The Economics of Entrepreneurship

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The Economics of Entrepreneurship

Entrepreneurship is an integral part of economic change and growth. Yet until recently it has been largely neglected by economists. In The Economics of Entrepreneurship, Simon C. Parker draws on theoretical insights and recent empirical findings to show how economics can contribute to our understanding of entrepreneurship. The book is based on an earlier work, The Economics of Self-employment and Entrepreneurship (Cambridge University Press, 2004), that has quickly become an essential reference for academics researching the economics of entrepreneurship. Written in a more accessible style, this book contains much that made this earlier work so successful and, in addition, includes improved pedagogical features and new material on the theory of the firm, spin-offs, nascent entrepreneurship, growth-enhancing knowledge spillovers and social entrepreneurship. It can be used both as a reference text for academics from a variety of disciplines and as a textbook for graduate students. s i m o n c . p a r ke r is Director of the Entrepreneurship CrossEnterprise Leadership Centre at the Richard Ivey School of Business, University of Western Ontario. He is also a fellow of the IZA Research Institute for the Study of Labour in Germany and a fellow of the Amsterdam Centre for Entrepreneurship.

The Economics of Entrepreneurship

Simon C. Parker

CAMBRIDGE UNIVERSITY PRESS

Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, São Paulo, Delhi, Dubai, Tokyo Cambridge University Press The Edinburgh Building, Cambridge CB2 8RU, UK Published in the United States of America by Cambridge University Press, New York www.cambridge.org Information on this title: www.cambridge.org/9780521899604 © Simon C. Parker 2009 This publication is in copyright. Subject to statutory exception and to the provision of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published in print format 2009

ISBN-13

978-0-521-89960-4

Hardback

ISBN-13

978-0-521-72835-5

Paperback

Cambridge University Press has no responsibility for the persistence or accuracy of urls for external or third-party internet websites referred to in this publication, and does not guarantee that any content on such websites is, or will remain, accurate or appropriate.

To Charlie, Lydia, Olivia and Julia

Contents

List of figures

1

List of tables

xvi

Preface

xvii

Introduction 1.1 What economics adds to the study of entrepreneurship 1.2 Coverage and structure of the book 1.3 Defining and measuring entrepreneurship 1.3.1 New venture creation and nascent entrepreneurs 1.3.2 Small firms 1.3.3 Self-employment/business ownership 1.3.4 Appraisal 1.4 International evidence about entrepreneurship rates in developed countries 1.5 The transition economies of Eastern Europe 1.6 Developing countries 1.7 Appendix: habitual entrepreneurs

Part I 2

page xv

Selection

Theories of entrepreneurship 2.1 ‘Early’ theories of entrepreneurship 2.2 The occupational choice model of entrepreneurship I: homogeneous agents 2.2.1 Definitions of risk aversion and risk 2.2.2 Simple static models 2.2.3 Dynamic models 2.3 The occupational choice model II: heterogeneous ability – the Lucas (1978) model 2.3.1 The Lucas model

1 2 5 6 7 10 10 15 15 19 22 24 29 31 32 36 37 38 39 41 41 vii

viii

Contents

2.4 2.5

2.6

2.7

2.8 2.9 2.10

3

2.3.2 Criticisms of the Lucas model 2.3.3 Variants and extensions of the Lucas model The occupational choice model III: heterogeneous risk attitudes – the Kihlstrom and Laffont (1979) model The very existence of an entrepreneurial option 2.5.1 Theory of the firm considerations 2.5.2 Non-profit-maximising ventures Incumbents’ characteristics: entrepreneurial spawning 2.6.1 Organisational limitations of incumbent firms 2.6.2 Agency cost theories 2.6.3 Learning theories Macroeconomic theories of entrepreneurship and growth 2.7.1 Wealth-based theories 2.7.2 Technology-based theories 2.7.3 Knowledge-based theories Multiple equilibrium models Conclusion Appendices 2.10.1 Technical definitions of risk aversion and risk 2.10.2 A simple ‘hire or outsource’ model 2.10.3 Landier’s serial entrepreneurship multiple equilibrium model 2.10.4 Parker’s human capital multiple equilibrium model

Empirical methods in entrepreneurship research 3.1 Cross-section regression models: sample selection bias and IV 3.1.1 Sample selection bias 3.1.2 Endogeneity and IV 3.2 Cross-section binary models of occupational choice 3.3 Extensions of the cross-section binary model 3.3.1 The inclusion of relative incomes 3.3.2 Multiple occupational choices 3.3.3 Multiple equation systems 3.3.4 Non-binary occupational choices 3.3.5 Heteroscedastic probit 3.4 Time-series models 3.5 Panel-data models 3.6 Entrepreneurial duration models 3.7 Appendices 3.7.1 The reduced form of the two-equation simultaneous equation model 3.7.2 Fraser and Greene’s (2006) heteroscedastic probit model

43 44 49 51 52 57 61 63 65 67 67 68 70 72 74 76 77 77 79 81 82 86 87 87 88 90 92 92 94 94 96 96 96 98 100 101 101 102

Contents

ix

4

Evidence about the determinants of entrepreneurship 4.1 Pecuniary and non-pecuniary incentives 4.1.1 Pecuniary incentives: relative earnings 4.1.2 Desire for independence and job satisfaction 4.2 Human capital 4.2.1 Age 4.2.2 Experience 4.2.3 Formal education 4.3 Social capital 4.4 Risk attitudes, over-optimism and other psychological traits 4.4.1 Risk attitudes and risk 4.4.2 Over-optimism and over-confidence 4.4.3 Other psychological trait variables 4.5 Demographic and industry characteristics 4.5.1 Marital status 4.5.2 Health issues 4.5.3 Family background 4.5.4 Industry characteristics 4.6 Macroeconomic factors 4.6.1 Technology as a determinant of entrepreneurship 4.6.2 Knowledge spillovers and growth 4.6.3 Entrepreneurship and the business cycle 4.6.4 Unemployment 4.6.5 Regional factors 4.7 Nascent entrepreneurship 4.7.1 Characteristics of nascent entrepreneurs 4.7.2 Venture development paths of nascent entrepreneurs 4.8 Dependent starts and firm characteristics 4.9 Conclusion

106 107 109 110 113 113 115 117 119 121 121 124 128 132 132 133 134 138 139 139 140 142 143 147 151 151 152 155 157

5

Ethnic entrepreneurship and immigration 5.1 Discrimination 5.1.1 Discrimination in the labour market 5.1.2 Discrimination in the capital market 5.1.3 Discrimination in the product market 5.2 Positive factors 5.2.1 Positive expected relative returns in entrepreneurship 5.2.2 Ethnic enclaves 5.2.3 Culture 5.2.4 Role models and inculcation of positive attitudes 5.3 Further evidence on determinants of ethnic differences in entrepreneurship

163 165 165 166 170 171 171 172 173 174 174

x

Contents

5.4

5.5

6

Female entrepreneurship 6.1 Some basic facts about female entrepreneurship 6.2 Family factors 6.2.1 Marriage and household production 6.2.2 The impact of children 6.3 Performance of women entrepreneurs 6.3.1 The gender earnings gap 6.3.2 Explanations of the earnings gap 6.3.3 Other performance gaps: growth and survival rates 6.4 Women and entrepreneurial finance 6.5 Conclusion

Part II 7

Immigration and entrepreneurship 5.4.1 Immigrants’ entrepreneurial propensities 5.4.2 The determinants of immigrant entrepreneurship 5.4.3 The effects of immigration on entrepreneurship Appendix 5.5.1 The Borjas (1986) decomposition

Financing

Debt finance for entrepreneurial ventures 7.1 Background and useful terminology 7.1.1 Background 7.1.2 Terminology 7.2 Theories of credit rationing and redlining 7.2.1 Type I credit rationing 7.2.2 Type II credit rationing, redlining and under-investment 7.3 Rebuttals of the credit rationing hypothesis and counter-rebuttals 7.3.1 Rebuttals 7.3.2 Counter-rebuttals 7.4 Over-investment 7.4.1 The de Meza and Webb (1987) over-investment model 7.4.2 Over-optimism 7.5 Conclusion 7.6 Appendices 7.6.1 Bernhardt’s (2000) model of Type I rationing 7.6.2 Stiglitz and Weiss’ (1981) model of Type II credit rationing 7.6.3 Bester’s (1985a) screening model

176 176 177 180 181 181 184 184 187 187 188 189 190 191 194 195 197 201 203 205 205 209 212 212 212 216 216 221 223 223 225 226 228 228 229 230

Contents

xi

8

Venture capital and other sources of finance 8.1 Venture capital and entrepreneurs 8.1.1 Organisational structure 8.1.2 Size of the entrepreneurial venture capital market 8.2 Advantages of venture capital finance for entrepreneurs 8.2.1 Value-adding activities by VCs 8.2.2 Equity finance as an optimal financial contract 8.3 Drawbacks of venture capital and equity finance for entrepreneurs 8.3.1 Factors inhibiting the use of equity finance 8.3.2 Equity rationing, funding gaps and under-investment 8.4 Informal equity finance: business angels 8.5 Other informal sources of finance 8.5.1 Family finance 8.5.2 Micro-finance schemes 8.5.3 Other non-profit-making lending organisations and schemes 8.5.4 Co-operative schemes 8.5.5 Trade credit 8.6 Conclusion

234 235 235 238 239 239 242 244 244 246 248 250 250 251 257 257 259 261

9

Wealth and entrepreneurship 9.1 The role of entrepreneurs in aggregate wealth accumulation and inequality 9.2 The ‘private equity premium puzzle’ 9.3 Entrepreneurial wealth diversification 9.4 Wealth and entrepreneurship: theories 9.4.1 The Evans and Jovanovic (1989) model 9.4.2 The Banerjee and Newman (1993) model 9.4.3 The Aghion and Bolton (1997) model 9.4.4 Newman’s (2007) moral hazard model 9.5 Wealth and entrepreneurship: evidence 9.6 Alternative interpretations of a wealth/entrepreneurship relationship 9.7 Wealth and performance in entrepreneurship 9.7.1 Effects of wealth on venture survival 9.7.2 Effects of wealth on venture investment decisions 9.7.3 Borrowing constraints and profitability 9.8 Evidence relating to Type II credit rationing 9.8.1 What does not constitute evidence of credit rationing 9.8.2 Berger and Udell’s (1992) approach 9.8.3 Other evidence 9.9 Conclusion 9.10 Appendices 9.10.1 Preferences for skewness and entrepreneurship 9.10.2 The Paulson et al. (2006) structural model

263 264 266 268 269 269 271 272 272 273 275 279 279 280 280 281 282 282 283 284 285 285 286

xii

Contents

Part III Performance

291

10

Entrepreneurship, job creation and innovation 10.1 Job creators 10.1.1 Some basic facts about entrepreneurs’ labour demand 10.1.2 Theoretical analysis 10.1.3 Evidence 10.2 Job creation by small firms 10.3 Innovation by small firms 10.3.1 Theoretical arguments 10.3.2 Evidence about innovation 10.4 Conclusion 10.5 Appendix 10.5.1 Carroll et al.’s (2000a) model of labour demand and taxation

293 293 293 294 295 297 300 301 303 305 306

11

Entrepreneurship and growth 11.1 Theories of venture growth 11.1.1 Gibrat’s Law 11.1.2 Jovanovic’s (1982) model of industry selection 11.1.3 Innovation, growth and shakeouts 11.1.4 Other theories of growth 11.2 Evidence about the growth of entrepreneurial ventures 11.2.1 Definitional and measurement issues 11.2.2 Evidence about determinants of venture growth 11.3 Entrepreneurship and economic growth 11.4 Appendix

310 311 311 312 314 315 316 317 319 324 330

12

Entrepreneurial effort 12.1 Unproductive and destructive entrepreneurship 12.1.1 Conceptual issues 12.1.2 Evidence 12.2 Work effort 12.2.1 Hours of work 12.2.2 Explaining entrepreneurs’ work hours 12.3 Ageing, retirement and entrepreneurship 12.4 Entrepreneurial learning 12.5 Appendices 12.5.1 Frank’s model of entrepreneurs’ labour supply and ageing 12.5.2 Parker and Rougier’s model of the entrepreneurial retirement decision

333 334 334 338 340 341 343 351 356 357

306

357 359

Contents

xiii

13

Entrepreneurs’ incomes and returns to human capital 13.1 Measurement issues: tax evasion and income under-reporting 13.2 Other measurement problems 13.3 Evidence relating to entrepreneurs’ relative incomes 13.4 The inequality and volatility of entrepreneurs’ incomes 13.5 Determinants of entrepreneurs’ incomes: theory and methods 13.6 Determinants of entrepreneurs’ incomes: findings 13.6.1 Entrepreneurs’ rate of return to education 13.6.2 Other explanatory variables and extensions

363 364 368 369 372 375 378 378 380

14

Survival 14.1 Closure is not necessarily failure 14.2 Survival rates and their distribution 14.3 Theoretical determinants of survival 14.3.1 The liabilities of newness and smallness 14.3.2 Other factors 14.4 Empirical determinants of survival 14.5 Conclusion

385 386 388 389 389 391 391 396

Part IV Public policy

401

15

Principles of entrepreneurship policy 15.1 The case for and against pro-entrepreneurship public policies 15.1.1 The case for pro-entrepreneurship policies 15.1.2 The case against pro-entrepreneurship policies 15.2 Principles of entrepreneurship policy design 15.3 Entrepreneurship policy evaluation

403

Finance and innovation policies 16.1 Loan guarantee schemes 16.1.1 Organisation of LGSs 16.1.2 Theoretical perspectives on LGSs 16.1.3 Evaluation of LGSs 16.2 Other credit market interventions 16.3 Policies to promote equity finance 16.3.1 Regulatory policies 16.3.2 Taxation policies 16.4 Innovation policy and entrepreneurship 16.5 Appendix: the Keuschnigg-Nielsen (2004a, 2006) double moral hazard model

412 412 412 413 415 417 418 419 421 423

16

405 405 407 408 409

425

xiv

17

Contents

Taxation, regulation and other policies 17.1 Taxation 17.1.1 Income and payroll taxes: theoretical issues 17.1.2 Income and payroll taxes: empirical evidence 17.1.3 Corporation tax 17.1.4 Wealth and inheritance taxes 17.2 Labour and product market schemes 17.2.1 Employment assistance schemes 17.2.2 Procurement and affirmative action schemes 17.2.3 Business advice and assistance 17.3 Regulation 17.3.1 The regulation of entry 17.3.2 The regulation of exit (bankruptcy) 17.3.3 Property rights and corporate governance 17.3.4 The regulation of employment 17.3.5 The regulation of credit markets 17.3.6 Costs of compliance with regulations 17.3.7 Conclusions 17.4 The welfare state, trade unions, enterprise culture and instability 17.4.1 The size of the welfare state 17.4.2 Social security transfers and trade unions 17.4.3 Fostering an enterprise culture 17.4.4 Political and economic instability 17.5 Conclusion

References Index

428 428 429 433 436 437 438 438 440 440 442 442 446 448 450 451 453 454 455 455 457 457 458 459 466 541

Figures

2.1 2.2 2.3

7.1 7.2 7.3 7.4 9.1 11.1 12.1 12.2

Utility functions page 37 Occupational choice with heterogeneous entrepreneurial ability 42 Occupational choice with two occupations, entrepreneurship (EN) and paid employment (PE). (a) PE attracts the ablest entrepreneurs: x > x˜ enter PE (b) EN attracts the ablest entrepreneurs: x > x˜ enter EN (c) Multiple marginal entrepreneurs, x˜ and x˜ 2 48 The supply of and demand for loans 210 Stiglitz and Weiss’ credit rationing model. (a) Banks’ expected returns (b) The market for funds 215 Redlining 216 The use of two-term contracts to separate hidden types 218 The convex outer envelope of returns in entrepreneurship, V(B), and paid employment, R(B) 267 The lag structure of the impact of new business formation on regional employment change 326 Payoffs to entrepreneurship and rent-seeking in Murphy et al.’s (1993) model 336 Work mixing in entrepreneurship and paid employment 343

xv

Tables

1.1 1.2 1.3 1.4 2.1 4.1 4.2 4.3 4.4 4.5 9.1 10.1 10.2 11.1 11.2 12.1 12.2 14.1 14.2 16.1

xvi

International rates of nascent entrepreneurship page 9 Aggregate self-employment rates in some selected OECD countries, 1960–2000 (per cent) 16 Aggregate self-employment rates in some selected transition economies, 1980–1998/99 (per cent) 20 Aggregate self-employment rates in some selected developing countries, the 1960s to the 1990s (per cent) 23 Demand- and supply-side factors influencing the ‘make or buy’ decision 55 Summary of determinants of entrepreneurship 108 Reasons given for becoming self-employed in the UK (per cent) 109 Observations about social capital and implications for entrepreneurship 120 Self-employment rates in the British regions, 1970 and 2000 147 What happens to nascent entrepreneurs 154 Estimates of a simultaneous equation model of entrepreneurship and wealth 275 Characteristics of job creators 296 Innovation and entrepreneurship 301 Determinants of entrepreneurial venture growth 320 Useful empirical growth models 330 Average work hours in Britain by gender and occupation 342 Estimates of the determinants of entrepreneurs’ work hours 346 Summary of economic determinants of venture survival 392 Firm birth and death interactions 396 Features of LGSs in the UK, the USA, France, Germany and Canada 413

Preface

This book follows directly in the footsteps of its predecessor, The Economics of Self-employment and Entrepreneurship (referred to hereafter as Parker, 2004). That book was primarily a monograph for the specialist which could be used secondarily as a course text. By updating the post-2003 literature (to mid-2008) and covering more of the pre-2003 literature, the present book is more comprehensive than its predecessor. It has also been completely rewritten to facilitate its use as a course text for undergraduate and postgraduate courses in entrepreneurship, taught from an economics perspective. In addition, the emphasis of the present book is on entrepreneurship rather than self-employment. For all of these reasons, this book both is, and is not, a ‘second edition’. To help enhance the appeal of this book as a course text I have made two presentational changes to Parker (2004). First, I have wherever possible removed mathematical formalism from the core text. When particular formal models are deemed to be of especial interest, they are discussed in footnotes or in chapter appendices. These can be skipped entirely by readers of a non-technical persuasion. Second, I have made greater efforts to explain economics ideas, terminology and jargon more clearly than before, to help readers not principally trained in economics. Both changes are designed to enhance the accessibility of the text, which I use to teach my Economics of Entrepreneurship students on the doctoral entrepreneurship programme at the University of Louisville, KY. No doubt some unexplained jargon remains, for which I apologise. Compared with its predecessor, the book extends its coverage to discuss several important areas of literature, including social (not-for-profit) entrepreneurship; spin-offs and intrapreneurship; and nascent entrepreneurship. There is also a deeper treatment of several topics covered in Parker (2004), including venture capital, knowledge spillovers, female entrepreneurship, regulation and economic growth. Three areas which I lack the space to cover in detail in the present volume are academic entrepreneurship (including university spinoffs), family firms and entrepreneurship education. Finally, I would like to thank the Social Sciences editor at Cambridge University Press, Chris Harrison, and his colleagues for ongoing support of this ‘economics of entrepreneurship’ project and help in bringing it to fruition. xvii

1

Introduction

The entrepreneur is at the same time one of the most intriguing and one of the most elusive characters…in economic analysis. He has long been recognised as the apex of the hierarchy that determines the behaviour of the firm and thereby bears a heavy responsibility for the vitality of the free enterprise society. (Baumol, 1968, p. 64)

Entrepreneurship is increasingly in the news. Governments all over the world extol its benefits and implement policies designed to promote it. There are several reasons for this interest in, and enthusiasm for, entrepreneurship. Owner-managers of small enterprises run the majority of businesses in most countries. These enterprises are credited with providing specialised goods and services that are ignored by the largest firms. Entrepreneurs generate productivity gains from dynamic entry and exit, which spurs economic development. This comes about either by selection or by competition. Selection involves replacing incumbents who are inefficient or do not satisfy consumer demand by entrants who are more efficient or better meet demand by offering new or better-quality products. Entrants intensify competition and thereby discipline incumbents to provide cheaper or more innovative goods. The most dynamic entrepreneurs pioneer new markets for innovative products, creating jobs and enhancing economic growth. As a striking example, four of the largest US companies by market capitalisation in 1999, accounting between them for about one-eighth of US GDP (Microsoft, Dell, Cisco Systems and MCI), did not exist twenty years earlier (Jovanovic, 2001). Hence it is reasonable to expect that some of today’s new start-ups will grow to become tomorrow’s industrial giants. Even those which do not do so can create positive externalities, for example by developing supply chains that help attract inward investment, or by creating wealth and facilitating social mobility. It is sometimes also claimed that the decentralisation of economic production into a large number of small firms is good for society and democracy, promoting the ethos of a self-reliant and hardy ‘entrepreneurial spirit’. As the wellspring of industrial dynamism, wealth creation and innovation, entrepreneurship is an integral part of economic change and growth. Yet entrepreneurship has only recently come to be regarded as a field. A complete view of it recognises its multi-disciplinary academic underpinnings, drawing from economics, finance, business studies, sociology, psychology and other subjects. This heterogeneous provenance 1

2

Introduction

reflects the multidimensional nature of entrepreneurship, which partly contributes to the elusiveness of the entrepreneur alluded to by William Baumol. 1.1 What economics adds to the study of entrepreneurship

Today, the economics of entrepreneurship is a thriving research field. Although the ‘business studies’ approach to entrepreneurship research remains dominant in terms of field journals, conference activity and academic posts – in other words, in most practical respects – the economics of entrepreneurship literature continues to develop rapidly, generating numerous insights about how entrepreneurship interacts with the economy. However, many non-economists continue to ignore the economics of entrepreneurship literature, while a minority actively denigrates economics, sometimes claiming that the discipline itself is intrinsically unsuited to the study of entrepreneurship. One of the objectives of this book is to rebut the anti-economics arguments, by demonstrating constructively what the subject can and does say about entrepreneurship. It is the author’s belief that anti-economics arguments mainly reflect ignorance about the current state of economics. Before going on to define what the economics of entrepreneurship is, and what it brings to the analysis of entrepreneurship as an academic field, it is worth briefly trying to understand these claims, which can be summarised as follows: 1. Economics (it is alleged) assumes that agents know prices and goods and, automatonlike, optimise resource usage via mathematical rules. But entrepreneurs cannot optimise because they cannot know the prices of goods or services which do not yet exist; they must therefore use heuristics and exercise idiosyncratic judgement. 2. Economics entails the analysis of equilibrium. But the essence of entrepreneurship is that entrepreneurs recognise disequilibrium opportunities and exploit them, destroying the status quo in a ceaseless progression of disequilibrium states. 3. Economics assumes perfect information and competition, so in equilibrium profits are eliminated. But without a profit motive there can be no entrepreneurship; and in the real world imperfect information and imperfect competition prevail so even small entrepreneurial ventures can possess some market power. 4. Economists have chosen not to write the entrepreneur into their models. For this reason the entrepreneur is absent from economics textbooks. But the entrepreneur is central to economic growth so neoclassical growth theory is at best incomplete and at worst misleading. I will take these criticisms point by point. The first one is based on a simple misunderstanding about optimisation in economics. For example, Bayesian methods are ideally suited to modelling situations of entrepreneurial uncertainty (Alvarez and Parker, 2009); and economists have a long tradition of assuming that agents act on the basis of subjective probabilities about the future, even if subjective probabilities differ from objective probabilities. That is, it is recognised that individual agents can and do make mistakes. Although the ‘rational expectations hypothesis’ does not allow agents to

Introduction

3

make systematic errors, this is far from being the only school of thought in modern economics. Economic models are increasingly beginning to incorporate persistent overoptimism, bounded rationality and other cognitive biases into individual behaviours and choices (Minniti and Lévesque, 2008). So nowadays the criticism of hyper-rationality in economics is wide of the mark. The second criticism seems to be based on another misunderstanding, this time about the notion of equilibrium in economics. ‘Equilibrium’ describes a resting point which is eventually obtained after some change occurs. Even if the economy never arrives at a predicted equilibrium, because it is disrupted by another event, it is still helpful to predict the eventual likely outcomes of a given change. As it happens, many economic models now analyse the behaviour of individuals in environments which undergo continual unpredictable change, and deal with equilibrium as a dynamic concept (captured, for example, by the notion of an ‘equilibrium growth path’). A further example relates to innovation, where some economists model the dynamic processes that generate new knowledge and opportunities, rather than taking them to be exogenous as in much of the business studies entrepreneurship literature (King and Levine, 1993; Audretsch, 2003). It is surprising to see some critics continuing to make the third point, which is now hopelessly out of date. As numerous examples in this book attest, imperfect information and imperfect competition play a central role in modern economic analysis, including applications to entrepreneurship. It is essential not to erroneously conflate ‘normal’ and ‘supernormal’ profits. The former is the return needed to keep factors of production employed in their present use. It is not competed away to zero. Economists merely claim that when markets are competitive or contestable, ‘supernormal profits’ (i.e. profits in excess of normal profits) will eventually be competed away. It is a mistake to claim that this precludes exploitation of temporary or even ongoing entrepreneurial opportunities. Indeed, economists would say that one manifestation of entrepreneurship is precisely entry by new firms to compete for profits with incumbents. Other manifestations and definitions of entrepreneurship are also possible, including those based on innovation, managing uncertainty and owning a business; these come well within economists’ambit too (Bianchi and Henrekson, 2005). The first part of the fourth criticism states that economists do not write entrepreneurs into their models, firms or the broader economy. That might have been true when Baumol wrote that ‘the theoretical firm is entrepreneur-less – the Prince of Denmark has been expunged from the discussion of Hamlet’ (1968, p. 66); but with the development of new theories, perspectives and subject areas such as agency theory, personnel economics and game theoretic work on innovation, this is no longer the case. As this book will hopefully show, numerous economics journal articles now treat the entrepreneur as a distinctive economic actor, albeit (to use the terminology of Baumol, 1993b) usually as a ‘firm-organising’ rather than an ‘innovating’ entrepreneur. Baumol (1993b) points out that it is the innovating entrepreneur, and not the firm organiser entrepreneur, whose role is inherently difficult to describe and analyse systematically, and who is really absent from conventional economic models of the firm. As he wrote at an earlier time, ‘one

4

Introduction

hears of no…brilliant innovations, of no charisma or any of the other stuff of which entrepreneurship is made’ (Baumol, 1968, p. 67). But this entrepreneur is doomed to be absent from all scientific theories, economic or otherwise. Criticising economics for this state of affairs is hardly fair. The second part of the fourth criticism has greater substance, however. The terms ‘entrepreneur’ and ‘entrepreneurship’ are still missing from leading economics textbooks in microeconomics, macroeconomics and industrial organisation (Rosen, 1997; Kent and Rushing, 1999). In my opinion these are unfortunate and unnecessary omissions and this criticism is a fair one. In short, and allowing that economists can do more to incorporate the entrepreneur into mainstream textbooks, it is time for the anti-economists to stop caricaturing economics as a subject locked in a 1970s neoclassical time-warp, where economies are characterised by perfect information, perfect foresight, perfect markets and perfect price flexibility. They should instead start to consider what economics can add to our understanding of entrepreneurship. In essence, the economics of entrepreneurship analyses how economic incentives affect entrepreneurial behaviour, and how entrepreneurial behaviour in turn affects the broader economy.1 This is clearly a broad definition and covers a wide variety of issues, as the various chapters of this book amply testify. Consider by way of example a manager’s decision problem of whether to retain employees who develop new innovations within the firm, in order to foster ‘intrapreneurship’, or whether to let them quit and start up in independent entrepreneurship. In this problem, economic incentives are clearly a key issue. Of course, incentives also shape behaviour more generally. Individuals do not have to become entrepreneurs, but choose to do so when the incentives (not necessarily financial) are sufficiently favourable. Indeed, the whole idea of public policy towards entrepreneurship is premised on the notion that government interventions (through taxation, regulation, grants, etc.) affect entrepreneurs’ incentives and thereby their behaviour. One could in fact go further and argue that one cannot fully understand issues like female entrepreneurship, ethnic minority and immigrant entrepreneurship, or entrepreneurial effort without some knowledge of labour economics. Labour economics is also at the heart of participation choices and work participation decisions, as is the microeconomics of incentives. The latter in turn underpin much of the contemporary cutting-edge research on entrepreneurial finance, both debt finance and venture capital. And for their part, these issues cannot be understood without some knowledge of financial economics. Likewise, public economics informs the analysis of public policy towards entrepreneurship. Finally, one can also point out some limitations in some non-economics approaches to entrepreneurship which the economics approach appears well placed to avoid. One is a lack of predictive theory, and ad hoc (or post hoc) hypothesis generation. For instance, it is not much of a theory which merely states that people lacking entrepreneurial intentions are less likely than others to become entrepreneurs; or that individuals who lack access to resources needed to start a business are less likely to actually start a business.

Introduction

5

This type of obvious reasoning, which is deemed uninteresting and therefore unpublishable by mainstream economics journals, can nevertheless be found frequently in other approaches to entrepreneurship. Nor does the economics approach to entrepreneurship content itself with merely listing descriptive and anecdotal evidence which lacks conceptual or causal interpretation and which is not obviously generalisable. By applying its armoury of sophisticated theoretical and econometric methods, the economics of entrepreneurship seeks to extend the understanding of all entrepreneurship scholars, whether they are economists or not. My hope is that this book will help to convince the sceptical reader of this potential. 1.2

Coverage and structure of the book

This book builds on my previous volume (Parker, 2004) by continuing to organise and assess the current state of the branching, acquisitive and rapidly growing literature on the economics of entrepreneurship. The book is intended to serve as a comprehensive overview and guide to researchers and students of entrepreneurship in a variety of disciplines, not just in economics. I have tried to make the text more accessible by providing verbal explanations of analysis in the text, and relegating technical details wherever possible to chapter appendices. This way, the non-mathematically inclined reader can skip the maths altogether without missing any of the major points and insights. For brevity and focus, some topics will be mentioned only in passing and will not be explored in depth. These include academic entrepreneurship (see Rothaermel et al., 2007, for a review); family firms (see Anderson and Reeb, 2003); and entrepreneurship education (see Lee and Wong, 2006). Some alternative approaches will also receive only fleeting attention, including organisational, strategic and managerial decision-making by entrepreneurs; ‘organisational ecology’ and ‘evolutionary economics’ approaches to entrepreneurship; and practical advice (‘how to’ information) to entrepreneurs. Nor will I provide descriptive case studies of individual entrepreneurs, small firms or the industries in which they operate. These topics are ably covered in numerous business studies texts, and will not be repeated here. The book is organised in four parts. The first part deals with selection into entrepreneurship, analysing which people become entrepreneurs and why. Chapter 2 discusses prominent theories in the economics of entrepreneurship, while chapter 3 describes some useful econometric techniques commonly employed in applied research. Chapter 4 provides evidence derived from testing theoretical constructs articulated in chapter 2, using the methods outlined in chapter 3. This evidence base explains what drives some people to engage in entrepreneurship. Chapters 5 and 6 then focus on entrepreneurial selection for some particular groups of interest: ethnic minorities, immigrants and women. The second part of the book analyses the financing of entrepreneurial ventures. There are two major chapters here. Chapter 7 deals with debt (bank) finance, while chapter 8 treats venture capital and other ‘informal’ sources of finance. The third part

6

Introduction

of the book then considers several aspects of entrepreneurial inputs and performance, from the standpoint of individual entrepreneurs and the broader economy. Chapter 9 explores one important aspect of performance, namely wealth accumulation. Chapters 10 and 11 examine several more: job creation, innovation, venture growth and the relationship between entrepreneurship and aggregate economic growth. Chapter 12 analyses the essential venture input of entrepreneurial effort. Chapter 13 discusses entrepreneurial incomes and the returns to human capital, while chapter 14 considers a different performance outcome, namely venture survival. The final part of the book deals with public policy. There are three chapters here. Chapter 15 sets out some principles of entrepreneurship policy. Chapter 16 analyses finance and innovation policies towards entrepreneurship, while chapter 17 concludes the book with a discussion of taxation, labour and product market policies towards entrepreneurship; regulation as it impacts on entrepreneurs; and other important macro issues including the role of the welfare state, trade unions, the role of ‘enterprise culture’ and macroeconomic instability.

