICROLOAN 0.2414** [0.1071] BANKLOAN 0.0021 [0.0558] REMITABROAD -0.0423 [0.1168] PAWNING 0.0200 [0.0768] Predicted 0.8436 0.8436 0.8436 0.8436 0.8436 probabilities No. of 241 238 241 241 239 observations LR chi2 41.36 42.35 42.61 45.68 47.19 Prob > chi2 0.0003 0.0006 0.0009 0.0006 0.0021 Pseudo 푅2 0.1699 0.1750 0.1751 0.1877 0.1946 Log-likelihood -101.01 -99.83 -100.39 -98.85 -97.64 Correctly 82.16% 81.09% 82.16% 81.33% 82.43% classified *p<0.10, **p<0.05 and ***p<0.01 denote respective significance levels; dependent variable opportunity entrepreneur; probit regressions; marginal effects and numbers in parentheses standard errors.

186

Table 6.3A: Coefficients: estimated probability of being opportunity entrepreneur

Variable Model 1 Model 2 Model 3 Model 4 Model 5 KILINOCHCHI - - - - -1.2349*** 1.1310***[0.2599] 1.1381***[0.2649] 1.1645***[0.2644] 1.2431***[0.2724] [0.2954] TAMIL 0.2907 [0.4375] 0.3445 [0.4438] 0.2426 [0.4591] 0.1755 [0.4566] 0.1288 [0.4662] MALE -0.0505 [0.3617] -0.0202 [0.3704] -0.0926 [0.3658] -0.1687 [0.3690] -0.0339 [0.3653] AGE -0.0757 [0.0757] -0.0668 [0.0768] -0.0795 [0.0763] -0.0891 [0.0773] -0.0638 [0.0778] AGESQ/1000 0.8224 0.7432 0.8592 1.0114 0.6118 [0.9039] [0.9155] [0.9119] [0.9250] [0.9362] CHILDREN 0.3105 [0.2922] 0.2651 [0.2982] 0.3237 [0.2964] 0.3294 [0.2996] 0.2890 [0.3045] PRIMARY 0.6599*[0.3497] 0.6335*[0.3547] 0.6834**[0.3529] 0.7948**[0.3595] 0.6823*[0.3630] UPPER 0.2263 [0.2389] 0.2817 [0.2447] 0.2310 [0.2404] 0.1636 [0.2462] 0.2579 [0.3125] SECONDARY GRADUATE & 0.6410* [0.3625] 0.6752* [0.3682] 0.6531* [0.3679] 0.6285* [0.3740] 0.6120 [0.3805] POST INFORMAL --- 0.2980 [0.3210] UNEMPLOYED --- 0.1017 [0.2572] ABLE-BODIED -0.5121 [0.3839] -0.4704 [0.3888] -0.5235 [0.3835] -0.6103 [0.3883] -0.5281 [0.3847] HOME 0.3567 [0.2315] 0.3628 [0.2343] 0.3605 [0.2319] 0.3791* [0.2336] 0.4197* [0.2396] OWNERSHIP ENTREPRENEU -0.5181** -0.5448** -0.5416** -0.5696** -0.6184*** RIAL FAMILY [0.2321] [0.2346] [0.2344] [0.2360] [0.2440] LOCAL -1.1752*** -1.2030*** -1.2156*** -1.1281*** -1.2018*** RESIDENT [0.3727] [0.3801] [0.3745] [0.3806] [0.3873] TRADE 0.0686 [0.2309] 0.0640 [0.2352] 0.0642 [0.2333] 0.0043 [0.2373] 0.1404 [0.2441] MEMBERSHIP PREVIOUS BUS 0.5482**[0.2554] 0.5465**[0.2581] 0.5527**[0.2563] 0.5295**[0.2593] 0.5525**[0.2620] EXP TRADE 0.0355 [0.3278] 0.0386 [0.3296] 0.0422 [0.3440] SERVICES 0.1668 [0.3259] 0.2347 [0.3317] 0.1788 [0.3377] CONST & MANU 0.4014 [0.4340] 0.3797 [0.4348] 0.4189 [0.4441] TRADESOUTH 0.4193*[0.2402] --- INFORMALLOA -0.1085 [0.2463] N MICROLOAN 1.0475** [0.4788] BANKLOAN 0.0093 [0.2421] REMITABROAD -0.1839 [0.5067] PAWNING 0.0869 [0.3334] Constant 1.5151[1.4689] 1.1765[1.5069] 1.6158[1.4912] 1.7736[1.5057] 1.3795[1.5353] Predicted 0.8436 0.8436 0.8436 0.8436 0.8436 probabilities No. of 241 238 241 241 239 observations LR chi2 41.36 42.35 42.61 45.68 47.19 Prob > chi2 0.0003 0.0006 0.0009 0.0006 0.0021 Pseudo 푅2 0.1699 0.1750 0.1751 0.1877 0.1946 Log-likelihood -101.01 -99.83 -100.39 -98.85 -97.64 Correctly 82.16% 81.09% 82.16% 81.33% 82.43% classified *p<0.10, **p<0.05 and ***p<0.01 denote respective significance levels; dependent variable opportunity entrepreneur; probit regressions; coefficients and numbers in parentheses standard errors.

187

Appendix E: Table 7.3A: Correlation Matrix Between Key Variables – Gross Income Growth

(1) (2) (3) (4) (5) (6) (7)- LS (8) -US (9) (10) (11)- (12) (13) (14) (15) (16) (17) (18) (19) (20) UNEM EMP P (1) Growth 1.00

(2) Kilinochchi -0.50 1.00

(2) ln Firm Size -0.48 0.52 1.00

(3) ln Firm Age 0.27 -0.36 -0.16 1.00

(4) Formal -0.16 0.12 0.23 0.23 1.00

(5) Male -0.07 0.18 0.16 -0.01 0.18 1.00

(6) Primary -0.07 0.29 0.08 -0.09 -0.02 0.05 1.00

(7) Lower Secondary (LS) -0.01 -0.01 -0.09 -0.02 -0.05 0.03 -0.34 1.00

(8) Upper Secondary (US) 0.02 -0.11 -0.14 0.11 0.01 0.01 -0.27 -0.56 1.00 (9) Graduate and Post- grad 0.11 -0.18 0.03 0.01 0.09 -0.14 -0.11 -0.24 -0.19 1.00 (10) Unemployed (UNEMP) 0.06 0.01 -0.14 0.14 -0.02 0.05 -0.06 0.05 -0.01 0.03 1.00

(11) Employed (EMP) 0.04 -0.19 -0.15 -0.06 -0.05 -0.13 0.03 -0.11 0.07 0.04 -0.55 1.00

(12) Ex-combatant -0.13 0.09 0.02 0.06 0.05 0.04 -0.06 -0.006 -0.03 -0.04 -0.10 -0.14 1.00

(13) Sole Proprietorship -0.17 0.05 0.05 0.08 0.04 -0.08 0.11 -0.08 -0.03 0.006 0.05 -0.04 -0.008 1.00 (14) Past Business Experience 0.002 0.28 0.11 0.01 0.11 0.17 0.08 0.09 -0.18 -0.07 -0.05 -0.06 0.01 -0.06 1.00 - (15) Trade Membership -0.157 0.43 0.28 -0.05 0.19 0.16 0.20 -0.06 -0.04 0.004 0.11 -0.15 0.08 0.004 0.12 1.00

(16) Trade 0.02 0.01 0.13 0.05 0.02 -0.04 -0.06 -0.007 0.03 0.08 0.04 -0.05 -0.07 -0.04 -0.05 0.10 1.00

(17) Service -0.13 -0.003 -0.10 -0.03 -0.08 0.05 0.04 -0.02 -0.03 -0.05 0.05 0.04 0.08 0.13 0.007 -0.08 -0.65 1.00 (18) Construction and Related Manufacturing 0.19 0.04 -0.05 0.03 0.03 0.08 0.10 0.02 -0.06 -0.01 -0.06 -0.05 -0.05 -0.06 0.03 0.06 -0.25 -0.23 1.00

(19) Rented Premises -0.14 0.19 0.21 -0.19 0.03 0.17 0.08 -0.05 0.11 -0.17 -0.11 0.05 0.01 0.03 0.01 -0.04 0.06 -0.05 -0.16 1.00

Notes: see Table 7.1 for variable definitions. The highest correlations are between LS and US, and UNEMP and EMP, with the respective correlation coefficients -0.56 and -o.55. while the other coefficients remain low.

188

References Abadie, A & Dermisi, S 2008, 'Is terrorism eroding agglomeration economies in Central Business Districts? lessons from the office real estate market in downtown Chicago', Journal of Urban Economics, vol. 64, no. 2, pp. 451-463. Abadie, A & Gardeazabal, J 2003, 'The economic costs of conflict: a case study of the Basque Country', American Economic Review, vol. 93, no. 1, pp. 113-132. Abeyratne, S 2004, 'Economic roots of political conflict: the case of Sri Lanka', World Economy, vol. 27, no. 8, pp. 1295-1314. Abor, J & Quartey, P 2010, 'Issues in SME development in Ghana and South Africa', International Research Journal of Finance and Economics, vol. 39, no. 6, pp. 215-228. Acs, ZJ 2006, 'How is entrepreneurship good for economic growth?', Innovations: Technology, Governance, Globalization, vol. 1, no. 1, pp. 97-107. Acs, ZJ & Audretsch, DB 1990, Innovation and small firms, MIT Press, Cambridge. Acs, ZJ, Audretsch, DB, Braunerhjelm, P & Carlsson, B 2012, 'Growth and entrepreneurship', Small Business Economics, vol. 39, no. 2, pp. 289-300. Acs, ZJ, Audretsch, DB & Strom, R 2009, Entrepreneurship, growth, and public policy, Cambridge University Press, Cambridge. Acs, ZJ, Carlsson, B & Karlsson, C 1999, Entrepreneurship, small and medium-sized enterprises and the macroeconomy, Cambridge University Press, Cambridge. Acs, ZJ, Desai, S & Hessels, J 2008, 'Entrepreneurship, economic development and institutions', Small Business Economics, vol. 31, no. 3, pp. 219-234. Acs, ZJ & Naude, WA 2011, 'Entrepreneurship, stages of development, and industrialization', World Institute for Development Economics Research, Working Papers No. 2011/80, Helsinki, Finland. Acs, ZJ & Naude, WA 2013, 'Entrepreneurship, stages of development, and industrialization', in A Szirmai, WA Naude and L Alcorta (eds), Pathways to Inustrialization in the 21st Century, Oxford University Press, Oxford, pp. 373-392 Adams, RH & Cuecuecha, A 2013, 'The impact of remittances on investment and poverty in Ghana', World Development, vol. 50, pp. 24-40. Aggarwal, R, Demirguc-Kunt, A & Peria, MSM 2011, 'Do remittances promote financial development?', Journal of Development Economics, vol. 96, no. 2, pp. 255-264. Aghion, P & Howitt, P 1998, Endogenous growth theory, MIT Press, Cambridge, Mass. Aghion, P, Fally, T & Scarpetta, S 2007, 'Credit constraints as a barrier to the entry and post-entry growth of firms', Economic Policy, vol. 22, no. 52, pp. 731-779. Alchian, AA 1950, 'Uncertainty, evolution, and economic theory', Journal of Political Economy, vol. 58, no. 3, pp. 211-221. Alderman, H, Hoddinott, J & Kinsey, B 2006, 'Long term consequences of early childhood malnutrition', Oxford Economic Papers, vol. 58, no. 3, pp. 450-474. Aldrich, H & Zimmer, C 1986, 'Entrepreneurship through social networks', University of Illinois at Urbana-Champaign's Academy for Entrepreneurial Leadership Historical Research Reference in Entrepreneurship. Alesina, A & Dollar, D 2000, 'Who gives foreign aid to whom and why?', Journal of Economic Growth, vol. 5, no. 1, pp. 33-63. Alesina, A & La Ferrara, E 2000, 'Participation in heterogeneous communities', Quarterly Journal of Economics, vol. 115, no. 3, pp. 847-904. Allen, F, Chakrabarti, R, De, S, Qian, J & Qian, M 2012, 'Financing firms in India', Journal of Financial Intermediation, vol. 21, no. 3, pp. 409-445. Almus, M 2002, 'What characterizes a fast-growing firm?', Applied Economics, vol. 34, no. 12, pp. 1497-1508. Amit, R & Muller, E 1995, '“Push” and “pull” entrepreneurship', Journal of Small Business & Entrepreneurship, vol. 12, no. 4, pp. 64-80.