1.3

Defining and measuring entrepreneurship

The first and most pressing task is to define entrepreneurs and entrepreneurship. It should be said immediately that there is no general agreement about the meaning of these terms. Some researchers identify entrepreneurs with residual claimants such as small business owners or the self-employed; others restrict their definition of entrepreneurs to business owners who employ other workers. Others again take a Schumpeterian standpoint and argue that entrepreneurship entails the introduction of new paradigmshifting innovations rather than a particular occupation. A popular definition of an entrepreneur in business studies is someone who ‘perceives an opportunity, and creates an organisation to pursue it’ (Bygrave and Hofer, 1991, p. 14). This definition implies that new venture creation is the essence of entrepreneurship. Part of the divide between the economics and business studies approaches to entrepreneurship is attributable to the different definitions of entrepreneurship they utilise. Economists are often content to utilise business owners (in industrial organisation and macroeconomics), the self-employed (in labour and microeconomics) and small firms (in industrial organisation) as working definitions. These definitions all rely implicitly on residual-claimant and risk-taking aspects of entrepreneurship, and facilitate the analysis of incentives, investments, resource allocation decisions and occupational choices. In contrast, many business studies researchers feel there is nothing entrepreneurial about merely being an owner-manager of a small business. They usually prefer to study behaviours entailed by starting a new business, and speculate about cognitive and perceptual constructs entailed with it. Economists tend to eschew this approach as overly subjective, insisting instead on inferring motives only from actual observed behaviour. This is the so-called ‘revealed preference’ principle.

Introduction

7

In empirical work, researchers of all persuasions either have to gather their own data or are obliged to use whatever measure of entrepreneurship comes to hand. The present section presents three of the most commonly used empirical measures, and discusses their advantages and drawbacks. These are new venture creation, small firms and self-employment/business ownership. The final subsection concludes with a brief appraisal.

1.3.1

New venture creation and nascent entrepreneurs

Equating entrepreneurship with opportunity recognition and new venture creation is now standard practice in the business studies approach to entrepreneurship (Shane and Venkataraman, 2000). It is operationalised empirically in the ongoing Global Entrepreneurship Monitor (GEM) data collection exercise (Reynolds et al., 2005). GEM defines as an ‘entrepreneur’ an adult who is engaged in setting up or operating a new venture which is less than forty-two months old. The index of ‘Total Entrepreneurial Activity’ (TEA) is the proportion of the population who are entrepreneurs according to this definition. For example, the 2005 GEM reports that the TEAs of most industrialised countries lie in the 5–10 per cent range. An advantage of GEM data is that definitions and measurement constructs are largely comparable across countries. Thirty-one countries participated in GEM in 2003, involving interviews of some 100,000 adults. GEM also compiles some individual-level data. However, the new venture creation conception of entrepreneurship suffers from several drawbacks. First, many new ventures are mundane, hobby businesses which generate little private or social value. These are included in TEA, despite being far from ‘entrepreneurial’ in a Schumpeterian sense. Second, by excluding businesses over forty-two months old, GEM implicitly categorises even dynamic and enterprising business owners as ‘non-entrepreneurial’. This hardly chimes with popular views about entrepreneurs. Third, focusing only on new ventures excludes growth and exit as part of the entrepreneurship phenomenon, even though many people regard growth and strategic closure (e.g. ‘harvesting’) as essential aspects of entrepreneurship.2 GEM also suffers from limited numbers of covariates and a short time-series. This has resulted in numerous cross-country studies based on as few as twenty or thirty observations. It is unclear what can be learned from such small and heterogeneous samples. Another problem is substantial year-to-year volatility in TEA as a result of excluding older firms. While annual movements of countries up and down the TEA ‘league table’ no doubt make good headlines, it is questionable whether this measure fully reflects the range of entrepreneurial activities. By failing to net out the numerous business exits which occur, the new venture creation and GEM approaches probably overstate sustained, wealth-creating entrepreneurship (Gartner and Shane, 1995). A useful distinction operationalised within GEM is the difference between ‘necessity’ and ‘opportunity’ entrepreneurs. Necessity entrepreneurs are those who face no better alternative to work than entrepreneurship, while opportunity entrepreneurs are those who pursue an entrepreneurial opportunity even though attractive alternative ways of

8

Introduction

earning a living are open to them.3 The 2001 GEM report argued that economic growth is associated with opportunity entrepreneurship while social welfare programmes affect necessity entrepreneurship (Reynolds et al., 2002). A different part of the GEM data collection effort specifically measures ‘nascent entrepreneurs’. Someone is classified as a ‘nascent entrepreneur’ (NE) if they answer ‘yes’ to each of the following questions: (i) ‘Are you, alone or with others, now trying to start a new business?’, (ii) ‘Do you expect to be owner or part owner of the new firm?’, (iii) ‘Have you been active in trying to start the new firm in the past twelve months?’, and (iv) ‘Has your start-up not yet generated a positive monthly cash flow that covers expenses and the owner-manager’s salary for more than three months?’. In addition, respondents must still be in the start-up or gestation phase of an independent firm (Gartner et al., 2004). There are two advantages of studying NEs when exploring the entry process. These are the avoidance of ‘survival’ and ‘hindsight’ biases. Survival bias arises because only about one-half of all aspiring business founders ultimately succeed in creating new organisations which eventually appear in public records (Aldrich, 1999). Firms which ultimately start up are not generally representative of all those which originally tried, and contain relatively few of the smallest and youngest start-up efforts. So inferring aspects of NEs from data sets of established firms is akin to ‘studying gamblers by exclusively investigating winners’ (Davidsson, 2006, p. 3). ‘Hindsight bias’ occurs when established entrepreneurs misreport events which occurred prior to start-up, perhaps because of memory loss or selective re-interpretation of the past. Comparing expectations with outcomes, Cassar (2007) showed that NEs are prone to substantial recall bias. This problem is avoided by interviewing NEs at the time they start up. Two major types of data set focus explicitly on NEs. Both types screen large random samples of households or individuals and use the definition of NE given above. GEM is one; the other is the Panel Study of Entrepreneurial Dynamics (PSED) (Reynolds et al., 2004; Gartner et al., 2004). The PSED originated in the USA, but versions are now available in many other countries too, as well as a new version (PSED II) in the USA. The original US PSED I identified NEs from 64,622 random telephone interviews conducted between July 1998 and January 2000. PSED II identifies NEs from 31,845 random telephone interviews conducted between October 2005 and January 2006. Both GEM and PSED have advantages and drawbacks with respect to measuring nascent entrepreneurship. GEM data on dependent and independent variables are comparable across countries while the various versions of PSED are not. On the other hand, unlike PSED, GEM lacks rich information about individual-level variables. This together with its limited sophistication of measurement makes GEM less useful than PSED for micro-level analysis (Davidsson, 2006). Arguably, both data sets are vulnerable to the charge that despite their emphasis on individual-level factors, their conceptualisations of NE and measurement instruments refer to the venture rather than the person. As many as one-fifth of NEs are starting a new venture for the second, third or nth time (Alsos and Kolvereid, 1998). Another problem is that both data sets probably underestimate entrepreneurial activities by failing to register ‘spontaneous’ starts.

Introduction

9

Table 1.1. International rates of nascent entrepreneurship Venezuela Chile New Zealand USA Australia Brazil Ireland Canada Spain China

0.192 0.109 0.093 0.081 0.066 0.065 0.051 0.051 0.044 0.043

Finland Germany UK Singapore South Africa Italy Netherlands Hong Kong Japan France

0.041 0.035 0.034 0.030 0.027 0.020 0.017 0.017 0.014 0.009

Source: GEM 2003. Nascent entrepreneurship rates by country, extracted from Wagner (2006b, Table 2.1).

Thus, building on earlier longitudinal data analysis by Katz (1990), Henley (2007) finds that the majority of actual transitions observed in Britain are not preceded by declarations of NE status to survey interviewers a year earlier. This might mean that the majority of start-ups are ‘hastily conceived’, having less than a year of preparation. Lack of preparation might in turn explain the high closure rates of many new ventures (see chapters 4 and 14). Let us now turn to evidence about the prevalence of nascent entrepreneurship. According to PSED I data cited by Reynolds et al. (2004), 6.2 per cent of American adults are NEs, corresponding to over 10 million people and 5.6 million new firms. Wagner (2006b, Table 2.1) provides NE rates for all thirty-one countries participating in the 2003 GEM; an abstract of these data appears in Table 1.1. Note the higher estimate of US nascent entrepreneurship in this table compared with the PSED I. Arobust finding both for the USAand many other countries is that men are about twice as likely to be an NE as women.4 But there seem to be few gender differences in venture organisation structures and performance outcomes once NEs are actually engaged in the process (Davidsson, 2006). Another important feature of nascent entrepreneurship is team starts, which involve just over one-half of American NEs. Seventy-four per cent of NE teams comprise two members, followed by 17, 7 and 5 per cent for three, four and five or more members, respectively (Aldrich et al., 2004). Most team members are spouses, with non-spouse teams mainly comprised of people who are similar to each other (‘homophilious’) in terms of ethnicity, gender and occupational background (Ruef et al., 2003). ‘Homophily’ is most pronounced along ethnic and occupational lines, and is even stronger in large teams. Among non-spousal teams, homophily also has a strong gender aspect. Ruef et al. (2003) conjectured that homophily is valued because it embodies familiarity and makes trust easier to establish. This issue is explored further in chapter 4. Despite the relatively recent emergence of this topic, there is already a vast business studies literature devoted to nascent entrepreneurship. Davidsson (2006) reviews some

10

Introduction

of this literature; another useful review, with more of an economics emphasis, is Wagner (2006b).5 Evidence about the characteristics of NEs and their venture development paths are discussed further in chapter 4. 1.3.2 Small firms A more ‘traditional’ measure of entrepreneurship, which pre-dates the 1980s, is the number (or share) of small and medium sized firms (SMEs) in the economy. This definition has the advantage of being easily measurable, since most national statistics agencies tabulate data on economic outcomes by firm size ranges. Nowadays, few researchers or practitioners believe that SMEs are congruent with entrepreneurship. Firm size definitions are arbitrary and industry-specific, and do not obviously represent notions of entrepreneurship. Not all entrepreneurs run small firms, and not every small firm is run by an entrepreneur (Brock and Evans, 1986; HoltzEakin, 2000). The number of SMEs also includes part-time and ‘hobby’ businesses that are not truly entrepreneurial in the sense of being innovative, growth- or profitdriven (Carland et al., 1984). And it can be objected that small business is about firms, whereas entrepreneurship is about the individual, in particular individuals exploiting new opportunities.6 1.3.3

Self-employment/business ownership

The rationale for using self-employment or business ownership as a measure of entrepreneurship is that entrepreneurship is a risk-taking activity. Since all entrepreneurs do not have an employer and own their own business, these measures possess the merit of inclusivity. A problem with them though, as we shall see, is that they can include individuals who are unlikely to be entrepreneurs by other criteria. Self-employment also fails to capture many nascent entrepreneurs. According to GEM data, about 80 per cent of nascent entrepreneurs either have a current job or are managing another business while they work on developing their new business. Hence they are not measured in household surveys (the primary source of self-employment data) as self-employed. And while (as we have seen) just over one-half of all new business creation efforts by nascent entrepreneurs are performed by teams, the conception of self-employment is always at the level of a single individual. Self-employment and business ownership classifications overlap but are not identical. For example, some employees own businesses or shares of businesses ‘on the side’, while other ‘casual’ self-employees do not own a business in any concrete sense. However, for expositional ease, I will talk mainly about self-employment hereafter: the reader should recognise that similar arguments generally apply to business owners too. A practical advantage of using self-employment as a measure of entrepreneurship is that it is widely implemented – both at the individual level within household surveys and at the national level, via the OECD Labour Force Statistics database, allowing

Introduction

11

international comparisons to be performed. The OECD data date back to the 1960s. Useable international comparisons on a large panel of countries go back as far as 1972 (Parker and Robson, 2004) and the data series continues to be published. Although there are some problems of comparability between countries, van Stel (2005) presents algorithms to resolve these problems. In short, given the widespread availability of data on the self-employed in government surveys worldwide, self-employment is one of the easiest measures of entrepreneurship to operationalise in empirical research (Katz, 1990). The self-employed are usually classified formally as individuals who earn no regular wage or salary but who derive their income by exercising their profession or business on their own account and at their own risk. Many of them operate sole proprietorships. This is the simplest form of business organisation. A sole proprietorship is an unincorporated business owned by one person, which makes no distinction between the assets of the business and the personal liabilities of the owner. Likewise, partners of an unincorporated business are usually classified as self-employed. It is sometimes helpful to partition the self-employed into employers and own-account workers (the latter work alone); and into owners of incorporated or unincorporated businesses. Most self-employed people in most countries own unincorporated businesses, rendering their incomes liable to income tax. For example, CPS data suggest that 62 per cent of self-employed Americans run unincorporated businesses (Bregger, 1996). Incorporated business-owners tend to be older and employ others, which might explain why incorporation rates among new (and typically small) entrants to self-employment are about one-half of this rate (Evans and Jovanovic, 1989). Incorporated businesses face corporation tax (see also chapter 17). To appreciate the economic importance of self-employment and business ownership, consider the following facts:

1. Around 10 per cent of the workforces in most OECD economies are self-employed. The figure climbs to about 20 per cent when individuals who work for the selfemployed are also included (Haber et al., 1987). Two-thirds of people in the US labour force have some linkage to self-employment either by having experienced self-employment themselves; by coming from a background in which the household head was self-employed; or by having a close friend who is self-employed (Steinmetz and Wright, 1989). By the end of their working lives, about two-fifths of theAmerican workforce has experienced at least one spell of self-employment (Reynolds and White, 1997). 2. Between 80 and 90 per cent of all businesses in the economy are operated by selfemployees (Acs et al., 1994; Selden, 1999). 3. Many employees in industrialised countries claim that they would like to be self-employed. For example, according to Blanchflower (2000), 63 per cent of Americans, 48 per cent of Britons and 49 per cent of Germans declared a preference for self-employment over paid employment.7

12

Introduction

The remainder of this subsection focuses on the problems that can be encountered when researchers use self-employment data. Many of these problems boil down to difficulties of classifying self-employed people accurately in sample surveys. A further problem, of the measure being too broad to capture genuine entrepreneurship, is discussed at the end. The first classification problem is that in many countries (including the UK and USA), owners of incorporated businesses are defined for tax purposes as employees rather than self-employed. Yet they resemble in all other respects (e.g. in terms of residual claimant status) the ‘self-employed’. In cross-country comparisons of self-employment rates, it is therefore important for consistency to count these individuals as self-employed (van Stel, 2005). Second, for some individuals, legal and tax-based definitions of self-employment are sometimes at variance with each other (Harvey, 1995; Dennis, 1996). In law, the issue comes down to whether there is a contract of service or a contract for services. The first indicates paid employment, the second self-employment. For example, Harvey (1995) cites the UK legal case of Young and Woods v. West, whereby the criteria for a worker being under a contract of service includes the worker not determining their own hours, not supplying their own materials and equipment, not allocating or designating their own work, not being able to nominate a substitute to work in their place, and not setting their rate of pay (see also Leighton, 1983). UK legislation IR35, announced in 1999, has given these restrictions on self-employment legal force, to help Her Majesty’s Revenue and Customs better tackle tax evasion by ‘spurious self-employed’ workers (see below). This measure has helped to harmonise the UK’s legal and tax definitions of self-employment. The tax system can have other perverse effects as well, leading to wrongful classification of self-employment. For example, married partners might declare themselves self-employed purely to enable the chief income earner to minimise tax payments by ‘splitting’ tax liabilities with the spouse who pays tax at a lower marginal rate. Canadian evidence suggests that this problem entails over-estimation of the number of genuinely self-employed people (Schuetze, 2006; and see chapter 17). Third, many household surveys stipulate that self-employment status is to be self-assessed by the survey respondents. This can lead to further differences in the classification of workers, compared with legal and tax-based definitions (Casey and Creigh, 1988; Boden and Nucci, 1997). Partly for this reason, some surveys (e.g. the UK Labour Force Survey (LFS) and the US Characteristics of Business Owners (CBO)) use tax-based definitions of self-employment. Fourth, as intimated above, many people who create new businesses are categorised as employees in national surveys because wage and salary work is currently their primary source of income. Thus self-employment typically only measures entrepreneurship once a venture is up and running. It does not capture the process of creation itself, which some (but by no means all) scholars identify most closely with entrepreneurship. Fifth, there is a ‘grey area’ between paid employment and self-employment. Some workers classified as self-employed, with apparent autonomy over their work hours,

Introduction

13

are effectively employees. These are peripheral workers subordinated to the demands of one client firm (Pollert, 1988; Harvey, 1995). They are sometimes referred to as ‘dependent self-employed workers’ (Böheim and Muehlberger, 2006; Cabeza Pereiro, 2008). According to an ILO Report cited by Böheim and Muehlberger (2006), these workers ‘provide work or perform services to other persons within the legal framework of a civil or commercial contract, but…are dependent on or integrated into the firm for which they perform the work or provide the service in question’. Defining a dependent self-employed worker as a sole proprietor with only one customer, Böheim and Muehlberger (2006) report that the dependent self-employed amount in number to about one-tenth of the number of non-dependent self-employed in the UK. Böheim and Muehlberger argue that the dependent self-employed lose their rights under labour law, are entitled to fewer social security benefits and are often (but not always) beyond trade union representation and collective bargaining.8 According to these authors, the dependent self-employed tend to be white, older men, with modest education and job tenures but persistence as dependent self-employees. They are concentrated in construction, financial services and skilled trades. It is sometimes argued that employers actively seek to organise their workforce in self-employment contracts, to cut costs and to avoid their social obligations (Parker, 2007b). Böheim and Muehlberger (2006) estimate that fewer worker entitlements in self-employment, together with the removal of employer payroll contributions for selfemployed workers, can reduce an employer’s labour costs by 20–30 per cent. Firms do appear to exercise some discretion about the mode of employment contract they offer. For example, Moralee (1998) reported that in response to new laws penalising companies that misclassify employees as self-employed to avoid tax payments, the number of ‘employees’ in the construction industry apparently increased sharply while the number of ‘self-employed’ workers decreased sharply. This debate is related to one on contracting out of labour by large firms, a phenomenon that was thought to have been particularly pronounced in the 1980s, a decade when self-employment rates in several countries increased dramatically. The theory behind this issue is discussed further in chapter 2. Other examples of workers in the ‘grey area’ between employment and selfemployment include commission salespersons, freelancers, homeworkers and teleworkers, workers contracted through temporary employment agencies and franchise holders. According to Moralee (1998), 13 per cent of the UK self-employed in 1997 were homeworkers, with little change in self-employed homeworking taking place over the 1990s. Moralee (1998) also observed that 61 per cent of teleworkers were self-employed. It is difficult to know whether to classify franchisees as ‘entrepreneurial’ or merely ‘managerial’. Much of the distinction comes down to the behaviours and actions of the franchisees themselves. Felstead (1992) argued that many ‘self-employed’ franchisees are effectively directed by their franchisor, who holds most of the ownership rights and has a senior claim on profits. With relatively little discretion about the format of their business, one could certainly claim that some franchisees resemble branch managers more than independent entrepreneurs.9 Likewise, one could argue that a

14

Introduction

self-employed retailer who is pressurised into stocking the goods of mainly one goods manufacturer also has limited discretion about the nature of his or her business. Like other self-employed workers, franchisees face risk and uncertainty. In their case, not only is their income risky, but there is also a possibility that the franchisor will either go out of business or refuse to renew the franchise agreement at the expiration of its term.10 Two other ‘grey’ categories include unpaid family workers who work in a business run by a self-employed person; and members of worker co-operatives, who are not obviously either employees or self-employed workers in the conventional sense of the terms. Both groups tend to be more numerous in developing than in developed countries, and in rural than in urban areas. According to Bregger (1996, p. 5), ‘Unpaid family workers are persons who work on a family farm or in a family business for at least 15 hours a week and who receive no earnings or share of the profits of the enterprise.’ Blanchflower (2000) detected substantial variation within developed countries in the proportion of self-employed workers who are unpaid family workers, being one-third in Japan, compared with one-seventh in Italy and just 1.7 per cent in the USA. As Blanchflower points out, it may not make sense to simply discard unpaid family workers from the self-employment count, since they often share indirectly (e.g. via consuming household goods) in the proceeds generated by the business. Worker co-operatives are also relatively uncommon in developed countries, although there are exceptions, such as France, Italy and Spain.11 Finally, one should stress that the self-employed are a very diverse group of people. They include small business proprietors, whose businesses may or may not be incorporated; independent professionals such as doctors and lawyers; skilled manual and craftworkers; farmers; some categories of homeworkers; and ‘labour-only’ subcontractors such as construction workers (Meager and Bates, 2004). Guiso et al. (2004) claim that a measure of ‘pure’ entrepreneurship should exclude from self-employed samples all professionals, artisans, plumbers, electricians and other tradesmen. This can be quite easily implemented in practice using micro-data containing detailed occupational codes.12 What are we to make of this discussion? It seems that self-employment is a convenient but imperfect measure of entrepreneurship. It has the merit of versatility, capturing a stock measure of entrepreneurship in a cross-section sense, as well as flow measures if entries to and exits from this state are identified longitudinally. Arguably, future work should seek to refine the self-employment definition to produce a more focused group of subjects who are in some sense ‘entrepreneurial’, yet which is still wide enough to cover a variety of different aspects of ‘entrepreneurship’ – a concept whose theoretical dimensions will be analysed in chapter 2.

1.3.4 Appraisal We have seen that there is no common definition of entrepreneurship, with three main empirical measures dominating the literature. Although this might seem to be

Introduction

15

unsatisfactory or even problematic at first blush, Baumol (1993b) argues that disagreement about ‘who is an entrepreneur’, or ‘what is entrepreneurship’, scarcely matters in practice. Many of the definitions are ‘complementary rather than competitive, each seeking to focus attention on some different feature of the same phenomenon. It is surely appropriate for two authors to differ in the aspects of the entrepreneur that they find it useful to emphasize, and the choice of one author need constitute no impediment to the pursuit of a slightly different side of the subject by the other’ (Baumol, 1993b, p. 198). Indeed, claims that the field cannot progress without resolving this issue in a single agreed definition seems to be a counsel of despair. It is unlikely if not impossible that any single measure of entrepreneurship could or even should ever be regarded as portraying all the nuances of entrepreneurship. One could certainly argue that the existence of more than one practical entrepreneurship measure is a positive advantage rather than a drawback. The researcher has greater choice to employ an empirical measure that relates more closely to their theoretical construct, whatever that may be. Different measures contain different information, which make them complements rather than substitutes. Some researchers have recognised this, suggesting that researchers should use a mixture of entrepreneurship measures in empirical work (Gartner and Shane, 1995). It is more likely in practice, however, that researchers will either take an agnostic approach and use whatever measure is available, flying as it were under a flag of convenience; or that they will remain wedded to their own strict disciplinary paradigms and continue doggedly to use only one type of measure. For clarity, the following convention is adopted in this book. At the conceptual level, the terms ‘entrepreneur’and ‘entrepreneurship’will be used throughout. At the practical level, where issues of measurement, estimation and policy are involved, I will try to refer to the particular measure that was either used in applied research or which appears most suitable. That could be any of new venture creator, nascent entrepreneur, small firm, self-employed or business owner. Finally, it is sometimes convenient to disaggregate entrepreneurs into ‘habitual’ and ‘novice’ subgroups. ‘Habitual’ entrepreneurs run multiple businesses, either sequentially (‘serial’ entrepreneurs) or simultaneously (‘portfolio’ entrepreneurs) (Westhead and Wright, 1998, 1999). In contrast, ‘novice’ entrepreneurs are entrepreneurs who establish a single business. The appendix to this chapter contains more information about these distinctions, which are slowly but rightfully beginning to attract the attention of entrepreneurship researchers. 1.4

International evidence about entrepreneurship rates in developed countries

This section documents some international evidence about levels of, and trends in, entrepreneurship in developed countries. An advantage of using a long time span of data is that it can capture the effects of fundamental changes in important environmental influences (e.g. technological changes), which shorter time spans cannot do.As explained in the previous section, the data for which the longest time spans are available relate to

16

Introduction

Table 1.2. Aggregate self-employment rates in some selected OECD countries, 1960–2000a (per cent) 1960

1970

1980

1990

2000

13.83 18.81 22.68 34.25 15.86c 30.51 25.93 21.87 21.79 38.97 7.28

8.94 13.20 19.18 31.29 14.09 22.17 23.59 16.65 17.90 35.59 7.36

8.70 9.74 17.18 21.67 16.16 16.79 23.26 12.23 10.03 30.47 8.05

8.50 9.52 14.05 25.64 15.05 13.26 24.53 9.64 9.24 26.27 13.32

7.33 10.66 11.34 28.53 13.49 10.56 24.48 10.46d 7.03 20.49 11.34

B. Non-agricultural workers USA 10.45 10.17 Canadab Japan 17.38 Mexico 23.01 Australia 11.01c 16.90 Franceb Italy 20.60 15.08 Netherlandsb Norway 10.14 23.60 Spainb UK 5.89

6.94 8.33 14.44 25.20 10.00 12.71 18.97 12.02 8.61 21.55 6.27

7.26 7.05 13.75 14.33 12.73 10.71 19.20 9.06 6.53 20.63 7.11

7.51 7.40 11.50 19.89 12.34 9.32 22.24 7.84 6.12 20.69 12.41

6.55 9.46 9.35 25.48 11.72 8.06 23.21 9.25d 4.83 17.69 10.83

A. All workers USA Canadab Japan Mexico Australia Franceb Italy Netherlandsb Norway Spainb UK

Notes: a Self-employment rates defined as employers plus persons working on their own account, as a proportion of the total workforce. b Includes unpaid family workers. c 1964 not 1960. d 1999 not 2000. Source: OECD Labour Force Statistics, issues 1980–2000, 1970–81 and 1960–71.

aggregate self-employment rates. Hence the results in this and the following sections present evidence of levels and trends in aggregate self-employment rates. It should be borne in mind however that to the extent that different countries use different definitions of self-employment, cross-country comparisons of levels should be treated with caution. Table 1.2 demonstrates considerable diversity in the levels and time-series patterns of self-employment rates across OECD countries. Results are presented separately for both all workers and non-agricultural workers, reflecting the substantial difference agriculture makes to measured self-employment rates in many countries. Indeed,

Introduction

17

most researchers tend to exclude agricultural workers from their definitions of selfemployment, on the grounds that farm businesses have very different characteristics from non-farm businesses. The agricultural sector tends to decline as an economy develops, which may distort self-employment trends in other sectors of the economy (Blanchflower, 2000). I will first single out for discussion a few countries which have received substantial attention from the research community, and then chart some broad historical trends. Consider the USA first. Although its self-employment rates look relatively low, the primary source for the US entries in Table 1.2 is the CPS Monthly Household Labour Force Survey, which unlike comparable surveys in other countries excludes incorporated business owners as self-employed (see the previous section). Were these business owners to be included in the figures, the US rates would rise by about 2.5 percentage points, making them more similar to other industrialised countries (Manser and Picot, 1999). Looking at trends, if agricultural workers are included, the US self-employment rate has been in continual decline since at least 1960. According to Steinmetz and Wright (1989), this decline can actually be traced back to the 1870s, when the selfemployment rate stood at just over 40 per cent of the labour force; and it underwent an especially steep decline between 1950 and 1970 (see also Table 1.2). This led Phillips (1962) in his historical study to characterise US self-employment as a ‘shrinking world within a growing economy’. Phillips (1962) predicted that self-employment would eventually serve as a refuge only for older, handicapped or unproductive workers as a safeguard against unemployment. Few experts would agree with this characterisation today. Excluding agricultural workers, Table 1.2 shows that a revival in US self-employment occurred during the 1970s and 1980s, a finding that has been highlighted by several authors.13 But unlike previous authors, Table 1.2 reveals that this revival in nonagricultural US self-employment apparently came to an end by 2000, by which time the non-agricultural self-employment rate had fallen back to below its 1970 level. Thus the last two decades of the last century can best be interpreted as witnessing selfemployment rates that stabilised after a long period of decline punctuated by a brief window in the mid-70s to mid-80s of modest but consistent growth (Arum and Müller, 2004). The US experience is mirrored by France, which has also seen its overall selfemployment rate decline steadily since the start of the twentieth century (Steinmetz and Wright, 1989). It should be noted that low-skilled workers in France such as childcarers and gardeners are still mainly classed as wage earners (with multiple employers) rather than self-employed (Amossé and Goux, 2004). This could partly explain the relatively low self-employment rates in France, although not its inexorable decline. However, France’s downward trend is not replicated in every OECD country. For example, Table 1.2 shows that both Canadian self-employment rates exhibited U-shaped patterns, increasing particularly strongly in the 1990s, where it was concentrated among own-account workers and accounted for the bulk of overall job growth.14

18

Introduction

In contrast, both measures of the UK self-employment rate increased dramatically in the 1980s, a finding that attracted substantial research interest when it was first discovered (Hakim, 1988; Campbell and Daly, 1992). But the expansion of UK selfemployment ceased in the 1990s, at the end of which decade male self-employment rates declined, while the female self-employment rate was static (Ajayi-obe and Parker, 2005). Empirical work by Taylor (2004) attributed the increase in self-employment during the 1980s to an increase in the inflow rate; and the cessation of the trend increase in the 1990s to a rise in the outflow rate. According to Storey (1994a), the UK historical trend in self-employment was one of steady decline between 1910 and 1960, followed by increase from 1960 to 1990, with the rate in 1990 being similar to that in 1910. The data in Table 1.2 reveal a striking variety of patterns over 1960–2000. Four countries (Japan, France, Norway and Spain) had steadily declining self-employment rates. Six witnessed a revival in self-employment at some point within the period (USA, Canada, Mexico, Italy, the UK and the Netherlands); and one (Australia) had a relatively stable self-employment rate. This prevents grand conclusions from being drawn about trends or ‘revivals’in entrepreneurship across the developed world, at least in the second half of the last century. In contrast, between the mid-nineteenth and mid-twentieth century the picture seems to have been less ambiguous, with declining self-employment shares in Germany, Switzerland, Canada, the UK, the USA and France.15 One reason why it is hard to draw sweeping cross-country conclusions is that different countries experienced different events which masked any common trends (Blanchflower, 2004). It does seem clearer, however, that the last two decades of the previous century saw increased heterogeneity of self-employment in most developed countries, as witnessed by the decline of the traditional artisan class and the rise of the managerial-professional class as well as unskilled self-employment. At the same time, more people had greater exposure to self-employment over the course of their lives (Arum and Müller, 2004). Consistent with this, UK evidence points to greater instability in the labour market with more movements into and out of self-employment, which involved more frequent spells of self-employment for a greater number of people (Taylor, 2004). Finally, I will say a word about the relative importance of small firms in the economy. The overwhelming majority of US businesses employ fewer than five workers.16 There are high rates of business formation and dissolution among small firms, especially in industries like retailing, where low capital requirements make entry easy and keep profits modest. The aggregate number of small businesses grew in the USA in the post-war period, but their relative economic importance (measured in terms of their employment share or share of GDP) declined over that period. Recent evidence suggests that the share of private non-farm GDP accounted for by small businesses in the USA has now stabilised, at around 50 per cent over the last two decades (SBA, 2002a). That the decline was not greater is mainly attributable to the growth of the service sector, in which small firms are disproportionately concentrated. We will say more about the role of changing industrial structure in chapter 4.

Introduction

19

1.5 The transition economies of Eastern Europe

Of special interest are the so-called transition economies of Eastern Europe, which underwent a switch from communist central planning to a more market-based system at the end of the 1980s. In the words of Earle and Sakova (2000, p. 583): ‘it is difficult to imagine a regime more hostile towards self-employment and entrepreneurship than the centrally planned economies of Eastern Europe’. These regimes fixed prices and wages, placed restrictions on hiring workers and acquiring capital and levied confiscatory taxes on entrepreneurs. This is reflected in the low non-agricultural selfemployment rates, shown in Table 1.3, which held in Poland in 1980 and in Russia in 1992.17 Part of the interest in studying the transition economies is that they serve as a testbed for the strength of dormant entrepreneurial vigour that could be released after market liberalisation. According to Blanchflower et al. (2001), Poles topped the list of respondents to a survey of 25,000 people in twenty-three countries asking whether they would prefer to be self-employed to being a wage worker: 80 per cent responded in the affirmative. Blanchflower et al. (2001) concluded that there is no shortage of potential entrepreneurs in the transition economies. Numerous opportunities to make profits and run businesses became available after liberalisation that had been proscribed before. Many of these opportunities involved supplying services and consumer goods which were not provided by state-run producers. Rates of new entry and profits tended to be very high in transition economies just after liberalisation, before declining over time as niches became filled and competition intensified (McMillan and Woodruff, 2002). Estrin et al. (2006) describe in detail the kinds of entrepreneurial opportunities that emerged from the collapse of central planning. They include the liquidation of vertically integrated state enterprises, which created niche markets; privatisations, which released fixed and working capital that could be used to finance entrepreneurship; the rise of grey and black markets, which responded to the lack of consumer goods; and the emergence of politically networked middlemen with access to crucial production inputs. This led to a dramatic expansion in the share of small firms in many transition economies throughout the 1990s, towards Western levels. The incentive to become an entrepreneur in the transition economies was no doubt further enhanced by declining opportunities in wage employment and growing unemployment as the sprawling former state-run companies began to contract in the 1990s. Perhaps surprisingly in this regard, GEM data identifies relatively few ‘necessity’-type entrepreneurs in eastern Europe, compared with the West (Estrin et al., 2006). Although legal barriers to private enterprise and self-employment came down after 1989, bureaucracy and the limited rule of law continues to stifle productive entrepreneurship in many transition economies.18 For example, Baumol (1990, 1993a) contends that an important factor which retards productive entrepreneurship in countries that are prone to bureaucracy and lawlessness is the scope to engage in privately profitable (but socially unproductive) rent-seeking activities or (socially destructive)

20

Introduction

Table 1.3. Aggregate self-employment rates in some selected transition economies, 1980–1998/99 (per cent)

Poland (all workers) (non-agricultural) Russian Federation Czech Rep. (all workers) (non-agricultural) Hungary (all workers) (non-agricultural) Slovak Rep. (all workers) (non-agricultural)

1980

1990

25.44 3.37

27.17 9.16

1992

1994

1998/99

10.18 10.25 16.93 16.94 6.21 6.49

22.44 11.70 5.29 14.59 14.50 14.56 12.81 7.80 8.00

0.76

Notes: Self-employment rates defined as employers plus persons working on their own account, as a proportion of the total workforce. Source: UN Yearbook of Labour Statistics, various issues. ‘1998/99’ is 1998 for Poland and 1999 for the Russian Federation.

organised crime. If payoffs to unproductive and destructive activities are sufficiently high, entrepreneurs will rationally divert effort from productive innovation to exploit them. This general issue is discussed further in chapter 12. In the remainder of this section, I will discuss some specific obstacles to entrepreneurship in the transition economies, as well as some empirical evidence on the issue. Researchers have highlighted the following obstacles to entrepreneurship in transition economies (Estrin et al., 2006): • Individuals lacked wealth when communism ended because they had hitherto been prevented from legally accumulating assets. This reduced personal financing of new ventures. The problem was exacerbated by a ‘missing middle class’ and underdeveloped financial markets reluctant to lend to new small ventures. Formal trading was further restricted by reliance on a variety of informal rules and behaviour which were remnants of the communist past. • Massive economic uncertainty because of major restructuring of entire industries simultaneously, and ongoing imbalances between supply and demand. Recessionary and inflationary conditions in the early years of transition restricted purchases of shares of newly privatised companies for many citizens and led to a chaotic business environment which fostered corruption and mafia operations. • Weak and fragile institutions resulted in uneven, lengthy and arbitrary enforcement of contracts, which created fertile ground for organised rent-seeking. Entrepreneurs in these economies remain unusually vulnerable to corruption because they cannot rely on secure property rights enshrined in the rule of law and usually lack bargaining power against powerful gangster-capitalists and the public bureaucracy. • Negative social attitudes towards private business and entrepreneurship.

Introduction

21

Some commentators claim that although rates of entrepreneurship are comparatively low in all transition economies, advanced Central and Eastern European countries face superior entrepreneurial prospects compared with former Soviet republics and Russia in particular (Estrin et al., 2006; Aidis et al., 2008). Although one must be careful about making cross-country comparisons because of data limitations and the sparseness of the empirical research base, there are several reasons why Russia in particular faces even steeper challenges to promote entrepreneurship than its neighbours (Table 1.3). First, as we will see in chapter 4, one of the strongest influences on an individual’s likelihood of becoming an entrepreneur is having a parent who was an entrepreneur. Russia experienced communism for the longest of all the former Soviet countries, and suffered from not just one but three missing generations of entrepreneurs. Second, Russia has a limited productive heritage, given its tradition of trading natural resources for goods produced by satellite countries in the former USSR. Its reliance on natural resource extraction continues to this day. Third, Russia has an adverse age profile, with low average rates of male longevity (only fifty-eight years), a declining population and relatively few young people. In asking whether government can help address these problems, one should recognise that the role of public policy in the promotion of entrepreneurship is quite subtle. McMillan and Woodruff (2002) argue that government may not be initially important to the success of liberalisation and entrepreneurship in transition economies, but becomes more important as these economies begin to develop. At first, entrepreneurs can find ways around missing institutions. The establishment of reputations can substitute for legal enforcement of contracts between new firms and suppliers. Entrepreneurs play a repeated game with their suppliers and creditors, so incentives to cheat each other are diluted. This is especially important in the stages immediately following liberalisation, when search costs for new partners are the greatest if trade breaks down. Also, trade credit can substitute for bank credit, and reinvestment of profits can substitute for outside equity. But this can only go so far. Entrepreneurs need access to reliable contracts if they are to expand their companies into markets where they have fewer personal contacts; and they need financial regulation to access bank loans and outside shareholding if they are to grow and achieve economies of scale. This is where governments can play a key role by upholding contracts and property rights, effectively setting the ‘rules of the game’. Government may also be able to tackle impediments to investment such as corruption and mafia, costs of licensing and registering new starts, as well as macroeconomic instability. McMillan and Woodruff (2002) argue that governments in transition economies differ considerably in these respects. Turning to the evidence base about personal characteristics of entrepreneurs in transition economies, Djankov, Miguel et al. (2005) surveyed 400 entrepreneurs and a similar number of non-entrepreneurs in Russia in 2003, and looked for significant differences in characteristics and backgrounds. Entrepreneurs in Russia tend to be more willing to gamble with their incomes than non-entrepreneurs. They also have stronger links with other entrepreneurs. For example, the proportion of parents, aunts and uncles running a business is 42 per cent among entrepreneurs but is only 20 per cent among

22

Introduction

non-entrepreneurs. Also, more than one-quarter of Russian entrepreneurs claim that having friends who were entrepreneurs influenced their decision to become one too. Broadly similar findings hold for China in most but not in all respects (Djankov et al., 2006). According to Aidis et al. (2008), Russian entrepreneurs are disproportionately older, male and well educated compared with their Western counterparts. Where Russian and Chinese entrepreneurs appear to differ most is in terms of the influence of business networks. In principle, business networks may have positive or negative impacts in transition economies. They can be positive because they can foster trust and substitute for missing institutions. But they can have negative effects if they create barriers to entry and promote business inertia. Estrin et al. (2006) report positive effects in China, but negative ones in Russia. In particular, Russian business networks strongly promote the interests of established firms at the expense of new entrants (Aidis et al., 2008). It is noteworthy that relatively little is known about ethnic and female entrepreneurship in transition economies. This situation, which contrasts with what is known about them in developed economies (see chapters 5 and 6), constitutes an important gap in our knowledge base which needs to be closed. 1.6

Developing countries

In the early post-war period, researchers attached great importance to fostering entrepreneurship in developing countries. In the words of W. Arthur Lewis (1955, p. 82): ‘economic growth is bound to slow unless there is an adequate supply of entrepreneurs looking out for new ideas, and willing to take the risk of introducing them’. According to Leff (1979), interest in the issue had dwindled by the 1970s. Leff asserted that this was because of a perception that the entrepreneurial problem had been ‘solved’, with high rates of real output growth serving as evidence of entrepreneurial vigour. Subsequently, slower growth, high rates of population growth, widespread failures of state-owned enterprises, constraints on public sector employment and the spread of free-market beliefs reactivated interest in promoting entrepreneurship in developing economies. Table 1.4 shows that, on average, developing countries have markedly higher selfemployment rates than developed countries. Nevertheless, Table 1.4 also reveals considerable variation in these rates. It has been noted elsewhere that many Asian countries have very high self-employment rates, sometimes exceeding half the workforce. This includes the Philippines and Indonesia (Le, 1999) and Nepal (Acs, Audretsch and Evans, 1994). According to Acs, Audretsch and Evans (1994), the self-employment rate in Nepal in the 1980s exceeded 85 per cent, compared with only 3.1 per cent in Botswana. However, in contrast to claims by some previous researchers that the trend in developing countries is away from self-employment (Blau, 1987; Schultz, 1990), the evidence in Table 1.4 reveals that no such trend can be generally established. It is noteworthy that the evidence in Table 1.4 relates to relatively small developing countries. Conspicuous by their absence are the ‘BRIC’ (Brazil, Russia, India and

Introduction

23

Table 1.4. Aggregate self-employment rates in some selected developing countries, the 1960s to the 1990s (per cent)

Africa Mauritius Egypt Americas Bolivia Costa Rica Dominican Rep. Ecuador Asia Bangladesh Korean Republic Pakistan Sri Lanka Thailand

1960s

1970s

1980s

1990s

13.03 29.19

10.30 26.14

n.a. 28.20

16.72 27.19

n.a. 20.78 44.79 42.97

48.86 17.10 29.44 37.81

40.27 21.80 36.46 37.27

34.81 24.70 37.11 37.03

7.33 44.04 21.94 26.94 29.83

45.56 33.92 46.90 22.90 29.65

38.83 33.07 55.95 24.74 29.75

29.59 28.02 48.18 26.68 28.45

Notes: Self-employment rates defined as employers plus persons working on their own account, as a proportion of the total workforce. Includes agricultural workers. Source: UN Yearbook of Labour Statistics, various issues. ‘1960s’ is either 1960, 1961, 1962 or 1963 for all countries; ‘1970s’ is some year between 1970 and 1976; ‘1980s’ is 1980 or 1981 except Ecuador (1982), Costa Rica (1984) and Bolivia (1989); and ‘1990s’ is some year between 1990 and 1996.

China) economies. The last two countries in particular are rapidly becoming major players in the world economy, making it likely that entrepreneurship researchers will pay them greater attention in future (Bruton et al., 2008). Why are self-employment rates so high in developing countries? I will return to the broad issue of economic development and entrepreneurship later in the book; here just three specific factors will be mentioned. First, the data in Table 1.4 (and those used in most other studies of these countries) include agriculture. The agricultural workforce is dominated by the self-employed; and agriculture usually plays a prominent role in the economies of developing countries. Second, much labour market engagement in urban locations in developing countries (at least one-fifth according to Yamada, 1996) takes the form of informal self-employment.19 Third, high self-employment rates may reflect limited development of formal economic and financial markets. For example, Leibenstein (1968) argued that entrepreneurship in developing countries often simply involves overcoming constraints caused by poor economic and financial infrastructure, and is quite basic in nature. This viewpoint is related to the long-standing dual labour market model of development, comprising a formal urban sector in which employees earn premium wages, and an informal rural sector in which entrepreneurs receive belowaverage incomes (Lewis, 1955; Harris and Todaro, 1970). That model predicts that as poor economies develop, labour will move from the informal to the formal sector, with

24

Introduction

a decline in self-employment rates. However, evidence from the field refutes both the prediction of higher average incomes in paid employment in developing countries,20 as well as the prediction that workers shift from self- to paid employment as they gain experience. This evidence could have an important bearing on entrepreneurship policies in developing countries. If entrepreneurship really does improve living standards and reflects ‘development’ rather than ‘distress’ (or ‘opportunity’ rather than ‘necessity’) then proentrepreneurship policies seem appropriate, at least in principle. An interesting insight into this question comes from rural China, which has experienced a huge rise in rural self-employment, with up to 30 million new rural self-employed emerging between 1988 and 1995 alone. Almost 40 per cent of all new off-farm jobs in rural China now belong to the newly self-employed. Self-employment as a proportion of the rural labour force was 4 per cent in 1981 but had grown to 16 per cent by 1995 (Mohapatra et al., 2007). Mohapatra et al.’s (2007) survey evidence shows that relatively capital-intensive self-employment is driving this trend; low-productivity self-employment (handicrafts and custom labour services) has declined in importance. Furthermore, education and wealth are positively correlated with entry into and persistence within high-productivity self-employment. This evidence is indicative of a ‘development’ rather than a ‘distress’ story. Arguably, therefore, economic theories of growth and development offer greater scope for explaining high rates of entrepreneurship in developing countries than stories of deprivation alone. I will return to this issue in subsequent chapters of this book. 1.7 Appendix: habitual entrepreneurs

According to Wright et al. (1998), nearly two-thirds of British entrepreneurs are novice entrepreneurs whose current business is their first business. One-quarter are serial entrepreneurs who owned a previous business but no longer own it. The remaining 10 per cent are portfolio entrepreneurs who own two or more businesses contemporaneously. Together, serial and portfolio entrepreneurs are referred to as ‘habitual’ entrepreneurs. From their interviews of 9,553 Norwegians, Alsos and Kolvereid (1998) reported that 205 individuals (2.2 per cent of the randomly sampled population) were nascent entrepreneurs (NEs). Of these NEs, 64 per cent were novice entrepreneurs, 20 per cent were serial entrepreneurs and 16 per cent were portfolio entrepreneurs. These rates are similar to British estimates reported by Westhead and Wright (1999). Univariate comparisons reveal similar personal characteristics by NE type, with a slightly higher proportion of young people and women among novice NEs. More recent Swedish evidence suggests that human and social capital are both associated with portfolio entrepreneurship, and that habitual entrepreneurs tend to exploit new opportunities in the form of new organisations – unlike novice entrepreneurs who are more likely to incorporate them within their current business (Wiklund and Shepherd, 2008). US evidence shows that 20 per cent of new starts in the 1987 CBO were businesses operated by portfolio entrepreneurs (Astebro and Bernhardt, 2003), while the founders

Introduction

25

of 20–30 per cent of all small business owners had started other businesses in the past (Baird and Morrison, 2005, p. 2329; Hyytinen and Ilmakunnas, 2007). According to Hyytinen and Ilmakunnas (2007), Finns with both greater aspirations and capabilities to become serial entrepreneurs are significantly more likely to do so. In summary, despite some agreement across studies, estimates of the proportions of habitual versus novice entrepreneurs still vary (Ucbasaran et al., 2006, pp. 464–65). This may suggest a need for more representative survey data. Some, but by no means all, serial entrepreneurship entails entrepreneurs bouncing back from business failure. The German Regional Entrepreneurship Monitor reveals that about one-fifth of active NEs are ‘failed’ entrepreneurs (Wagner, 2003b), while Landier (2004) cites a pilot study by the SBA showing that 50 per cent of bankrupt ‘Chapter 7’ business owner filers between 1989 and 1993 went on to start another new business in 1993. These rates are high enough to suggest that stigma from failure is not very widespread in either Germany or the USA. Wagner (2003b) observed that the probability of ‘taking a second chance’ is significantly lower among older and more risk-averse entrepreneurs located in regions characterised by high past business failure rates. This probability is however significantly higher for individuals who know other entrepreneurs and who are located in regions with a high share of NEs. Several conceptual and empirical problems arise in studies of habitual entrepreneurs. Rosa (1998) observes, for example, that portfolio business activity is not necessarily entrepreneurial: diversification is a standard efficiency-enhancing strategy of large, established firms as well as of new and small ventures. Rosa (1998) also criticises the ‘cross-section’ emphasis on the latest businesses of habitual entrepreneurs, rather than the performance of entrepreneurs evaluated across all of their businesses. At the time of writing, the body of theory about serial entrepreneurship is relatively under-developed. One is left instead to conjecture about its determinants. One interesting possibility of a non-economic nature is that some people become serial entrepreneurs in order to escape corporate bureaucracy. Entrepreneurs grow their firms to the point where the internal bureaucracy becomes unbearable, at which point they sell them and start new small-scale companies. Thus it is possible that the inevitably recurring nature of both bureaucracy and the emergence of new opportunities in the firms that entrepreneurs found, rather than any innate entrepreneurial ability or characteristics, might explain the serial entrepreneurship phenomenon (Dobrev and Barnett, 2005). More prosaically, tax advantages might provide an alternative explanation of serial entrepreneurship. In some jurisdictions, entrepreneurs receive tax credits on their positive income ‘draw’ if their company profit is negative up to a given time limit – after which they lose the favourable income tax treatment even if the company continues to make losses net of the draw. This gives entrepreneurs incentives to draw excessive incomes out of their businesses, putting their companies into the red, before closing and re-forming the same ventures as ‘new’ businesses after the tax-credit time-limit expires. As owners of an ostensibly new business, such entrepreneurs can continue to claim the tax credit. Clearly, though, these are fairly sketchy and heuristic accounts

26

Introduction

of the phenomenon. More formal economic theories of serial entrepreneurship will be discussed in the next chapter. Also surprising is the fact that relatively little research has so far been conducted on portfolio entrepreneurs. Storey et al. (1987) observed that 80 per cent of the directors of ‘fast-growth’ companies (those hiring more than fifty employees within five years of incorporation) owned other businesses. In quoting these results, Storey later concluded that ‘portfolio owners are therefore of key [economic] importance’ (Storey 1994a, p. 112). This highlights the need for further theoretical and empirical research on portfolio entrepreneurship.

Notes 1. The introduction to a recent special issue on the Economics of Entrepreneurship in the Journal of Business Venturing stated: ‘Economics helps us understand how individuals make decisions, why and how they create and grow organisations, and what the intended and unintended consequences of these actions are at both the micro and macroeconomic levels. Economics further helps us analyze how entrepreneurship influences growth and development and how, in turn, the macro structure of a region or country influences the type and quantity of entrepreneurship. Economic analysis provides insights for scholars and road maps for practitioners and policymakers’ (Minniti and Lévesque, 2008, p. 603). 2. In fact, GEM does collect some data on established firms, though these are rarely used or discussed in public forums. 3. Taking this distinction further, Buenstorf (2007) distinguishes between ‘opportunity’ and ‘necessity’ spinoffs. The former are new ventures formed by employees who commercialise ideas that would otherwise be shelved by the parent firm (Klepper and Thompson, 2006). The latter are new ventures stimulated by adverse developments at the parent firm which render future employment at this firm less attractive. According to Buenstorf (2007), both types of spin-off performed similarly well in the German laser industry. 4. See Reynolds (1997), Delmar and Davidsson (2000), Davidsson and Honig (2003), Reynolds et al. (2004), Wagner and Sternberg (2004), Arenius and Minniti (2005), Rotefoss and Kolvereid (2005) and Wagner (2006b). 5. Johnson, Parker and Wijbenga (2006) introduce a special interdisciplinary issue of Small Business Economics on the topic. 6. The same drawbacks also apply to Gartner and Shane’s (1995) little-used measure of the number of organisations per capita, whose rationale is Hawley’s (1907) argument that entrepreneurship depends on ownership rights. 7. Of course, these types of survey can be criticised for asking hypothetical questions, without forcing individuals to bear the constraints of self-employment as they would if they acted upon their declared preferences. Indeed, much lower rates of serious entrepreneurial intention emerge from longitudinal analyses of wage and salary workers (Katz, 1990), implying that much ‘latent entrepreneurship’ is probably only cheap talk. This has not stopped some researchers studying its determinants (Grilo and Irigoyen, 2006; Masuda, 2006). Because of the danger of cheap talk, I will usually adopt in this book the economist’s standard practice of ignoring studies that report interviewees’ declared preferences, focusing instead on revealed preferences (i.e. actual behaviour). The principal exception relates to a discussion of job satisfaction (see chapter 4). 8. See Bone (2006) for a highly entertaining though hopelessly tendentious account of ‘direct selling’ self-employed salesmen and women, in which it is claimed that managerial control over these self-employees can be even greater than in conventional wage contracts.

Introduction

27

9. ‘They [franchisees] operate without close and direct supervision, but are required to operate within procedures laid down and subject to unilateral change; they appropriate the profits generated by the business, but only after they have made turnover-related payments to their franchisor; and, despite buying or leasing the means of production, are subject to restrictions on how they are used both during and after the currency of the agreement’ (Felstead, 1991, pp. 38–9). 10. Williams (1999) argues that franchisees take less risk than independent self-employed business owners, because of profit-sharing arrangements with franchisors and lower demand uncertainty resulting from selling a known product. In support of his contention, Williams went on to observe a lower variance of self-employment incomes among franchisees than non-franchisees in his 1987 CBO sample of full-time self-employed workers. However, this might overstate the case, not least because exit rates can be higher among franchisees than independent business owners (Bates, 1994a). 11. See Spear (1992). Pérotin (2006) supplies a recent analysis. 12. According to Glaeser (2007), only 1 per cent of American self-employees are engaged in skilled manufacturing. Also, San Jose in California has among the lowest self-employment rates in the USA, despite being home to Silicon Valley, often regarded as the most entrepreneurial cluster in the world. Glaeser (2007) distinguishes high-value from low-value entrepreneurs by disaggregating this group by industry sector and income level. 13. Blau (1987), Steinmetz and Wright (1989), Aronson (1991), Bregger (1996) and Williams (2000). 14. See Lin et al. (2000), Manser and Picot (1999), Kuhn and Schuetze (2001), and Moore and Mueller (2002). 15. See Kuznets (1966, Table 4.2). Numerous studies report self-employment rates which are significantly negatively correlated with GDP per capita over shorter time spans. See Ilmakunnas and Kanniainen (2001), Torrini (2005), Bergmann and Sternberg (2007), Ho and Wong (2007) and Wennekers et al. (2005). Yamada (1996) reports a correlation coefficient of −0.85 between self-employment rates and GDP per capita for over thirty developing countries. 16. See e.g. White (1984) and Brock and Evans (1986, chap. 2) for details. 17. Agriculture was never fully collectivised in Poland, which accounts for its high rate of selfemployment for all workers inclusive of agriculture. 18. Baumol (1990, 1993a); Dutz et al. (2000) and Aidis et al. (2008). 19. Some researchers contend that entrepreneurship in developing countries has been unfairly linked to low-value informal sector work, owing to early work in the development area which linked this sector to a makeshift outcome when mobility between agriculture and industry was impeded (van der Sluis et al., 2005). 20. See Yamada (1996) for evidence from a Peruvian sample and Zhang et al. (2006) for evidence from rural China. In the latter study, the rural self-employed earn more than twice as much than rural employees, albeit with nine times the standard deviation of income. Early studies also generally report higher returns to the self-employed in developing economies (see Parker, 2004, chap. 1, n. 19, for references), although there are exceptions. In Kenya, only 10 per cent of the self-employed appear to earn more than the average wage (Daniels, 1999; Daniels and Mead, 1998).

Part I Selection

2

Theories of entrepreneurship

Numerous thinkers have speculated about both the origin and function of the entrepreneur and the nature of entrepreneurship. A large body of research in economics now addresses these topics. Section 2.1 briefly surveys ‘early’(chiefly pre-1975) views about entrepreneurship. These are mainly concerned with defining and identifying salient aspects of entrepreneurship in a general way. The remaining sections treat ‘modern’ (post-1975) contributions to the economics literature on entrepreneurship. These are typically framed as models of occupational choice between entrepreneurship and paid employment. They tend to be less concerned with definitional issues and usually take entrepreneurship to be any activity where individuals work for themselves and trade off risk and returns, rather than opting for safe returns in a different occupation. For simplicity, the latter will generally be called simply ‘paid employment’ hereafter. Section 2.2 introduces a basic occupational choice model of entrepreneurship, in which all agents are treated as homogeneous. The analysis is broadened out in section 2.3, where individuals are endowed with heterogeneous entrepreneurial abilities, and also in section 2.4, which endows them with heterogeneous risk attitudes. The theories contained in these two sections predict which individuals become entrepreneurs and which do not on the basis of differences in personal characteristics. In contrast, section 2.5 asks why a profit-making entrepreneurial occupational choice exists at all if entrepreneurs’ tasks can also be performed within existing firms. I also consider here the existence of non-profit-making ventures (social enterprises) and family firms. Section 2.6 asks what types of firm ‘spawn’ new enterprises. Spawned ventures can be either ‘dependent’ or ‘independent’ spin-offs. Dependent spinoffs are ventures formed in collaboration with an incumbent firm (sometimes termed ‘intrapreneurship’), whereas independent spin-offs are pursued entirely separately from an incumbent (‘entrepreneurship’). Section 2.7 traces out the implications of entrepreneurship as an occupational choice for economic growth and development. Section 2.8 briefly sketches out several multiple equilibrium models which carry implications for regional variations in entrepreneurship. Section 2.9 concludes. 31

32

Selection

This chapter provides an up-to-date account of theoretical perspectives on the economics of the entrepreneur. The discussion is deliberately kept as accessible as possible; technical details are relegated to the appendices located at the end of the chapter. 2.1

‘Early’ theories of entrepreneurship

My treatment of early theories of entrepreneurship will be fairly brief, since much of this literature has been summarised by other authors. I will group these theories by theme rather than chronologically, unlike Hébert and Link (2006), for example, in the updated version of their ‘classic’ enquiry into the identity of the entrepreneur. 1. Arbitrage and the bearing of risk and uncertainty. Richard Cantillon (1755) stressed the importance of the entrepreneur as an arbitrageur or speculator, who conducts all exchanges and bears risk as a result of buying at certain prices and selling at uncertain ones. Cantillon’s is a risk theory of profit: anyone who receives an uncertain income can essentially be regarded as an entrepreneur. According to Cantillon, successful entrepreneurs perform a key role in the economy by relieving the paralysis engendered by uncertainty, allowing production and exchange to occur and market equilibrium to be attained. Unsuccessful entrepreneurs go out of business: only the ‘fittest’ survive. Entrants appear when profits persist. Cantillon’s entrepreneur is not an innovator, nor does he change supply or demand. Instead, he is perceptive, intelligent and willing to take risks. His role is to bring the two sides of the market together, bearing all the risks involved in this process. Subsequent researchers have developed Cantillon’s thoughts in two separate and well-trodden directions. First, Kirzner (1973, 1985) emphasised the importance of the entrepreneur as a middleman or arbitrageur, who is alert to profitable opportunities that are in principle available to everyone. According to Kirzner, individuals have different access to, or views about, entrepreneurial opportunities, which can explain why some people become entrepreneurs while others do not. Successful entrepreneurs merely notice what others have overlooked and profit from their exceptional alertness. Kirzner neither explained where alertness comes from, nor whether individuals or government can deliberately cultivate it. More recently, Gifford (1998) has tried to endogenise alertness in a model of limited entrepreneurial attention. Entrepreneurs endowed with high levels of managerial ability optimally spend their time operating numerous projects: they therefore face a high opportunity cost of perceiving new innovative opportunities, which renders them less ‘alert’. Following Knight (1921), the second line of research highlights the importance of risk and uncertainty. According to Knight, entrepreneurs have limited information about the availability of natural resources, technological change and prices. Although factor prices are contractible, consumer demand and competitors’ actions can change, so output prices (and hence profits) fluctuate.1 Hence entrepreneurs need to possess particular characteristics such as self-confidence, judgement, a venturesome nature, foresight – and luck. These characteristics are not tradeable, and being complementary

Theories of entrepreneurship

33

to other productive assets, make it logical for entrepreneurs to own rather than lease those other assets and so form a firm. This provides a basic entrepreneurial theory of the firm (Foss et al., 2007). One of Frank Knight’s key contributions was to recognise that the decision to become a worker or an entrepreneur depends on the risk-adjusted relative rewards in each sector. In his own words: The labourer asks what he thinks the entrepreneur will be able to pay, and in any case will not accept less than he can get from some other entrepreneur, or by turning entrepreneur himself. In the same way the entrepreneur offers to any labourer what he thinks he must in order to secure his services. (Knight, 1921, p. 273).