189

Amoros, JE 2009, 'Entrepreneurship and quality of institutions: a developing-country approach', UNU-WIDER Research Paper No. 2009/07, Available at http://www.econstor.eu/handle/10419/45174 Amoros, JE & Bosma, N 2014, Global Entrepreneurship Monitor 2013 Global Report, Fifteen years of assessing entrepreneurship across the globe, Babson Park, MA: Global Entrepreneurship Monitor. Amoros, JE & Cristi, O 2008, 'Longitudinal analysis of entrepreneurship and competitiveness dynamics in Latin America', International Entrepreneurship and Management Journal, vol. 4, no. 4, pp. 381-399. Anand, S & Ravallion, M 1993, 'Human development in poor countries: on the role of private incomes and public services', The Journal of Economic Perspectives, vo. 7, no. 1, pp. 133-150. Anderson, MB 1999, Do no harm: how aid can support peace-or war, Lynne Rienner Publishers, Boulder. Annan, J, Blattman, C & Horton, R 2006, 'The state of youth and youth protection in Northern Uganda', UNICEF Report Uganda. Available at http://www.sway uganda.org/SWAY.Phase1.FinalReport.pdf. Ardagna, S & Lusardi, A 2008, 'Explaining international differences in entrepreneurship: the role of individual characteristics and regulatory constraints', National Bureau of Economic Research Working Paper No. 14012. Arenius, P & Minniti, M 2005, 'Perceptual variables and nascent entrepreneurship', Small Business Economics, vol. 24, no. 3, pp. 233-247. Arrow, KJ 1962, 'The economic implications of learning by doing', The Review of Economic Studies, vol. 29, no. 3, pp. 155-173. Arunatilake, N, Jayasuriya, S & Kelegama, S 2001, 'The economic cost of the war in Sri Lanka', World Development, vol. 29, no. 9, pp. 1483-1500. Aterido, R, Hallward-Driemeier, M & Pages, C 2011, 'Big constraints to small firms' growth? business environment and employment growth across firms', Economic Development and Cultural Change, vol. 59, no. 3, pp. 609-647. Athukorala, P & Jayasuriya, S 1994, Macroeconomic policies, crises, and growth in Sri Lanka, 1969-90, The World Bank, Washington, DC. Athukorala, P & Jayasuriya, S 2013, 'Economic policy shifts in Sri Lanka: the post-conflict development challenge', Asian Economic Papers, vol. 12, no. 2, pp. 1-28. Athukorala, P & Rajapatirana, S 2000, 'Liberalization and industrial transformation: lessons from the Sri Lankan experience', Economic Development and Cultural Change, vol. 48, no. 3, pp. 543-572. Audretsch, DB 2003, Entrepreneurship: a survey of the literature, Institute for Development Strategies Indiana, University & Centre for Economic Policy, London. Audretsch, DB, Houweling, P & Thurik, AR 2004, 'Industry evolution diversity, selection and the role of learning', International Small Business Journal, vol. 22, no. 4, pp. 331-348. Audretsch, DB, Keilbach, MC & Lehmann, EE 2006, Entrepreneurship and economic growth, Oxford University Press, New York. Audretsch, DB, Klomp, L, Santarelli, E & Thurik, AR 2004, 'Gibrat's law: are the services different?', Review of Industrial Organization, vol. 24, no. 3, pp. 301-324. Audretsch, DB & Thurik, AR 2000, 'Capitalism and democracy in the 21st century: from the managed to the entrepreneurial economy', Journal of Evolutionary Economics, vol. 10, no. 1-2, pp. 17-34. Ayyagari, M, Demirguc-Kunt, A & Maksimovic, V 2010, 'Formal versus informal finance: evidence from China', Review of Financial Studies, vol. 23, no. 8, pp. 3048-3097. ——— 2011, 'Small vs. young firms across the world: contribution to employment, job creation, and growth', World Bank Policy Research Working Paper No. 5631, vol. Available at SSRN: http://ssrn.com/abstract=1807732, no. 5631.

190

Bandara, J & Jayasuriya, S 2009, 'Sri Lanka in distortions to agricultural incentives in Asia', in K Anderson and W Martin (eds), Distortions to agricultural incentives in Asia, The World Bank, Washington, DC, pp. 409-440. Bandara, JS & Jayasuriya, S 2011, 'Regional disparities: limitations in the application of new economic geography', in S Kelegama and D Gunewardena (eds), Economic and socail development under a market economy regime in Sri Lanka, Vol. 1, Colombo, Sri Lanka, pp. 195-228. Baptista, R, Esccria, V & Madruga, P 2008, 'Entrepreneurship, regional development and job creation: the case of Portugal', Small Business Economics, vol. 30, no. 1, pp. 49-58. Baptista, R, Lima, F & Mendonca, J 2012, 'Human capital and the performance of firms over time', Available at http://ssrn.com/abstract=2009673. Barker, T, Dunne, P & Ron, S 1991, 'Measuring the peace dividend in the United Kingdom', Journal of Peace Research, vol. 28, no. 4, pp. 345-358. Barkham, R, Gudgin, G & Hart, M 2012, The determinants of small firm growth: an inter- regional study in the United Kingdom 1986-90, Routledge, Oxon. Barney, JB 1986, 'Strategic factor markets: expectations, luck, and business strategy', Management Science, vol. 32, no. 10, pp. 1231-1241. Bates, T 1990, 'Entrepreneur human capital inputs and small business longevity', The Review of Economics and Statistics, vol. 72, no. 4, pp. 551-559. ——— 1995, 'Self-employment entry across industry groups', Journal of Business Venturing, vol. 10, no. 2, pp. 143-156. Baumann, F & Brandle, T 2012, 'Self-employment, educational attainment and employment protection legislation', Labour economics, vol. 19, no. 6, pp. 846- 859. Baumol, WJ 1967, Business behavior, value and growth, Harcourt, Brace & World, New York. ——— 1990, 'Entrepreneurship: productive, unproductive, and destructive', Journal of Political Economy, vol. 98, no. 5, pp. 893-921. Bazzi, S & Blattman, C 2014, 'Economic shocks and conflict: evidence from commodity prices', American Economic Journal: Macroeconomics, vol. 6, no. 4, pp. 1-38. Beck, T, Demirguc-Kunt, A & Levine, R 2005, 'SMEs, growth, and poverty: cross-country evidence', Journal of Economic Growth, vol. 10, no. 3, pp. 199-229. Beck, T, Demirguc-Kunt, A & Maksimovic, V 2008, 'Financing patterns around the world: are small firms different?', Journal of Financial Economics, vol. 89, no. 3, pp. 467- 487. Bellows, J & Miguel, E 2006, 'War and institutions: new evidence from Sierra Leone', The American Economic Review, vol. 96, no. 2, pp. 394-399. ——— 2009, 'War and local collective action in Sierra Leone', Journal of Public Economics, vol. 93, no. 11-12, pp. 1144-1157. Belsley, DA, Kuh, E & Welsch, RE 1980, Regression diagnostics : identifying influential data and sources of collinearity, Wiley, New York. Bennett, J 2010, 'Informal firms in developing countries: entrepreneurial stepping stone or consolation prize?', Small Business Economics, vol. 34, no. 1, pp. 53-63. Benoit, E 1973, Defense and economic growth in developing countries, Lexington Books, Lexington, Mass. Benz, M & Frey, BS 2004, 'Being independent raises happiness at work', Swedish Economic Policy Review, vol. 11, no. 2, pp. 95-134. Bergmann, H & Sternberg, R 2007, 'The changing face of entrepreneurship in Germany', Small Business Economics, vol. 28, no. 2-3, pp. 205-221. Bernstein, I 1996, Guns or butter: the presidency of Lyndon Johnson, Oxford University Press, Cambridge. Besley, T & Mueller, H 2012, 'Estimating the peace dividend: the impact of violence on house prices in Northern Ireland', American Economic Review, vol. 102, no. 2, pp. 810-833. 191

Besley, T & Persson, T 2008, 'The incidence of civil war: theory and evidence', National Bureau of Economic Research (NBER) Working Paper No. 14585. Besley, T & Persson, T 2011, 'The logic of political violence', Quarterly Journal of Economics, vol. 126, no. 3, pp. 1411-1445. Bhagwati, JN 1982, 'Directly unproductive, profit-seeking (DUP) activities', Journal of Political Economy, vol. 90, no. 5, pp. 988-1002. Bhola, R, Verheul, I, Thurik, R & Grilo, I 2006, 'Explaining engagement levels of opportunity and necessity entrepreneurs', EIM Business and Policy Research Research Report No. H200610. Bigsten, A, Collier, P, Dercon, S, Fafchamps, M, Gauthier, B, Gunning, JW, Oduro, A, Oostendorp, R, Patillo, C & Soderbom, M 2003, 'Credit constraints in manufacturing enterprises in Africa', Journal of African Economies, vol. 12, no. 1, pp. 104-125. Birley, S & Westhead, P 1994, 'A taxonomy of business start-up reasons and their impact on firm growth and size', Journal of Business Venturing, vol. 9, no. 1, pp. 7-31. Blanchflower, DG 2000, 'Self-employment in OECD countries', Labour economics, vol. 7, no. 5, pp. 471-505. Blanchflower, DG & Oswald, AJ 1998, 'What makes an entrepreneur?', Journal of Labor Economics, vol. 16, no. 1, pp. 26-60. Blattman, C & Annan, J 2010, 'The consequences of child soldiering', Review of Economics and Statistics, vol. 92, no. 4, pp. 882-898. Blattman, C & Miguel, E 2010, 'Civil war', Journal of Economic Literature, vol. 48, no. 1, pp. 3-57. Block, J & Koellinger, P 2009, 'I can't get no satisfaction—necessity entrepreneurship and procedural utility', Kyklos, vol. 62, no. 2, pp. 191-209. Block, J & Sandner, P 2009, 'Necessity and opportunity entrepreneurs and their duration in self-employment: evidence from German micro data', Journal of Industry, Competition and Trade, vol. 9, no. 2, pp. 117-137. Block, J, Sandner, P & Spiegel, F 2015, 'How do risk attitudes differ within the group of entrepreneurs? the role of motivation and procedural utility', Journal of Small Business Management, vol. 53, no. 1, pp. 183-206. Block, J & Wagner, M 2010, 'Necessity and opportunity entrepreneurs in Germany: characteristics and earnings differentials', Schmalenbach Business Review, vol. 62, no. 2, pp. 154-174. Borjas, GJ 1986, 'The self-employment experience of immigrants', The Journal of Human Resources, vol. 21, no. 4, pp. 485-506. Bose, S 1995, 'State crises and nationalities conflict in Sri Lanka and Yugoslavia', Comparative Political Studies, vol. 28, no. 1, pp. 87-116. Bosma, N, Acs, ZJ, Autio, E, Coduras, A & Levie, J 2009, 'Global Entrepreneurship Monitor: executive report 2008'. Babson Park, MA: Global Entrepreneurship Research Consortium, Available at: http://www.gemconsortium. org/download/1287850267108/GEM_Global_08. pdf. Bosma, N, Stam, E & Schutjens, V 2011, 'Creative destruction and regional productivity growth: evidence from the Dutch manufacturing and services industries', Small Business Economics, vol. 36, no. 4, pp. 401-418. Bosma, N, Van Praag, M, Thurik, R & De Wit, G 2004, 'The value of human and social capital investments for the business performance of startups', Small Business Economics, vol. 23, no. 3, pp. 227-236. Bosma, N, Wennekers, S & Amoros, JE 2012, 'Global Entrepreneurship Monitor, 2011 extended report: Entrepreneurs and entrepreneurial employees across the globe', Global Entrepreneurship Research Association (GERA), London. Bottazzi, G, Coad, A, Jacoby, N & Secchi, A 2011, 'Corporate growth and industrial dynamics: evidence from French manufacturing', Applied Economics, vol. 43, no. 1, pp. 103-116.

192

Bottazzi, G & Duenas, M 2012, 'The evolution of the business cycles and growth rates distributions', Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy. Boudreaux, K 2007, 'The business of reconciliation: entrepreneurship and commercial activity in post-conflict Rwanda', Economic Affairs, vol. 27, no. 2, pp. 6-13. Bozzoli, C, Bruck, T & Sottsas, S 2010, 'A Survey of the global economic costs of conflict', Defence and Peace Economics, vol. 21, no. 2, pp. 165-176. Bradley, DE & Roberts, JA 2004, 'Self‐Employment and job satisfaction: investigating the role of self‐efficacy, depression, and seniority', Journal of Small Business Management, vol. 42, no. 1, pp. 37-58. Braunerhjelm, P, Acs, ZJ, Audretsch, DB & Carlsson, B 2010, 'The missing link: knowledge diffusion and entrepreneurship in endogenous growth', Small Business Economics, vol. 34, no. 2, pp. 105-125. Brenner, R & Reuven, B 1985, Betting on ideas : wars, inventions, inflation, University of Chicago Press, Chicago. Brown, RPC, Connell, J & Jimenez-Soto, EV 2014, 'Migrants' remittances, poverty and social protection in the South Pacific: Fiji and Tonga', Population, Space and Place, vol. 20, no. 5, pp. 434-454. Bruck, T, Justino, P, Verwimp, P & Avdeenko, A 2010, 'Identifying conflict and violence in micro-level surveys', Discussion paper no. 5067, Forschungsinstitut zur Zukunft der Arbeit. Bruck, T, Justino, P, Verwimp, P & Tedesco, A 2013, 'Measuring conflict exposure in micro-level surveys', Household in Conflict Network (HiCN) Working Paper No. 153. Bruck, T, Naude, WA & Verwimp, P 2011, 'Small business, entrepreneurship and violent conflict in developing countries', Journal of Small Business and Entrepreneurship, vol. 24, no. 2, pp. 161-78. ——— 2013, 'Business under fire: entrepreneurship and violent conflict in developing countries', Journal of Conflict Resolution, vol. 57, no. 1, pp. 3-19. Bruck, T & Schindler, K 2009, 'Smallholder land access in post-war Northern Mozambique', World Development, vol. 37, no. 8, pp. 1379-1389. Bruckner, M 2011, 'Country fixed effects and unit roots: A comment on poverty and civil war: Revisiting the evidence', School of Economics Research Paper No. 2011- 17, University of Adelaide. Bruckner, M & Ciccone, A 2010, 'International commodity prices, growth and the outbreak of civil war in Sub‐Saharan Africa', The Economic Journal, vol. 120, no. 544, pp. 519-534. Brunjes, J & Diez, JR 2012a, 'Opportunity entrepreneurs–potential drivers of non-farm growth in rural Vietnam?', Working Papers on Innovation and Space 2012-01, Philipps University Mar-burg, Department of Geography, Marburg. ——— 2012b, '‘Recession push’ and ‘prosperity pull’ entrepreneurship in a rural developing context', Entrepreneurship & Regional Development, vol. 25, no. 3-4, pp. 251-271. Bruton, GD, Ketchen, DJ & Ireland, RD 2013, 'Entrepreneurship as a solution to poverty', Journal of Business Venturing, vol. 28, no. 6, pp. 683-689. Bundervoet, T, Verwimp, P & Akresh, R 2009, 'Health and civil war in rural Burundi', Journal of Human Resources, vol. 44, no. 2, pp. 536-563. Burke, A 2011, 'The entrepreneurship enabling role of freelancers: theory with evidence from the construction industry', International Review of Entrepreneurship, vol. 9, no. 3, pp. 1-28. Burke, MB, Miguel, E, Satyanath, S, Dykema, JA & Lobell, DB 2009, 'Warming increases the risk of civil war in Africa', Proceedings of the National Academy of Sciences, vol. 106, no. 49, pp. 20670-20674.