Thus Knight viewed individuals not as born entrepreneurs or non-entrepreneurs, but as opportunists, who can turn their hand to entrepreneurship when the risk-adjusted returns there are relatively favourable – or alternatively to paid employment when they are not. The remainder of the chapter shows how modern economic research has followed directly in this tradition, making explicit the risk-adjusted returns Knight referred to. One aspect of Knight’s writing that has arguably generated more heat than light is the distinction between ‘risk’and ‘uncertainty’. Alvarez and Parker (2009) define risk as the case where individuals do not know in advance the (random) outcome of a draw from a given probability distribution, but do know the parameters and structure of the true underlying probability distribution. In contrast, uncertainty arises when individuals are not only ignorant of the outcomes of the random draw, but also of the true probability distribution. Some authors from the business studies tradition (e.g. Shane, 2003) claim that uncertainty precludes the use of economic optimisation methods. However, this claim is false because individuals can form subjective (albeit possibly incorrect) beliefs about the probability distribution; and optimisation methods can still be applied in this context (Alvarez and Parker, 2009). Alternatively, Knight might have meant that even the possible outcomes themselves are unknown, rather than just the probabilities (Langlois and Cosgel, 1993). Arguably, though, it is hard to imagine any practical situation where outcomes are completely unthinkable, rather than just very unlikely. For example, the concept of the Internet was anticipated even in the 1950s, as was space travel many decades before that; and these are very radical developments compared with the more mundane market uncertainties that entrepreneurs typically deal with. This makes Langlois and Cosgel’s (1993) argument difficult to sustain, quite apart from the fact that other researchers have reached very different verdicts about ‘what Frank Knight really meant’ (see, e.g., LeRoy and Singell, 1987). 2. Co-ordination of factors of production. According to Jean-Baptiste Say (1828), the chief contribution of the entrepreneur is to combine and co-ordinate factors of production. The entrepreneur stands at the centre of the economic system, directing and rewarding the various factors of production, and taking the residual as profit. Personal characteristics such as judgement, perseverance and experience required for successful entrepreneurship are in scarce supply, providing supernormal profits

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to these entrepreneurs. Furthermore, all of these characteristics have to be present simultaneously in order for an entrepreneur to be successful. Entrepreneurs need to be resourceful, knowing how to overcome unexpected problems and to exploit (although not develop) existing knowledge. Although some have criticised Say’s view of the entrepreneur as just a superior kind of worker with managerial duties (e.g. Hébert and Link, 2006), others have offered modern re-statements and developments of Say’s perspective. For example: ‘an entrepreneur is someone who specialises in taking judgemental decisions about the co-ordination of scarce resources’ (Casson, 2003, p. 20).2 3. Innovation and creative destruction. According to Josef Schumpeter (1934, 1939), entrepreneurship entails innovation. The entrepreneur does not operate within conventional technological constraints, making small gradual changes to existing production methods. Instead, he or she develops new technologies or products that make discrete discontinuous changes which shift the paradigm altogether, breaking organisational routines and driving economic development (Santarelli and Pesciarelli, 1990). In Schumpeter’s words, the entrepreneur as innovator is responsible for ‘the doing of new things or the doing of things that are already being done in a new way’ (1947, p. 151). This could involve: (i) the creation of a new product; (ii) a new method of production; (iii) the opening of a new market; (iv) the capture of a new source of supply; or (v) a new organisation of industry. Similar to Say, the entrepreneur is an exploiter rather than an inventor of new knowledge. Schumpeter regarded entrepreneurial actions as the principal cause of business cycles and economic development. In his grand vision of ‘creative destruction’, a wave of entrepreneurial innovation hits the economy, displacing old products and production processes, followed by rapid imitation by new competitors. Ultimately stability is restored and entrepreneurship reaches a temporary cessation before the next wave occurs. Both entrepreneurial activity and the ensuing profits are temporary, unless the entrepreneur continues to innovate: ‘everyone is an entrepreneur only when he actually carries out new combinations and loses that character as soon as he has built up his business, when he settles down to running it as other people run their business’ (Schumpeter, 1934, p. 78). This argument can be seen as a rationale for defining entrepreneurship in terms of new venture creation, as discussed in the previous chapter. Schumpeter seems to have viewed the entrepreneur not as a calculating utility maximiser but as a rare, unusual creature driven by instinctive motives. Schumpeter regarded profit as a residual, not a return to the entrepreneur as a ‘factor of production’, and even claimed that ‘the entrepreneur is never a risk bearer’ – ‘capitalists’ (financiers) bearing the risk instead (1934, p. 137). However, the view that only capitalists and not entrepreneurs bear risks has been roundly and justly criticised by subsequent writers (e.g. Kanbur, 1980), for imposing an arbitrary distinction between ‘capitalists’ and ‘entrepreneurs’, and for ignoring entrepreneurs’ actual and opportunity costs in operating ventures that can (and often do) fail. Another limitation of Schumpeter’s thinking centres on his prediction of the eventual demise of the entrepreneur, caused by the rise of large monopolistic firms, which have

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advantages at co-ordinating teams of workers to perform R&D. This prediction has not come to pass, partly because new and small firms continue to be important innovators (see chapter 10), and partly because the majority of US basic research is funded not by large firms but by the federal government (Schultz, 1980). Of all the early writers about entrepreneurship, Schumpeter is perhaps the most widely cited today. In several respects, this is puzzling. His insistence that entrepreneurs do not bear risk is frankly bizarre. And his gloomy prediction about the demise of entrepreneurship has been clearly disproved. Perhaps his ideas were overly influenced by the spread of socialism at the time he was writing. More generally, Schumpeter ignored the forces of competition which challenge and overturn incumbents in modern capitalist economies (Acs and Armington, 2006, chap. 1). Furthermore, arguably not one but several Schumpeters survive in his writings. His ideas underwent continual development, and it is hard to know whether it is this or the sometimes biblical opaqueness of his verbal argumentation which generates the confusion that has spawned a large if ultimately rather sterile academic industry interpreting ‘what Schumpeter really meant’. 4. Leadership and motivation. In stark contrast to Schumpeter, others have claimed that a defining feature of entrepreneurs is that they bring about changes of a gradual nature to existing products and processes, through a combination of leadership, motivation, the ability to resolve crises and risk-taking (Leibenstein, 1968). Some supportive if anecdotal evidence about the incremental nature of innovations, discussed by Bhide (2000), is discussed in chapter 10. 5. Personal or psychological traits. This line of thought relates entrepreneurship to the possession of special innate personal characteristics. It has rather fallen out of favour among entrepreneurship researchers, but it will be briefly discussed in chapter 4. While not exhaustive, the above list includes many of the most influential ‘traditional’ views about entrepreneurs. The brevity of this overview was deliberate. Much of this material has been discussed extensively before and, as others have noted, ‘a tome could be written on the connections and contradictions between these theories’(Barreto, 1989, p. 43; see also van Praag, 1999). One of the more interesting connections appears in a recent article by Langlois (2007), who identifies the ‘opportunity discovery, exploitation and evaluation’ definitions of entrepreneurship with Kirzner, Knight and Schumpeter, respectively. I close this discussion by drawing two conclusions. First, the early theories can be dichotomised between those in the neoclassical tradition (such as Knight, 1921; Marshall, 1930; Schultz, 1980), based on the notion that entrepreneurs lead markets into equilibrium, and those in the Austrian tradition (such as Kirzner, 1973; 1997) which sees entrepreneurs as part of an ongoing disequilibrium process of indefinite or infinite duration. On a related theme, Shane and Venkataraman (2000) argue that Kirzner’s and Schumpeter’s theories represent different types of opportunity. In Kirzner, opportunities embodied in existing information are discovered, by the most alert individuals (entrepreneurs); whereas in Schumpeter, opportunities are created as a result of new information and technology. Kirzner’s entrepreneurs move the market towards

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an economic equilibrium consistent with existing information, whereas Schumpeter’s create the prospect of entirely new equilibria. Rosen (1997) attempted to find some common ground between the two sides of this dichotomy. Second, it seems self-evident that none of the above theories offers a complete account of entrepreneurship. To be sure, one can cite selectively from these theories to support a particular viewpoint, but none of them provides necessary or sufficient conditions for entrepreneurship. For example, construction site managers co-ordinate factors of production, while corporate R&D employees contribute to the development of innovations; yet neither is obviously an ‘entrepreneur’. This exemplifies the subtleties which make entrepreneurship such an elusive, and almost certainly multi-dimensional, concept. 2.2 The occupational choice model of entrepreneurship I: homogeneous agents

Modern economic theories of entrepreneurship differ in at least two important respects from those described above. Perhaps the most important distinction relates to the dominance of the utility maximising paradigm in modern economic research. Modern economic theories take as their starting point the Knightian premise that individuals do not have to be entrepreneurs. They can choose between entrepreneurship and some outside option (usually taken to be paid employment); and they choose the occupation that offers them the greatest expected utility. Most theories treat occupational choice as a discrete, rather than a continuous, decision. This follows Kanbur (1981), who noted the difficulty of viewing occupational choice as an adjustment at the margin of a continuous process, such as ‘engaging a “little bit” more in entrepreneurial activity’ (p. 163). However, as will be discussed below, some researchers have also analysed how individuals mix their time between different occupations, which resembles more of a continuous than a discrete choice. A second distinctive feature of modern economic theories of entrepreneurship is that they make their simplifying assumptions explicit. These assumptions usually include the existence of competitive product markets, known technology, and price-taking workers and entrepreneurs. In many cases these assumptions are inessential to the results, and merely simplify the analysis. The present section analyses entrepreneurship as an occupational choice when agents are homogeneous. It first analyses the simplest cases, and then introduces additional layers of complexity (and realism) by introducing risk and risk aversion. The simplest ‘static’ models, in which events take place in a single period, are discussed first. This is followed by a treatment of ‘dynamic’ models, in which events unfold over several periods. Before turning to these models, I will first define some of the terms that will be used extensively in what follows. While they can be found in many standard economics texts, it is convenient to group them together and establish a common notation. I will give simple explanations of these concepts in the text; more formal definitions are collected in the first part of the chapter appendix.

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(a): Risk-averse

37

U(y) U(y)

Utility,

U

(b): Risk-loving

(c): Risk-neutral

U(y)

Income,

y

Figure 2.1 Utility functions

2.2.1

Definitions of risk aversion and risk

To commence, consider a utility function U (y) which is an increasing function of income, y. In principle, utility functions can take several shapes: three are illustrated in Figure 2.1. Utility function (a) is a concave function of income. While extra units of income increase utility, they increase it by progressively smaller amounts. This utility function is also said to embody risk aversion. Individuals with utility functions exhibiting a greater degree of curvature than in (a) are said to be more risk-averse. Technically, this utility function has a positive first and a negative second derivative with respect to y. More risk-averse people are willing to pay a higher insurance premium to avoid risk than their less risk-averse counterparts. Utility function (b) in Figure 2.1 is a convex function of income. Extra units of income increase utility by progressively greater amounts. This utility function is said to embody risk-loving preferences. Individuals with utility functions exhibiting a greater degree of curvature are said to be more risk-loving. Technically, this utility function has positive first and second derivatives with respect to y. Utility function (c) is linear in income. It embodies risk neutrality. As the name suggests, risk-neutral people are indifferent to risk. A wide array of evidence suggests that in the real world, most individuals, including entrepreneurs, are risk-averse.3 This debunks the popular view that entrepreneurs are gamblers, and is consistent with evidence that entrepreneurs’ behaviour is better described by moderate and calculated risk taking than outright gambling. Thus entrepreneurs ‘enjoy the excitement of a challenge, but they don’t gamble. Entrepreneurs avoid low-risk situations because there is a lack of challenge and avoid high-risk situations because they want to succeed. They like achievable challenges’ (Meredith et al., 1982, p. 25). Furthermore, individuals appear to exhibit decreasing absolute risk aversion (DARA: see the chapter appendix). This means that as they become wealthier, they

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become less reluctant to gamble a fixed sum. DARA plays a key role in some of the models of entrepreneurship analysed throughout this book. It is also helpful to have a precise definition of ‘an increase in risk’. Two useful and general definitions are second-order stochastic dominance (SOSD) and meanpreserving spread (MPS). These concepts are defined formally in part 1 of the chapter appendix. Essentially, both concepts refer to a more dispersed distribution of risky returns. These are more general notions of an increase in risk than an increase in the variance of a random variable, as explained in the chapter appendix.

2.2.2 Simple static models The simplest static models treat an economy without risk, where individuals choose between working for a wage of w and producing output independently as an entrepreneur in return for profit, π. If π > w, workers switch into entrepreneurship. By the laws of supply and demand, the extra output decreases the price it is sold for, reducing π until it comes into equality with w. Conversely, π cannot be less than w because then entrepreneurs would quit, reducing aggregate output and thereby increasing the price until equality was restored. It follows immediately that w = π is an equilibrium condition in this simple case. Likewise, any exogenous increase in w (caused by technological change, for example) will decrease the equilibrium number of entrepreneurs (de Wit, 1993). Richer versions of this simple model of homogeneous agents introduce risk. Risk can emanate from various sources. Entrepreneurs may be unsure about the demand for their good, their ability to produce, or future costs of production (Wu and Knott, 2006). Although employees can also face some risk, through income variation and redundancy for example, entrepreneurs face more variable incomes than workers do as well as higher business closure rates (see chapters 13 and 14). Most researchers assume for simplicity that entrepreneurs face some kind of idiosyncratic risk to their profits, whereas employees all face a certain wage, w. The assumption of perfect certainty in paid employment is usually innocuous, and can be relaxed without affecting analytical predictions. It will also be assumed that entrepreneurs cannot completely diversify or sell their risk. This also appears to be a reasonable assumption. Markets for private unemployment, accident and sickness insurance are limited and prone to moral hazard problems. Few entrepreneurs have access to stock markets to share risk, and realworld capital markets are imperfect, undermining entrepreneurs’ efforts to smooth consumption in the face of income risk. It might be thought that, given risk aversion among entrepreneurs, an increase in risk in entrepreneurship would necessarily decrease the equilibrium number of entrepreneurs. In fact, the opposite turns out to be the case, if entrepreneurs can choose output once risky demand outcomes are revealed. The reason is that risk can provide upside potential as well as downside outcomes, making riskier markets more attractive and hence liable to market entry even by risk-averse entrepreneurs (Sheshinski and Drèze, 1976). The possibility that occupations offering high upside potential can attract able individuals seeking rare but highly profitable opportunities was recognised

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over half a century ago (Roy, 1951). In practice, abler individuals are indeed more likely to try start-ups in risky markets like software development than in ‘safe’ markets like hairdressing (Bhide, 2000). Bhide contends that entrepreneurs who become very successful in sectors with highly skewed returns do not necessarily need to have special insights or a novel technological discovery. They might merely possess superior information and sell an already existing service or product more effectively than the competition. He concludes that ‘given…limited endowments, profitable start-ups tend to cluster in small, uncertain market niches’ (Bhide, 2000, p.113).4 I have so far considered the consequences of greater risk in the economy, perhaps caused by an increase in the volatility of trading conditions. But what if there is a general increase in risk aversion among individuals? That might reflect a change in tastes within an economy. Alternatively, it can be thought of as a device to analyse cross-country differences in risk attitudes. Kanbur (1979) studied the effects of greater risk aversion on the equilibrium number of entrepreneurs. Kanbur’s model generates two hypotheses: (i) if labour is hired after the outcome of the random shock is observed, an increase in absolute risk aversion decreases the equilibrium number of entrepreneurs, since risk-averse individuals avoid risky occupations; (ii) if labour is hired before the outcome of the random shock is observed, an increase in absolute risk aversion has an ambiguous effect on the number of entrepreneurs. Prediction (i) appears to give some theoretical backing for the popular view that Europe has less high-value-adding entrepreneurship than the USA because Europeans are more risk-averse than Americans are. However, there are at least two reasons to treat this argument with scepticism. First, most entrepreneurs who hire workers in practice do so continuously, i.e. before risk is resolved. That makes Kanbur’s hypothesis (ii) the relevant case – but this case is the theoretically ambiguous one. Second, the Kanbur model assumes that every individual is identical, and that all entrepreneurs hire workers. These assumptions appear unrealistic and are relaxed in some of the models discussed below.

2.2.3

Dynamic models

The models discussed so far assume costless switching between occupations. Thus if entrepreneurship becomes attractive relative to paid employment, workers are assumed to move immediately into entrepreneurship; the converse also applies. However, individuals might incur costs of switching occupation. These costs could be non-pecuniary involving, for example, the sudden loss of a pleasant compensating differential, disruption to an accustomed lifestyle, a feeling of rootlessness, stress from change, or stigma from failure (Gromb and Scharfstein, 2002; Landier, 2004). Or they could be economic in nature involving, for example, lost sector-specific experience, costs of raising start-up capital (if entering entrepreneurship), or retraining costs (if entering paid employment). Switching costs might also relate to exit barriers caused by incurring sunk costs of capital with limited resale value, prior commitments to customers, or a desire by entrepreneurs to avoid sending an adverse signal of ability by abandoning their ventures (Boot, 1992).

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Dixit (1989) showed that risk together with sunk costs can give agents an option value of waiting before switching. This reduces the total amount of entry and exit that occurs – as conditions have to become very bad before entrepreneurs close their business and relinquish their sunk costs, or very favourable before they are willing to incur the risk of jeopardising their assets by entering the market. Risk generates an ‘option value’ of remaining in the present occupation and deferring a costly switch. Only when average incomes in entrepreneurship reach some upper ‘trigger point’ will people become entrepreneurs. And they will only leave entrepreneurship in the presence of the adjustment cost if incomes drop to some lower trigger point. Between these two trigger points individuals remain in their current occupation (Dixit and Rob, 1994). Consequently, there may be hysteresis (i.e. path-dependence) in occupational choice. Individuals may remain in entrepreneurship even if the returns there at a given instant are less than those available in an alternative occupation. It is rational to remain in the occupation not only because of the switching cost, but also because there is an option value to wait and see if conditions in the currently unfavourable occupation improve. Only if this option value becomes sufficiently small does switching become worthwhile. Dixit and Rob (1994) went on to show that a socially suboptimal amount of switching takes place in equilibrium. There might therefore be a role in principle for government to improve on the free-market equilibrium by speeding up the reallocation of labour between occupations, for example by subsidising training costs (see chapter 17). Although Dixit and Rob (1994) did not discuss this implication of their work, the hysteresis result is useful because it goes some way to explaining why there is relatively little voluntary switching between entrepreneurship and paid employment from year to year, despite the abundance of latent entrepreneurs (q.v. chapter 1). US estimates of the proportion switching into self-employment in any given year are only 2–3.5 per cent (see Parker, 2004, note 14, p. 67, for citations). The figures for switching out of selfemployment into paid employment are higher, amounting to 10–20 per cent in the USA and the UK (Fairlie, 1999; Henley, 2005). This reflects the high closure rates of small businesses, especially newly established ones (see chapter 14). A dynamic optimisation model proposed by Parker (1996, 1997a) explores the implications of introducing risk into paid employment as well as entrepreneurship. These models also allow individuals to mix work hours between occupations, enabling occupational choice to be analysed as a continuous, rather than just a discrete, choice. Incomes in the two occupations are assumed to evolve as independent Brownian motions.5 The key result from this model is that if expected income growth is higher in entrepreneurship than in paid employment, and if there is no income risk in paid employment, then greater income risk in entrepreneurship unambiguously reduces the optimal fraction of time spent in entrepreneurship. But if there is income risk in paid employment as well, greater income risk in entrepreneurship has ambiguous effects on the optimal fraction of time spent in entrepreneurship. At first sight, it might appear surprising that risk-averse individuals could respond to an increase in risk in entrepreneurship by spending even more time in it. But the logic is analogous to that applying in the twoasset portfolio problem in finance, where the risk of the overall portfolio is a convex

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combination of the risk of each asset. Overall portfolio risk can sometimes be reduced by increasing the portfolio share of the riskiest asset (Parker, 1997a). 2.3 The occupational choice model II: heterogeneous ability – the Lucas (1978) model

In practice, it is likely that entrepreneurs differ from employees and among themselves in terms of their innate ‘entrepreneurial ability’. Lucas (1978) was one of the first researchers to trace out the economic implications of heterogeneous entrepreneurial ability. This section first explains the gist of the Lucas model before discussing some limitations and extensions. 2.3.1 The Lucas model Ability in entrepreneurship might derive from human capital (van Praag, 2005), idiosyncratic leadership qualities (Leibenstein, 1968) or judgement (Casson, 2003), among other possible sources. Whatever its provenance, to fix ideas and keep the exposition simple I will just assume that everybody, whether an entrepreneur or not, has some innate ability which describes how well they would perform in entrepreneurship were they to become an entrepreneur. Ability is measured as a single-dimensional quantity, x. The lowest ability in the population is x, while the highest is x. Denote the relative frequency of individuals with an entrepreneurial ability of x by f (x), and the cumulative relative frequency by F(x).6 It is also assumed that abilities are fixed and known with certainty by each individual. Jovanovic (1982) among others has relaxed this assumption, in a model where entrepreneurs learn about their x by observing their performance in entrepreneurship. Discussion of the Jovanovic model is deferred until chapter 11. Lucas (1978) assumed that x scales up an entrepreneur’s output of q to give net profit of π(x) = xq − c, where c is the cost of using capital and labour to produce q (similar results obtain if x scales down the entrepreneur’s costs, but it is simplest to work with the output assumption). The output price is normalised to unity and all people are taken to be risk-neutral. It follows directly that all and only individuals with x ≥ x˜ will become entrepreneurs, where x˜ is the identity of the ‘marginal entrepreneur’, defined as the person who is indifferent between the two occupations:

π(˜x) = w.

(2.1)

An implication of this ‘fundamental’ equation of entrepreneurial occupational choice is that there are a total of 1 − F(˜x) entrepreneurs; the remaining F(˜x) people work for the entrepreneurs as employees. This equilibrium is illustrated in Figure 2.2. A property of the Lucas production function xq is that entrepreneurs’ demands for labour and capital are greater among those with higher x. That is, abler entrepreneurs run larger firms, irrespective of whether size is defined in terms of employment or capital assets. This furnishes another reason why the ablest people become entrepreneurs.

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Retur ns

Profits, p(x)

W age , w

Ability

,x x~

x Employees

x Entrepreneurs

Figure 2.2 Occupational choice with heterogeneous entrepreneurial ability

Operating a firm enables able people to spread their ability over a larger scale and so reap greater returns (see also Murphy et al., 1991). Lucas (1978) obtained further results by making a dynamic extension to his model. He asked how entrepreneurs adjust their demand for factors of production (capital and labour) when the stock of capital increases as the economy develops. A key parameter in this regard is the technical elasticity of substitution, σ , which describes the sensitivity of entrepreneurs’ chosen capital–labour ratios to changes in the relative prices of capital and labour. Assuming that firm growth rates are independent of firm size (known as Gibrat’s Law, explained in chapter 11), Lucas showed that if σ is less than (greater than) (equal to) unity, increases in per capita capital in the economy decrease (increase) (leave unchanged) the equilibrium number of entrepreneurs, and increase (decrease) (leave unchanged) average firm size. The intuition behind this result is as follows. If the supply of capital increases, (a) labour becomes more productive and (b) the price of capital decreases relative to that of labour (i.e. the wage). Effect (a) increases the demand for labour while effect (b) decreases it. But if σ < 1, effect (b) is modest relative to effect (a), since relative factor usage does not respond much following the change in their relative prices. So the demand for labour rises overall, increasing the wage, and so encouraging marginal entrepreneurs to become employees. That increases x˜ and thereby also the average size of firms.7 It is interesting to interpret Lucas’ dynamic result in terms of a prediction about future trends in the fraction of the workforce who are entrepreneurs. Empirical estimates consistently point to an elasticity of substitution of less than unity (see, e.g., Hamermesh, 1993, pp. 92–104). Given also that capital per head tends to grow over time (Maddison, 1991), Lucas’model therefore predicts that the fraction of entrepreneurs will inexorably

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decline over time, while the average firm size and industrial concentration will both inexorably increase. Lucas (1978) concluded his article with an anecdote about a small, family-owned (‘entrepreneurial’) restaurant he visited on vacation. He conjectured that this organisational form would eventually come to be replaced by a large franchise outlet (‘paid employment’) as an inevitable consequence of rising real wages. More generally, one can observe the progressive disappearance of small, owner-managed convenience stores in Britain and America and the reallocation of their labour towards large supermarket chains. Consistent with these arguments, much of the observed productivity gains in the US retail sector in the 1990s involved the entry of new establishments associated with large multi-unit firms, and the exit of small independents (Haltiwanger, 2006). Crosssection comparisons between rich and poor nations bear this story out. For example, whereas fewer than 15 per cent of Ghanaian manufacturing workers are employed in establishments with more than ten workers, less than one per cent of total US valueadded in the manufacturing sector comes from establishments employing fewer than five employees (Gollin, 2008). Lucas (1978) also provided some evidence of his own that average firm size is positively related to the capital stock per head. He regressed the average number of employees per firm (as a proxy for average firm size) against per capita gross national product (as a proxy for the stock of capital per head). He estimated that a 1 per cent increase in GNP per capita is significantly associated with a 1 per cent increase in the average number of employees per firm. Gollin (2008) calibrates a ‘model economy’ based on a simple extension of Lucas’ model using Japanese data and also predicts a negative (‘L-shaped’) relationship between rates of entrepreneurship and GDP per capita. However, Lucas’ prediction that entrepreneurship is destined to inevitable decline as economies develop receives less support if the aggregate self-employment rate is used as a proxy for the size of the entrepreneurship sector. As shown in chapter 1, while the self-employment rate decreased steadily between the nineteenth and mid-twentieth centuries in most developed economies, the trend in some of them reversed in the last quarter of the twentieth century, at a time when real wages and the capital stock were both increasing. This leads one to ask whether something is amiss in Lucas’ model, or whether other factors are at work, overwhelming the mechanism proposed there. I consider these issues next. 2.3.2

Criticisms of the Lucas model Despite its elegance and profound influence on the economics of entrepreneurship, Lucas’ (1978) model can be criticised on several grounds. One technical objection is the assumption of Gibrat’s Law, which underlies the dynamic variant of Lucas’ model. Recent studies have cast doubt on the applicability of this ‘Law’, finding that firm growth rates are actually not invariant to firm size (see chapter 11). A more fundamental criticism of Lucas’ model is that it neglects technological change, which is arguably a more important source of macroeconomic growth than

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changes in the capital stock. Innovation and technological change may have endowed new entrepreneurial ventures with a competitive advantage over their larger counterparts in some industries, such as ICT (Acs and Audretsch, 1991). To the extent that this is true, more rather than less entrepreneurship would be expected to emerge as economies develop. A related problem is that Lucas’ model is highly aggregated and simplified, glossing over important industry composition effects. This is likely to be important in view of the concentration of entrepreneurs in particular industry sectors (see chapter 4). In particular, rising levels of prosperity often translate into greater demand for services (e.g. personal and customised services) that entrepreneurs are particularly efficient at supplying. Furthermore, Lucas did not define entrepreneurial ability, x, precisely, taking it to be exogenous with an unexplained provenance. As noted above, it is common for researchers to interpret ability in terms of human capital, such as schooling and experience. Unfortunately, the empirical evidence does not point to an unambiguous relationship between human capital and selection into entrepreneurship (see chapter 4). Occasionally, ability is defined in a more specific manner, for example as the ability to predict and adjust to idiosyncratic changes in consumer tastes, as in Takii (2008). However, this definition is more difficult to operationalise empirically. Another problem is that if entrepreneurs learn over time, it may be inappropriate to treat entrepreneurial managerial ability as fixed and exogenous (Otani, 1996). A simple test of whether entrepreneurial ability is fixed or improves with learning can be performed by estimating how entrepreneurs’ productivities (a proxy for ability) vary with the number of firms in a locality. If abilities are fixed, then more firms in a locality implies lower average entrepreneurial ability.8 But if entrepreneurs can learn from each other, thereby enhancing their ability, then more firms in a locality might imply higher average entrepreneurial ability. Measuring ability as average firm productivity in a sample of Italian manufacturing firms, Guiso and Schivardi (2005) detected a significant positive relationship between average firm productivity and the number of firms in the entrepreneur’s locality, supporting the learning hypothesis and rejecting Lucas’ static ability hypothesis. These findings may imply that instead of trying to reduce entry barriers, governments should promote entrepreneurial clusters since these can efficiently facilitate entrepreneurial learning and productivity. 2.3.3

Variants and extensions of the Lucas model Numerous variants of Lucas’ model have been proposed.9 In each of these models, the ablest people select into entrepreneurship. This section reviews three major extensions to Lucas’ model. The Lucas model assumes that ability is one-dimensional; that entrepreneurs compete in only one industry sector; and that ability only affects returns in entrepreneurship. In the first set of extensions, ability is allowed to be two-dimensional. The second extension introduces additional industry sectors for entrepreneurs to work in. The third recognises that ability can also affect wages in the outside option of paid employment. I now discuss each of these extensions in turn.

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One of the first models to make ability two-dimensional was Calvo and Wellisz (1980). Individuals are now distinguished by age as well as ability. Calvo and Wellisz showed that faster technological progress leads to an equilibrium outcome where older, inherently less able entrepreneurs are replaced by younger and inherently more able entrepreneurs. Gifford (1998) proposed a different two-dimensional model of ability, comprising ‘managerial ability’ (valuable for operating existing projects) and ‘entrepreneurial ability’ (valuable for innovating new projects). An implication of her model is that firms might do best passing up entrepreneurial opportunities because finite time and limited cognitive abilities enable them to pay only limited attention to new opportunities. That might help explain why new firms can exploit ideas which time-constrained incumbents choose not to develop themselves. The most influential model with multiple abilities is Lazear’s (2005). Suppose individuals differ among themselves by having different endowments of two skills, x1 and x2 . It is plausible that specialists in paid employment earn the maximum of x1 and x2 , i.e. max{x1 , x2 }. That is because they can choose to specialise in jobs where their highest skill is rewarded the most, while relying on other workers in the organisation to supply the other skill type. In contrast, solo entrepreneurs have to supply both skills themselves; and the performance of an entrepreneurial venture is only as good as its weakest link. Hence entrepreneurs earn λ min{x1 , x2 }, where λ > 1 is the market value of entrepreneurial ability. For any joint distribution F(x1 , x2 ), it follows that the more similar are x1 and x2 , the likelier any given individual is to do best by choosing entrepreneurship. Hence Lazear (2005) predicts that entrepreneurs are ‘jacks of all trades’, unlike employees who are specialists. Some evidence on this issue is reviewed in chapter 4. The second major extension of the Lucas model introduces multiple industry sectors. There are two principal models here. The first, by Murphy et al. (1991), departs from the Lucas model by showing that free occupational choice involving entrepreneurship can be inefficient. Murphy et al. (1991) consider two industrial sectors indexed by j ∈ {1, 2}, which use different technologies whose productivities at time t are denoted by Ajt . Productivity is assumed to evolve according to best practice (ability) in that sector: Ajt = Aj,t−1 max{xj,t−1 }, where {xj,t−1 } is the set of entrepreneurial abilities in industry j at time t − 1. That is, productivity in industry j at t is assumed to grow most in industries where the most talented entrepreneurs congregate. It is easy to think of several reasons why that might be so. For example, the most able individuals could pioneer radical new innovations specific to the industry which increase the productivity of every producer in that industry. In both industries, an entrepreneur’s output depends on the product of their own ability and the number of workers they hire. Suppose that the productivity of hired workers differs between the two industries, with less rapidly diminishing marginal returns in industry 1. This means that, all else equal, an extra worker in industry 1 will increase output by more than an extra worker in industry 2. Murphy et al. (1991) assume that entrepreneurs can freely choose which sector to enter to maximise their private returns. They then show that the ablest individuals, x ∈ [˜x1 , x], all become entrepreneurs

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in industry 1; the least able individuals, x ∈ [x, x˜ 2 ] all become workers; and individuals of intermediate ability, x ∈ [˜x2 , x˜ 1 ], all become entrepreneurs in industry 2. In this model there are two marginal entrepreneurs, x˜ 1 and x˜ 2 < x˜ 1 . The ablest entrepreneurs all bunch in the sector with the lowest diminishing marginal returns because this enables them to run larger firms and spread their ability over a larger scale. Unfortunately, while these entrepreneurial occupational choices are privately optimal, they are socially inefficient, because bunching does not maximise aggregate productivity growth. Given the definition of Ajt above, the social optimum would have the ablest entrepreneur joining one industry while the next ablest joins the other. The problem is that each entrepreneur does not take into account the technological externality caused by their own occupational choice decision. Nocke (2006) analysed a second model of multiple sectors, where competition is more intense in larger sectors. Only the ablest entrepreneurs can effectively compete in large sectors, but relatively few entrepreneurs possess sufficient ability to make entry into those markets attractive. This can arguably help explain the puzzling real-world observation that many large markets contain relatively few entrepreneurs and therefore less product variety compared with smaller markets. Before turning to the third set of extensions to Lucas’ model, note that entrepreneurs can not only choose their industry sector, but also their mode of entry, for example whether: to start a new firm; to succeed their parents in a family firm (if this option is available); or to acquire an external firm. According to Dennis (1997), 78.5 per cent of new industry entrants in the USA are de novo (i.e. brand-new entrants), with the remainder acquiring a business through inheritance, takeover or franchising. Parker and van Praag (2006a) estimated that 83 per cent of Dutch entrepreneurs in 1994 started their venture de novo, while 10 per cent took over a family firm and 7 per cent took over an outside firm. Using a forward-looking rational choice model, Parker and van Praag (2006a) argue that individuals with a family-business background will optimally acquire less human capital. Having lower lifetime wealth from human capital these people are as a result more risk-averse (by DARA: see the first part of the chapter appendix) – and so are more likely to take over firms than to start risky new ones from scratch. Holmes and Schmitz (1990) explore a different angle of the ‘mode of entry’ issue: whether an entrepreneur should continue operating a venture, or transfer it to a possibly less able entrepreneur in order to release time and resources to explore new opportunities. Holmes and Schmitz (1990) predict that the least able types will take over and manage existing firms, while the most able will specialise in setting up new businesses. Those with intermediate ability optimally either manage the businesses they started, or replace them with higher-quality businesses purchased from abler entrepreneurs. The Holmes–Schmitz model incorporates and extends ideas from Schumpeter and Kirzner about opportunity recognition, and helps explain why some individuals are ‘portfolio’ entrepreneurs, running several businesses simultaneously, while others become ‘serial’ entrepreneurs, running a succession of businesses one after another (see chapter 1). This model can also explain why some entrepreneurs simply rely on buying businesses created by others.