193

Burki, AA & Terrell, D 1998, 'Measuring production efficiency of small firms in Pakistan', World Development, vol. 26, no. 1, pp. 155-169. Burnside, C & Dollar, D 2000, 'Aid, policies, and growth', American Economic Review, vol. 94, no. 4, pp. 847-868. Calvo, GA & Wellisz, S 1980, 'Technology, entrepreneurs, and firm size', The Quarterly Journal of Economics, vol. 95, no. 4, pp. 663-677. Camacho, A & Rodriguez, C 2013, 'Firm exit and armed conflict in Colombia', Journal of Conflict Resolution, vol. 57, no. 1, pp. 89-116. Cameron, AC & Trivedi, PK 2005, Microeconometrics : methods and applications, Cambridge University Press, New York, Cambridge. Cantillon, R 1755, Essay on the nature of trade in general, Henry Higgs (edition and translation 1959), London. Carter, NM, Gartner, WB, Shaver, KG & Gatewood, EJ 2003, 'The career reasons of nascent entrepreneurs', Journal of Business Venturing, vol. 18, no. 1, pp. 13-39. Cassar, G 2007, 'Money, money, money? A longitudinal investigation of entrepreneur career reasons, growth preferences and achieved growth', Entrepreneurship and Regional Development, vol. 19, no. 1, pp. 89-107. Cerra, V & Saxena, SC 2008, 'Growth dynamics: The myth of economic recovery', American Economic Review, vol. 98, no. 1, pp. 439-457. Chand, S 2011, ‘Data collection in difficult environments: measuring economic recovery in post-conflict Bougainville’, Civil-Military Effectiveness: Building Tomorrow’s Capabilities hosted by the Asia-Pacific Civil Military Centre of Excellence. Chand, S 2013, 'Building peace in Bougainville: measuring recovery post conflict', State, Society and Governance in Malanesia (SSGM) discussion paper 2013/5, Australian National University. Chand, S & Coffman, R 2008, 'How soon can donors exit from post-conflict states', Center for Global Development Working Paper No. 141, Washington, DC. Chandrakanthan, AJV 1994, ‘An exodous sans destination: refugees and the internally displaced in Sri Lanka’, In 4th International Research and Advisory Panel Conference, University of Oxford, Oxford, England. Chavis, LW, Klapper, LF & Love, I 2011, 'The impact of the business environment on young firm financing', The World Bank Economic Review, vol. 25, no. 3, pp. 486- 507. Chen, S, Loayza, NV & Reynal-Querol, M 2008, 'A symposium on post-conflict transitions: the aftermath of civil war', World Bank Economic Review, vol. 22, no. 1, pp. 63-85. Chlosta, S, Patzelt, H, Klein, S & Dormann, C 2012, 'Parental role models and the decision to become self-employed: the moderating effect of personality', Small Business Economics, vol. 38, no. 1, pp. 121-138. Chowdhury, MJA 2011, 'The determinants of entrepreneurship in a conflict region: evidence from the Chittagong Hill Tracts in Bangladesh', Journal of Small Business and Entrepreneurship, vol. 24, no. 2, pp. 265-281. Christopher, D & Andrew, H 2012, '“Push” versus “pull” entrepreneurship: an ambiguous distinction?', International Journal of Entrepreneurial Behaviour & Research, vol. 18, no. 6, pp. 697-719. Ciccone, A & Matsuyama, K 1996, 'Start-up costs and pecuniary externalities as barriers to economic development', Journal of Development Economics, vol. 49, no. 1, pp. 33-59. Clarke, R 2011, 'Conventionally defeated but not eradicated: Asian Arms Networks and the potential for the return of Tamil militancy in Sri Lanka', Civil Wars, vol. 13, no. 2, pp. 157-188. Co, CY, Gang, IN & Yun, MS 2005, 'Self-employment and wage earning in Hungary', Review of Development Economics, vol. 9, no. 2, pp. 150-165. Coad, A 2007, 'Firm growth: A survey', Centre d’Economie de la Sorbonne, CES working paper No. 2007/24. 194

——— 2009, The growth of firms: A survey of theories and empirical evidence, Edward Elgar Publishing, Cheltenham, UK. Coad, A & Tamvada, J 2012, 'Firm growth and barriers to growth among small firms in India', Small Business Economics, vol. 39, no. 2, pp. 383-400. Coase, RH 1937, 'The nature of the firm', Economica, vol. 4, no. 16, pp. 386-405. Cockayne, J, Mikulaschek, C & Perry, C 2010, The United Nations Security Council and civil war: first insights from a new dataset, International Peace Institute, New York. Collier, P 1999, 'On the economic consequences of civil war', Oxford Economic Papers, vol. 51, no. 1, pp. 168-183. Collier, P 2000, 'Ethnicity, politics and economic performance', Economics & Politics, vol. 12, no. 3, pp. 225-245. ——— 2007, The bottom billion : why the poorest countries are failing and what can be done about It, Oxford University Press, New York. ——— 2009, 'Post-conflict recovery: how should strategies be distinctive?', Journal of African Economies, vol. 18, no. 1, pp. 99-131. Collier, P & Dollar, D 2002, 'Aid allocation and poverty reduction', European Economic Review, vol. 46, no. 8, pp. 1475-1500. Collier, P & Duponchel, M 2013, 'The economic legacy of civil war: firm-level evidence from Sierra Leone', Journal of Conflict Resolution, vol. 57, no. 1, pp. 65-88. Collier, P & Hoeffler, A 2002, 'Aid, policy and peace: reducing the risks of civil conflict', Defence and Peace Economics, vol. 13, no. 6, pp. 435-450. ——— 2004a, 'Aid, policy and growth in post-conflict societies', European Economic Review, vol. 48, no. 5, pp. 1125-1145. ——— 2004b, 'Greed and grievance in civil war', Oxford Economic Papers, vol. 56, no. 4, pp. 563-595. ——— 2007, 'Unintended consequences: does aid promote arms races?', Oxford Bulletin of Economics and Statistics, vol. 69, no. 1, pp. 1-27. Collier, P, Hoeffler, A & Rohner, D 2009, 'Beyond greed and grievance: feasibility and civil war', Oxford Economic Papers, vol. 61, no. 1, pp. 1-27. Collier, P, Hoeffler, A & Soderbom, M 2008, 'Post-conflict risks', Journal of Peace Research, vol. 45, no. 4, pp. 461-478. Collins, WJ & Margo, RA 2007, 'The economic aftermath of the 1960s riots in American cities: Evidence from property values', The Journal of Economic History, vol. 67, no. 4, pp. 849-883. Cooper, AC & Dunkelberg, WC 1986, 'Entrepreneurship and paths to business ownership', Strategic Management Journal, vol. 7, no. 1, pp. 53-68. Cooray, A 2014, 'Ethnic or political fractionalisation? A district level analysis of the provision of public goods in Sri Lanka', Growth and Change, vol.45, no.4, pp. 640-666 Cosh, A, Hughes, A & Weeks, M 2000, 'The relationship between training and employment growth in small and medium-sized enterprises', RfEE Research Report No. 7. Coyne, CJ, Dempster, GM & Isaacs, JP 2010, 'Asset values and the sustainability of peace prospects', The Quarterly Review of Economics and Finance, vol. 50, no. 2, pp. 146-156. Coyne, CJ & Leeson, PT 2004, 'Plight of underdeveloped countries', Cato Journal, vol. 24, no. 3, pp. 205-235. Creswell, JW 2009, Research design : qualitative, quantitative, and mixed methods approaches, 3rd ed. Thousand Oaks, Sage Publications, Califonia. Crost, B, Felter, J & Johnston, P 2014, 'Aid under fire: development projects and civil conflict', American Economic Review, vol. 104, no. 6, pp. 1833-1856. Cuthbertson, AG 1997, 'The trade policy review of Sri Lanka', The World Economy, vol. 20, no. 5, pp. 633-648.

195

Cuthbertson, AG & Athukorala, P 1990, 'Liberalizing foreign trade: the experience of Sri Lanka', in D Papageorgiou, M Michaely, and AM Choksi (eds), Liberalizing foreign trade: the experience of Indonesia, Pakistan and Sri Lanka, Blackwell, Oxford, pp. 287–414. Das, S 1995, 'Size, age and firm growth in an infant industry: the computer hardware industry in India', International Journal of Industrial Organization, vol. 13, no. 1, pp. 111-126. Davidsson, P, Delmar, F & Wiklund, J 2006, Entrepreneurship and the growth of firms, Edward Elgar Publishing, Cheltenham, UK. Davidsson, P & Honig, B 2003, 'The role of social and human capital among nascent entrepreneurs', Journal of Business Venturing, vol. 18, no. 3, pp. 301-331. Davidsson, P & Wiklund, J 2000, 'Conceptual and empirical challenges in the study of firm growth', in DL Sexton and H Landström (eds), The Blackwell handbook of entrepreneurship, Blackwell, Oxford, pp. 26-44. Davis, SJ, Haltiwanger, JC & Schuh, S 1996, Job creation and destruction, MIT Press, Cambridge, Mass. DCS (Department of Census and Statistics) 2012a, Census of population and housing 2012, Department of Census and Statistics, Colombo, Sri Lanka. ——— 2012b, Sri Lanka labour force survey 2012, Department of Census and Statistics, Colombo, Sri Lanka. ——— 2014, Poverty headcount ratio brief: decomposition of consumption poverty, Department of Census and Statistics, Colombo, Sri Lanka. de Groot, OJ 2010, 'The spillover effects of conflict on economic growth in neighbouring countries in Africa', Defence and Peace Economics, vol. 21, no. 2, pp. 149-164. de la Calle, L & Sanchez-Cuenca, I 2012, 'Rebels without a territory an analysis of nonterritorial conflicts in the world, 1970–1997', Journal of Conflict Resolution, vol. 56, no. 4, pp. 580-603. de Meza, D & Webb, DC 1987, 'Too much investment: a problem of asymmetric information', The Quarterly Journal of Economics, vol. 102, no. 2, pp. 281-292. de Paula, A & Scheinkman, JA 2007, 'The informal sector', National Bureau of Economic Research Working Paper No. 08‐018, Philadelphia: Pennsylvania Institute for Economic Research (May). Available at SSRN:http://ssrn.com/abstract=1135724. de Ree, J & Nillesen, E 2009, 'Aiding violence or peace? the impact of foreign aid on the risk of civil conflict in sub-Saharan Africa', Journal of Development Economics, vol. 88, no. 2, pp. 301-313. de Silva, SBD 1982, The political economy of underdevelopment, Routledge & Kegan Paul, London. de Walque, D & Verwimp, P 2010, 'The demographic and socio-economic distribution of excess mortality during the 1994 genocide in Rwanda', Journal of African Economies, vol. 19, no. 2, pp. 141-162. Deaton, A & Miller, R 1996, 'International commodity prices, macroeconomic performance and politics in Sub-Saharan Africa', Journal of African Economies, vol. 5, no. 3, pp. 99-191. Deininger, K 2003, 'Causes and consequences of civil strife: micro-level evidence from Uganda', Oxford Economic Papers, vol. 55, no. 4, pp. 579-606. Dejardin, M 2011, 'Linking net entry to regional economic growth', Small Business Economics, vol. 36, no. 4, pp. 443-460. Delmar, F 2012, 'Measuring growth: Methodological considerations and empirical results', in P Davidsson, F Delmar and J Wiklind (eds), Entrepreneurship and the growth of firms, Edward Elgar, Cheltenham, UK, pp. 62-84 Delmar, F, Davidsson, P & Gartner, WB 2003, 'Arriving at the high-growth firm', Journal of Business Venturing, vol. 18, no. 2, pp. 189-216.