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The third set of extensions to Lucas’ model allows the outside wage w also to be a function of ability x: i.e. w = w(x).10 There are two possibilities (Jovanovic, 1994). One arises if more able individuals earn more in both occupations. Then π(x) and w(x) are both increasing functions of x. The marginal entrepreneur x˜ is defined by π(˜x) = w(˜x). Several outcomes can arise, including multiple marginal entrepreneurs (Figure 2.3(c)). Another outcome, illustrated in Figure 2.3(a), is that the least able individuals become entrepreneurs. Another possibility is that π(x) is an increasing function of ability x, while w(x) is a decreasing function of x. This could arise, for example, if x measures productive rebelliousness, which pays off in entrepreneurship but is penalised in team-based paid employment. In this case, Figure 2.3(b) is applicable and, as in Lucas’ model, only the highest-ability types become entrepreneurs. However, this case seems less realistic than the one where abilities increase in both occupations, but do so more steeply in entrepreneurship. As Frank Knight put it: It may well be true that able leaders are in general also more competent workers, or operatives, but the gain in superior direction is so much more important than that from superior concrete performance that undoubtedly the largest single source of the increased efficiency through organisation results from having work planned and directed by the exceptionally capable individuals, while the mass of the people follow instructions. (Knight, 1921, p. 17)

If w(x) does not depend on x, the outside return is simply a constant w, the case considered hitherto. This case might arise even if workers have heterogeneous abilities, provided that there is asymmetric information in the labour market, whereby employers cannot discover the abilities of their workers and workers cannot credibly signal their abilities to employers. Then employers have to offer all workers a single pooled wage, irrespective of their hidden ability. Interestingly, this can cause occupational choice to be inefficient. Asymmetric information gives able individuals an additional incentive to turn entrepreneur, since some of them who would choose paid employment under full information might now choose entrepreneurship under asymmetric information to avoid being penalised by a fixed wage in paid employment which does not reflect their above-average ability. The result is too many entrepreneurs in equilibrium, since some individuals choose entrepreneurship purely as a result of imperfect information about their ability (Laussel and Le Breton, 1995).11 Laussel and Le Breton (1995) speculate that their findings might have special implications for transition or developing economies, which lack institutions for screening workers efficiently, and which might therefore be burdened with too many (rather than too few) small-scale entrepreneurs. One problem with Laussel and Le Breton’s model is that workers and employers have incentives to engage in monitoring to reveal hidden abilities. Parker (2003c) allows for this possibility in a model in which individuals again possess private information about their abilities, and can choose freely between producing a good with either a safe or a risky technology. Under plausible conditions, it is shown that employers optimally deploy the safe technology and screen workers to reveal their abilities at the outset, whereas banks deploy the risky technology and monitor entrepreneurs only if and when

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j

(a)

Expected retur n in occupation

j = PE

j = EN

~ x

0 Ability

x

Expected retur n in occupation

j

(b)

j = EN

j = PE

~ x

0

Ability

x

Expected retur n in occupation

j

(c)

j = EN

j = PE

0

~ x2

~ x1 Ability

x

Figure 2.3 Occupational choice with two occupations, entrepreneurship (EN) and paid employment (PE). (a) PE attracts the ablest entrepreneurs: x > x˜ enter PE (b) EN attracts the ablest entrepreneurs: x > x˜ enter EN (c) Multiple marginal entrepreneurs, x˜ 1 and x˜ 2

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they default. The reason is that banks have to monitor entrepreneurs ex post anyway to guard against opportunistic defaults (see chapter 7); while efficient production in large firms requires accurate separation of worker types from the outset.12 A variety of possible occupational choice outcomes emerges from the Parker (2003c) model, including all of those illustrated in Figure 2.3. This model also has some implications for the efficiency of bank-financing of entrepreneurial investments – an issue followed up in chapter 7. 2.4 The occupational choice model III: heterogeneous risk attitudes – the Kihlstrom and Laffont (1073) model

What if individuals can choose freely between entrepreneurship and paid employment, as in the models just discussed, but face risk in entrepreneurship and have heterogeneous aversion to risk rather than heterogeneous entrepreneurial ability? The economic implications of this question have been analysed by Kihlstrom and Laffont (1979) (henceforth KL79). In Kihlstrom and Laffont’s own words, their model is ‘a formalisation, for a special case, of Knight’s discussion of the entrepreneur’ (1979, p. 745). This is because Knight proposed diversity among individuals with regard to confidence in their judgement to run firms: those who are ‘confident and venturesome “assume the risk” or “insure” the doubtful and timid by guaranteeing to the latter a specified income in return for an assignment of the actual results’ (Knight, 1921, p. 269). KL79 abstract from unequal managerial abilities: risk attitude is the only source of heterogeneity in their model. As noted earlier in the chapter, more risk-averse individuals are willing to pay a premium in order to insure themselves against risk. Put another way, faced with the choice between receiving a safe return in paid employment, w, and a risky return in entrepreneurship, a more risk-averse person is likelier to take the safe option. KL79 proved that if there is a continuum of agents differentiated only by their risk attitude, only the least risk-averse will become entrepreneurs. This can be understood within the usual occupational choice framework in terms of a marginal entrepreneur who is indifferent between risky entrepreneurship and safe paid employment. Everyone who is less risk-averse than the marginal entrepreneur becomes an entrepreneur, and everyone who is more risk-averse becomes an employee (see Figure 2.2, where x is now interpreted as an inverse measure of risk aversion). To be a viable occupation in this model, entrepreneurship must pay a risk premium. This is only attractive to the least risk-averse; it is insufficient to compensate more risk-averse individuals. The prediction that less risk-averse individuals are more likely to become entrepreneurs is intuitive and has received independent empirical support (see chapter 4). KL79 went on to derive several further results from their model. First, more riskaverse entrepreneurs are predicted to operate smaller firms, i.e. use less labour than less risk-averse entrepreneurs, under reasonably general conditions. Second, a general increase in individual risk aversion reduces the equilibrium wage. This is implied by the previous two results, because greater risk aversion decreases the demand for labour by

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each entrepreneur and increases the equilibrium number of employees. Both changes reduce the aggregate demand for labour and hence w (cf. Kanbur, 1979). If all individuals were risk-neutral in the KL79 model, industry equilibrium would be Pareto efficient. That is, there would be no allocations that could make one individual better off without making another individual worse off. However, when some individuals are risk-averse, three types of inefficiency can arise. First, maximisation of aggregate output requires all firms to produce the same output when the production function is concave. However, entrepreneurs with heterogeneous risk aversion operate firms of different sizes, as noted above. Second, individuals could be made better off if risks were shared, but there is no mechanism for facilitating this. Third, in general the wrong number of individuals become entrepreneurs. On the one hand risk aversion causes too few individuals to become entrepreneurs (from the standpoint of efficiency), but on the other hand risk aversion causes too small a demand for labour, reducing w and so causing too many individuals to choose entrepreneurship. In general, the two effects will offset each other and the net effect on efficiency cannot be predicted without further information about tastes and technology. For example, in the special case where all individuals are equally risk-averse, it can be shown that there will be too many entrepreneurs in equilibrium. Another special case is constant returns-to-scale technology, under which only one firm is optimal, compared to the greater number that would emerge in KL’s competitive equilibrium.13 One way of enhancing efficiency would be to introduce a risk-sharing mechanism such as a stock market (Kihlstrom and Laffont, 1983; Grossman, 1984). For example, in the context of international trade with foreigners who have a comparative advantage in entrepreneurship-rich goods, Grossman (1984) argues that establishing risk-sharing mechanisms to stimulate domestic entrepreneurship is a better solution than imposing welfare-reducing tariffs or other trade restrictions on foreign entrepreneurs. However, a problem with the specific solution of a stock market to share risks is that it is likely to be impractical for small enterprises. The high fixed costs incurred by a stock market listing are likely to deter small firms from diversifying their risks in this way. One might also ask whether investors can write financial contracts to insure riskaverse entrepreneurs. In fact, standard principal-agent models in microeconomics predict that variable returns are generally required to elicit the high levels of discretionary non-contractible entrepreneurial effort needed to make ventures succeed. If individuals faced a fixed return irrespective of what they do, they would have incentives to economise on privately costly discretionary effort. This is a theme which will recur several times in this book. The implication is that it can be optimal for lenders to write contracts that avoid fully insuring entrepreneurs (Keuschnigg and Nielsen, 2004b; Rampini, 2004). With regard to partial insurance on the other hand, the results are more subtle and surprisingly far-reaching, as an important paper by Newman (2007) demonstrates. Newman envisages an insurance company offering entrepreneurs a predetermined and relatively smooth income stream in return for entrepreneurs’ risky stream of profits. Entrepreneurs still have to bear some risk, in order to induce them to supply effort;

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but they receive partial insurance through the less risky income stream provided by the insurer. Newman (2007) contends that the conceptually modest extension of partial income insurance creates a serious conceptual problem for the KL79 model, since he shows it implies that the poorest, and hence the most risk-averse individuals (under DARA) become the entrepreneurs, while the wealthy work for them as their employees! The rationale underlying this counter-intuitive result is that it is easier for an insurance company to incentivise poor individuals to exert effort via an income-smoothing scheme than the wealthy. Poor individuals are more risk-averse, so it is cheaper for the insurance company to devise an income stream which provides the correct incentives for them to exert effort in entrepreneurship. In contrast, wealthy people need to bear a large amount of risk under partial insurance in order to induce them to supply effort – and that can make entrepreneurship expensive and unattractive for these agents relative to ‘safe’ paid employment. Newman (2007) claims that the preposterous prediction that wealthy people work for poor entrepreneurs calls into question any theory of entrepreneurship based on heterogeneous risk attitudes in which entrepreneurs’ primary role is to insure workers. As he put it, ‘The fragility of this theory’s [KL79’s] empirical predictions suggests that we probably should look elsewhere for explanations of the roles and causes of entrepreneurship’ (Newman, 2007, p. 12). However, there are two limitations to Newman’s contention. First, if risk attitudes are independent of wealth, then KL79’s analysis continues to apply regardless of whether entrepreneurs can be insured. And even if risk attitudes are not independent of wealth, a private market that insures entrepreneurs’ incomes is not guaranteed to exist. In practice, insurance for business owners tends to be against specific risks (e.g. loss or damage of business equipment, or travel insurance), and not of the form Newman studied, which has insurers claiming entrepreneurs’ payoffs in return for payouts of a partially smoothed stream of income. One reason why Newman’s insurance system is not observed is that there are obvious incentives for entrepreneurs to under-report their incomes to the insurers. If enough entrepreneurs did this, an income-transfer system of this kind would quickly become infeasible. With a missing market for income insurance, one immediately returns to the world of KL79.

2.5 The very existence of an entrepreneurial option

This section changes the emphasis from characteristics of individuals to characteristics of firms. I first ask why a profit-making entrepreneurial occupational choice exists at all, if entrepreneurs’ tasks can also be performed within incumbent firms. Some answers to this question are offered in the first part of this section and in section 2.6. In the second part of this section I then ask why enterprises which do not maximise profits, or which are less efficient than incumbents, can survive and even thrive in competitive markets. Such enterprises include small firms which lack economies of scale; family firms with objectives other than pure profit maximisation; and not-for-profit (social) enterprises.

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2.5.1

Selection

Theory of the firm considerations Some industries and occupations are characterised by high levels of independent smallscale business ownership and self-employment. Examples include taxicab and truck driving, sales, draughtsmen services and freelance journalism, among others. Hitherto, we have asked why individuals might choose to become entrepreneurs. But one can also ask why entrepreneurship should ever be an option in the first place. Putting it another way, one can ask when it is more efficient for the owners of large firms (called ‘principals’) to unbundle their firm and buy in some parts of their production from independent contractors, rather than organising production activity within the firm. In the latter case, principals pay workers (called ‘agents’) a contracted wage. If only the most efficient producers can survive under competition, firm organisation can come to represent a binding constraint on the occupations available to individuals to choose from. An extreme articulation of this position might even claim that firm characteristics, rather than individual characteristics, lie at the heart of why some individuals are observed to be entrepreneurs. Essentially, this question goes back to the classic work of Coase (1937). Coase argued that firms exist because they economise on transaction costs of exchange compared with individual producers contracting in spot markets. But internal exchange can also be costly, leading to cases where individual contracting is preferred. Holmstrom and Milgrom (1994) refer to the choice between hiring employees or outside contractors as the ‘make or buy’ decision. For example, personal ownership of their own vehicle gives independent self-employed truck drivers greater incentives to care for and maintain it than if they are hired as employees driving the company’s vehicle. An employee’s lack of incentives to care for a company asset represents an agency problem. Given this (and the relative ease of measuring delivery performance), one might therefore expect logistics companies to hire independent self-employed truckers rather than to maintain an expensive payroll of employee truck drivers (Milgrom and Roberts, 1992, pp. 249, 311). In fact, contrary to this argument, independent contractors accounted for less than one-third of US trucking activity conducted by interstate trucking firms with $1 million or more in revenues in 1991 (Nickerson and Silverman, 2003). And in a study of the US taxicab industry, Sherer et al. (1998) found that taxicab companies make greater use of employee drivers than of independent contractors. To explore the forces at work here, consider a simple principal-agent problem in which employers (principals) can either hire individuals (agents) to produce output via an employment contract, or buy in the production from independent contractors. Contractor status is equated with independent entrepreneurship (self-employment or business ownership) in what follows. A simple way of conceiving this is to imagine employers ‘contracting out’employee work to the self-employed by changing the nature of their contract, as happens in some software companies, for example.14 The text below provides a verbal account of this simple ‘hire or outsource’ model; the second part of the chapter appendix presents it formally.15 An important consideration for employers wondering whether to produce output inhouse using an employment relationship or to buy in production from independent

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entrepreneurs is the degree to which in-house production generates other ‘asset specificity’ benefits which would be lost if it were to be contracted out. For example, in-house production might generate knowledge or output spillovers which increase production elsewhere in the firm. If these asset-specificity benefits are valuable enough, employment (‘hire’) might dominate entrepreneurship (‘outsource’) and prevent the latter from ever emerging as a feasible occupational choice. But if these benefits are not very valuable, or absent altogether, employers might be able to do better by buying in the services of outside independent contractors. Independents might be cheaper than direct employees if the latter but not the former are covered by legally binding minimum wages, costly employment protection legislation and trades union collective bargaining (Parker, 2007b). Under these circumstances, an employer might be able to share the benefits of outsourcing by offering workers attractive contracts to become independent contractors. It is notable that by making the agent rather than the employer the residual claimant, the agent will always supply high levels of non-contractible effort. Thus the avoidance of agency costs provides a further reason why entrepreneurship might dominate paid employment and emerge as a feasible organisational form. It also highlights a potential efficiency benefit of entrepreneurship relative to paid employment, since it is not always possible for employers to incentivise employees to supply high levels of discretionary effort. The formal ‘hire or outsource’ model outlined in part 2 of the chapter appendix provides further details. Several examples of asset-specific benefits which encourage incumbents to keep work in-house arise in particular industries. For example, in the trucking industry asset-specificity benefits can be embodied in a logistic company’s reputation value of a good service record, or the value derived from co-ordinating interdependent freight deliveries (Nickerson and Silverman, 2003). These benefits might be lost if logistics companies outsourced trucking services to independent contractors, since the latter care little about the company’s reputation and have few incentives to take account of the company’s overlapping schedules with other deliveries. In the direct selling industry, the asset-specific benefit from keeping work in-house could be complementary with non-selling activities (Anderson and Schmittlein, 1984). In the specific context of the market for taxicab drivers, Sherer et al. (1998) highlighted the importance of training to safeguard product quality and the value of co-ordinating workers to ensure they engage in mutually co-operative behaviour. These considerations can also promote the use of employees rather than contractors. For example, contractors who compete against each other in an ‘eat what you kill’ environment might overuse scarce resources in an effort to gain a competitive advantage over rival contractors serving the same principal. And small firms and the self-employed are known to perform less training than large firms (see chapter 10), which can reduce the quality of outsourced work and hence the attractiveness of contracting out (Briscoe et al., 2000; Meager and Bates, 2004). More generally, Sherer et al. (1998) highlight an advantage of hiring employees over contractors based on the notion that employees can be more easily redeployed into other jobs in the firm when unforeseen contingencies arise. Employment contracts can therefore be thought of as incomplete contracts which are superior to spot

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(entrepreneurial) contracts when some circumstances cannot be perfectly anticipated. Employment contracts can also avoid continual renegotiations, which entail search and transaction costs and are prone to opportunism, including hold-up problems (Grossman and Helpman, 2002). An example of opportunism arises in the trucking industry. Most independent contractors optimally buy trucks which are readily deployable, so there is a thin market for specialised trucks. To avoid hold-up problems, it pays logistics companies to buy specialised non-standard vehicles themselves. The evidence is consistent with this outcome (Nickerson and Silverman, 2003). Evidence also suggests that spot contracting is more common when local markets are thicker, not only in trucking (Hubbard, 2001), but also in business services (Ono, 2007), and across industries in general (Holmes, 1999). If asset-specific benefits from keeping work in-house are negative, entrepreneurship can be expected to dominate paid employment quite generally. This is likely to occur in vertically integrated firms which suffer from diseconomies of scale and scope, since it can pay these firms to focus on their core competencies in order to safeguard product quality. They can do so by accessing externally provided specialised services that are unavailable within the firm (Gramm and Schnell, 2001; Grossman and Helpman, 2002). In principle, specialised outside contractors can centralise expertise, reap economies of scale and exploit smoother production schedules, especially for intermediate goods or services which are only needed irregularly. The formal ‘hire or outsource’ model set out in part 2 of the chapter appendix is consistent with several aspects of Holmstrom and Milgrom’s (1994) theoretical analysis, as well as some independent empirical evidence. One prediction is that independent entrepreneurship is more likely to emerge when output is hard to measure. A second prediction is that entrepreneurship is more likely to emerge when it is costly to monitor employees. Garen’s (2006) analysis of US Dictionary of Occupational Titles data supports this argument. Garen (2006) showed that independent contractors tend to occupy jobs that require greater worker expertise and judgement (such as analysing data and undertaking various and complex duties) – for which worker monitoring is difficult and costly. Being more difficult to monitor, jobs with these characteristics can make independent entrepreneurship an optimal form of firm organisation, since being residual claimants, entrepreneurs have direct incentives to perform well.16 The first two columns of Table 2.1 summarise the firm-level determinants of the decision to ‘make or buy’ discussed in the foregoing. The evidence to date is scant but supports some of the above arguments. Harrison and Kelley (1993) surveyed plants in the US metalworking sector in 1986–7, and analysed the determinants of contracting out of a particular production process: metal machining or metal-cutting. Nearly 60 per cent of the firms which responded to the survey claimed that they regularly contracted out some machining work. Using logistic regression, Harrison and Kelley reported that the size of the parent company, the wages of the machinists and the diversity of the plant’s product mix were all significantly associated with a greater propensity to contract out machining. These findings are consistent with explanations 6 and 9 in column 1 of Table 2.1. A second study, by Abraham and Taylor

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Table 2.1. Demand- and supply-side factors influencing the ‘make or buy’ decision Firm choice (Demand side)

Individual choice (Supply side)

Make (hire) employee

Buy from entrepreneur

Become an entrepreneur

Become an employee

1. Assets specific to firm; externalities

1. Assets not specific to firm; no externalities 2. Output hard to measure 3. Effort, care of assets and motivation unimportant 4. High monitoring costs 5. Thick markets

1. Autonomy (flexible schedules) desired 2. Income smoothing not desired 3. No borrowing constraints

1. Autonomy not desired

2. Output easy to measure 3. Effort, care of assets and motivation important 4. Low monitoring costs 5. Thin markets: hold-up problems 6. No diseconomies of scale or scope 7. Training needed to enhance quality and uphold routines 8. Gains from co-ordinating or redeploying workers 9. Low employment costs and restrictions

2. Income smoothing desired 3. Borrowing constraints

6. Diseconomies of scale or scope 7. Training and worker quality inessential

8. No gains from co-ordinating or redeploying workers 9. High employment costs and restrictions

(1996), using data from the US Bureau of Labor Statistics Industry Wage Surveys, found that smaller firms in urban locations and firms paying above-average wages were more likely to contract out services. Their interpretation was that small firms have greater need of, and urban firms have easier access to, specialised services provided by outside contractors. To complete the model’s treatment of the ‘make or buy’decision, the last two columns of Table 2.1 suggest motives from the worker’s perspective for choosing between independent contractor status (entrepreneurship) and paid employment. In the formal ‘hire or outsource’ model, autonomy (enabling flexible work schedules: row 1) can explain why individuals might be willing to forgo income smoothing provided by employers (row 2). On the other hand, highly risk-averse individuals would prefer income smoothing in paid employment over the income variability on offer in independent entrepreneurship.17 And borrowing constraints might render workers incapable of

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Selection

raising the necessary funds to go independent in any case (row 3; this is T ∗ in the formal model; see also chapter 9 for an analysis of borrowing constraints). Finally, I briefly discuss how ‘theory of the firm’considerations might affect the structure and feasibility of ‘team’start-ups involving multiple founders. A natural question to pose is whether members of a team of founders are likely to continue to share ownership rights in the future, or whether one member will end up with exclusive ownership and control rights (the ‘entrepreneur’), employing the others (‘employees’) for a salary. In a model which explores this question, Barzel (1987) considered two founders in separate jobs, one of which (A, say) makes output that is intrinsically easy to monitor, while the other (B) makes a productive contribution that is intrinsically difficult to monitor. For example, A could be the producer of widgets, whereas B is the co-ordinator of contracts to supply clients, the keeper of accounts and records, and the searcher for new clients and cheaper raw materials. Barzel predicts that B will become the residual claimant (‘entrepreneur’), employing A using a standard wage contract. The reason is simply that, by the nature of her job, B finds it easier to shirk. But B has fewer incentives to shirk if she is given residual claimant status rather than a wage contract, since as a residual claimant she bears directly the costs of her own foregone effort (a similar point applies in the ‘hire or outsource’ model described above). Hence this arrangement maximises the joint product from collaboration and hence the payoffs of the two agents. An important point is that the identity of the entrepreneur is not fixed, but depends on the relative wages commanded by the two collaborators. In particular, if A’s productivity (and hence wage) increases relative to B’s, then A could eventually replace B as the entrepreneur. The reason is that the loss from shirking by A under a fixed wage contract may eventually become so great that it becomes imperative to eliminate it – which is achieved by shifting the residual claimant status from B to A. Barzel’s insight is consistent with a prediction from incomplete contract theory (Grossman and Hart, 1986) that individuals who objectively have the most to gain from ownership become the residual claimants and monitor other members of the production team (see also Alchian and Demsetz, 1972). Alvarez and Parker (2009) demonstrate that this prediction holds under conditions of risk, but not necessarily under ‘Knightian’ uncertainty, in the sense of the terms as defined earlier in this chapter. In practice, many founders of new ventures operate under conditions of uncertainty and often do not agree about the distribution of states of nature. In which case, Alvarez and Parker (2009) show that both the founders’ subjective beliefs and their respective productivities determine the identity of the residual claimant throughout the evolution of the firm. In contrast to incomplete contract theory, the residual claimant need not be the ablest entrepreneur with the most to gain objectively, but could instead be the most optimistic member of the founding team. Naturally, ventures founded on the basis of over-optimism are less likely to take objectively sound decisions and survive than those founded on the basis of a hard-headed evaluation of relative ability. Perhaps counter-intuitively in this context, Parker and Alvarez (2009) point out that extreme uncertainty can be a more benign venture-founding environment than moderate uncertainty. The reason is that highly uncertain environments force entrepreneurs to ‘keep an open mind’ and update their

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beliefs using market data more rapidly than they would in less uncertain environments. In the long run, uncertainty eventually dissolves into risk as entrepreneurs learn the true distribution of states of nature. Then the predictions of incomplete contract theory once again prevail – provided the venture survives the consequences of early decisions made on the basis of possibly incorrect subjective beliefs under uncertainty.

2.5.2

Non-profit-maximising ventures Enterprise is frequently carried out in non-profit-maximising organisations as well as in conventional profit-maximising private sector ventures. Two obvious questions then arise: Why would any entrepreneur not want to maximise profits? And how can nonprofit-maximising, or inefficiently small ventures, survive in competition with efficient profit-maximising rivals who can be expected to undercut them in competitive markets? These questions will be answered in two specific contexts below: that of small (and often family-owned) firms and that of social enterprises.

Small and family-owned firms A fundamental question is why, when large firms enjoy scale economies in production, small firms which lack scale economies can survive in competition with them. Not only can large companies price below smaller enterprises, but their greater diversification enables them to take risks which smaller firms cannot, including investments which take a long time to generate returns. Yet we saw earlier in this chapter that both the Lucas (1978) and the Kihlstrom and Laffont (1979) models give rise to a size distribution of firms in which there is no intrinsic penalty to smallness. These models can therefore explain the coexistence of small entrepreneurial and large corporate firms without having to appeal to special factors that give small firms an advantage over large ones. Nevertheless, some special factors which do give small ventures a positive advantage over their larger counterparts are of intrinsic interest, to be sure, and are considered in various places throughout this book. Among these, it has been suggested that entrepreneurial firms enjoy superior innovation performance, being more efficient at supplying small batches of goods or customised goods and services than their larger rivals, who tend to have a comparative advantage at producing standardised products in large production runs. Small firms can also avoid diseconomies of scale (Williamson, 1985; Reid and Jacobsen, 1988). But a different and sometimes overlooked aspect of non-profit-maximising entrepreneurship is the existence of non-pecuniary benefits from business ownership. These benefits can explain why non-profit-maximisers can compete with or even undercut profit-maximisers, since the former may be willing to accept below-market incomes in return for non-pecuniary benefits. As one researcher has put it, ‘A casual look at neighbourhood pharmacies, groceries, small farming and independent truckers suggests that competitive pressures have not driven out small businessmen with preferences for self-employment, despite sometimes meagre returns’ (Feinberg, 1980, p. 1166). This could be another reason why Lucas’ (1978) argument

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that capital accumulation progressively drives these types of firm to the wall might be incomplete.18 Even so, one can still ask why it is that in almost every industry large firms, with all their advantages of scale, do not supplant small enterprises on efficiency grounds. Wiggins (1995) proposed an answer to this question based on legal costs and incentive contracts. In circumstances when employees know that their employer has an incentive to renege on promised compensation for their effort, it becomes costly for employers to commit to strong incentives. When these costs outweigh the benefits of scale, the small independent firm can replace the large firm as the efficient form of organisation. Thus when costs of employers reneging are low, workers who anticipate employer reneging to be likely and who cannot afford the legal costs of tort can only guarantee being compensated for their work by becoming entrepreneurs themselves and owning the rights to the fruits of their own effort. This insight might possibly explain why some sectors of the economy are dominated by small firms rather than by large ones, including high-tech research-intensive enterprises with long development lead times which afford ample opportunities for employer reneging. Another question is how family firms can survive against non-family firms. Family firms might compromise efficiency by utilising narrow (within-family) pools of managerial expertise, or by pursuing objectives of family harmony rather than growth opportunities (Westhead and Howarth, 2006). But family firms might also enjoy efficiency advantages over conventional profit-maximising firms, for example by commanding greater loyalty and trust from their workforce. Perhaps reflecting these offsetting advantages and drawbacks, the balance of evidence about the relative performance of family compared with non-family firms is mixed (Bertrand and Schoar, 2006; Westhead and Howarth, 2006). Social enterprises According to a recent definition: The term ‘social enterprise’ describes organisations…that aim to achieve financial sustainability through trading for a social purpose. Any surplus they make is usually re-invested to further the approved mission of the organization, and not distributed for private gain. They are located mainly in the non-profit sector, although several, such as The Big Issue and the Grameen Bank, have been incorporated as for-profit social enterprises. (Haugh, 2006, p. 401)

Social enterprises are part of the not-for-profit (NP) sector, which plays an important role in many modern economies. According to Steuerle and Hodgkinson (1999, p. 77), NPs accounted for roughly 7 per cent of US GDP in the 1990s, and their number grew at a faster rate (5 per cent per annum) than those of for-profit (FP) enterprises (1.4 per cent). NPs enjoy relatively high survival rates, with about 60 per cent of the 130,000 new NPs formed between 1992 and 1996 in the USA still being in existence in 1996 (Cordes et al., 2004). They also employ about 4.4 per cent of the economically active population across the world (Haugh, 2006, p. 403). In many countries NPs are heavily concentrated in particular services, especially labour-intensive ones like childcare, medical care, education and care for the elderly (Rose-Ackerman, 1996; Haugh, 2006).

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An important question is how NP enterprises can coexist with FP firms. Altruism has been proposed as one answer.19 Investors do not bear the costs of corporate social responsibility or other NP activities in the stocks that they hold, because in an efficient market these costs are already impounded in the prices of the shares when they purchase them. A lower share price simply increases the cost of capital to entrepreneurs, so NP entrepreneurs bear the cost, which they might be willing to do if they are altruistic (Baron, 2007). And altruism does not seem to prevent NP entrepreneurs from exploiting profit-making opportunities when they arise (James, 1998). It is less clear why non-altruistic entrepreneurs running FPs do not enter and drive NPs out of the market. One reason might be favourable tax treatment of NPs, which takes the form of tax exemptions on eligible income and tax deductibility on donations. In some countries such as the USA, there are also additional exemptions from sales and property taxes, and even postage subsidies. But tax benefits are not the essential characteristic of NP social enterprises (Glaeser and Shleifer, 2001). Many NPs were founded before tax benefits were introduced; and these benefits are limited or non-existent for noncharitable social enterprises (Weisbrod, 1988, pp. 4–5). All in all, some two-thirds of US NP do not qualify for tax deductions on contributions or subsidised postal rates. Their greatest benefit is probably the (non-deductible) effort input of volunteer workers (see below). An alternative explanation for NPs coexisting with FP enterprises is based on the notion of selfish behaviour combined with a Non-Distribution Constraint, NDC (Easley and O’Hara, 1983; Hansmann, 1987). An NDC is a legal constraint that prevents any surpluses generated by NPs being distributed to their owners in the form of stock, dividends or above-market salaries and perks to their employees. Instead, any surpluses must be reinvested in the venture. Easley and O’Hara (1983) derive the NDC as the outcome of a game between society and owner-managers, where the latter produce some benefit valued by society. When the benefit is not directly observable by members of society, it may be optimal to restrict owner-managers’ compensation to provide incentives for the desired output to be produced. In this case, the NP-NDC organisational form emerges. But when the social benefit is transparent, managerial compensation is left unrestricted in the optimal contract, which corresponds to an FP organisational form. More generally, an NDC may offer society benefits by protecting investments made by donors, volunteers, consumers and employees from ex post appropriation by rentseeking entrepreneurs. An NDC signals a credible commitment to outside stakeholders that an entrepreneur running a social enterprise will not exploit their donations by, for example, expropriating them and cutting back on other investments. Conventional FP firms cannot make this commitment, which puts them at a competitive disadvantage, at least for consumers for whom product quality matters and is hard to verify (Glaeser and Shleifer, 2001). Instead, FPs have to appeal to better-informed consumers. This can explain why many industries are characterised by mixed NP and FP enterprise provision. As an example, consider nursing care homes. FP care homes tend to use more sedatives – a cheap way of keeping patients calm – than NP care homes, which provide more

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intensive patient care instead (Weisbrod, 1988, chap. 8). Sedatives are a cost-reducing strategy that adversely affects non-contractible quality; consumers anticipating such incentives in the FP sector demand NP care homes instead. The NDC in an NP eliminates the profit incentive to compromise on quality, which FPs lack. This may even enable NPs to command a higher market price than FPs (Glaeser and Shleifer, 2001).20 Other evidence supports this argument too. Weisbrod (1998, chap. 8) reports that NP healthcare providers offer higher levels of hard-to-detect quality than their FP counterparts do, including higher levels of consumer satisfaction and greater provision of information to customers. Rose-Ackerman (1996, sec. 5) cites evidence that quality in child daycare tends to be higher in NPs than in FPs, though this outcome often entails higher production costs. The NDC can also confer on NPs a competitive advantage by helping them to attract donations (Glaeser and Shleifer, 2001) and greater worker effort (Francois, 2003). That is because the NDC ensures that donations of money and labour effort will not simply be converted by the NP into profit, something that cannot be guaranteed by FPs. Volunteer labour turns out to be very important to many NPs, accounting for over 40 per cent of their workforces on average, compared with virtually none for FPs (Haugh, 2006, p. 403). FPs can be further out-competed if workers care about an NP’s social mission, since then NPs can attract the effort of paid employees with a smaller wage premium than FPs do. However, this particular argument is not supported by evidence from Mocan and Tekin’s (2003) analysis of data from 398 day care centres in four US states, which showed that workers in NPs receive substantial premiums in their wages and compensation compared with their counterparts in the FP sector. That might be because workers share in the proceeds, whose disbursement is restricted by the NDC. The discussion so far suggests that the NDC may be a major reason why social enterprises can compete with for-profit enterprises. There are other possible reasons as well. For instance, NPs can cater for heterogeneous demands. Governments are often under pressure to provide uniform levels of services on a majoritarian basis, leaving gaps that can only be filled by specialised NPs (Weisbrod, 1988). Some social enterprises furnish opportunities for ideologically motivated entrepreneurs to influence their environment, and connect with consumers with similar motivations. On the other hand, the NP organisational form is not a unique signal of quality. Reputation, competition and certification are others (Francois, 2003). And it should be borne in mind that social entrepreneurs might achieve their goals more effectively by running an FP and devoting the profits to charitable purposes. It is possible that some public goods would not be forthcoming at all without NDCs or NPs – which provides a possible social welfare justification for the (institutionally imposed) NDC. More controversial from a policy standpoint is the tax deductibility of NPs. Weisbrod (1988) observes a growing trend for NPs to engage in profit-making activities. This brings them into direct competition with FPs, possibly unfairly if they are tax-advantaged; and it may also divert NPs from their central purpose of enhancing social welfare. For example, the YMCA has been criticised for using its charitable status to help undercut private sector health and fitness clubs. The YMCA responds

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by claiming it uses membership revenues in affluent areas to cross-subsidise provision in poor areas, which would not otherwise be forthcoming. Weisbrod (1988) recommends replacing targeted tax deductibility with neutral tax credits to address this problem. One can also argue that NDCs might put NPs at a competitive disadvantage by blunting the profit motive and hence incentives to be efficient. That can lead to shirking in NPs, unless NP managers are highly motivated by non-pecuniary factors. Lacking market discipline, and buoyed by favourable tax treatment and subsidies, NPs can survive longer than is efficient, and lack external scrutiny that can remove underperforming managers (Rose-Ackerman, 1996). Of course, the separation of ownership and control can also blunt efficiency in large FPs. Unfortunately, there are serious difficulties in comparing efficiency in FP and NP enterprises, owing to differing quality levels, different strategic objectives and disparate consumer needs (Weisbrod, 1988, chap. 2; Rose-Ackerman, 1996, sec. 5). It does seem, however, that NPs as well as FPs provide fewer pure charity services in more competitive markets (Rose-Ackerman, 1996, p. 720). Finally, we still know relatively little about the identity of the social entrepreneurs who operate NPs. Weisbrod (1988, pp. 32–3) cites some anecdotal evidence that NP managers attach greater importance to being cheerful, forgiving and helpful than FP managers. The latter state that financial prosperity and ambition are more important to them, and are likelier to claim that they are driven by a ‘need for power’. Other research shows that lawyers working for NP ‘public interest’ law firms earn less than their FP counterparts, but are likelier to declare themselves content with their career choice. Haugh’s (2006) survey of character trait studies finds that social entrepreneurs are unusually motivated to make a contribution to society; but then one would probably expect this. Based on a GEM-type survey, Harding and Cowling (2004) find social entrepreneurs in the UK to be older and better qualified, and to earn higher incomes than FP entrepreneurs. Harding and Cowling (2004) also report high levels of social entrepreneurial activity among disadvantaged groups, defined in terms of those on low incomes, the unemployed, women and ethnic minorities. These predictions tally in several respects with Parker’s (2008c) neoclassical life-cycle theory of social entrepreneurs, which predicts two dominant career types engaged in this form of entrepreneurship: idealistic individuals who operate social enterprises when young and who make little money over their lifetimes; and wealthy individuals who become social entrepreneurs later in life after they have achieved success either as an employee or as an entrepreneur in the for-profit sector. 2.6

Incumbents’ characteristics: entrepreneurial spawning

Workers often leave incumbent firms to start new ones. They can do so by forming independent start-ups, or new ventures that are related in some way to their employer (‘dependent start-ups’). Gompers et al. (2005) have called the process by which large firms create new independent ventures ‘entrepreneurial spawning’.