196

Demirguc-Kunt, A, Klapper, LF & Panos, GA 2011, 'Entrepreneurship in post-conflict transition: the role of informality and access to finance', Economics of Transition, vol. 19, no. 1, pp. 27-78. Denny, EK & Walter, BF 2014, 'Ethnicity and civil war', Journal of Peace Research, vol. 51, no. 2, pp. 199-212. Desai, S 2011, 'Measuring entrepreneurship in developing countries', in WA Naude (eds), Entrepreneurship and economic development, Palgrave Macmillan, Basingstoke and New York. Desai, S, Acs, ZJ & Weitzel, U 2013, 'A model of destructive entrepreneurship: insight for conflict and postconflict recovery', Journal of Conflict Resolution, vol. 57, no. 1, pp. 20-40. Devins, D 2009, 'Enterprise in deprived areas: what role for start-ups?', International Journal of Entrepreneurship and Small Business, vol. 8, no. 4, pp. 486-498. DeVotta, N 2000, 'Control democracy, institutional decay, and the quest for Eelam: explainning ethnic conflict in Sri Lanka ', Pacific affairs, vol. 73, no. 1, pp. 55-76. DeVotta, N 2005, 'From ethnic outbidding to ethnic conflict: the institutional bases for Sri Lanka's separatist war', Nations & Nationalism, vol. 11, no. 1, pp. 141-159. ——— 2007, 'Sinhalese Buddhist nationalist ideology: implications for politics and conflict resolution in Sri Lanka', East-West Center, Washington, DC. ——— 2009, 'The Liberation Tigers of Tamil Eelam and the lost quest for separatism in Sri Lanka', Asian Survey, vol. 49, no. 6, pp. 1021-1051. ——— 2014, 'Parties, political decay, and democratic regression in Sri Lanka', Commonwealth & Comparative Politics, vol. 52, no. 1, pp. 139-165. DiAddario, S 1997, 'Estimating the economic costs of conflict: an examination of the two- gap estimation model for the case of Nicaragua', Oxford Development Studies, vol. 25, no. 1, pp. 123-141. Dixon, J 2009, 'What causes civil wars? integrating quantitative research findings', International Studies Review, vol. 11, no. 4, pp. 707-735. Djankov, S, Qian, Y, Roland, G & Zhuravskaya, E 2006a, 'Entrepreneurship in China and Russia compared', Journal of the European Economic Association, vol. 4, no. 2-3, pp. 352-365. ——— 2006b, 'Who are China's entrepreneurs?', American Economic Review, vol. 96, no. 2, pp. 348-352. Djankov, S & Reynal-Querol, M 2010, 'Poverty and civil war: revisiting the evidence', The Review of Economics and Statistics, vol. 92, no. 4, pp. 1035-1041. Doern, R & Goss, D 2013, 'From barriers to barring: why emotion matters for entrepreneurial development', International Small Business Journal, vol. 31, no. 5, pp. 496-519. Dormann, CF, Elith, J, Bacher, S, Buchmann, C, Carl, G, Carre, G, Marquez, JRG, Gruber, B, Lafourcade, B, Leitao, PJ, Munkemuller, T, McClean, C, Osborne, PE, Reineking, B, Schroder, B, Skidmore, AK, Zurell, D & Lautenbach, S 2013, 'Collinearity: a review of methods to deal with it and a simulation study evaluating their performance', Ecography, vol. 36, no. 1, pp. 27-46. Doyle, MW & Sambanis, N 2000, 'International peacebuilding: a theoretical and quantitative analysis', The American Political Science Review, vol. 94, no. 4, pp. 779-801. Doyle, MW & Sambanis, N 2006, Making war and building peace: United Nations peace operations, United Nations Peace Operations, Princeton University press, Princeton, NJ. Dreyer, DR 2010, 'Issue conflict accumulation and the dynamics of strategic rivalry', International Studies Quarterly, vol. 54, no. 3, pp. 779-795. Du Rietz, A & Henrekson, M 2000, 'Testing the female underperformance hypothesis', Small Business Economics, vol. 14, no. 1, pp. 1-10.

197

Dube, O & Naidu, S 2010, 'Bases, bullets, and ballots: the effect of US military aid on political conflict in Colombia', Center for Global Development Working Paper no.197. Dunham, D & Jayasuriya, S 2000, 'Equity, growth and insurrection: liberalization and the welfare debate in contemporary Sri Lanka', Oxford Development Studies, vol. 28, no. 1, pp. 97-110. Dunham, D & Kelegama, S 1997, 'Does leadership matter in the economic reform process? liberalization and governance in Sri Lanka, 1989–1993', World Development, vol. 25, no. 2, pp. 179-190. Dunne, JP 2013, 'Armed Conflicts', in B. Lomborg (eds), Global Crises: Smart Solutions, Cambridge University Press, Cambridge. Dunne, T, Roberts, MJ & Samuelson, L 1989, 'The growth and failure of US manufacturing plants', The Quarterly Journal of Economics, vol. 104, no. 4, pp. 671-698. Earle, JS & Sakova, Z 2000, 'Business start-ups or disguised unemployment? evidence on the character of self-employment from transition economies', Labour economics, vol. 7, no. 5, pp. 575-601. Easterly, W 2003, 'Can foreign aid buy growth?', Journal of Economic Perspectives, vol.17, no. 3, pp. 23-48. Easterly, W, Levine, R & Roodman, D 2004, 'Aid, policies, and growth: comment', American Economic Review, Vol. 94, no.3, pp. 774-780. Economist 2014, 'Sri Lanka's Muslims-Buddhist power: another minority is under threat', The Economist, 21 June 2014, Available at http://www.economist.com/news/asia/21604601-another-minority-under-threat- buddhist-power. Efendic, A, Mickiewicz, T & Rebmann, A 2014, 'Growth aspirations and social capital : young firms in a post-conflict environment', International Small Business Journal, Epublication ahead of print, February 6, 2014. DOI: 10.1177/0266242613516987. Elbadawi, IA, Kaltani, L & Schmidt-Hebbel, K 2008, 'Foreign aid, the real exchange rate, and economic growth in the aftermath of civil wars', The World Bank Economic Review, vo. 22, no. 1, pp. 113-140. Elston, JA & Audretsch, DB 2011, 'Financing the entrepreneurial decision: an empirical approach using experimental data on risk attitudes', Small Business Economics, vol. 36, no. 2, pp. 209-222. Esteban, J, Mayoral, L & Ray, D 2012, 'Ethnicity and conflict: an empirical study', Amercan Economic Review, vol. 102, no. 4, pp. 1310-1342. Evans, DS & Jovanovic, B 1989, 'An estimated model of entrepreneurial choice under liquidity constraints', Journal of Political Economy, vol. 97, no. 4, pp. 808-827. Evans, DS & Leighton, LS 1989, 'Some empirical aspects of entrepreneurship', The American Economic Review, vol. 79, no. 3, pp. 519-535. ——— 1990, 'Small business formation by unemployed and employed workers', Small Business Economics, vol. 2, no. 4, pp. 319-330. Fafchamps, M 2000, 'Ethnicity and credit in African manufacturing', Journal of Development Economics, vol. 61, no. 1, pp. 205-235. Falter, J-M 2001, ‘Self-employment entry and duration in Switzerland’, in papers from 13th EALE Annual Conference, 13-16 September 2001, Jyväskylä, Finland. Fearon, JD 2010, 'Do governance indicators predict anything? the case of ‘fragile states’ and civil war', Background paper prepared for the World Development Report 2011 on conflict, security, and development. Fearon, JD 2003, 'Ethnic and cultural diversity by country', Journal of Economic Growth, vol. 8, no. 2, pp. 195-222. ——— 2004, 'Why do some civil wars last so much longer than others?', Journal of Peace Research, vol. 41, no. 3, pp. 275-301.

198

Fearon, JD, Humphreys, M & Weinstein, JM 2009, 'Can development aid contribute to social cohesion after civil war? evidence from a field experiment in post-conflict Liberia', American Economic Review, vol. 99, no. 2, pp. 287-291. Fearon, JD, Kasara, K & Laitin, DD 2007, 'Ethnic minority rule and civil war onset', American Political Science Review, vol. 101, no. 1, pp. 187-193. Fearon, JD & Laitin, DD 2003, 'Ethnicity, insurgency, and civil war', American Political Science Review, vol. 97, no. 1, pp. 75-90. ——— 2011, 'Sons of the soil, migrants, and civil war', World Development, vol. 39, no. 2, pp. 199-211. Fernando, P & Moonesinghe, S 2012, 'Livelihoods, basic services and social protection in Sri Lanka', Working Paper 6. Secure Livelihoods Research Consortium, London. Fortna, VP 2004a, Peace time: cease-fire agreements and the durability of peace, Princeton University Press, Princeton. Fortna, VP 2004b, 'Does peacekeeping keep peace? international intervention and the duration of peace after civil war', International Studies Quarterly, vol. 48, no. 2, pp. 269-292. Foss, T, Myrtveit, I & Stensrud, E ‘A comparison of LAD and OLS regression for effort prediction of software projects’, 12th European Software Control and Metrics Conference (ESCOM 2001), pp. 9-15. Fossen, FM & Buttner, TJM 2013, 'The returns to education for opportunity entrepreneurs, necessity entrepreneurs, and paid employees', Economics of Education Review, vol. 37, pp. 66-84. Frerks, G 2013, 'Discourses on war, peace and peacebuilding in Sri Lanka', in D Hilhorst (eds), Disaster, conflict and society in crises: everyday politics of crisis response, Routledge, Oxon, pp. 19-37. Frey, BS, Luechinger, S & Stutzer, A 2009, 'The life satisfaction approach to valuing public goods: the case of terrorism', Public Choice, vol. 138, no. 3-4, pp. 317- 345. Friedman, E, Johnson, S, Kaufmann, D & Zoido-Lobaton, P 2000, 'Dodging the grabbing hand: the determinants of unofficial activity in 69 countries', Journal of Public Economics, vol. 76, no. 3, pp. 459-493. Fritsch, M, Kritikos, A & Rusakova, A 2012, 'Who starts a business and who is self- employed in Germany', IZA Discussion Paper No. 6326, Available at SSRN: http://ssrn.com/abstract=2003659. Fuhrmann, M & Tir, J 2009, 'Territorial dimensions of enduring internal rivalries', Conflict Management and Peace Science, vol. 26, no. 4, pp. 307-329. Fujita, M, Masahisa, F & Venables, A 1999, The spatial economy : cities, regions and international trade, MIT Press, Cambridge, Mass. Ganegodage, KR & Rambaldi, AN 2014, 'Economic consequences of war: evidence from Sri Lanka', Journal of Asian Economics, vol. 30, pp. 42-53. Garba, AS, Djafar, F & Mansor, SA 2013, 'Evidence of opportunity and necessity driven entrepreneurship in Nigeria', Journal of Entrepreneurship, Management and Innovation, vol. 9, no. 3, pp. 57-78. Gedajlovic, E, Honig, B, Moore, CB, Payne, GT & Wright, M 2013, 'Social capital and entrepreneurship: a schema and research agenda', Entrepreneurship Theory and Practice, vol. 37, no. 3, pp. 455-478. Geertz, C 1978, 'The bazaar economy: information and search in peasant marketing', The American Economic Review, vol. 68, no. 2, pp. 28-32. Gerace, MP 2002, 'US military expenditures and economic growth: some evidence from spectral methods', Defence and Peace Economics, vol. 13, no. 1, pp. 1-11. Geroski, PA 2001, 'Exploring the niche overlaps between organizational ecology and industrial economics', Industrial and Corporate Change, vol. 10, no. 2, pp. 507- 540.

199

Ghani, E, Kerr, WR & O'Connell, SD 2011, 'Who creates jobs?', Economic premises, vol. 70, pp. 1-7. Ghatak, M & Nien-Huei Jiang, N 2002, 'A simple model of inequality, occupational choice, and development', Journal of Development Economics, vol. 69, no. 1, pp. 205-226. Ghobarah, HA, Huth, P & Russett, B 2003, 'Civil wars kill and maim people-long after the shooting stops', American Political Science Review, vol. 97, no. 2, pp. 189- 202. Giacomin, O, Guyot, J-L, Janssen, F & Lohest, O 2007a, Novice creators: personal identity and push pull dynamics, CRECIS working paper 07/2007, Louvain School of Management, Centre for Research in Research in Change, Innovation and Strategy. Giacomin, O, Janssen, F, Guyot, J-l & Lohest, O 2011, 'Opportunity and/or necessity entrepreneurship? the impact of the socio-economic characteristics of entrepreneurs', MPRA Paper No. 29506, Munich Personal RePEc Archive. Gibrat, R 1931, Les inégalités économiques, Recueil Sirey, Paris. Goedhuys, M & Sleuwaegen, L 2000, 'Entrepreneurship and growth of entrepreneurial firms in Cote d'Ivoire', The Journal of Development Studies, vol. 36, no. 3, pp. 123-145. Goldstone, JA, Bates, RH, Epstein, DL, Gurr, TR, Lustik, MB, Marshall, MG, Ulfelder, J & Woodward, M 2010, 'A global model for forecasting political instability', Vol. 54, pp. 190-208. Goodhand, J 2010, 'Stabilising a victor's peace? Humanitarian action and reconstruction in Eastern Sri Lanka', Disasters, vol. 34, no. 3, pp. 342-367. Goodhand, J 2012, 'Sri Lanka in 2011: consolidation and militarization of the post-war regime', Asian Survey, vol. 52, no. 1, pp. 130-137. Gries, T & Naude, WA 2010, 'Entrepreneurship and structural economic transformation', Small Business Economics, vol. 34, no. 1, pp. 13-29. ——— 2011, 'Entrepreneurship and human development: a capability approach', Journal of Public Economics, vol. 95, no. 3-4, pp. 216-224. Grobar, LM & Gnanaselvam, S 1993, 'The economic effects of the Sri Lankan civil war', Economic Development and Cultural Change, vol. 41, no. 2, pp. 395-405. Grossman, G & Helpman, E 1991, 'Innovation and growth in the world economy', MIT Press, Cambridge. Guglielmetti, C 2010, 'Measuring the business environment for entrepreneurship in fragile states', UNU-Wider working paper 2010/14. Helsinki: Finland. Guidolin, M & Ferrara, EL 2007, 'Diamonds are forever, wars are not: is conflict bad for private firms?', The American Economic Review, vol. 97, no. 5, pp. 1978-1993. Gujarati, DN 2003, 'Basic econometrics. 4th edn, McGraw-Hill, New York. Gurtoo, A & Williams, CC 2009, 'Entrepreneurship and the informal sector: some lessons from India', The International Journal of Entrepreneurship and Innovation, vol. 10, no. 1, pp. 55-62. Haapanen, M & Tervo, H 2009, 'Self-employment duration in urban and rural locations', Applied Economics, vol. 41, no. 19, pp. 2449-2461. Haltiwanger, J, Jarmin, RS & Miranda, J 2013, 'Who creates jobs? small versus large versus young', Review of Economics and Statistics, vol. 95, no. 2, pp. 347-361. Hannan, MT 2005, 'Ecologies of organizations: Diversity and identity', The Journal of Economic Perspectives, vol. 19, no. 1, pp. 51-70. Hannan, MT & Freeman, J 1977, 'The population ecology of organizations', American journal of sociology, vol. 82, no. 5, pp. 929-964. Harbom, L & Wallensteen, P 2010, 'Armed conflicts, 1946—2009', Journal of Peace Research, vol. 47, no. 4, pp. 501-509. Harris, G 1999, 'The costs of armed conflict in developing countries', in G Harris (eds), Recovery from armed conflict in developing countries, Routledge, London, pp. 12-28. 200