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Employees of small firms are more likely to change their job than employees of large firms (Parker, 2009bb). In part, this reflects the higher wages and fringe benefits available in large firms. But what is less obvious is why, among workers who change jobs, those leaving small firms are much more likely to become entrepreneurs than those leaving large firms. The evidence on this point is consistent across a wide range of countries, including the UK, the USA and European countries.21 According to Parker (2009b), at least four different theories can explain this phenomenon. First, a ‘transmission theory’ predicts that small firms are particularly effective at transmitting useful business experience and pro-entrepreneurship attitudes to their workers, possibly via inspirational role models. Second, a ‘blocked mobility’ theory associates small firms with ‘bad’ jobs and proposes entrepreneurship as an escape route for frustrated employees trapped in the small-firm sector. Third, a ‘self-selection’ theory suggests that less risk-averse individuals sort themselves into both small firms and entrepreneurship at different stages of their lives (Parker, 2008d).Afourth theory is that bureaucracy imparts conservative attitudes, specialised skills, inward-facing experience and stable rewards, which are all antithetical to entrepreneurship. Bureaucracy is especially pervasive in large firms (Sørensen, 2007). Longitudinal matched employee–employer Danish data on labour market transitions over 1990–7 show that current or previous work experience in a large firm prompts workers to move into self-employment later rather than sooner (Sørensen, 2007).22 In contrast, an empirical analysis based on thirteen waves of BHPS (British Household Panel Survey) data suggests that the self-selection theory is most consistent with the evidence (Parker, 2009b). The analysis so far has focused on independent start-ups, whereby entrepreneurs develop ideas or technologies independently of their parent firm. These types of startup are more common than dependent starts, in which employees operate a new venture as a quasi-independent arm of the parent firm. Individuals engaged in dependent startups are sometimes referred to as ‘intrapreneurs’. Note that intrapreneurship is distinct from management buy-outs (MBOs), whereby employees take over the ownership and control of a firm. Parker (forthcoming) analyses the decision to practice intrapreneurship versus entrepreneurship. It has long been known that large incumbent firms play a central role in the creation of new ventures, especially in the high-tech sector. Rothwell (1984) observed this phenomenon in the semiconductor and computer-aided design industries, where several large incumbents were responsible for the initial technological breakthroughs and furnished both the entrepreneurs and the risk-capital required to found the new technology-based small firms which diffused these innovations into general use. Importantly, the process by which incumbents generate innovative ideas which become commercialised in new independent ventures is not observed only in high-tech firms (Bhide, 2000). In many industries, it is not uncommon for the ‘parent’ firm to maintain a close relationship with its offshoots, even several years after the date of start-up, either as a partial equity holder or as a customer for its products or technologies. A useful way of thinking about dependent starts is in terms of an employee, e.g. an R&D scientist, making a discovery. If the parent company (‘employer’) is unwilling

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or unable to exploit the discovery, the employee may have to quit their job and found a new venture in order to exploit it. On other occasions, though, it can be worthwhile for an employee to exploit an opportunity in conjunction with their employer in a joint venture. The remainder of the chapter offers various reasons why entrepreneurs sometimes leave firms to pursue new opportunities instead of developing them inhouse with their employer. Following Klepper’s (2001) taxonomy, these theories relate to organisational limitations of incumbent firms; agency costs; and learning behaviour.

2.6.1 Organisational limitations of incumbent firms Why would incumbents be willing to pass up on valuable opportunities, lose talented employees and watch them create new firms, possibly competing in related markets to their own? Often, to be sure, some incumbents go out of their way to stop employees leaving to start new ventures – including, perhaps most famously, Intel (Jackson, 1998, pp. 211–338). But it is also clear that some incumbent firms play a benevolent, or at least not actively obstructive, role in the independent venture creation process. This can happen, for example, when a new business idea falls outside the firm’s core line of business and the firm does best by sticking to its core competencies. In dynamic industries, incumbents may simply face more opportunities than they can exploit. Pursuing every innovation opportunity could easily require more capital than can be raised, and could exceed the firm’s capacity to grow. If so, incumbents can do best by rejecting some projects in order to be ready to exploit more complementary opportunities later on (Cassiman and Ueda, 2006). Sometimes, incumbents are powerless to stop employees leaving to start new ventures, or cannot afford to retain them. In general, though, the potential mobility of scientific personnel need not be a problem for incumbents if the incumbent’s managers can design a labour contract ‘which only induces the scientist to leave and join a rival if the sum of the benefits to the two agents [incumbent and scientist] increases as a result of the scientist’s leaving’ (Pakes and Nitzan, 1983, p. 360). Another possibility, explored in the remainder of this subsection, is that incumbents lose skilled personnel because of organisational limitations. Large firms can be bureaucratic and inert, developing inflexible but valuable internal organisational ‘routines’ which discourage radical change.23 Employees who discover radical new opportunities within these firms are not permitted to develop them in-house because that would disrupt the firm’s routines (Freeman and Engel, 2007). Employees can then become frustrated and come into conflict with their employer (Garvin, 1983; Klepper, 2006). Ultimately, employees might have to leave and start up on their own if they are ever to commercialise their ideas, especially if other existing firms are also unwilling to take up the ideas. This logic can explain why, even though large bureaucratic firms generate plentiful ideas, they can also be associated with numerous independent breakaway starts (Klepper and Sleeper, 2005). Gompers et al. (2005) exemplified the large bureaucratic firm by Xerox, which refused to develop numerous valuable ideas discovered by its employees, including the

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Ethernet, the graphical user interface, laser printing and personal distributed computing. Employees who left Xerox to exploit these opportunities founded numerous new companies, including Adobe Systems and 3Com. Somewhat ironically, Chester Carlsson originally started Xerox after his proposal to produce a (new) copy machine was rejected by Kodak, on the grounds that the new copy machine would not earn very much money, and in any case, Kodak was in a different line of business – photography. Entrepreneurial spawning sometimes spans several generations: ‘the ‘Traitorous Eight’ left Shockley Labs to create Fairchild Semiconductor, which itself saw its employees starting, among others, National Semiconductor, Intel, AMD and LSI Logic, which in turn became parents to Cypress, Zilog, Sierra Semiconductor, and many other semiconductor companies’(Hellmann, 2007b, p. 919). As another example, Christiansen (1993) observed that many founders of new companies in the US rigid disk drive industry over 1976–89 were members of an engineering team at a previous disk drive company who had developed a new disk drive architecture that their employer was unwilling to pursue. A particularly salient problem for large incumbent firms is the high levels of uncertainty often entailed by radical innovations. Responding to ‘short-termist’ investor pressure, large established incumbents often screen out ambiguous projects, preferring to invest in ones offering more predictable outcomes (Bhide, 2000; Freeman and Engel, 2007). Uncertainty also promotes divergent opinions between employees and employers about the value of a discovery. That makes it less likely they will agree on initiating a corporate venture, making independent starts more likely.24 Klepper (2006) develops a theory in which an incumbent firm’s managers agree about the firm’s current course until one manager comes into possession of a new piece of information about a valuable opportunity for the firm. This manager tries to persuade the other managers of the value of pursuing the opportunity. If this effort fails and the managers disagree, and if the opportunity’s expected value exceeds the cost of starting a new firm, then the well-informed manager may quit and start their own venture. This model generates an impressive number of empirical findings about spin-offs. One is that, because of his or her superior information, the manager’s spin-off venture will perform better than the parent firm and it will be socially beneficial for the spin-off to be formed. Another is that high-performing managers run high-performing new ventures. Furthermore, any event that decreases the decision-making influence of an informed manager (e.g. acquisition by an outside firm) is predicted to increase the chances of a spin-off. Another important consideration is that incumbents have incentives to avoid cannibalising their operations or risking their reputations on new ideas which may prove unsuccessful (Holmstrom, 1989; Freeman and Engel, 2007). These considerations are especially pronounced if customers prefer incremental to radical product changes (Christiansen, 1993; Christiansen and Bower, 1996). In addition, organisations undergoing crises are more likely to lose employees and spawn independent companies (Cooper, 1986; Klepper, 2006). The threat of job loss from a current employer, associated with closure or acquisition of the parent firm, or downsizing of management, can encourage employees to leave and start their own firms (Eriksson and Kuhn, 2006).

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Market conditions characterised by numerous paradigm-shifting innovations can be another source of crisis, making incumbents more conservative while simultaneously creating positive opportunities for new independent ventures. This line of argument associates independent new starts with environments containing plentiful opportunities for radical innovation.

2.6.2 Agency cost theories Agency cost theories of entrepreneurial spawning centre on the incentive problems that occur when an employee discovers a significant invention and faces a choice between either leaving a firm to exploit it independently or remaining to develop it jointly with their employer. One source of conflict arises if the incumbent can exploit the opportunity more effectively owing to asset complementarity with the new innovation. Another emerges from asymmetric information problems which impede the ability of the employee to contract with the employer to develop an innovation jointly. Anton and Yao (1995) studied this problem in a setting where intellectual property rights (IPR) are weak. IPR is assumed to be weak both from the standpoint of the entrepreneur (whose idea can be expropriated by the employer) and from that of the employer (which cannot use trade-secret protection to prevent a private start-up). Anton and Yao (1995) showed that although joint profits are maximised if a joint venture outcome is chosen, imperfect IPR protection means that the incumbent sometimes does best by providing insufficient compensation to the entrepreneur, encouraging the latter to start up independently. Anton and Yao (1995) predict that the frequency of independent start-ups is greater:

• The more limited are the employees’ funds relative to the value of the invention, since limited funds prevent the employee from pre-committing to joint development by posting a bond; • The more similar are profits under an independent start-up to profits under a joint venture; • The greater the employer’s negotiating power to appropriate value from revealed inventions; • The less that innovations require distinctive complementary assets of existing businesses, and the more they rely on footloose human capital. Agency conflicts can come in other guises too. We saw earlier in this chapter that when firms cannot credibly commit to refrain from stealing workers’ compensation, the latter can be incentivised to start new firms (Wiggins, 1995). An implication of the Wiggins (1995) model is that workers are more likely to found new firms the longer they have to wait before output from their effort is observed. As noted earlier, an example is path-breaking innovations that take a long time to yield output, which might help explain why radical innovations are sometimes undertaken by small independent worker-owned firms backed with risk-capital, rather than by established firms.

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Of course, incumbents wishing to discourage workers from quitting to found potentially competing start-ups need to design incentive contracts which deter workers from diverting effort into researching new opportunities in the first place. One solution to this moral hazard problem involves employers adjusting remuneration packages to deter quits and promote jointly developed spin-offs (Subramanian, 2005). Perhaps contrary to expectations, another solution is for employers actually to underbid for the rights to develop promising new ideas. By underbidding, the firm decreases workers’ incentives to divert effort from their main tasks. This can be optimal even if the firm possesses advantages over outside financiers such as venture capitalists owing to asset complementarities, or if underbidding runs the risk of losing employees to VCbacked start-ups (Bankman and Gilson, 1999). On other occasions, though, a potential entrepreneur might be more worried about an outside investor expropriating their idea, and might therefore prefer a joint corporate venture with their present employer. In joint ventures, employers presumably have fewer incentives to engage in opportunistic behaviour at the expense of entrepreneurs. On the other hand, entrepreneurs might have to cope with time-consuming internal ‘politics’ which impede development of their joint ventures. Hellmann (2007b) further explores the idea of effort diversion in a model where employees are paid to focus on a ‘main’ task but can privately divert unobservable effort into the development of new innovations. When the value of a new innovation is modest, Hellmann (2007b) shows that it can be optimal for employers to pre-commit not to develop any of them in-house, even though ex post it would seem to gain by reneging on these commitments and seizing the new opportunities. The strategy of reneging can be dominated by the employer’s policy of building a reputation for keeping promises in a repeated employment game, thereby focusing their employees most effectively on their main task. The cost to the firm is that it must resign itself to losing some employees who start up as entrepreneurs, if they hold the IPR. But if the value of new innovations is high enough, the firm might decide instead to break with its non-commercialisation policy and ‘go with the flow’ (assuming it holds the IPR) – choosing internal venturing (spinouts) instead. In other cases, entrepreneurial spin-offs can occur. Hellmann (2007b) provides a taxonomy of these cases. Klepper (2001) criticises the realism of agency theories which focus on strategic conflicts of interest between employers and employees. Klepper (2001) argues that although legal conflicts between parents and employee spin-offs do happen, they are relatively uncommon events compared with strategic disagreements, and are also less common than the simple dissatisfaction which employees experience owing to their employers refusing to develop new ideas. In any case, strategic conflicts of interest need not occur at all if the new firms compete in different markets from the incumbents, or if they supply the incumbents with intermediate inputs, as frequently is the case. Finally, asymmetric information in credit markets can help explain why intrapreneurship is more commonly observed in some industries and regions than in others. Suppose an investor can choose whether to fund a risky investment project within an existing firm (‘intrapreneurship’) or within a new firm (‘entrepreneurship’). The investor does

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not know the individual abilities of recipients of the funds, but does know that if intrapreneurs fail in their investment projects, they will be redeployed within their firm if they are unlucky but talented, and will have to leave the firm immediately and apply for funds outside the firm if the firm realises that they are simply untalented. Gromb and Scharfstein (2002) show that two equilibria can emerge. In one equilibrium, there is a lot of intrapreneurship, so the pool of independents seeking finance is dominated by lots of low-ability failed intrapreneurs and entrepreneurs. This leads financiers to shun entrepreneurship and to back intrapreneurship as the preferred mode of commercialisation. In the other equilibrium, entrepreneurship is common and the pool of independents seeking finance is of high quality. This leads financiers to prefer entrepreneurship as the mode of commercialisation. 2.6.3 Learning theories According to these theories, employees use their work experience to acquire specific capabilities and learn about new venture opportunities (Buenstorf, 2007). This can facilitate independent start-ups in related technological areas. Parent firms might not find the costs of pre-empting these start-ups worthwhile if they occur rarely. In practice, the benefits of direct transfers of technology and specific capabilities from incumbents to employees appear to be less important than the value of general experience and knowledge gained from working in the parent firm, including those relating to internal firm ‘routines’ (Klepper, 2001). Gompers et al. (2005) had this idea in mind when they delineated a second type of spawning firm, exemplified by Fairchild Semiconductors, which (they argued) inadvertently prepared employees to be entrepreneurs by educating them about the entrepreneurial process and exposing them to a network of other entrepreneurs and venture capitalists. Klepper (2001) suggests that start-ups are well placed to adopt high-quality organisational routines from their parents, thereby disseminating good practice and technological progress across industries. This might explain why successful high-tech firms tend to spawn more rather than less successful firms. In particular, high-tech firms tend to have relatively complex production processes, so more of their employees are exposed to high-quality routines which they are able to replicate independently. This observation might in turn explain the high survival rates of spawned firms (see chapter 4). It might also explain why having multiple founders (who have presumably been exposed to numerous good routines) assists the survival of spawned firms, and why the product lines of spawned firms are often so similar to those of their parent firms, since this allows them to exploit their knowledge about similar routines.

2.7

Macroeconomic theories of entrepreneurship and growth

There are three broad macrotheoretical approaches to the study of entrepreneurship and economic growth. The first approach builds on wealth-based theories of entrepreneurship by proposing that as economies develop they accumulate wealth

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through entrepreneurship, which promotes growth and further entrepreneurship. The role of entrepreneurs in this process, as capitalists who hire labour, borrow capital and produce output, is centre stage. Many of these models focus on the implications for economic development of entrepreneurial borrowing constraints. The second broad category of theories contains technology-based models, in which entrepreneurs respond to incentives created by technological change. The third category focuses on entrepreneurship as a means of exploiting knowledge spillovers, regarded by some researchers as the fundamental mechanism facilitating endogenous growth. Each of these broad approaches is now discussed in turn. 2.7.1 Wealth-based theories

Perhaps the best-known and most influential wealth-based theory of entrepreneurship and economic growth is that of Banerjee and Newman (1993). I will first sketch out the implications of this model before exploring other contributions to the literature. Banerjee and Newman (1993) proposed a model in which there are three occupations: ‘entrepreneurship’ (in which business owners pay a fixed investment cost to start up and hire a fixed number of workers), ‘self-employment’ (in which business owners operate small businesses with a smaller investment cost and hire no workers) and ‘paid employment’. Notice that the self-employed and entrepreneurs are defined as distinct occupational groups in this model. Not everyone can become self-employed or an entrepreneur: poor individuals lack the wealth needed to overcome borrowing constraints and so cannot raise the requisite investment cost (see also chapter 9). There are three social classes in the model, with workers being the poorest and entrepreneurs the wealthiest. The primary role of entrepreneurship in this model is to create wealth. But the distribution of wealth affects and is in turn affected by (constrained) occupational choices. The evolution of this dynamic feedback process traces out the development process. Every individual bequeaths a fraction of their lifetime wealth to their offspring, who similarly face constrained occupational choices. The offspring of the wealthy are also wealthy and can bypass borrowing constraints, giving rise to entrepreneurial dynasties. The existence of borrowing constraints injects sufficient friction into the model to ensure that the initial distribution of wealth has persistent effects on selection into entrepreneurship over the generations and on long-run economic development. Banerjee and Newman (1993) demonstrated that several possible development paths can emerge from their model. One is a ‘capitalist’development process, in which almost everyone is a worker and there are only a few very wealthy entrepreneurs. The large supply of workers depresses the average wage, which ensures that few workers in subsequent generations can overcome the borrowing constraints. That in turn ensures that wages remain low in the future. Entrepreneurs benefit from low wages since they entail lower production costs. This keeps them and their offspring wealthy, and perpetuates inegalitarian entrepreneurial dynasties as an equilibrium outcome.25 A different equilibrium in Banerjee and Newman’s (1993) model is associated with ‘artisanal’ development, in which self-employment is common and there are relatively

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few workers. The small supply of workers keeps the average wage high, making entrepreneurship unattractive and perpetuates self-employment. The high wage also enables workers to accumulate enough resources to overcome the borrowing constraint and to join the ranks of the self-employed (but not of entrepreneurs). The equilibrium in this economy is one where self-employed artisans predominate. Banerjee and Newman (1993) suggest that the most beneficent equilibrium is one where there are not too many poor workers. Then high wages prevail, enabling subsequent generations of workers to rise up the wealth and social scales, become entrepreneurs in their turn, and making the economy increasingly prosperous and entrepreneurial. A poor economy can become rich if it does not start too far away from the rich one. But if it starts sufficiently poor it will be doomed to remain so, and entrepreneurship will stagnate. A policy implication of this line of thinking is that by relaxing borrowing constraints and allowing productive investment projects to be funded, financial development might be capable of boosting entrepreneurship (see also King and Levine, 1993). It should be borne in mind, however, that economic and financial development often go together, implying that financial development by itself is unlikely to be a panacea for slow economic development. Nevertheless, a recent calibration exercise suggests that financial intermediation enabling entrepreneurs to borrow to start businesses could be responsible for 7.3 per cent of the US economy’s total output, delivering an average welfare gain of 11.1 per cent relative to the case where financial intermediation is unavailable and people have to rely on their endowments of wealth to invest in new ventures (Bohác˘ ek, 2007). Some alternative wealth-based models of entrepreneurship and growth yield even more optimistic predictions about the prospects for long-run development. Aghion and Bolton (1997) proposed a model of moral hazard (outlined in greater detail in chapter 9) in which wealth incentivises effort. Economic development increases wealth and effort, thereby reducing moral hazard problems restricting lending, and so enabling more people to overcome borrowing constraints which formerly prevented them from becoming entrepreneurs. Eventually, everyone can become an entrepreneur and creditbased constraints cease to bind entirely as the economy passes a critical threshold. Two subsequent studies extended these wealth-based theories of entrepreneurship and growth by exploring how the distribution of heterogeneous entrepreneurial ability affects economic development. Lloyd-Ellis and Bernhardt (2000) show that two development paths can arise, depending on the distribution of entrepreneurial ability. If the latter is not too skewed, i.e. there is not a scarcity of innately talented entrepreneurs, wages as well as output, wealth and aggregate income continue to grow over time. The distributions of income and wealth eventually converge to unique limiting distributions which (unlike Banerjee and Newman, 1993) are independent of the initial distributions. Notably, entrepreneurial efficiency eventually replaces wealth as the primary determinant of occupational choice. The second development path is less benign, however. If most individuals have low entrepreneurial ability, i.e. the distribution of ability is positively skewed and efficient entrepreneurs are scarce, then a development path can

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arise whereby the economy exhibits endogenous long-term cycles in production. Wages evolve pro-cyclically, so income inequality falls during booms and rises during recessions. The mechanism is as follows. Development-led wage increases transfer wealth from rich entrepreneurs (employers) to their less wealthy workers (employees). If there are few efficient entrepreneurs, wealthy entrepreneurs who lack much talent are easily persuaded to switch into paid employment as their wage costs rise and their profits fall. The increase in supply of workers then reduces the average wages. That stimulates the opposite process: having to pay lower wages, the supply of entrepreneurs increases – which in turn bids wages back up again. And so the cyclical process continues. In an interesting recent contribution, Acemoglu et al. (2006) propose a simple model of innovation, growth and entrepreneurial selection in which entrepreneurs reinvest their profits in their enterprises to bypass credit constraints. Economies can develop either by adopting existing technology or by innovating at the technology frontier. Acemoglu et al. (2006) argue that at early stages of economic development, re-investment enables mediocre entrepreneurs to predominate; and development proceeds by entrepreneurs adopting existing technologies. This is akin to wealth rather than entrepreneurial efficiency being the primary determinant of agents’ choices. But at later stages of development, as the economy approaches the technology frontier and credit constraints ease, able entrepreneurs replace mediocre entrepreneurs and carry out high-level innovation activities. Then entrepreneurial efficiency replaces wealth as the primary determinant of agents’ choices. On the basis of these predictions, Acemoglu et al. (2006) suggest that governments should choose institutional structures and policies which favour adopting ‘catch-up’ technology at early stages of development, followed by a switch into subsidising ‘high-tech’ innovation later on.

2.7.2 Technology-based theories Two broad theoretical perspectives will be discussed under this heading. One relates to the effects of technological progress on the occupational choice of entrepreneurship, while the other focuses on implications of technological progress for the prospects of small relative to large firms. The first set of theories is more formally based than the latter, which tend to be more conjectural in nature. Calvo and Wellisz (1980) and Schaffner (1993) propose formal models of firm-sizeneutral exogenous technological progress. Both articles predict a Lucas (1978)-type outcome – continually increasing numbers of workers and decreasing numbers of entrepreneurs. For example, Schaffner (1993) assumes that technological progress increases expected returns and risk for both employers and entrepreneurs. But by being able to costlessly smooth workers’ wages, risk-neutral employers gain an advantage over entrepreneurs who are assumed to lack opportunities to insure their incomes and so obtain a desired risk–return profile. This entices marginal entrepreneurs to quit and switch into paid employment. Furthermore, new (possibly de alio) risk-neutral employers can enter the market to exploit this advantage over entrepreneurs, bidding up wages and drawing even more individuals out of entrepreneurship and into paid employment.

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Lazear’s (2005) jack-of-all-trades hypothesis suggests another reason why technological progress might erode the size of the entrepreneurial sector over time. If entrepreneurs need a mix of skills in production, then technological progress which demands additional skills requirements will decrease the number of suitably equipped individuals and therefore also the number of entrepreneurs. On the other hand, if a rapid pace of technological change is complementary with entrepreneurial ability, one would expect the number of entrepreneurs undertaking the most efficient projects to increase, with a reduction in the number of entrepreneurs undertaking the least efficient projects. ‘A higher rate of technological change is therefore likely to induce a cleansing effect on entrepreneurial activity and to alter the market perception of business creation’ (Crifo and Sami, 2008, p. 585). Another set of formal theories treats innovation and technological progress as endogenous outcomes. A literature on ‘patent races’ has emerged in which firms compete against each other to discover new patentable innovations that yield monopoly profits while rendering previous products obsolete à la Schumpeter.26 Profits are only temporary, however, because new innovations are eventually rendered obsolete, in Schumpeterian fashion, by future innovations. In some flavours of this model, imperfect competition is needed for Schumpeterian entrepreneurs to be willing to conduct growth-generating research (Aghion and Howitt, 1992). But subsequent extensions of this research programme suggest that under some circumstances competition and capital can be growth enhancing too (Aghion and Howitt, 1992; Howitt and Aghion, 1998). Arguably, however, this class of models does not bear closely on entrepreneurship. As Bianchi and Henrekson (2005) point out, these models primarily apply to large established firms which devote resources to large-scale ‘routinised’ R&D activities. Patent races therefore have little to do with entrepreneurial innovations and ignore issues of entrepreneurial choice by not really distinguishing between entrepreneurs and inventors. In contrast, several alternative approaches, analysed below, place entrepreneurs at the heart of models of endogenous technological progress and economic growth. Less formal theories in contrast speculate about the changing nature of production functions wrought by technological change, and their effects on the relative advantages of small relative to large firms. Some authors, from at least the time of Schumpeter, have claimed that technological progress tends to be biased in favour of large firms, making the latter’s economies of scale increasingly dominant over time, thereby squeezing out smaller producers and hence (by implication) entrepreneurs. This could be one factor behind the ongoing decline of the small independent producer in agriculture in particular; it has long been known for instance that the size share of the agricultural selfemployment sector and per capita GNP are strongly negatively correlated (Kuznets, 1966; Schultz, 1990). Garicano and Rossi-Hansberg (2006) propose another reason why independent entrepreneurship may decline with technological change. Technology reduces communication costs, working to the advantage of team-based production in large firms, which leverages high levels of communication between team members. Thus independent entrepreneurship can be progressively replaced by corporations on efficiency grounds.

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On the other hand, it has been suggested that technological change can also work the other way, favouring smaller rather than larger businesses. Arguably, the advent of more flexible manufacturing techniques has begun to make scale economies less important, reducing minimum efficient scale and facilitating competition from smaller firms.27 These changes might also have endowed small firms with a comparative advantage at supplying markets with low or fluctuating levels of demand. The emergence of new technologies, including ICT and telecommunications, and the growing demand for labour-intensive services and ‘niche’ customised products might also have favoured smaller firms, by requiring less physical capital and more human capital (although see Bhide, 2000, chap. 14, for a sceptical view). Further theory-grounded quantitative research is needed to shed light on the extent to which technological change of this sort has impacted on entrepreneurship. 2.7.3

Knowledge-based theories Neoclassical theories of economic growth in the 1960s emphasised the role of savings and capital accumulation in promoting growth. Key to those theories was the notion of an aggregate production function q = Aq(k) linking per capita income q with capital per head k via an increasing concave function q(k), where A is the exogenous state of technology. For simplicity, these theories often also assumed a constant savings rate 0 < γ < 1 and constant rates of population growth g and capital depreciation δ > 0. According to the classic Solow growth model, economies will grow for as long as the supply of capital, γ Aq(k), exceeds the demand for capital, (g + δ)k. The concavity of q(·) and the straight-line nature of (g + δ)k ensures that the two must come into equality for some k > 0: this characterises the steady-state equilibrium. In this model, long-run growth is unaffected by the savings rate but instead is predicted to be driven by exogenous increases in A. By the late 1980s neoclassical growth theory had fallen out of favour. It was unable to explain the determinants of A (famously described as ‘falling like manna from heaven’) and hence long-run growth. And mounting evidence rejected the theory’s prediction that savings rates and economic growth are uncorrelated in steady-state. Romer (1986) offered a solution to these problems by proposing an endogenous theory of growth. This theory essentially replaces a concave q(·) (diminishing marginal returns to capital) with a straight-line function: q(k) ∝ k. Now it is possible for γ Aq(k) to permanently exceed (g + δ)k, leading to ongoing (‘permanent’) growth and a positive relationship between savings rates and growth rates – consistent with the evidence. The crucial contribution made by Romer is the replacement of a concave aggregate production function with a straight-line one. This makes no sense at all at the level of the individual firm, where it implies increasing returns-to-scale to all factors of production taken together, with the counter-intuitive implication that one single firm would end up dominating the entire economy. Of course, nothing like this ever remotely happens in reality. Romer’s insight was to propose that individual firms continue to operate with a concave production function, but that external spillovers give the economy as a whole the straight-line production function associated with continuous ongoing growth.

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This raises the question about what the external spillovers are and where they come from. At a general level, it has been known for a long time (at least since Arrow, 1962) that knowledge and ideas are public goods, whose benefits are only partially captured by their creators. Knowledge creation has the tendency to carry within it the seeds of further knowledge creation, which might not be obvious to the creator but which can be recognised and developed by others. This is a positive externality which is commonly referred to as ‘knowledge spillovers’. Knowledge spillovers can ‘end up facilitating other agents’ innovation efforts (either unintentionally, as happens when inventions are imitated), or intentionally, as may happen when scientists divulge the results of their research’ (Breschi and Lissoni, 2001, p. 975). Economists have been fairly quick to recognise the role of entrepreneurs in promoting and exploiting knowledge spillovers. In an early contribution, Schmitz (1989) argued that entrepreneurs’ search and investment activities inadvertently add to the stock of knowledge which others can exploit. Consequently, entrepreneurship is a possible source of positive externalities at the level of the economy. An implication of positive externalities is that competitive equilibria are not welfare-maximising. There will in general be too little entrepreneurship relative to the social optimum. Hence policy-makers should actively promote technology-based entrepreneurship in order to stimulate economic growth. It is helpful to delve more deeply into the origins of knowledge spillovers. Two important sources are universities and private R&D laboratories. Both are important producers of ‘codified’ and ‘tacit’ knowledge. Codified knowledge is easily documented, transferable and reproducible (e.g. information published in a scientific article), and virtually costless in the age of the Internet to diffuse across local and national boundaries (Krugman, 1991). Such knowledge can be transmitted instantaneously through specialist interest groups such as online user communities. In contrast, tacit knowledge cannot be completely documented; it is often context-specific and best transmitted via face-to-face interactions and frequent repeated contacts (von Hippel, 1994). Costs of transmitting tacit knowledge rise with distance, so it tends to be locally concentrated. An example is new ideas developed through close discussion between a few experts. If tacit knowledge is a crucial ingredient of new commercial innovations, then geographical proximity is essential to access it or even to become aware of its existence. Propinquity also facilitates networking, trust, co-operation and social capital needed to exploit new opportunities arising from access to this type of knowledge (Saxenian, 1994; Thornton and Flynn, 2003). It remains to explain how entrepreneurs exploit knowledge spillovers. As noted in the previous section, one conduit is employees of firms which perform R&D but which do not develop the new opportunities in-house. That might be because of inflexible internal ‘routines’ or because the managers of R&D firms do not value the ideas’ potential as much as entrepreneurs do (Audretsch and Thurik, 2001a, p. 12). In which case, new venture creation can be the only way of diminishing the ‘knowledge filter’ between the creation and exploitation of knowledge. Naturally, entrepreneurship is not the only way of exploiting knowledge spillovers: others include

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corporate venturing and opportunistic exploitation by existing firms which specialise in adapting new technologies originating in other firms (Cohen and Levinthal, 1989). But entrepreneurship is likely to play an important role in the process. In short, growth can be created by individual entrepreneurs who exploit knowledge via new ventures even though they do not contribute directly to the production of knowledge themselves. In contrast to the theories of patent races mentioned above, entrepreneurs and new venture creation play a central role in these theories of knowledge-based technological change and economic growth. Broadening out the implications of these ideas, Acs et al. (2004a) argue that entrepreneurship will become progressively more important in knowledge-based economies by providing one of the main channels through which new economic knowledge can be commercialised. As globalisation drives down wages and employment in traditional tradable sectors (especially manufacturing) in developed countries, and as multinational employers become increasingly footloose and stateless, entrepreneurship becomes increasingly important by enabling developed countries to exploit their comparative advantages in developing innovative knowledge-intensive goods. This has prompted Audretsch (2003) to argue that in an era of rapid globalisation, public policy ought to involve the ‘strategic management of places’ rather than the management of plants or firms. A fuller discussion of public policy relating to knowledge spillovers is deferred until chapter 16; evidence about the existence and importance of knowledge spillovers is reviewed in chapter 4. 2.8

Multiple equilibrium models

Some formal models attempt to explain why regions with similar or identical socioeconomic profiles can end up with markedly different qualities and quantities of entrepreneurship. This section briefly discusses three such models. The first is a model of serial entrepreneurship by Landier (2004), which predicts how similar regions can find themselves in different equilibria in terms of the quality of entrepreneurship. The second, by Ghatak et al. (2007), is a productivity-based explanation of differences in the quality of entrepreneurship. The third is a human capital model by Parker (2005b) which predicts how similar regions can end up with different quantities of entrepreneurship. The last two parts of the chapter appendix sketch out simplified technical versions of the first and third of these models for the interested reader. In Landier’s (2004) model, entrepreneurs possess either high or low ability. Able entrepreneurs periodically encounter attractive new business opportunities which they can only pursue by closing down their existing business and raising new capital. By assumption, new projects are more attractive to entrepreneurs than existing ones. At the same time, less able entrepreneurs can also apply for new capital for banks, which they secretly consume before defaulting. Banks face an asymmetric problem, because when confronted with a set of loan applicants they cannot distinguish the able serial entrepreneurs (who generate profit for the bank) from the less able serial entrepreneurs (who generate losses for the bank).

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Two distinct equilibria arise in this model. In one, which Landier (2004) calls the ‘conservative’ equilibrium, the cost of capital is high. This deters able types from becoming serial entrepreneurs, as they have to cross-subsidise the less able types. So the borrower pool is swamped with less able types, justifying the high cost of capital and locking in this outcome as an equilibrium. The other equilibrium, called ‘experimental’, is associated with a low cost of capital. Now able types have incentives to exploit their profitable opportunities to become serial entrepreneurs. That creates a more favourable borrower pool from banks’ perspective, allowing them to break even with a lower interest rate. Neither of the equilibria in Landier’s model is unambiguously more efficient than the other, although the experimental equilibrium is more efficient in sectors close to the technological frontier. The conservative equilibrium generates too little serial entrepreneurship to exploit valuable opportunities efficiently in these sectors. Which initial conditions determine whether an economy finds itself in a conservative or an experimental equilibrium? Landier speculates that in some places the ‘stigma of failure’is high, which makes serial entrepreneurship unattractive. Then the conservative equilibrium is more likely to prevail. Landier identifies the USA with an experimental equilibrium and Europe with a conservative equilibrium, although one could also imagine multiple equilibria to arise within countries with non-integrated (i.e. regionally separate) markets. Landier’s (2004) model neatly analyses both multiple equilibria and serial entrepreneurship within one simple framework; but it is not the only or even necessarily the most convincing theoretical model of either multiple equilibria or serial entrepreneurship. Ghatak et al. (2007) point out that if a non-pecuniary benefit to being a successful entrepreneur is sufficient to attract very able entrepreneurs into the market despite low financial payoffs, high average entrepreneurial ability makes employees very productive, resulting in high wages (thereby confirming low entrepreneurial profits) and establishing an equilibrium with numerous well-paid workers. But if payoffs in entrepreneurship are high, this can attract less able people to become entrepreneurs, reducing the average productivity of the workforce and resulting in low wages (thereby confirming high entrepreneurial profits). The quality and quantity of the pool of entrepreneurs is very different in both equilibria, as are average living standards. In principle, either equilibrium can arise. Multiple equilibria in the quality of entrepreneurship also arise as a natural consequence of an observation, noted in the previous section, that technological progress increases the quality of entrepreneurial projects while being spurred in turn by improvements in the average quality of entrepreneurs (Crifo and Sami, 2008). In a ‘high-level’ equilibrium, technological progress and the probabilities of entrepreneurial success are high. Banks reward entrepreneurs with low interest rates, which encourages entrepreneurs to select high-quality technology-rich projects, which further adds to the technological progress underpinning this equilibrium. The ‘low-level’ equilibrium, in contrast, is characterised by low levels of technological progress, and the self-selection of less able entrepreneurs who perpetuate this state of affairs as an equilibrium outcome.

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I now turn to the issue of multiple entrepreneurship equilibria in the quantity of entrepreneurship. In Parker (2005b), individuals have to decide how much human capital to acquire before learning about their idiosyncratic entrepreneurial ability. Human capital is assumed to be valuable in both entrepreneurship and paid employment, but generates a higher marginal return for the ablest people in entrepreneurship compared with paid employment (à la Lucas, 1978). To make optimal human capital choices, individuals must estimate expected returns in entrepreneurship in their region, which they can only do by observing local entrepreneurs. The fact that individuals make decisions based on observing local levels of entrepreneurship in their own region is what gives rise to multiple entrepreneurship equilibria. A ‘low-level’ entrepreneurship equilibrium arises when individuals deduce from the current occupational structure that they have a low probability of being able enough to prosper in entrepreneurship, and so acquire relatively little human capital. These choices predispose them to choose paid employment over entrepreneurship once their true ability is revealed, because having low human capital they cannot access high marginal returns in entrepreneurship while having to pay a fixed entry cost. But that perpetuates the initial occupational structure and locks in low levels of human capital and entrepreneurship as an equilibrium. Conversely, a ‘high-level’ entrepreneurship equilibrium can arise and persist for the opposite reason. Clearly an important assumption in this model is that entrepreneurs remain in their region and are not well informed about entrepreneurial ability in other locations. Evidence that entrepreneurs have low levels of geographic mobility lends some credence to this assumption (see chapter 4).28 In this model as in Landier’s (2004) and Ghatak et al. (2007), one does not need to appeal to exogenous behavioural, cultural or institutional differences to generate different equilibria in different regions. Instead, multiple equilibria arise from imperfect information and incomplete co-ordination of agents. However, Parker (2005b) concludes that the scope for practical policy interventions to shift entrepreneurship equilibria from low to high level outcomes is probably limited. This might account, for example, for the limited practical success of regional development agencies in changing regional profiles of entrepreneurship within the EU.

2.9

Conclusion

This chapter has reviewed a wide range of influential economic theories of entrepreneurship, both microeconomic and macroeconomic. Microeconomic theories predict what types of individual are likely to become entrepreneurs; why an entrepreneurial occupational choice exists at all; and the role of incumbent firms in the entrepreneurial entry process. Macroeconomic theories trace out the implications of these microeconomic choices for the broader economy and for aggregate rates of entrepreneurship in different countries and regions. Both sets of theories generate a range of hypotheses which can be tested and falsified in the accepted scientific tradition. Chapter 4 will review the salient evidence on these issues.

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Modern economic theories of entrepreneurship clarify what can, and cannot, be said about the determinants of entrepreneurship. For example, we saw that greater market risk in entrepreneurship does not necessarily reduce the equilibrium number of entrepreneurs. One reason for this is that in general equilibrium, greater risk in entrepreneurship also affects employees indirectly by decreasing the equilibrium wage. The chapter also explained why rational individuals facing sunk-entry costs in a risky trading environment do not always switch between entrepreneurship and paid employment despite apparently being able to make clear-cut gains from doing so. As a further example, the theories reviewed in this chapter explained why some employees leave large incumbent firms to start up new ventures, while others do not. Nevertheless, numerous gaps in our knowledge remain. With regard to the famous Lucas (1978) model, for example, we still know relatively little about where entrepreneurial ability comes from, and to what extent it can be shaped by schooling and enterprise education. This relates to the age-old question of whether entrepreneurs are ‘born or made’. In addition, recent research about risk-sharing via partial income insurance questions the robustness of risk-attitude-based theories of entrepreneurship, notably Kihlstrom and Laffont’s (1979). It is presently unclear whether these models need to be amended or are intrinsically flawed. And theories about the boundaries of the firm and contracting out need to be developed further in future research, as they set limits on the extent to which an entrepreneurial occupational choice ever arises. To the extent that entrepreneurship is a viable occupational choice, the economics of entrepreneurship argues that individuals only become entrepreneurs if that occupation maximises their expected utility. This insight forms the starting point of empirical models of entrepreneurial choice reviewed in the next chapter. 2.10 Appendices 2.10.1

Technical definitions of risk aversion and risk Consider the case of risk-averse agents. Then Uyy (y) < 0 < Uy (y), where subscripts denote derivatives. The following definitions propose some useful ways of quantifying risk aversion.

Definition 1 Given a twice-differentiable utility function U (y), the Arrow–Pratt coefficient of absolute risk aversion at income y is defined as r A (y) = −Uyy (y)/ Uy (y) > 0. Definition 2 The utility function U (y) exhibits decreasing absolute risk aversion if r A (y) is a decreasing function of y. Definition 3 Given a twice-differentiable utility function U (y), the coefficient of relative risk aversion at income y is defined as r R (y) = −yUyy (y)/Uy (y). The concept of absolute risk aversion is useful for describing preferences over risky outcomes that involve absolute gains or losses of income. In contrast, relative risk aversion is more appropriate for risky situations where outcomes are percentage gains

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or losses of income. Individuals whose preferences are described by decreasing absolute risk aversion (Definition 2) take more risks as they become better off. While this often yields economically reasonable results about risk-taking behaviour, it is sometimes too weak and is complemented by the stronger assumption of non-increasing relative risk aversion. This assumption states that individuals become more willing to risk fractions of their income as their income increases. It is a stronger assumption than decreasing absolute risk aversion because, by r R (y) = yr A (y), decreasing relative risk aversion implies decreasing absolute risk aversion, but the converse does not necessarily follow. It is sometimes convenient to consider the case where both apply, encapsulated in the following assumption: Assumption 1 The utility function U (y) exhibits decreasing absolute risk aversion and non-increasing relative risk aversion. The notions of ‘an increase in risk’ considered in the text, namely second-order stochastic dominance (SOSD) and mean-preserving spread (MPS), rank two return distributions, with distribution functions F(y) and G(y). Consider the following ranking:



U (y) dF(y) ≥

U (y) dG(y),

(2.2)

where U (·) does not necessarily have to be (though often is) the utility function defined earlier. Definition 4 (Second-order stochastic dominance) For any distributions F(·) and G(·) with the same mean, F(·) second-order stochastically dominates (is less risky than) G(·) if, for every non-decreasing function U (·), (2.2) holds. Definition 5 (Mean preserving spread) For any distributions F(·) and G(·) with the same mean, G(·) is a mean preserving spread of (i.e. is in this sense riskier than) F(·) if, for U (·) some concave function, (2.2) holds. SOSD evidently places less structure on U (·) than MPS, which is in turn a more general measure of ‘increase in risk’ than an increase in variance because it implies (but is not implied by) the latter. Under both definitions, every risk-averter prefers F(·) to G(·). For completeness, I also state a further useful definition: Definition 6 (First-order stochastic dominance) The distribution F(·) first-order stochastically dominates G(·) if, for every non-decreasing function U (·), (2.2) holds. Definition 6 implies that every expected utility maximiser who prefers more to less prefers F(·) to G(·). Equivalently, for any amount of money income y, the probability of getting at least y is higher under F(·) than under G(·).

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2.10.2 A simple ‘hire or outsource’ model

Consider a principal, P, who wishes to elicit high levels of effort from an agent, A, who produces a good, S. If high effort is chosen (e = 1), A incurs a utility cost of 1 and produces a high level of output, S, with probability π1 > 0, and a low level of output S < S with probability 1 − π1 . If low effort is chosen (e = 0), A’s effort cost is zero, and the probabilities of high and low output are π0 > 0 and 1 − π0 respectively: π := π1 − π0 > 0. Thus high effort benefits P, but signals of effort are noisy: it is still possible that low effort yields high output, since π0 > 0. To elicit high effort from employee A, P contracts to pay A the wage t when S is observed, and the wage t < t when S is observed. The contracted values of (t, t) will be derived as solutions to P’s incentive problem described below. In entrepreneurship, A obtains total utility of U (t)+ξ −1 if they supply high effort, where U (t) is a concave function of remuneration t, and ξ > 0 is a non-pecuniary benefit from being an entrepreneur (see chapter 4). Let h = U −1 be the inverse function of U (·), which is increasing and convex (h
> 0,

h > 0). Hence writing U = U (t) and U = U (t) as the utilities of agents, we have t = h(U ) and t = h(U ). If P keeps the production of S in-house, her programming (moral hazard) problem is: max π1 [S − h(U )] + (1 − π1 )[S − h(U )] + φ

(2.3)

π1 u + (1 − π1 )U − 1 ≥ π0 U + (1 − π0 )U

(2.4)

(U ,U )

subject to

π1 U + (1 − π1 )U − 1 ≥ U (w),

(2.5)

where (2.4) is the incentive compatibility constraint designed to elicit high effort, and (2.5) is A’s participation constraint. The first two terms of (2.3) are P’s expected returns net of wage costs h(U ); the last term φ ≥ 0 is an ‘asset specificity’ value of producing S in-house, which is lost if S is made elsewhere and bought in by P. ∗ The solution to this standard principal-agent problem is a tuple of values (t , t ∗ ), where   (1 − π1 ) ∗ (2.6) t = h 1 + u(w) + π  π1  t ∗ = h 1 + u(w) − (2.7) π ∗

Notice that t ∗ < t ensures that the agent bears some risk: this is necessary for the agent to supply high levels of privately costly effort. By comparing P’s expected payoffs under high and low agent effort [which are given by (2.3) and π0 S + (1 − π0 )S + φ − w, respectively], it is only worthwhile for P to ∗ encourage high effort by offering ˜t ∗ ∈ {t ∗ , t } if the following condition holds: ∗

Sπ ≥ π1 t + (1 − π1 )t ∗ − w.

(2.8)

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If it does not hold, P does best by settling for low worker effort, and paying A the outside wage w. ∗ An alternative strategy to paying ˜t ∗ ∈ {t ∗ , t } (or w) is for P to transfer to A the residual claimant’s rights to S, in return for a certain upfront payment, T ∗ . This changes A’s employment status from an employee to an independent residual claimant (‘entrepreneur’). Notice that entrepreneurs will always supply the first best levels of effort to produce S because they are the residual claimants and bear the full costs from supplying low effort (see Parker, 2007b, for a formal proof). If entrepreneur A supplies the good to P, then A is willing to accept the change of employment status (including the payment to P of T ∗ ) if π1 U (S − T ∗ ) + (1 − π1 )U (S − T ∗ ) + ξ − 1 ≥ U (w),

(2.9)

T∗

= T (S, S, π1 , ξ , w). which implicitly solves at equality for Clearly, P only agrees to buy in S from A – and thereby replace a paid employment relationship with an entrepreneurial relationship – if this makes P better off relative to the case of retaining direct control, i.e. if  ∗ if high effort is induced π1 (S − t ) + (1 − π1 )(S − t ∗ ) + φ ∗ T > (2.10) π0 S + (1 − π0 )S + φ − w if low effort is induced Note that (2.9) and (2.10) embody the principle of Pareto optimality, since A is no worse off and P becomes strictly better off under contracting out. To see how Pareto gains can be made, observe that as an employee, A benefits from partial insurance provided by P, while P benefits from employing A in-house because of asset-specificity. But by becoming an entrepreneur, A can obtain the nonpecuniary benefit ξ , which by (2.9) can be sufficiently strong to compensate for losing her insurance. Effectively, ξ increases the value that A is willing to pay for the rights to S, which is necessary in order to convince P to relinquish the asset-specificity value, φ. It is noteworthy in this regard that evidence discussed elsewhere in this book supports the notion that entrepreneurs are willing to pay premiums in the capital and labour markets in return for non-pecuniary benefits (see chapters 9 and 13). Regarding performance measurement, consider a hypothetical firm in which output is either insensitive to variations in worker effort, or is hard to measure accurately. Then S is too small for (2.8) to hold, so P does best by settling for low worker effort and paying A the flat employment wage w. This prediction is exactly in line with the argument of Holmstrom and Milgrom (1994) mentioned in the text, who expect nonincentivised employment contracts to appear when output is hard to measure. Note that by ensuring that high effort is forthcoming, P’s decision to unbundle the firm and make A independent can result in greater effort-based efficiency. This simple model also predicts that costs of monitoring workers encourage firms to outsource work to independent contractors. If the expected value of in-house production S˜ is reduced by high worker monitoring costs, the contracting-out condition (2.10) is more likely to hold for any given firm. These costs are irrelevant for contractors, because being residual claimants they have no incentive to shirk.

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2.10.3

Landier’s serial entrepreneurship multiple equilibrium model Consider an economy with a unit mass of risk-neutral entrepreneurs. There are two periods and all entrepreneurs are active in both of them. Every entrepreneur borrows a unit of capital at the start of the first period, which is used to finance a project lasting two periods. Half of the agents are high quality or ‘good’ entrepreneurs, g, who end the second period able to repay principal plus interest of D > 0 to their lender. These entrepreneurs receive the good payoff Rg > D at the end of the second period with probability one and never default on their loans. However, at the start of the second period, a random proportion 0 < q < 1 of the g entrepreneurs are presented with an opportunity to close their first project, forego Rg and borrow another unit of capital to finance a second project, with a higher return R > Rg . If they take this opportunity, these entrepreneurs repay 2D at the end of the second period. The other half of the entrepreneurs are low quality or ‘bad’ entrepreneurs, b, who all operate a project generating a return of at least D if they are successful, with probability 1 − q. These (1 − q)/2 entrepreneurs repay the bank D at the end of the second period. If they are unsuccessful (with probability q), they secretly consume the income and borrow another unit of capital at the start of the second period to keep the project going. They dissemble to banks, claiming that they are g types with a profitable new project to invest in. For simplicity, suppose that even with this second round of finance all of these q/2 entrepreneurs end up defaulting at the end of the second period. All the bank can retrieve from these projects is Rb , where Rb < D. Banks are perfectly competitive and face an exogenous outside cost of capital, ρ > 1. Entrepreneur quality is private information and hidden from banks in both periods. There is therefore an asymmetric information problem (see chapter 7). Banks’ expected profits net of capital repayments if g types choose to become serial entrepreneurs, a case indexed by 1, are



π1B

1+q =D 2





1−q +D 2



q + Rb − ρ(1 + q) = 0, 2

where the first and second terms are expected repayments from successful g and b types, respectively; the third term is expected repayments from unsuccessful b types; and the last term is the cost of the 1 + q/2 + q/2 = 1 + q units of capital used. Setting π1B to zero yields the competitive repayment amount q D1 = ρ(1 + q) − Rb . 2

(2.11)

If the q/2 g types choose not to become serial entrepreneurs, a case indexed by 2, banks’ expected returns become π2B =

   1 q 1−q q D+D + Rb − ρ 1 + = 0, 2 2 2 2

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yielding the competitive repayment D2 =

  ρ 1 + q2 − q2 Rb 1−

q 2

> D1 .

(2.12)

Which of D1 and D2 prevails in equilibrium determines whether the good entrepreneurs with the opportunity become serial entrepreneurs. The key point is that this in turn determines which of D1 and D2 prevails. This realisation lies at the heart of Landier’s multiple equilibrium result. To see this formally, note that the q g types in question choose between payoffs of R − 2D if they become serial entrepreneurs and Rg − D if not. All entrepreneurs are price-takers: they take the required repayments in competitive credit markets as given. Suppose Rg is such that the following condition holds: D2 > R − Rg > D1 . These inequalities imply respectively R − 2D2 < Rg − D2

and

R − 2D1 > Rg − D1 .

(2.13)

These inequalities respectively state that entrepreneurs optimally choose not to close their businesses and become serial entrepreneurs in locations where the cost of capital is high, but take them in locations where the cost of capital is low. These decisions change the mix of the borrower pool and determine the respective costs of capital in the different locations. That is, entrepreneurs who return to the credit market in locations where the cost of capital is high will disproportionately be low-quality types who failed in their first business. This generates losses for banks, which therefore have to raise the cost of capital to break even. Of course, this deters serial entrepreneurship and reinforces this equilibrium, which Landier calls ‘conservative’. In the other, ‘experimental’, equilibrium where the cost of capital is low, it becomes worthwhile for high-quality entrepreneurs to close their current businesses and start a new one in the hope that they will achieve a very high return. Once these better entrepreneurs join the borrower pool, competitive lenders can charge a lower cost of capital. Of course, this is the condition which encouraged serial entrepreneurship to occur in the first place, and establishes this second equilibrium. 2.10.4

Parker’s human capital multiple equilibrium model √ Consider individuals whose return to human capital, h, is either ψ(h) = x h in entrepreneurship (where x ∈ [0, 1] is uniformly distributed entrepreneurial ability) √ or w(h) = α h in paid employment. Here α is a parameter restricted to lie between zero and one in order for a mixture of occupations to arise in equilibrium. The population size is normalised to unity. All individuals have to choose their optimal human capital investment h∗ before their ability is revealed later on; the best they can do when choosing h is to observe average ability among the 0 < n < 1 individuals observed to be entrepreneurs in the region, i.e. the conditional expectation E(x|n). Hence their best guess of the probability that they will end up with sufficient ability to become an entrepreneur is n. The probability that they do not, and so end up choosing paid

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employment, is 1 − n. If the marginal cost of human capital acquisition is c > 0, and if entrepreneurs pay an entry cost of D, then

√ √ h∗ = argmax n[E(x|n) h − D] + (1 − n)α h − ch The first term is the expected net return in entrepreneurship multiplied by the probability that individuals end up in it; the second term is analogous for paid employment. The first-order condition for this problem is nE(x|n) + (1 − n)α − c = 0. √ 2 h

(2.14)

Once individuals stop schooling they realise their entrepreneurial ability, x. They then choose their occupation, which is entrepreneurship for all and only those with x > x˜ , where the marginal entrepreneur x˜ is identified by √ J (˜x) := (˜x − α) h∗ − D = 0. (2.15) The uniform distribution implies that x˜ = 1 − n, and that E(x|n) = E(x|x ≥ x˜ ) = 1 − (n/2). Substituting this last expression into (2.14) yields   n(1 − α − n/2) + α 2 ∗ h = . 2c

(2.16)

Finally, putting (2.16) into (2.15) and noting that x˜ = 1 − n yields an occupational choice locus J (n) = EN (n) − PE(n) = 0, (2.17) where EN (n) := (1 − n)[n(1 − α − n/2) + α] − 2cD PE(n) := α[n(1 − α − n/2) + α]. The intersection of these two curves, EN (n) and PE(n), solves (2.17) and identifies up to two occupational choice equilibria, n ∈ {n1 , n2 }. Parker (2005b, pp. 838–9) provides necessary and sufficient conditions for both equilibria to exist, expressed in terms of the model parameters. One can as a result conceive of two regions identical in all respects except that one has high rates of entrepreneurship, n2 , and the other has low rates of entrepreneurship, n1 < n2 , where n1 = inf {n ∈ [0, 1]|J (n) = 0}

and

n2 = sup{n ∈ [0, 1]|J (n) = 0} .

Provided x2 < 1 − α (as seems to be the case in practice: Parker, 2005b, p. 837), the model also implies that chosen human capital is higher in the high-entrepreneurship region: h∗ |n2 > h∗ |n1 .

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Notes 1. Knight viewed profits as a fluctuating residual, not as a return to the entrepreneurial factor of production. In contrast, Hawley (1907) regarded profit as a reward to risk-taking. According to Hawley, the entrepreneur needs to be the owner of an organisation, in order to exercise decisionmaking power. What sets the entrepreneur apart from managers is his or her willingness to be the ultimately responsible agent in the productive process, liable for ownership of output but also for losses. 2. Also, according to Klein and Cook (2006), T. W. Schultz’s conception of entrepreneurship – articulated in an unpublished 1979 paper – is the ability to adjust or reallocate resources in response to changing economic circumstances. 3. See Stiglitz (1970), Carroll (2002) and Charles and Hurst (2003). See also chapter 4, for evidence on the question of whether entrepreneurs are less risk-averse than non-entrepreneurs. 4. More ambiguous effects of risk are observed if output prices rather than quantities are random: see Appelbaum and Katz (1986). When the random price realisation is low, both entrepreneurial profits and employee wages decrease. 5. Alternative stochastic income processes tend to be intractable. See e.g. Li (2002) for such a model with Markov processes in both paid employment and entrepreneurship.  6. The cumulative distribution function, or cdf, is F(x) = xx f (χ) d χ . Hence F(x) = 0 and F(x) = 1.  7. The average size of firms is q[1 − F(˜x)]−1 x˜x xf (x) dx, which is increasing in x˜ . 8. This is because average ability is increasing in x˜ , as implied by the previous footnote. But a higher x˜ implies fewer entrepreneurs, who number 1 − F(˜x). 9. See e.g. Calvo and Wellisz (1980), Oi (1983), Blau (1985), Bond (1986), Brock and Evans (1986), Kuhn (1988), Murphy et al. (1991), de Wit and van Winden (1991), Laussel and Le Breton (1995), Yamada (1996), Lloyd-Ellis and Bernhardt (2000), Fonseca et al. (2001), Parker (2003c, 2005b), Fender (2005), Lazear (2005), Bohà˘cek (2006), Acemoglu (2008), Antunes et al. (2008) and Gollin (2008). 10. This possibility was recognised as early as Roy (1951), who considered the implications of free occupational choice for productivity when ability affects payoffs differently in two distinct occupations. 11. As will be seen in chapter 7, de Meza and Webb (1987) obtained a similar result in the credit rather than the labour market. 12. Alternatively, able workers demand ex ante screening to reveal their abilities to the general public and so gain utility from higher status (Parker and van Praag, 2009). In either case, it is assumed to be too costly for banks to screen entrepreneurs ex ante as well as ex post. 13. The KL79 model is based on an assumption of perfect competition. Welfare effects have also been studied for this model under imperfect competition. In that case there are entrepreneurial rents which are not competed away. This attracts more entrepreneurs, which increases social welfare relative to the case of perfect competition (Clemens, 2006). These rents partly offset the negative effects of limited entrepreneurial risk taking, though the latter is still associated with reduced economic growth (Clemens, 2008). 14. Of course, other types of contracting out also exist, e.g. making workers redundant and buying in their services from non-entrepreneurial firms. These sorts of arrangement are not the focus of interest here. 15. This model, drawn from Parker (2007b), can also be used to analyse a different aspect of the make-or-buy decision, namely the ‘retail or franchise’ decision, in which the principal chooses between paying employees to sell the product through company-owned retail outlets or paying franchisees to do so. Mazzeo (2004) analyses this issue and shows how the market environment affects the choice of independent or franchisee status in the US motel industry.

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16. This provides an alternative reason to Casson’s (2003) association of entrepreneurship with the exercise of judgement, since judgement is likely to be needed in jobs which are costly to monitor and in which independent business ownership is the optimal organisational form. 17. In terms of the formal model, the contracting-out condition (2.9) would not hold for highly risk-averse workers. 18. Small scale and modest returns can make non-profit-maximisers unattractive acquisition targets for profit-maximisers, assisting their independent survival. Nevertheless, some not-for-profit enterprises protect themselves formally by forming foundations. 19. Drucker (1990), Rose-Ackerman (1996) and Baron (2007). 20. Proof: Suppose the entrepreneur sells a unit of a good at price P which embodies an innate quality q. The entrepreneur obtains utility βq from supplying a good of quality q, where β > 0 could reflect either altruism or a reputation advantage to quality. The cost of producing quality is c(q), where c(·) is a convex function. If the entrepreneur adopted FP status for his firm, he would choose q to maximise π = P − c(q) + bq, yielding a first-order condition c
(q) = b. Denote the quality that satisfies this condition by qf . If the entrepreneur adopts NP status, she cannot receive profits but receives perquisites instead, that are worth less to her than profits, namely d π, where d < 1. Hence her first-order condition is c
(q) = b/d . Let qn denote the quality that solves this equation. Clearly c
(qn ) = b/d > b = c
(qf ). By convexity of c(q), this implies qn > qf . Finally, if consumers value quality, the price P they are willing to pay is increasing in q, so P(qn ) > P(qf ). 21. See Storey (1994a), Boden (1996), Wagner (2004), Eriksson and Kuhn (2006), Mueller (2006b), Sørensen (2007), Hyytinen and Maliranta (2008) and Parker (2009b). Wagner (2004) and Sørensen (2007) also observe that young firms are associated with worker quits leading to entrepreneurship; but size seems to matter more than age (Sørensen, 2007). 22. However, Sørensen’s (2007) study uses a selected sample of people who enter entrepreneurship at some point in the sample frame. Hence sorting (self-selection) might still be an important part of the story, even if it cannot explain the entire story. 23. See Winter (1984), Henderson and Clark (1990) and Henderson (1993). 24. Audretsch (2001), Klepper and Sleeper (2005) and Klepper (2006). 25. A similar prediction of a persistent ‘class structure’ which separates people into rich employers and poor workers, who have no choice but to work for the employers for a low wage, emerges from the model of Matsuyama (2006). Endogenous investment decisions by entrepreneurs in Matsuyama’s (2006) model locks the class structure into place. 26. See e.g. Segerstrom et al. (1990), Segerstrom (1991), Aghion and Howitt (1992) and Peretto (1998). 27. See Piore and Sabel (1984), Dosi (1988), Carlsson (1989, 1992), Acs et al. (1990), Carlsson et al. (1994) and Wennekers and Thurik (1999). 28. The model also implies that all employees over-invest in human capital, while the ablest entrepreneurs under-invest in human capital relative to the first-best case of perfect information (Parker, 2005b, proposition 3).