Hartzell, CA 2009, 'Settling civil wars', Conflict Management and Peace Science, vol. 26, no. 4, pp. 347-365. Hassner, RE 2006, 'The path to intractability: time and the entrenchment of territorial disputes', International Security, vol. 31, no. 3, pp. 107-138. Hausmann, R & Rodrik, D 2003, 'Economic development as self-discovery', Journal of Development Economics, vol. 72, no. 2, pp. 603-633. Hegre, H & Sambanis, N 2006, 'Sensitivity analysis of empirical results on civil war onset', Journal of Conflict Resolution, vol. 50, no. 4, pp. 508-535. Herbst, JI 2000, States and power in Africa: comparative lessons in authority and control, Princeton University Press, New Jersey. Hernandez, L, Nunn, N & Warnecke, T 2012, 'Female entrepreneurship in China: opportunity- or necessity-based?', International Journal of Entrepreneurship and Small Business, vol. 15, no. 4, pp. 411-434. Heshmati, A 2001, 'On the growth of micro and small firms: evidence from Sweden', Small Business Economics, vol. 17, no. 3, pp. 213-228. Heston, A, Summers, R & Aten, B 2002, 'Penn World Table', Center for International Comparisons International Comparisons (CICUP), University of Pennsylvania, Philadelphia. Hettige, ST 2009, 'Youth and education in Sri Lanka: preliminary findings', University of Colombo, Colombo. Hintermaier, T & Steinberger, T 2005, 'Occupational choice and the private equity premium puzzle', Journal of Economic Dynamics and Control, vol. 29, no. 10, pp. 1765-1783. Hisrich, RD & Brush, C 1986, 'Characteristics of the minority entrepreneur', Journal of Small Business Management, vol. 24, p. 1. Hitt, MA, Ireland, RD, Camp, SM & Sexton, DL 2001, 'Strategic entrepreneurship: entrepreneurial strategies for wealth creation', Strategic Management Journal, vol. 22, no. 6‐7, pp. 479-491. Hoddie, M & Smith, JM 2009, 'Forms of civil war violence and their consequences for future public health', International Studies Quarterly, vol. 53, no. 1, pp. 175-202. Hoeffler, A, Ijaz, SS & von Billerbeck, S 2011, 'Post-conflict recovery and peacebuilding', Background Paper for the World Development Report 2011 on conflict, security, and development. Hoglund, K & Orjuela, C 2011, 'Winning the peace: conflict prevention after a victor's peace in Sri Lanka', Contemporary Social Science, vol. 6, no. 1, pp. 19-37. Holmes, TJ 1990, 'A theory of entrepreneurship and its application to the study of business transfers', Journal of Political Economy, vol. 98, no. 2, pp. 265-294. Holtzeakin, D, Joulfaian, D & Rosen, HS 1994, 'Entrepreneurial decisions and liquidity constrainsts ', Rand Journal of Economics, vol. 25, no. 2, pp. 334-347. Hoxha, D & Capelleras, J-L 2010, 'Fast-growing firms in a transitional and extreme environment: are they different?', Journal of Small Business and Enterprise Development, vol. 17, no. 3, pp. 350-370. Hsiang, SM, Meng, KC & Cane, MA 2011, 'Civil conflicts are associated with the global climate', Nature, vol. 476, no. 7361, pp. 438-441. Hurst, E & Lusardi, A 2004, 'Liquidity constraints, household wealth, and entrepreneurship', Journal of Political Economy, vol. 112, no. 2, pp. 319-347. ICG (International Crisis Group) 2010, The Sri Lankan diaspora after the LTTE, Asia Report No. 186, (Colombo/Brussels, International Crisis Group). Imai, KS, Arun, T & Annim, SK 2010, 'Microfinance and household poverty reduction: new evidence from India', World Development, vol. 38, no. 12, pp. 1760-1774. Imai, KS & Azam, MS 2012, 'Does microfinance reduce poverty in Bangladesh? new evidence from household panel data', Journal of Development Studies, vol. 48, no. 5, pp. 633-653.

201

Iyer, R & Schoar, A 2010, 'Are there cultural determinants of entrepreneurship?', in J Lerner and A Schoar (eds), International differences in entrepreneurship, University of Chicago Press, pp. 209-240. Jensen, MC 1986, 'Agency costs of free cash flow, corporate finance, and takeovers', The American Economic Review, vol. 76, no. 2, pp. 323-329. Jensen, MC & Meckling, WH 1976, 'Theory of the firm: managerial behavior, agency costs and ownership structure', Journal of Financial Economics, vol. 3, no. 4, pp. 305-360. Jiggins, J 1976, 'Dismantling welfarism in Sri Lanka', Development Policy Review, vol. 9, no. 2, pp. 84-104. Johnson, PS 1986, New firms: an economic perspective, Allen & Unwin, London. Johnson, PS & Darnell, A 1976, 'New firm formation in Great Britain', Working Paper no. 5, Department of Economics, Durham University, England. Johnson, S, Kaufmann, D, Shleifer, A, Goldman, MI & Weitzman, ML 1997, 'The unofficial economy in transition', Brookings papers on economic activity, Washington, DC, pp. 159-239. Johnson, S, Kaufmann, D & Zoido-Lobaton, P 1998, 'Regulatory discretion and the unofficial economy', American Economic Review, vol. 88, no. 2, pp. 387-392. Johnson, S, McMillan, J & Woodruff, C 2000, 'Entrepreneurs and the ordering of institutional reform: Poland, Slovakia, Romania, Russia and Ukraine compared', Economics of Transition, vol. 8, no. 1, pp. 1-36. ——— 2002, 'Property rights and finance', American Economic Review, vol. 50, no. 2, pp. 649-670. Jones, MK & Latreille, PL 2011, 'Disability and self-employment: evidence for the UK', Applied Economics, vol. 43, no. 27, pp. 4161-4178. Jovanovic, B 1982, 'Selection and the evolution of industry', Econometrica, vol. 50, no. 3, pp. 649-670. Jovanovic, B & MacDonald, G 1994, The life-cycle of a competitive industry, Journal of Political Science, vol. 102, no. 2, pp. 322-347. Juergensmeyer, M 1990, 'What the Bhikkhu said: reflections on the rise of militant religious nationalism', Religion, vol. 20, no. 1, pp. 53-75. Kanbur, SM 1979, 'Of risk taking and the personal distribution of income', The Journal of Political Economy, vol. 87, no. 4, pp. 769-797. Kangasharju, A & Pekkala, S 2002, 'The role of education in self–employment success in Finland', Growth and Change, vol. 33, no. 2, pp. 216-237. Kant, I 1939, Perpetual peace, (English translation originally publication 1796), Columbia U.P, New York. Kant, I 2006, 'Toward perpepetual peace and other writings on politics, peace, and history', in P Kleingeld (eds), Rethinking the western tradition, Yale University Press, New Haven and London. Kautonen, T & Palmroos, J 2010, 'The impact of a necessity-based start-up on subsequent entrepreneurial satisfaction', International Entrepreneurship and Management Journal, vol. 6, no. 3, pp. 285-300. Kelegama, S 2000, 'Development in independent Sri Lanka: what went wrong?', Economic and Political Weekly, vol. 35, no. 17, pp. 1477-1490. Kelegama, S 1999, 'Economic costs of conflict in Sri Lanka', in RI Rotberg (eds), Creating peace in Sri Lanka: civil war and reconciliation, Brookings Institution Press, Washington, DC, pp. 71-87. Kelley, DJ, Singer, S & Herrington, MD 2012, 'The Global Entrepreneurship Monitor', 2011 Global Report, Babson College, London. Kerr, WR & Nanda, R 2011a, Financing constraints and entrepreneurship, in DB Audretsch, O Falck, S Heblich and A Lederer (eds), Handbook of Research on Innovation and Entrepreneurship, Cheltenham: Edward Elgar, pp. 88-103. Khan, M & Ferdous, T 2012, 'Comparing the estimation methods of OLS, LMS and LAD by proposed model selection criteria for contaminated data', International Journal 202

of Advanced Scientific Engineering and Technological Research, vol. 1, no. 4, pp. 9-18. Khandker, SR 1998, Fighting poverty with microcredit: experience in Bangladesh, Oxford University Press, New York. Kihlstrom, RE & Laffont, J-J 1979, 'A general equilibrium entrepreneurial theory of firm formation based on risk aversion', The Journal of Political Economy, vol. 87, no. 4, pp. 719-748. Kiiru, J ‘Microfinance, entrepreneurship and rural development: empirical evidence from Makueni district, Kenya’, Available at http://www.csae.ox.ac.uk/conferences/2007. Kilby, P 1983, 'The role of alien entrepreneurs in economic development: an entrepreneurial problem', The American Economic Review, vol. 73, no. 2, pp. 107-111. Kirzner, IM 1973, Competition and entrepreneurship, University of Chicago press, Chicago. ——— 1985, Discovery and the capitalist process, University of Chicago Press Chicago. Klapper, L, Laeven, L & Rajan, R 2006, 'Entry regulation as a barrier to entrepreneurship', Journal of Financial Economics, vol. 82, no. 3, pp. 591-629. Klyver, K, Nielsen, SL & Evald, MR 2013, 'Women's self-employment: an act of institutional (dis)integration? a multilevel, cross-country study', Journal of Business Venturing, vol. 28, no. 4, pp. 474-488. Knight, FH 1921, 'Risk, uncertainty and proﬁt', Houghton Mifflin, Boston. Knight, M, Loayza, N & Villanueva, D 1996, 'The peace dividend: Military spending cuts and economic growth', Staff Papers - International Monetary Fund, vol. 43, no. 1, pp. 1-37. Kodithuwakku, SS & Rosa, P 2002, 'The entrepreneurial process and economic success in a constrained environment', Journal of Business Venturing, vol. 17, no. 5, pp. 431-465. Koellinger, PD & Thurik, AR 2012, 'Entrepreneurship and the business cycle', Review of Economics and Statistics, vol. 94, no. 4, pp. 1143-1156. Koster, S & Rai, SK 2008, 'Entrepreneurship and economic development in a developing country: a case study of India', Journal of entrepreneurship, vol. 17, no. 2, pp. 117-137. Kraaijenbrink, J, Spender, J-C & Groen, AJ 2010, 'The Resource-Based View: a review and assessment of its critiques', Journal of Management, vol. 36, no. 1, pp. 349- 372. Krasniqi, BA 2009, 'Personal, household and business environmental determinants of entrepreneurship', Journal of Small Business and Enterprise Development, vol. 16, no. 1, pp. 146-166. ——— 2012, 'Building an expanded small firm growth model in a transitional economy: evidence on fast growing firms', Journal of East-West Business, vol. 18, no. 3, pp. 231-273. Kreutz, J 2010, 'How and when armed conflicts end: introducing the UCDP Conflict termination dataset', Journal of Peace Research, vol. 47, no. 2, pp. 243-250. ——— 2012, ‘Dismantling the conflict trap: Essays on civil war resolution and relapse’, PhD thesis, Uppsala University. Krishnamurty, K & Shome, S 2008, 'Economics of arms reduction and peace dividend: a survey', Indian Economic Journal, vol. 56, no. 1, pp. 36-69. La Porta, R, Lopez-de-Silanes, F, Shleifer, A & Vishny, R 1999, 'The quality of government', Journal of Law, Economics, and Organization, vol. 15, no. 1, pp. 222-279. Lahimer, N, Dash, S & Zaiter, M 2013, 'Does microfinance promote entrepreneurship and innovation? a macro analysis', African Journal of Science, Technology, Innovation and Development, vol. 5, no. 1, pp. 19-29.

203

Lal, D 2006, Reviving the invisible hand: the case for classical liberalism in the twenty- first century, Princeton University Press, Princeton. Lazear, EP 2004, 'Balanced skills and entrepreneurship', American Economic Review, vol. 94, no. 2, pp. 208-211. Lazear, EP 2005, 'Entrepreneurship', Journal of Labor Economics, vol. 23, no. 4, pp. 649-680. Lee, DY & Tsang, EW 2001, 'The effects of entrepreneurial personality, background and network activities on venture growth', Journal of Management Studies, vol. 38, no. 4, pp. 583-602. Leitch, C, Hill, F & Neergaard, H 2010, 'Entrepreneurial and business growth and the quest for a “comprehensive theory”: tilting at windmills?', Entrepreneurship Theory and Practice, vol. 34, no. 2, pp. 249-260. Levesque, M & Shepherd, DA 2004, 'Entrepreneurs' choice of entry strategy in emerging and developed markets', Journal of Business Venturing, vol. 19, no. 1, pp. 29-54. Lewis, AW 1954, 'Economic development with unlimted supplies of labour', The Manchester School of Economics and Social Studies, vol. 22, no. 2, pp.139-191. Licklider, R 1995, 'The consequences of negotiated settlements in civil wars, 1945-1993', The American Political Science Review, vol. 89, no. 3, pp. 681-690. Licklider, R 2009, 'Civil war outcomes', in MI Midlarsky (eds), Handbook of War Studies III: The Intrastate Dimension, The University Michigan Press, Ann Arbor, pp. 193- 226. Lilja, J & Hultman, L 2011, 'Intraethnic dominance and control: Violence against co- ethnics in the early Sri Lankan civil war', Security Studies, vol. 20, no. 2, pp. 171- 197. Lin, Z, Picot, G & Compton, J 2000, 'The entry and exit dynamics of self-employment in Canada', Small Business Economics, vol. 15, no. 2, pp. 105-125. Liu, J-T, Tsou, M-W & Hammitt, JK 1999, 'Do small plants grow faster? evidence from the Taiwan electronics industry', Economics Letters, vol. 65, no. 1, pp. 121-129. Loayza, NV 1996, 'The economics of the informal sector: a simple model and some empirical evidence from Latin America', Carnegie-Rochester Conference Series on Public Policy, vol. 45, pp. 129-162. Lotti, F, Santarelli, E & Vivarelli, M 2003, 'Does Gibrat's Law hold among young, small firms?', Journal of Evolutionary Economics, vol. 13, no. 3, pp. 213-235. Lu, J & Tao, Z 2010, 'Determinants of entrepreneurial activities in China', Journal of Business Venturing, vol. 25, no. 3, pp. 261-273. Lucas, RE 1978, 'On the size distribution of business firms', The Bell Journal of Economics, vol. 98, no. 2, pp. 508-23. Luttwak, EN 1999, 'Give war a chance', Foreign Affairs, vol. 78, no. 4, pp. 36-44. MacDougal, LT 2010, 'How production firms adapt to war: a case of Liberia', UNI-WIDER working paper no. 2010/69. Malchow-Moller, N, Schjerning, B & Sorensen, A 2011, 'Entrepreneurship, job creation and wage growth', Small Business Economics, vol. 36, no. 1, pp. 15-32. Maloney, WF 2004, 'Informality revisited', World Development, vol. 32, no. 7, pp. 1159- 1178. Marris, R 1963, 'A model of the “managerial” enterprise', The Quarterly Journal of Economics, vol. 77, no. 2, pp. 185-209. ——— 1964, The economic theory of managerial capitalism, Vol. 258, Macmillan, London. Mason, CM 1989, 'Explaining recent trends in new firm formation in the UK: some evidence from South Hampshire', Regional Studies, vol. 23, no. 4, pp. 331-346. Mavoothu, D 2009, 'A literature review on the benefits of ethnic and minority entrepreneurship', Journal of Asia Entrepreneurship and Sustainability, vol. 2, no. 2, pp. 46-53.

204

McPherson, MA 1991, 'Micro and small-scale enterprises in Zimbabwe: results of a country-wide survey', GEMINI Working Paper No. 25, Available at http://pdf.usaid.gov/pdf_docs/pnabk267.pdf. ——— 1996, 'Growth of micro and small enterprises in Southern Africa', Journal of Development Economics, vol. 48, no. 2, pp. 253-277. Mead, DC & Liedholm, C 1998, 'The dynamics of micro and small enterprises in developing countries', World Development, vol. 26, no. 1, pp. 61-74. Michelacci, C 2003, 'Low returns in R&D due to the lack of entrepreneurial skills', The Economic Journal, vol. 113, no. 484, pp. 207-225. Miguel, E & Roland, G 2011, 'The long-run impact of bombing Vietnam', Journal of Development Economics, vol. 96, no. 1, pp. 1-15. Miguel, E, Satyanath, S & Sergenti, E 2004, 'Economic shocks and civil conflict: an instrumental variables approach', The Journal of Political Economy, vol. 112, no. 4, pp. 725-753. Millan, JM, Congregado, E & Roman, C 2012, 'Determinants of self-employment survival in Europe', Small Business Economics, vol. 38, no. 2, pp. 231-258. Millan, JM, Hessels, J, Thurik, AR & Aguado, R 2013, 'Determinants of job satisfaction: a European comparison of self-employed and paid employees', Small Business Economics, vol. 40, no. 3, pp. 651-670. Miller, TL, Grimes, MG, McMullen, JS & Vogus, TJ 2012, 'Venturing for others with heart and head: how compassion encourages social entrepreneurship', Academy of Management Review, vol. 37, no. 4, pp. 616-640. Minniti, M & Naude, WA 2010, 'What do we know about the patterns and determinants of female entrepreneurship across countries?', European Journal of Development Research, vol. 22, no. 3, pp. 277-293. MOD (Ministry of Defence) 2011, Humanitarian and Factual Analysis, Ministry of Defence, Colombo, Sri Lanka. MOFP (Ministry of Finance and Planning) 2013, Annual Report 2013, Ministry of Finance and Planning, Colombo, Sri Lanka. Mokyr, J & Joel, M 1992, The Lever of Riches Technological Creativity and Economic Progress, Oxford University Press, New York. Montalvo, JG & Reynal-Querol, M 2007, 'Fighting against Malaria: Prevent wars while waiting for the “miraculous” vaccine', The Review of Economics and Statistics, vol. 89, no. 1, pp. 165-177. ——— 2010, 'Ethnic polarization and the duration of civil wars', Economics of Governance, vol. 11, no. 2, pp. 123-143. Moore, CS & Mueller, RE 2002, 'The transition from paid to self-employment in Canada: the importance of push factors', Applied Economics, vol. 34, no. 6, pp. 791-801. Moore, M 1994, '‘Guided democracy’in Sri Lanka: the electoral dimension', Journal of Commonwealth & Comparative Politics, vol. 32, no. 1, pp. 1-30. Morales-Gualdrón, ST & Roig, S 2005, 'The new venture decision: an analysis based on the GEM project database', The International Entrepreneurship and Management Journal, vol. 1, no. 4, pp. 479-499. Mueller, DC 1969, 'A theory of conglomerate mergers', The Quarterly Journal of Economics, vol. 83, no. 4, pp. 643-659. Murdoch, JC, Pi, CR & Sandler, T 1997, 'The impact of defense and non‐defense public spending on growth in Asia and Latin America', Defence and Peace Economics, vol. 8, no. 2, pp. 205-224. Murdoch, JC & Sandler, T 2002, 'Economic growth, civil wars, and spatial spillovers', Journal of Conflict Resolution, vol. 46, no. 1, pp. 91-110. ——— 2004, 'Civil wars and economic growth: Spatial dispersion', American Journal of Political Science, vol. 48, no. 1, pp. 138-151. Murphy, KM, Shleifer, A & Vishny, RW 1991, 'The allocation of talent: implications for growth', The Quarterly Journal of Economics, vol. 106, no. 2, pp. 503-530.

205

Nabi, G, Walmsley, A & Holden, R 2013, 'Pushed or pulled? exploring the factors underpinning graduate start-ups and non-start-ups', Journal of Education and Work, Epublication ahead of print, 14 June 2013, DOI:10.1080/13639080.2013.805189. Nagler, P & Naude, WA ‘Performance and survival of non-farm entrepreneurship in rural Africa: evidence from the LSMS-ISA surveys’, Paper prepared for the Annual Bank Conference on Harnessing Africa's Growth for Faster Poverty Reduction, Paris, France, 23 June 2014. Naranpanawa, A, Bandara, JS & Selvanathan, S 2011, 'Trade and poverty nexus: a case study of Sri Lanka', Journal of Policy Modeling, vol. 33, no. 2, pp. 328-346. Naude, WA 2010, 'Entrepreneurship, developing countries, and development economics: new approaches and insights', Small Business Economics, vol. 34, no. 1, pp. 1-12. ——— 2011a, Entrepreneurship and economic development, Palgrave Macmillan Basingstoke, New York. ——— 2011b, 'Entrepreneurship is not a binding constraint on growth and development in the poorest countries', World Development, vol. 39, no. 1, pp. 33-44. ——— 2013, 'Entrepreneurship and economic development: theory, evidence and policy', IZA Discussion Paper No. 7507, Available at http://www.econstor.eu/handle/10419/80532. Naude, WA, Amoros, J & Cristi, O 2014, '“Surfeiting, the appetite may sicken”: entrepreneurship and happiness', Small Business Economics, vol. 42, no. 3, pp. 523-540. Naude, WA & Rossouw, S 2010, 'Early international entrepreneurship in China: extent and determinants', Journal of International Entrepreneurship, vol. 8, no. 1, pp. 87-111. Nelson, RR & Pack, H 1999, 'The Asian miracle and modern growth theory', The Economic Journal, vol. 109, no. 457, pp. 416-436. Newman, AF 2007, 'Risk-bearing and entrepreneurship', Journal of Economic Theory, vol. 137, no. 1, pp. 11-26. Nichter, S & Goldmark, L 2009, 'Small firm growth in developing countries', World Development, vol. 37, no. 9, pp. 1453-1464. Nillesen, E & Verwimp, P 2010, 'A phoenix in flames? portfolio choice and violence in civil war in rural Burundi', UNU-WIDER Working Paper no. 2010/44, Available at http://www.econstor.eu/handle/10419/53984. Nocke, V 2006, 'A gap for me: entrepreneurs and entry', Journal of the European Economic Association, vol. 4, no. 5, pp. 929-956. North, DC 1990, Institutions, institutional change and economic performance, Cambridge university press, Cambridge, New York. North, DC, Wallis, JJ, Webb, SB & Weingast, BR 2013, In the shadow of violence politics, economics, and the problems of development, Cambridge University Press, Cambridge, New York. North, DC, Wallis, JJ & Weingast, BR 2009, Violence and social orders: a conceptual framework for interpreting recorded human history, Cambridge University Press Cambridge, New York. Nunn, N & Qian, N 2012, 'Aiding conflict: the impact of US food aid on civil war', National Bureau of Economic Research Working paper 17794, Available at http://www.nber.org/papers/w17794. ——— 2014, ‘US food aid and civil conflict’, The American Economic Review, vol. 104, no. 6, pp. 1630-1666. Nurjannah, I, Mills, J, Park, T & Usher, K 2014, 'Conducting a grounded theory study in a language other than English: procedures for ensuring the integrity of translation', Sage Open, vol. 4, no. 1, pp. 1-10. O’Brien, RM 2007, 'A caution regarding rules of thumb for variance inflation factors', Quality & Quantity, vol. 41, no. 5, pp. 673-690. 206

O’Farrell, PN & Hitchens, D 1988, 'Alternative theories of small-firm growth: a critical review', Environment and Planning, vol. 20, no. 10, pp. 1365-1383. Obeng, BA, Robson, P & Haugh, H 2012, 'Strategic entrepreneurship and small firm growth in Ghana', International Small Business Journal, vol. Epublication ahead of print, 24 December 2012, DOI:10.1177/0266242612463946. Orjuela, C 2003, 'Building peace in Sri Lanka: a role for civil society?', Journal of Peace Research, vol. 40, no. 2, pp. 195-212. Osmani, SR 1994, 'Is there a conflict between growth and welfarism? the significance of the Sri Lanka debate', Development and Change, vol. 25, no. 2, pp. 387-421. Oxenfeldt, AR 1943, New firms and free enterprise: pre-war and post-war aspects, American Council on Public Affairs, Washington, DC. Parker, SC 2004, The economics of self-employment and entrepreneurship, Cambridge University Press, New York. Parker, SC 2009, The economics of entrepreneurship, Cambridge University Press, Cambridge, New York. Parker, SC & Rougier, JC 2007, 'The retirement behaviour of the self-employed in Britain', Applied Economics, vol. 39, no. 6, pp. 697-713. Paulson, AL & Townsend, R 2004, 'Entrepreneurship and financial constraints in Thailand', Journal of Corporate Finance, vol. 10, no. 2, pp. 229-262. Penrose, E 1959, The theory of the growth of the firm, Blacewell, Oxford. Peretto, PF 1999, 'Industrial development, technological change, and long-run growth', Journal of Development Economics, vol. 59, no. 2, pp. 389-417. Piffaretti, NF 2010, 'From rent-seeking to profit-creation: private sector development and economic turnaround in fragile states', MPRA Paper no. 26558, Available at http://mpra.ub.uni-muenchen.de/26558/1/MPRA_paper_26558.pdf Pisani, MJ & Pagán, JA 2004, 'Self-employment in the era of the new economic model in Latin America: a case study from Nicaragua', Entrepreneurship & Regional Development, vol. 16, no. 4, pp. 335-350. Pissarides, F, Singer, M & Svejnar, J 2003, 'Objectives and constraints of entrepreneurs: evidence from small and medium size enterprises in Russia and Bulgaria', Journal of Comparative Economics, vol. 31, no. 3, pp. 503-531. Polman, L 2010, The crisis caravan: what's wrong with humanitarian aid?, Macmillan, New York. Portes, A 1998, 'Social capital: its origins and applications in modern sociology', Annual Review of Sociology, vol. 24, pp. 1-24. Poschke, M 2013, 'Who becomes an entrepreneur? labor market prospects and occupational choice', Journal of Economic Dynamics and Control, vol. 37, no. 3, pp. 693-710. PSI (Provincial Statistical Information) 2012, Provincial Statistical Information, Northern Provincial Council, Provincial Planning Secretariat, Northern Province. Quatraro, F & Vivarelli, M 2013, Drivers of entrepreneurship and post-entry performance of newborn firms in developing countries, IZA Discussion paper no. 7436, Available at http://www.econstor.eu/handle/10419/80699. Rada, C 2007, 'Stagnation or transformation of a dual economy through endogenous productivity growth', Cambridge Journal of Economics, vol. 31, no. 5, pp. 711- 740. Rajan, RG & Zingales, L 1998, 'Financial dependence and growth', The American Economic Review, vol. 88, no. 3, pp. 559-586. Rajasingham-Senanayake, D 2005, 'Sri Lanka and the violence of reconstruction', Development, vol. 48, no. 3, pp. 111-120. Ram, M 1994, 'Unravelling social networks in ethnic minority firms', International Small Business Journal, vol. 12, no. 3, pp. 42-53. Regan, PM 2009, Sixteen million one : understanding civil war, Paradigm Publishers, New York.

207

Reynolds, P, Bosma, N, Autio, E, Hunt, S, De Bono, N, Servais, I, Lopez-Garcia, P & Chin, N 2005, 'Global Entrepreneurship Monitor: data collection design and implementation 1998–2003', Small Business Economics, vol. 24, no. 3, pp. 205- 231. Reynolds, PD 1997, 'Who starts new firms? – preliminary explorations of firms-in- gestation', Small Business Economics, vol. 9, no. 5, pp. 449-462. Reynolds, PD, Camp, SM, Bygrave, W, Autio, E & Hay, M 2002, Global Entrepreneurship Monitor 2001 summary report, London Business School and Babson College, London Richardson, J 2005, Paradise poisoned: learning about conflict, terrorism and development from Sri Lanka's civil wars, International Center for Ethics Studies, Kandy, Sri Lanka. Richardson, JM & Samarasinghe, SWRdA 1991, 'Measuring the economic dimensions of Sri Lanka’s ethnic conflict' in SWRdA Samarasinghe and R Coughlan (eds), Economic dimensions of ethnic conflict, St. Martin's Press, New York, pp. 194- 223. Ritsila, J & Tervo, H 2002, 'Effects of unemployment on new firm formation: micro-level panel data evidence from Finland', Small Business Economics, vol. 19, no. 1, pp. 31-40. Roberts, PW, Klepper, S & Hayward, S 2011, 'Founder backgrounds and the evolution of firm size', Industrial and Corporate Change, vol. 20, no. 6, pp. 1515-1538. Robichaud, Y, LeBrasseur, R & Nagarajan, K 2010, 'Necessity and opportunity-driven entrepreneurs in Canada: an investigation into their characteristics and an appraisal of the role of gender', Journal of Applied Business and Economics, vol. 11, no. 1, pp. 59-79. Robinson, PB & Sexton, EA 1994, 'The effect of education and experience on self- employment success', Journal of Business Venturing, vol. 9, no. 2, pp. 141-156. Robson, PJ & Bennett, RJ 2000, 'SME growth: the relationship with business advice and external collaboration', Small Business Economics, vol. 15, no. 3, pp. 193-208. Robson, PJ & Obeng, BA 2008, 'The barriers to growth in Ghana', Small Business Economics, vol. 30, no. 4, pp. 385-403. Rogers, JD, Spencer, J & Uyangoda, J 1998, 'Sri Lanka: Political violence and ethnic conflict', American Psychologist, vol. 53, no. 7, pp. 771-777. Romer, PM 1986, 'Increasing returns and long-run growth', The Journal of Political Economy, vo. 94, no. 5, pp. 1002-1037. ——— 1990, 'Endogenous technological change', Journal of Political Economy, vo. 98, no. 5, pp. 71-102. Rosa, PJ, Kodithuwakku, S & Balunywa, W 2006, 'Entrepreneurial motivation in developing counttries: what does "necessity" and "opportunity" really mean?', Frontiers of Entrepreneurship Research, vol. 26, no. 20, pp. 1-15. Rotberg, RI 1999, Creating peace in Sri Lanka: civil war and reconciliation, Brookings Institution Press, Washington, DC. Rubin, J & Wagner, R 2014, 'Destroying collateral: asset security and the financing of firms', Applied Economics Letters, Epublication ahead of print, 28 October 2014, DOI:10.1080/13504851.2014.969823. Rubio, FF 1991, 'Microenterprise growth dynamics in the Dominican Republic: the ADEMI case', GEMINI Wording Paper Number 21, Washington, DC. Rupesinghe, K (eds) 2006, 'Negotiating peace in Sri Lanka: efforts, failures and lessons', Vol. 2, Research & Documentation Centre, Foundation for Co-Existence, Colombo, Sri Lanka. Rustad, SCA, Buhaug, H, Falch, A & Gates, S 2011a, 'All conflict is local: modeling sub- national variation in civil conflict risk', Conflict Management and Peace Science, vol. 28, no. 1, pp. 15-40.

208

Salaff, J, Greve, A & Ping, LXL 2002, 'Paths into the economy: Structural barriers and the job hunt for skilled PRC migrants in Canada', The International Journal of Human Resource Management, vol. 13, no. 3, pp. 450-464. Samarasinghe, SWRdA 2009, Nationalism & Ethnic Politics, vol. 15, no. 3-4, pp. 436- 461. Sambanis, N 2002, 'A review of recent advances and future directions in the quantitative literature on civil war', Defence and Peace Economics, vol. 13, no. 3, pp. 215- 243. ——— 2004, 'What is civil war?: Conceptual and empirical complexities of an operational definition', Journal of Conflict Resolution, vol. 48, no. 6, pp. 814-858. Sambanis, N & Elbadawi, IA 2000, 'External interventions and the duration of civil wars', World Bank Policy Research working Paper 2433, Washington, DC. Sanders, M & Weitzel, U 2013, 'Misallocation of entrepreneurial talent in postconflict environments', Journal of Conflict Resolution, vol. 57, no. 1, pp. 41-64. Santarelli, E & Tran, HT 2012, 'Growth of incumbent firms and entrepreneurship in Vietnam', Growth and Change, vol. 43, no. 4, pp. 638-666. Sarkees, MR & Wayman, FW 2010, Resort to war: a data guide to inter-state, extra- state, intra-state, and non-state wars 1816-2007, CQ Press, Washington ,DC. Sarvananthan, M 2003, 'Economic revival in North and East Sri Lanka: what are the impediments?', Economic and Political Weekly, vol. 38, no. 19, pp. 1844-1850. ——— 2007, 'Economy of the conflict region in Sri Lanka: from embargo to repression', Policy Studies 44, East West Centre. Washington, DC, Available at http://scholarspace.manoa.hawaii.edu/handle/10125/3498 ——— 2009, 'The path to economic and political emancipation in Sri Lanka', Conflict Trends, no. 4, pp. 13-22 ——— 2010, 'Sri Lanka: economy poised for take-off', Global: The International Briefing 4, September: 70-, Available at http://www.global-briefing.org/2010/09/economy- poised-for-take-off. ——— 2011, 'Sri Lanka: putting entrepreneurship at the heart of economic revival in the north, east, and beyond', Contemporary South Asia, vol. 19, no. 2, pp. 205-213. Schindler, K & Bruck, T 2011, 'The effects of conflict on fertility in Rwanda', World Bank Policy Research Working Paper No. WPS 5715, Wahington DC. Schumpeter, JA 1934, The theory of economic development : an inquiry into profits, capital, credit, interest, and the business cycle, Harvard University Press, Cambridge, Mass. ——— 1939, Business cycles, McGraw-Hill, New York. ——— 1950, Capitalism, Socialism and Democracy, Allen & Unwin, London. Sebigunda, EN 2013, 'How does Investment climate and business environment Impact firms’ efficiency in a post-conflict setting? Evidence from Democratic Republic of Congo', International Journal of Business and Management, vol. 8, no. 10, pp. 169-184. Segal, G, Borgia, D & Schoenfeld, J 2005, 'The motivation to become an entrepreneur', International Journal of Entrepreneurial Behaviour & Research, vol. 11, no. 1, pp. 42-57. Sen, A 1981, 'Public action and the quality of life in developing countries', Oxford Bulletin of Economics and Statistics, vol. 43, no. 4, pp. 287-319. ——— 2001, Development as freedom, Oxford University Press, Oxford. Shane, S 2000, 'Prior knowledge and the discovery of entrepreneurial opportunities', Organization Science, vol. 11, no. 4, pp. 448-469. Shane, S & Venkataraman, S 2000, 'The promise of entrepreneurship as a field of research', Academy of Management Review, vol. 25, no. 1, pp. 217-226. Shanmugam, K & Bhaduri, SN 2002, 'Size, age and firm growth in the Indian manufacturing sector', Applied Economics Letters, vol. 9, no. 9, pp. 607-613. Shastri, A 1990, 'The material basis for separatism: the Tamil Eelam movement in Sri Lanka', The Journal of Asian Studies, vol. 49, no. 1, pp. 56-77. 209

Shleifer, A & Vishny, RW 1993, 'Corruption', The Quarterly Journal of Economics, vol. 108, no. 3, pp. 599-617. Simoes, N, Moreira, SB & Crespo, N 2013, 'Individual determinants of self-employment entry–what do we really know?', MPRA Paper No. 48403, Available at http://mpra.ub.uni-muenchen.de/48403 Singer, MR 1992, 'Sri Lanka's Tamil-Sinhalese ethnic conflict: alternative solutions', Asian Survey, vol. 32, no. 8, pp. 712-722. Sleuwaegen, L & Goedhuys, M 2002, 'Growth of firms in developing countries, evidence from Cote d'Ivoire', Journal of Development Economics, vol. 68, no. 1, pp. 117- 135. Smith, RP 1980, 'Military expenditure and investment in OECD countries, 1954–1973', Journal of Comparative Economics, vol. 4, no. 1, pp. 19-32. Snodgrass, DR 1966, Ceylon: An export economy in transition, RD Irwin, Homewood. ——— 1998, 'Economic development of sri lanka: a tale of missed opportunities', in RI Rotberg (eds), Creating peace in Sri Lanka: civil war and reconciliation, Brookings Institution Press, Washington, DC. Soderbom, M & Teal, F 2001, 'Firm size and human capital as determinants of productivity and earnings', Working paper No.9, Centre for the Study of African Economies, Oxford University. Sonobe, T, Akoten, JE & Otsuka, K 2011, 'The growth process of informal enterprises in Sub-Saharan Africa: a case study of a metalworking cluster in Nairobi', Small Business Economics, vol. 36, no. 3, pp. 323-335. Sridharan, S, Maltz, E, Viswanathan, M & Gupta, S 2014, 'Transformative subsistence entrepreneurship: a study in India', Journal of Macromarketing, vol. 34, no. 4, pp. 486-504. Stedman, SJ 1997, 'Spoiler problems in peace processes', International Security, vol. 22, no. 2, pp. 5-53. Stewart, DB 1991, 'Economic growth and the defense burden in Africa and Latin America: simulations from a dynamic model', Economic Development and Cultural Change, pp. 189-207. Stiglitz, JE 2006, 'Civil strife and economic and social policies', Economics of Peace and Security Journal, vol. 1, no. 1, pp. 6-9. Stiglitz, JE & Weiss, A 1981, 'Credit rationing in markets with imperfect information', The American Economic Review, vol. 71, no. 3, pp. 393-410. Storey, DJ 1994a, Understanding the small business sector, Routledge, London, New York. ——— 1994b, 'The role of legal status in influencing bank financing and new firm growth', Applied Economics, vol. 26, no. 2, pp. 129-136. Storey, DJ 2011, 'Optimism and chance: The elephants in the entrepreneurship room', International Small Business Journal, vol. 29, no. 4, pp. 303-321. Storey, DJ & Jones, AM 1987, 'New firm formation-a labout market approach to industrial entry ', Scottish Journal of Political Economy, vol. 34, no. 1, pp. 37-51. Stuart, RW & Abetti, PA 1990, 'Impact of entrepreneurial and management experience on early performance', Journal of Business Venturing, vol. 5, no. 3, pp. 151-162. Szirmai, A, Naude, WA & Alcorta, L 2013, Pathways to industrialization in the 21st century, Oxford University Press, Oxford. Tambiah, SJ 1986, Sri Lanka : Ethnic fratricide and the dismantling of democracy, University of Chicago Press, Chicago. Tamvada, JP 2010, 'Entrepreneurship and welfare', Small Business Economics, vol. 34, no. 1, pp. 65-79. Tan, WC & Tay, RS 1995, 'Factors contributing to the growth of SMEs: the Singapore case', Journal of Enterprising Culture, vol. 3, no. 2, pp. 197-210. Tarway-Twalla, AK 2011, 'The contribution of grassroots businesses to post-conflict development in Liberia', Journal of Enterprising Communities: People and Places in the Global Economy, vol. 5, no. 1, pp. 58-67. 210

Taylor, M 2004, 'Self-employment in Britain: when, who and why', Swedish Economic Policy Review, vol. 11, no. 2, pp. 139-173. Taylor, T 1996, 'Cashing in the dividend - quietly', The World Today, vol. 52, no. 6, pp. 150-151. Themner, L & Wallensteen, P 2011, 'Armed conflict, 1946–2010', Journal of Peace Research, vol. 48, no. 4, pp. 525-536. ——— 2014, 'Armed conflicts, 1946–2013', Journal of Peace Research, vol. 51, no. 4, pp. 541-554. Tobias, JM & Boudreaux, KC 2011, 'Entrepreneurship and conflict reduction in the post- genocide Rwandan coffee industry', Journal of Small Business & Entrepreneurship, vol. 24, no. 2, pp. 217-242. Tobias, JM, Mair, J & Barbosa-Leiker, C 2013, 'Toward a theory of transformative entrepreneuring: poverty reduction and conflict resolution in Rwanda's entrepreneurial coffee sector', Journal of Business Venturing, vol. 28, no. 6, pp. 728-742. Toft, MD 2002, 'Indivisible territory, geographic concentration, and ethnic war', Security Studies, vol. 12, no. 2, pp. 82-119. ——— 2003, The geography of ethnic violence: Identity, interests, and the indivisibility of territory, Princeton University Press, Princeton, NJ. ——— 2010, 'Ending civil wars: A case for rebel victory?', International Security, vol. 34, no. 4, pp. 7-36. ——— 2014, 'Territory and war', Journal of Peace Research, vol. 51, no. 2, pp. 185-198. Toft, MD & Nathan, L 2011, 'Civil war settlements and the prospects for peace', International Security, vol. 36, no. 1, pp. 202-210. Tokila, A 2009, ‘Start-up grants and self-employment duration’, Paper presented at the Spring Meeting of Young Economists, Istanbul, Turkey, Available at http://www.iza.org/conference_files/SSch2009/tokila_a5053.pdf. Tonoyan, V, Strohmeyer, R, Habib, M & Perlitz, M 2010, 'Corruption and entrepreneurship: how formal and informal institutions shape small firm behavior in transition and mature market economies', Entrepreneurship Theory and Practice, vol. 34, no. 5, pp. 803-831. Train, KE 2009, Discrete choice methods with simulation, Cambridge University Press, Cambridge. Tybout, JR 2000, 'Manufacturing firms in developing countries: how well do they do, and why?', Journal of Economic Literature, vol. 38, no. 1, pp. 11-44. UNESCO 2011, The hidden crisis: armed conflict and education, United Nations Educational, Scientific and Cultural Organization (UNESCO), Paris, France. Uyangoda, J 2005, 'Ethnic conflict, the state and the tsunami disaster in Sri Lanka', Inter- Asia Cultural Studies, vol. 6, no. 3, pp. 341-352. ——— 2010, 'Sri Lanka in 2009: From civil war to political uncertainties', Asian Survey, vol. 50, no. 1, pp. 104-111. Van Praag, CM 2003, 'Business survival and success of young small business owners', Small Business Economics, vol. 21, no. 1, pp. 1-17. Van Praag, CM & Versloot, PH 2007, 'What is the value of entrepreneurship? a review of recent research', Small Business Economics, vol. 29, no. 4, pp. 351-382. Van Stel, A, Wennekers, S & Scholman, G 2014, 'Solo self-employed versus employer entrepreneurs: determinants and macro-economic effects in OECD countries', Eurasian Business Review, vo. 4, no. 1, pp. 107-136. Venkataraman, S 2004, 'Regional transformation through technological entrepreneurship', Journal of Business Venturing, vol. 19, no. 1, pp. 153-167. Verheul, I, Thurik, AR, Hessels, J & van der Zwan, P 2010, 'Factors influencing the entrepreneurial engagement of opportunity and necessity entrepreneurs', EIM Research Report 201011, Scales. Zoetermeer: EIM, pp. 1-24.

211

Verwimp, P 2005, 'An economic profile of peasant perpetrators of genocide: micro-level evidence from Rwanda', Journal of Development Economics, vol. 77, no. 2, pp. 297-323. Verwimp, P, Justino, P & Bruck, T 2009, 'The analysis of conflict: a micro-level perspective', Journal of Peace Research, vol. 46, no. 3, pp. 307-314. Verwimp, P & Nillesen, E 2010, 'A phoenix in flames-Portfolio choice and violence in civil war in rural Burundi', UNU-WIDER Working Paper 2010/44. Viner, J 1932, 'Cost curves and supply curves', Zeitschrift für Nationalökonomie, vol. 3, no. 1, pp. 23-46. Vivarelli, M 1991, 'The birth of new enterprises', Small Business Economics, vol. 3, no. 3, pp. 215-223. ——— 2007, Entry and post-entry performance of newborn firms, Vol. 32, Routledge, London. ——— 2012, 'Drivers of entrepreneurship and post-entry performance: microeconomic evidence from advanced and developing countries', The World Bank, Policy Research Working Paper no. 6245, Washington DC. ——— 2013, 'Is entrepreneurship necessarily good? microeconomic evidence from developed and developing countries', Industrial and Corporate Change, vol. 22, no. 6, pp. 1453-1495. Wagner, J 2005, 'Nascent necessity and opportunity entrepreneurs in Germany: Evidence from the Regional Entrepreneurship Monitor (REM)', University of Lone burg Working Paper Series in Economics no. 10. Wagner, RH 1993, 'The causes of peace', in R. licklider (eds), Stopping the killing: how civil wars end, New York University Press, New York and London, pp. 235-268. Walter, BF 1997, 'The critical barrier to civil war settlement', International Organization, vol. 51, no. 3, pp. 335-364. ——— 2011, 'Conflict relapse and the sustainability of post-conflict peace', Background paper for the World Development Report 2011 on conflict, security, and development. ——— 2014, 'Why bad governance leads to repeat civil war', Journal of Conflict Resolution, epublication ahead of print, 31 March 2014, doi: 10.1177/0022002714528006. Wang, DH-M, Yu, TH-K & Liu, H-Q 2013, 'Heterogeneous effect of high-tech industrial R&D spending on economic growth', Journal of Business Research, vol. 66, no. 10, pp. 1990-1993. Wang, H, Li, G & Jiang, G 2007, 'Robust regression shrinkage and consistent variable selection through the LAD-Lasso', Journal of Business & Economic Statistics, vol. 25, no. 3, pp. 347-355. Wang, S-W 2006, 'Determinants of new firm formation in Taiwan', Small Business Economics, vol. 27, no. 4, pp. 313-321. Warnapala, WAW 1994, Ethnic strife and politics in Sri Lanka: an investigation into demands and responses, Navrang, New Delhi, India. Warnecke, T 2013, 'Entrepreneurship and gender: an institutional perspective', Journal of Economic Issues, vol. 47, no. 2, pp. 455-464. Wayland, S 2004, 'Ethnonationalist networks and transnational opportunities: the Sri Lankan Tamil diaspora', Review of International Studies, vol. 30, no. 3, pp. 405- 426. Weidmann, NB 2009, 'Geography as motivation and opportunity: group concentration and ethnic conflict', Journal of Conflict Resolution, vol. 53, no. 4, pp. 526-543. Weidmann, NB, Rød, JK & Cederman, L-E 2010, 'Representing ethnic groups in space: a new dataset', Journal of Peace Research, vol. 47, no. 4, pp. 491-499. Weiner, M 1978, Sons of the soil : migration and ethnic conflict in India, Princeton University Press, Princeton, N.J. Wennekers, S & Thurik, AR 1999, 'Linking entrepreneurship and economic growth', Small Business Economics, vol. 13, no. 1, pp. 27-56. 212

Wennekers, S, van Stel, A, Thurik, AR & Reynolds, P 2005, 'Nascent entrepreneurship and the level of economic development', Small Business Economics, vol. 24, no. 3, pp. 293-309. Wernerfelt, B 1984, 'A resource‐based view of the firm', Strategic Management Journal, vol. 5, no. 2, pp. 171-180. Westermann-Behaylo, M 2009, 'Institutionalizing peace through commerce: engagement or divestment in South African and Sudan', Journal of Business Ethics, vol. 89, no. 4, pp. 417-434. Wickramasinghe, N 2014, 'Sri Lanka in 2013: Post-war oppressive stability', Asian Survey, vol. 54, no. 1, pp. 199-205. Wijeweera, A & Webb, MJ 2009, 'Military spending and economic growth in Sri Lanka: a time series analysis', Defence and Peace Economics, vol. 20, no. 6, pp. 499-508. ——— 2010, 'A peace dividend for Sri Lanka: the case for a return to prosperity following the end of hostilities', Global Economy Journal, vol. 10, no. 2, pp. 1-9. Wiklund, J, Patzelt, H & Shepherd, DA 2009, 'Building an integrative model of small business growth', Small Business Economics, vol. 32, no. 4, pp. 351-374. Willard, KL, Guinnane, TW & Rosen, HS 1996, 'Turning points in the civil war: views from the Greenback market', The American Economic Review, vol. 86, no. 4, pp. 1001- 1018. Williams, CC 2008, 'Beyond necessity-driven versus opportunity-driven entrepreneurship: a study of informal entrepreneurs in England, Russia and Ukraine', The International Journal of Entrepreneurship and Innovation, vol. 9, no. 3, pp. 157-165. Williams, CC & Nadin, S 2010, 'Entrepreneurship and the informal economy: an overview', Journal of Developmental Entrepreneurship, vol. 15, no. 04, pp. 361- 378. Williams, CC & Round, J 2009, 'Evaluating informal entrepreneurs' motives: evidence from Moscow', International Journal of Entrepreneurial Behaviour & Research, vol. 15, no. 1, pp. 94-107. Williams, CC & Youssef, Y 2014, 'Is informal sector entrepreneurship necessity-or opportunity-driven? some lessons from urban Brazil', Business and Management Research, vol. 3, no. 1, pp. 41-53. Williams, N & Williams, CC 2014, 'Beyond necessity versus opportunity entrepreneurship: some lessons from English deprived urban neighbourhoods', International Entrepreneurship and Management Journal, vol. 10, no. 1, pp. 23- 40. Williams, OF 2008, Peace through commerce : responsible corporate citizenship and the ideals of the United Nations global compact, University of Notre Dame Press, Notre Dame. Williamson, OE 1967, The economics of discretionary behavior: Managerial objectives in a theory of the firm, Markham Publishing Company, Illinois, Chicago. ——— 1975, Markets and hierarchies: Antitrust analysis and implications, The Free Pres, New York. Wilson, AJ 1988, The Break-up of Sri Lanka: The Sinhalese-Tamil conflict, University of Hawaii Press, Honolulu. Wilson, C & Wilson, P 2006, Make poverty business: increase profits and reduce risks by engaging with the poor, Greenleaf Publishing, Sheffiled, UK Winter, SG 2003, 'Understanding dynamic capabilities', Strategic Management Journal, vol. 24, no. 10, pp. 991-995. Wong, PK, Ho, YP & Autio, E 2005, 'Entrepreneurship, innovation and economic growth: evidence from GEM data', Small Business Economics, vol. 24, no. 3, pp. 335- 350. Wooldridge, J 2009, Introductory econometrics: a modern approach, 4th edn, Cengage Learning.

213

World Bank, 2009, World Development Report 2009: reshaping Economic Geography, The World Bank, Washington, DC. ——— 2011, World Development Report 2011: conflict, security and development, The World Bank, Washington, DC. ——— 2013, World Development Report 2013: Jobs, The World Bank, Washington, DC. Wriggins, WH 1960, Ceylon : dilemmas of a new nation, Princeton University Press, Princeton. Xu, B 1998, 'A reestimation of the Evans–Jovanovic entrepreneurial choice model', Economics Letters, vol. 58, no. 1, pp. 91-95. Yang, X, Cheng, Y & Gao, J ‘The changing motives for nascent entrepreneurship in China: using Global Entrepreneurship Monitor data’, IEEE Communications Society, pp. 5474-5478. Yasuda, T 2005, 'Firm growth, size, age and behavior in Japanese manufacturing', Small Business Economics, vol. 24, no. 1, pp. 1-15. Zissimopoulos, J, Karoly, LA & Gu, Q 2010, 'Liquidity constraints, household wealth, and self-employment', RAND Working Papers WR-725, RAND Corporation Publications Department, California. Zussman, A & Zussman, N 2006, 'Assassinations: Evaluating the effectiveness of an Israeli counterterrorism policy using stock market data', Journal of Economic Perspectives, vol. 20, no. 2, pp. 193-206. Zussman, A, Zussman, N & Nielsen, MØ 2008, 'Asset market perspectives on the Israeli–Palestinian conflict', Economica, vol. 75, no. 297, pp. 84-115.

214