3

Empirical methods in entrepreneurship research

The economics of entrepreneurship is notable for its careful use of econometric methods, and for eschewing the practice of asking entrepreneurs or other agents what they think they will do in various situations. Responses to these kinds of question are known to be prone to self-serving bias, or ‘cheap talk’, and respondents are liable to give answers which they think interviewers regard as ‘appropriate’. Instead, the ‘revealed preference’ principle trains economists to distrust individuals’ declared intentions, forcing them to undertake the harder but more objective task of inferring preferences and constraints from their actual behaviour. This often necessitates the use of advanced statistical techniques to overcome econometric problems that might otherwise vitiate empirical estimates. Examples of such problems include: • Sample selection bias, whereby sample membership is not random but instead is generated by some (at least partially) observable systematic process; • Unobserved heterogeneity, whereby important unmeasured idiosyncratic variables are omitted from a regression model; • Endogeneity, whereby an ‘independent’ variable is itself codetermined within the structural model of interest; and • Non-stationarity, whereby time-series variables follow unit root processes that violate a key assumption of the classical linear regression model and lead to invalid statistical inference. The present chapter outlines several ‘canonical’empirical models in the economics of entrepreneurship which address all of the problems on this list. Throughout, knowledge of regression analysis is taken as given; readers not versed in it are referred to Greene (2003) or one of innumerable alternative standard textbooks. It is worth noting at the outset that I will focus primarily on multivariate models. Such models enable the researcher to control for a potentially wide range of explanatory variables when conducting an empirical analysis. Correlations and (nonparametric) difference-in-means approaches are useful as simple descriptive tools and they continue to be deployed in some parts of the non-economics entrepreneurship literature. However, they are ill-suited for inference about structural relationships in entrepreneurship, because the omission of other conditioning variables generally creates 86

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bias (Greene, 2003). For this reason, and also because they are widely taught, simple descriptive methods will not be discussed in this chapter either. The chapter is arranged according to the type of data available to the researcher: cross-section, time-series or panel. The first three sections deal with cross-section models, introducing sample selection and instrumental variables (IV) methods; binary occupational choice models; and several useful extensions of those models. The fourth section deals with issues which arise when the researcher possesses a sample of timeseries data. The fifth section discusses panel-data models. The sixth section focuses on entrepreneurial duration, or ‘survival’, models. Details of an especially technical nature are relegated to footnotes and the chapter appendix.

3.1

Cross-section regression models: sample selection bias and IV

Consider the simple regression model, written in matrix form as Yi = γ
Xi + vi

i = 1, . . . , n ,

(3.1)

where Yi is some continuous variable, Xi is a set of regressors (including a column of ones for an intercept), γ is a vector of parameters to be estimated, and vi is a random disturbance, assumed to be normally distributed. Each observation is denoted by i: the sample size is n. For example, Yi might be the financial performance of entrepreneur i at a given point in time, while Xi contains relevant individual- and venture-specific characteristics. Such models are often estimated by ordinary least squares (OLS). The objective of OLS estimation is to obtain unbiased, or at least consistent, estimates γˆ of the true but unknown parameter vector γ . If γˆ is consistent, any bias (defined as |γˆ − γ |) progressively vanishes and estimates become more precise (efficient) as the sample size n increases. The OLS estimator only yields consistent or unbiased estimates if several assumptions hold. Two important cases where they do not hold include nonrandom composition of the sample {Yi , Xi }; and non-independence of Xi from vi . These two cases will now be discussed in turn.

3.1.1

Sample selection bias Suppose, for the sake of argument, that a researcher is interested in estimating (3.1) in order to discover the determinants of entrepreneurs’ sales or profits, Yi , in terms of a set of explanatory variables, Xi . Unbiased or consistent estimates of γ will allow the researcher to predict accurately the effects of changes in those variables on entrepreneurial profits, provided the sample is representative of all individuals in the population. The danger, however, is that the sample is not representative, because individuals have unmeasured characteristics which predispose them to perform particularly well in entrepreneurship and hence select into the sample. In this case, we say that sample selection bias occurs. Without dealing with this bias, researchers are unable to predict for example whether non-entrepreneurs with particular characteristics

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X could improve their lot by switching into entrepreneurship. A popular practical way of correcting for selection bias is Heckman’s (1979) method. Heckman’s method comprises two steps. The first step estimates a participation equation z i = ω
Wi + u i ,

(3.2)

where zi is an indicator variable equalling 1 if individual i is an entrepreneur and 0 otherwise; Wi is a set of explanatory variables, ω is a vector of coefficients, and ui is a disturbance term, with unit variance. Details about how to estimate a binary model of this sort appear in the next section. In the second step, the researcher computes fitted values zˆi from (3.2), and uses them to form a new variable called the ‘Inverse Mills Ratio’ λi = −φ(ˆzi )/(ˆzi ), where φ(·) and (·) are the density and cumulative distribution functions of the standard normal distribution. This variable is then added to the right-hand side of (3.1), yielding the augmented regression Yi = γ
Xi + αλi + i ,

(3.3)

where α > 0 implies positive self-selection into entrepreneurship and α < 0 implies negative selection. Equations (3.2) and (3.3) can be estimated jointly by maximum likelihood (ML) or in two separate steps. Most modern software packages do one or both automatically. The Heckman correction enables consistent estimates of the regression coefficients γ to be obtained. Applications of this method to earnings functions are discussed in section 3 of this chapter, while empirical estimates of α are reviewed in chapter 4. The Heckman correction method has been applied to such diverse topics as participation in public-sector entrepreneurship programmes (Wren and Storey, 2002) and models explaining entrepreneurs’ work hours (Parker, Belghitar and Barmby, 2005), among others. Arguably, however, it remains under-used in applied entrepreneurship research as a whole. 3.1.2

Endogeneity and IV If Xi is not independent of vi in (3.1), OLS no longer provides consistent estimates of γ . Specifically, if Xi includes endogenous, mis-measured, omitted or lagged dependent variables, OLS estimates of γ will generally be biased. As in Murray (2006), such variables will be termed ‘troublesome’ in what follows. Endogeneity biases in particular are especially widespread in the entrepreneurship field, for two principal reasons. First, researchers regularly explain performance in terms of variables whose values entrepreneurs choose themselves. But if choices are shaped by individual characteristics, these variables are likely to be correlated with unobserved characteristics captured within vi , rendering them endogenous. Second, several explanatory variables assumed to be causes of entrepreneurship are also consequences of it, such as wealth, income

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and economic growth. Cases like these will be highlighted in future chapters as and when the problem arises. As a constructive solution to these problems, Instrumental Variables (IV) estimators consistently estimate coefficients γ even when ‘troublesome’ variables appear in Xi . To fix ideas, write Xi = [Mi , Wi ] in (3.1), where Mi is a set of troublesome variables, and Wi is a vector of non-troublesome variables. IV is made possible by a set of variables Zi that are (i) uncorrelated with the error term vi ; (ii) correlated with the troublesome variables Mi ; and (iii) not members of Xi . The elements of Zi are called ‘identifying instrumental variables’, or simply ‘identifying instruments’. The most frequently used IV estimator is two-stage least squares (2SLS). At the first stage of 2SLS, the researcher identifies troublesome variables and identifying instruments and uses OLS to estimate Mi = α
Zi + β
Xi + i ˆi (where i is a random error term). In the second stage of 2SLS, the fitted values M are substituted for Mi in the regression of interest, (3.1). The second-stage estimates   ˆ i , Wi ]
[M ˆ i , Wi ] −1 [M ˆ i , Wi ]
Yi are called 2SLS estimates. γˆ = [M 2SLS requires at least as many instruments Zi as there are troublesome explanatory variables Mi . When there are as many (resp., more) instruments as (resp., than) troublesome explanatory variables, we say the equation of interest is exactly (resp., over-) identified. In principle, the more instruments there are the more efficient the IV estimators will be. In practice, however, it can be hard to find instruments which satisfy all of the criteria (i)–(iii) above. Ideally the choice of instruments should be guided by theory; however this is not always straightforward and suitable empirical constructs are not always available in actual data sets. Murray (2006) discusses the serious problems that can arise when the researcher implements IV using invalid or weak instruments, e.g. instruments which are not uncorrelated with the error term or are only weakly correlated with the troublesome variables. In cases like these, the cure can be worse than the disease. To illustrate the IV estimator, Parker and van Praag (2006a) estimated the following ‘triangular’ model of entrepreneurial performance: π = β0 + β1 x1 + · · · + βJ xJ + βs sch + βc con + u

(3.4)

sch = η0 + η1 x1 + · · · + ηJ xJ + θ1 z1 + v

(3.5)

con = γ0 + γ1 x1 + · · · + γJ xJ + γs sch + θ2 z2 + ω

(3.6)

where π is gross log entrepreneurial profit; x1 through xJ are exogenous control variables (i.e. the Wi above); sch measures years of schooling; and con is a measure of how capital-constrained an entrepreneur was when they started up. These last two variables are potentially endogenous, so correspond to the Mi variables in the preceding discussion. Now let z1 and z2 be vectors of ‘identifying’ instrumental variables which do not appear anywhere in (3.4). To generate consistent estimates of the parameters of interest (chiefly βs , βc and γs , predicted by Parker and van Praag’s theoretical model

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to be positive, negative and negative respectively), it is essential that z1 and z2 possess explanatory power and are genuinely independent of the disturbance terms u, v and ω. Based on a rationale explained in chapter 9, Parker and van Praag (2006a) chose father’s education and the number of siblings as instrumental variables z1 (yielding over-identification); and they chose capital intensity of the entrepreneur’s industry sector as the instrumental variable z2 (exact identification). Statistical tests described in Parker and van Praag (2006a) indicated that these identifying instruments were indeed exogenous. To demonstrate the practical importance of IV in terms of inference, Parker and van Praag (2006a) analysed a data sample of Dutch entrepreneurs taken from the mid1990s and obtained OLS estimates βˆs and βˆc (obtained by estimating (3.4) on its own) of 0.072 and −0.003 (with t statistics of 2.45 and 1.14 respectively). In contrast, IV estimates (obtained by estimating (3.4) through (3.6)) by 2SLS were 0.137 and −0.039 (with t statistics of 2.01 and 2.23 respectively). Thus IV implies larger and more significant effects from schooling and borrowing constraints on performance than OLS – illustrating the practical importance of correcting for endogeneity bias. Parker and van Praag (2006a) argue that these findings imply that government policies designed to promote entrepreneurship should seek to enhance the national stocks of both human and physical capital. There is growing use of IV estimation in the economics of entrepreneurship, as will become apparent from the discussion of various empirical findings throughout this book. However, applications of IV in ‘non-economics’ entrepreneurship research are still in their infancy; it is expected and hoped that many more applications will follow in the future. 3.2

Cross-section binary models of occupational choice

Binary limited dependent models are especially widely used in entrepreneurship research. We encountered one in the previous section, namely (3.2); below, the two most common binary models – probit and logit – are explained. Binary models are mainly, but not exclusively, used to model entrepreneurship as an occupational choice. Consider two occupations denoted by j: entrepreneurship, E, and paid employment, P. Each individual has a vector of observed characteristics Wi and derives utility Uij = U (Wi ; j) + uij if they work in occupation j, where U (·; ·) is utility that can be observed by the econometrician and uij is idiosyncratic unobserved utility. Denote by zi∗ a ‘latent’ variable which measures the relative utility advantage to i of being in occupation E relative to P. That is, zi∗ = U (Wi ; E) − U (Wi ; P) + uiE − uiP . If we assume that U (·; ·) is linear, taking the form U (Wi ; j) = βj
Wi , where βj are vectors of coefficients, then we can write zi∗ = α + β
Wi + vi ,

(3.7)

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where β
:= βE
−βP
is another vector of coefficients; α := E[uiE −uiP ] is an intercept; and vi := uiE − uiP − α ∼ IIN (0, σ 2 ) is a disturbance term. Henceforth the intercept term is incorporated in Wi as a column of ones, so β will be treated as the complete set of coefficients. Individual i chooses entrepreneurship over paid employment if zi∗ ≥ 0. Hence as in (3.2) above, define the observable binary occupational indicator variable  1 if individual i is observed in E, i.e., if zi∗ ≥ 0 zi := 0 if individual i is observed in P, i.e., if zi∗ < 0 Therefore the probability that an individual is observed to be an entrepreneur in a representative sample, with characteristic vector Wi , is Pr(zi = 1) = Pr(zi∗ ≥ 0) .

(3.8)

The probit model assumes that the distribution of the disturbance term vi is normal. Hence Pr(zi = 1) = (β
Wi /σ ) and Pr(zi = 0) = 1 − (β
Wi /σ ), where (·) is the (cumulative) distribution function of the normal distribution. The model is estimated numerically by Maximum Likelihood (ML) and is programmed in most modern software packages.1 The logit model arises if the distribution function of vi is assumed to be that of the logistic distribution, in which case (3.8) becomes Pr(zi = 1) =

exp{β
Wi } . 1 + exp{β
Wi }

(3.9)

A likelihood function can be formed in a similar way as for the probit model and β estimated in the same manner (see the previous note). Logit models are routinely programmed in most software packages. Both logit and probit dominate OLS estimation of zi = β
Wi + vi (called the linear probability model), since OLS is an inefficient and heteroscedastic estimator in this context, and problematically can predict probabilities outside the unit interval (see e.g. Maddala, 1983). It makes little practical difference which of probit or logit is used, as estimates of β are relatively insensitive to this choice.2 Econometricians call the general approach outlined above ‘reduced form’. This is because the logit and probit empirical specifications are derived from a utility maximisation problem in which the indirect utility function U (Wi ; j) is simply assumed to depend on several observable covariates in a convenient way. In contrast, ‘structural’ occupational choice models relax this assumption and instead either estimate or calibrate the ‘deep’ parameters of agents’ objectives and constraints. A recent example of a structural occupational choice model is Paulson et al. (2006); but the structural approach in entrepreneurship research can be traced back at least as far as Brock and Evans (1986). There are at least three distinct applications of binary choice models to occupational choice. One (I) focuses on the probability that at a snapshot of time individuals

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are entrepreneurs rather than employees. A second (II) asks a different question: what factors affect the decision to become an entrepreneur, as opposed to remaining in paid employment (or unemployment)? A third (III) investigates entrepreneurs’ decision to leave rather than to continue in entrepreneurship. All of these applications can be handled by binary choice models: they merely require different definitions of the dependent variable, zi . However, it is possible to dispute the relative merits of each. For example, Evans and Leighton (1989b) argued that II is preferable to I, on the grounds that I (unlike II) confounds entry and survival effects. This is because the probability of being an entrepreneur at time t depends on the probability of switching into entrepreneurship at some previous time and then remaining as an entrepreneur until t. On the other hand, as Wellington (2001) has pointed out, application II excludes people who are already successfully practising entrepreneurship, which is evidently a group of some interest. Low annual switching rates from paid employment to entrepreneurship (see chapter 2, section 2.2) also means that II sometimes suffers from small numbers of observations of entrepreneurs; and the characteristics of switchers may well be different from those of non-switchers. There are more serious methodological problems with application III, which will be discussed separately below. I would simply point out that all three applications can generate useful information and play an important role in applied entrepreneurship research. To conclude, a vast number of studies have implemented probit and logit binary choice models. Many of the findings based on them will be reviewed in chapter 4. 3.3

Extensions of the cross-section binary model

Five useful extensions of the cross-section binary occupational choice model are considered below: 1. 2. 3. 4. 5.

The inclusion of relative incomes Multiple occupational choices Multiple equation systems Non-binary occupational choices Heteroscedastic probit

3.3.1

The inclusion of relative incomes

Microeconomic theory teaches us that relative prices generally influence individual choices. If this precept is true for occupational choice, then one of the explanatory variables in the matrix W above should be relative income, or its logarithm: (ln yiE − ln yiP ). However, we know from section 3.1 that occupational incomes are potentially endogenous and prone to selection effects, so the binary choice model needs to be modified somehow in order to obtain efficient and unbiased estimates of the parameters of interest.3 The first stage of the probit model with relative incomes estimates selectivitycorrected earnings functions (see chapter 13) separately for entrepreneurs and

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employees. Letting M denote the vector of (exogenous) explanatory variables used in the earnings functions, one estimates the equations z i = β
Wi + v i [ln yiE |zi = 1] = γE
Mi + ϑE λiE + uiE [ln yiP |zi = 0] =

γP
Mi

+ ϑP λiP + uiP

i ∈ {E, P}

(3.10)

i∈E

(3.11)

i ∈ P,

(3.12)

where λiE = −φ(ˆzi )/(ˆzi ) and λiP = φ(ˆzi )/[1 − (ˆzi )] are the Inverse Mills Ratios to correct for selectivity into each occupation. Eq. (3.10) is sometimes called the ‘reduced form probit’ and is not of direct interest: its principal role is to generate zˆi values used to correct for selection bias in the earnings functions (3.11) and (3.12). Stage two of the binary choice model with relative incomes generates the predicted
yiE log incomes from both occupations derived from (3.11) and (3.12), namely ln
and ln yiP . Predicted incomes must be obtained because individuals are usually only observed in one occupation at any point in time in a cross-section sample. The third stage estimates the misleadingly named (see the previous footnote) ‘structural probit’ model



zi = α[ln yiE − ln yiP ] + ω
Xi + v˜ i ,

(3.13)

where X is a further vector of explanatory variables, and where v˜ i , uiE , uiP and vi are all assumed to be normally distributed disturbance terms. If occupational choices do indeed depend on relative financial returns in each occupation, then α > 0. A t-statistic can therefore be used to test the hypothesis that an individual is more likely to be an entrepreneur the greater is their relative income in entrepreneurship. Estimation at each stage is easily accomplished using standard econometric software, taking note of the following four points. First, this model has a 2SLS structure, and is therefore an implementation of IV. Consequently identification issues arise. This model is only properly identified (i.e. the estimated parameters only relate to the model parameters of interest) if the researcher includes in X at least one variable not included in M or W , and vice-versa. For example, it is common practice to exclude variables relating to the presence of children in i’s household from Mi , but to include them in Wi and Xi , on the grounds that family size affects occupational choices but not earnings (see Hammarstedt, 2006; and chapter 4). It is conceptually harder to think of variables that affect earnings (Mi ) but not occupational choice (Wi and Xi ). Of course, any chosen restrictions should be tested for validity in empirical work, as observed in the discussion of IV above. Second, because the relative income variable in (3.13) has been generated from previous regressions (i.e. it is a ‘generated regressor’), Newey–West corrected standard errors should be used. Third, income under-reporting by entrepreneurs may bias yiE and hence αˆ in (3.13). This problem can be overcome by applying income underreporting corrections (if available) to the data at the outset (see chapter 13). Fourth, one should ensure that the disturbances of the earnings equations are normally distributed

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(Bernhardt, 1994). This can be achieved by suitable transformations of the earnings variables in (3.11) and (3.12) (see Parker, 2003b). Unfortunately, previous applications of the probit model with relative incomes have tended to ignore most or all of these points, leading to biases of unknown magnitude. Chapter 4 will review estimates of the binary model with relative incomes, focusing on estimates of α in (3.13). 3.3.2

Multiple occupational choices Sometimes there may be more than two occupations to choose from. For example, Earle and Sakova (2000) studied the problem of choosing between employer selfemployment, own-account self-employment, paid employment, or unemployment. As another example, entrepreneurs might also be able to choose between several venture development paths. Thus Parker and Belghitar (2006) modeled nascent entrepreneurs’ decisions between launching a nascent venture, continuing to prepare their venture for eventual launch, and terminating the founding effort altogether. Multiple occupational choices can be handled by using multinomial choice models. Perhaps the most popular model within this class is the multinomial logit (MNL) model, which can be regarded as an extension of the simple logit model described above. In this model, individual i must choose between j = 1, . . . , J alternatives. Define zij as equal to one if i chooses j, and zero otherwise. Then the MNL model proposes that the probability i chooses j is

exp{βj
Wi + γ
Xj } . Pr(zij = 1|Wi , Xj ) =

j exp{βj Wi + γ Xj }

(3.14)

Here Wi is a vector of variables whose values vary across individuals, whereas Xj is a vector of variables whose values vary across occupations. The βj coefficients must vary across occupations or else they cannot be identified. Analogous to the genesis of the simple binary model discussed above, (3.14) is the probability that results from a choice problem in which individuals maximise utility across each alternative, where the utilities are given by Uij = βj
Wi + γ
Xj + uij (see Parker and Belghitar, 2006, for details and an illustration). The βj and γ coefficients can be estimated by maximum likelihood, a procedure that is routinely incorporated in most standard econometric software packages. 3.3.3

Multiple equation systems I will discuss here two useful applications of multiple equation systems for occupational choice in entrepreneurship. First, although individuals may wish to become entrepreneurs, they may not have the opportunity to do so. This motivates the use of the bivariate probit model, which separates individuals’ opportunities to become ∗ be a latent variable representing entrepreneurs from their willingness to do so. Let z1i ∗ represent willingness. Potentially different factors impinge on opportunities and let z2i

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opportunity and willingness, suggesting the specifications ∗ z1i = β1
W1i + v1i

(3.15)

∗ z2i

(3.16)

=

β2
W2i

+ v2i ,

where (v1i , v2i ) are distributed as bivariate normal variates with correlation coefficient ρ: the joint distribution function is (·, ·; ρ).4 To identify this model, it is necessary that W1 = W2 .As with the IV and binary choice models with relative incomes, the researcher must impose testable identifying restrictions on the basis of a priori reasoning. For example, the height of industry entry barriers might affect entrepreneurs’ opportunities but not their willingness to enter. Applications of the bivariate probit model include van Praag and van Ophem (1995) in an analysis of occupational choice between entrepreneurship and paid employment; Henley (2007), who analysed determinants of aspirations to become an entrepreneur jointly with actual transitions to entrepreneurship; and Fairlie (2006a), who specified z1∗ and z2∗ in (3.15) and (3.16) as predispositions for business ownership and computer usage, respectively. Using matched CPS (Current Population Survey) and Computer and Internet Usage data from 1997–2001, Fairlie estimated ρ to be insignificant, implying that entrepreneurs do not have unmeasured characteristics in v1i that predispose them to use computers via v2i (or vice versa). A second extension generalises (3.15) and (3.16) to allow for interdependent occupational choices: ∗ ∗ z1i = β1
W1i + γ1 z2i + v1i

(3.17)

∗ ∗ z2i = β2
W2i + γ2 z1i + v2i .

(3.18)

The introduction of the γ terms in (3.17) and (3.18) allows, for example, a husband’s occupational choice to affect his wife’s, and vice versa. This type of structure appears to be well suited for explaining why a disproportionate number of entrepreneurs are married to other entrepreneurs (Parker, 2008a). The model (3.17) and (3.18) is identified if at least one variable, 1 say, appears in W1 but not W2 , and if at least one variable, 2 , appears in W2 but not W1 . The reduced form of this model is given in the first part of the chapter appendix. An important implication of this simultaneous equation model is that ignoring interdependence in entrepreneurial choices when it is actually present can generate seriously misleading inferences. This warning is of practical relevance because most empirical studies of business ownership do precisely that.5 There are two principal ways of estimating the simultaneous probit model (3.17) and (3.18). One is by a consistent two-step estimator (2SE) proposed by Maddala (1983, chap. 8.8).6 The second method is Full Information Maximum Likelihood (FIML). Parker (2008a) provides an application using a sample of Panel Study of Income Dynamics (PSID) data.

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3.3.4 Non-binary occupational choices All of the models discussed so far assume that individuals choose to spend all of their time in either entrepreneurship or paid employment. However, many people have broken spells in entrepreneurship. Even among those with unbroken spells, some individuals mix their work time between multiple occupations. In particular, employees commonly start new businesses part-time while continuing to work for their employer (Carter et al., 1996; Delmar and Davidsson, 2000). A simplistic way of dealing with the problem of broken spells would be to measure involvement in entrepreneurship as the number of hours worked as an entrepreneur over a particular time period (e.g. Kolvereid and Isaksen, 2006). But this approach includes numerous non-entrepreneurs in the sample (e.g. professors doing consulting on the side) – while from a technical point of view it does not fully exploit the structure of the data. Recognising these limitations, Burke et al. (2008) propose the following model. Define a variable hE as the time spent in entrepreneurship over the last T years: 0 ≤ hE ≤ T . In a typical sample, hE values will be bimodal, with the dominant mode at hE = 0 (most sample respondents have no involvement at all in entrepreneurship) and a secondary mode at some hE > 0 (the modal work hours of entrepreneurs). Burke et al. (2008) recommend a two-stage estimator to first estimate the determinants of the outcomes hE = 0 rather than hE > 0 (using a binary model), and then to estimate the determinants of hE |hE > 0, i.e. of hE conditional on hE > 0.7 3.3.5

Heteroscedastic probit Binary choice models such as (3.2) will yield biased and inconsistent results if the variance of the error term is not constant across sample observations (i.e. is not homoscedastic). In such cases, the error term is said to be heteroscedastic. If the form of the heteroscedasticity is known, σ [in (3.31), note 1] should be replaced with σi , where σi is an estimated function of some covariates, Mi , which can be specified by the researcher. A convenient form is σi = exp{γ
Mi }. Fraser and Greene (2006) propose a simple model of Bayesian learning by entrepreneurs which gives rise to the heteroscedastic probit model. It is described in the second part of the chapter appendix. Fraser and Greene (2006) went on to estimate it using a sample of British Social Attitudes Survey (BSAS) data. The heteroscedastic component of the model turned out to be statistically and economically important in their analysis.

3.4 Time-series models

Consider again (3.1), but now for data observed for time periods t rather than individuals i. For example, Yt might be an aggregate measure of entrepreneurship in a country, such as the self-employment rate, which is related to a set of economic conditions in that country, Xt . One rationale for estimating a time-series model of entrepreneurship is that, unlike cross-section studies, it becomes possible to analyse trends in entrepreneurship. Also, the time-series approach can identify determinants of

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entrepreneurship which are uniform for all or most members of a cross-section sample at a given point in time, e.g. income tax variables, interest rates and other macroeconomic variables. Prior to the 1990s, the preferred technique for estimating (3.1) using time-series data was ordinary or generalised least squares (OLS or GLS: see, e.g., Blau, 1987; Steinmetz and Wright, 1989). Since then, however, it has become known that least squares estimators are inappropriate when any of the variables in the set {Yt , Xt } are non-stationary. In simple terms, a non-stationary process is one in which there is no mechanism forcing values of a series to revert to its mean. The application of OLS or GLS to non-stationary data is known to generate misleading results in which genuinely independent variables spuriously appear to be related to each other, and in which classical inference is invalid (Phillips, 1986). The R2 goodness-of-fit measure is no longer informative and t and F statistics can no longer be used for hypothesis testing. The importance of this point is underlined by the fact that national self-employment rates in the USA, UK and most other OECD countries appear to be non-stationary in practice.8 Hence this point appears to be of considerable practical importance. When time-series variables are non-stationary, it is necessary to check whether they co-integrate, i.e. whether there exists at least one linear combination of the variables (called a co-integrating vector) that is stationary. If so, there is said to be a long-run (non-spurious) relationship between the variables. It is then possible to obtain consistent estimates of the coefficients of that relationship, and to perform appropriate hypothesis tests on the coefficients. It is also possible to examine a dynamic (‘short-run’) errorcorrection model that describes how agents behave out of equilibrium. The interested reader is referred to Greene (2003, chapter 20) for an introduction to co-integration analysis. Parker (1996) proposes the following ‘working guide’ for estimating and performing inference on the parameters of (3.1) using time-series data and co-integration methods: 1. Check each variable in (3.1) is non-stationary using unit root tests. 2. If at least two variables are non-stationary, use a multivariate co-integration estimator to identify the number of co-integration vectors. 3. If there is a unique co-integration vector, test if Yt is weakly exogenous; and perform significance tests on each element of γ . 4. Estimate an error-correction model using the co-integrating residuals vˆ t in order to determine the short-run determinants of entrepreneurship. Co-integration methods are slowly becoming more widely used in the economicsbased entrepreneurship literature (e.g. Torrini, 2005; Bruce and Mohsin, 2006). However, as a general rule, researchers in the business studies tradition seem to be less aware of these methods (see Shane, 1996, and Choi and Phan, 2006, as examples). Hopefully, this situation will change in the future. Also, as more years of data become available, results based on co-integration analysis gain greater credence, as the power

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of unit root and co-integration tests tends to be low in small samples covering only a short time span. 3.5

Panel-data models

If time-series data are available for the same set of individuals (or ventures or countries) then a ‘panel’ of data is available. Panel data combines the advantages of the rich casespecific variation of cross-section data with the temporal variation of time-series data. It also enables the researcher to control for cohort- and unobserved person-specific effects – something which cannot be done with (repeated) cross-sections. Furthermore, panel data can increase the explanatory power of cross-section binary choice models which often lack predictive power (Reynolds, 1997). Suppose that a panel comprises n individuals i observed over T time periods t. A simple pooled regression model corresponding to (3.1) is Yit = γ
Xit + α + vit , where α is an intercept common to all individuals. A more general specification allows the intercept to vary across individuals, giving rise to the fixed-effects model: Yit = γ
Xit + αi + vit .

(3.19)

For example, consider an application in which Yit measures the rate of entrepreneurship in region i at time t. Then the αi can be interpreted as the net effects of unobserved (and time-invariant) cultural or institutional factors affecting entrepreneurship in region i (e.g. Grilo and Thurik, 2006; Freytag and Thurik, 2007). It should be noted that covariates Xit which are not time-varying cannot be included in fixed-effect models, as such covariates are indistinguishable from the fixed effects, αi . A solution to this problem is to use a random-effects estimator. The random-effects model is similar to (3.19), except that it assumes the intercepts are drawn from a common distribution with mean α and variance σα2 . Fixed- and random-effects estimators are both routinely implemented in modern software packages. Robson and Wren (1999) and OECD (2000) are two examples of panel-data models of entrepreneurship which take the cross-section units i to be countries rather than individuals. Letting  denote the difference operator (e.g. Xt ≡ Xt − Xt−1 ), both of these studies estimate the following dynamic specification of self-employment rates, Yit :  ln Yit = αi + β
Xit − γ ln Yi t−1 + ω
Xi t−1 + ϑt + vit ,

(3.20)

where X is a matrix of explanatory variables; αi is a country-specific intercept term; and ϑt is a set of time dummies. The β coefficients capture short-run effects of variables on self-employment rates, whereas the ω coefficients pick up their long-run effects. Other noteworthy examples of fixed-effect regression models include Audretsch and Thurik (2001a), Acs et al. (2004) and Mueller (2006a), who all analyse the relationship between entrepreneurship and cross-country economic growth; and Georgellis and

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Wall (2006), who analyse entrepreneurship across US states and over time. Results of these studies are discussed in chapter 4. Random-effects models are more common in applications where the cross-section dimension relates to individuals rather than countries, partly because problems of time-invariant covariates (e.g. individuals’ genders and highest educational qualifications) are more prevalent in this case. When using panel-data models with a large time-series dimension, the researcher should remember that as in the time-series case, least squares estimates of panel models are biased if any of the variables in the model are non-stationary. Co-integrated panel techniques are appropriate in this case (see Parker and Robson, 2004, for details). In contrast, the remainder of this subsection turns its attention to the case where the time-series dimension of the panel is small (e.g. a few years) and the cross-section dimension is large (e.g. several thousand respondents). Examples of data sets with these characteristics include the US PSID, the UK BHPS and the European Household Panel Survey (EHPS). Panels with a large cross-section dimension allow researchers to generalise binary models like (3.7) in two especially useful ways. As noted above, one is by introducing fixed effects to control for unobserved heterogeneity. The other is by modelling statedependent choices, which captures the tendency for individuals to remain in the same occupation for many periods, perhaps because of switching costs (see chapter 2).9 In an application using BHPS data on self-employment participation over 1991–99, Henley (2004)foundthatfixedeffectsandstatedependencebothplayedamajorroleinexplaining self-employment propensities. First, the fixed effects αˆ i accounted for nearly 60 per cent of the unexplained variance in self-employment propensities, consistent with the view that many determinants of self-employment choices are idiosyncratic and not easily amenable to explanation by available socio-economic variables. Second, in terms of state dependence, Henley found that a Briton who was self-employed last year is 30 per cent more likely to be self-employed this year than someone who was in paid employment last year. Together with the finding that local unemployment rates explain little of selfemployment participation, the results suggest that persistence in self-employment is a structural rather than a cyclical phenomenon. That provides a possible explanation for the unit root processes in aggregate self-employment rates reported in the previous section. Strikingly, Henley (2004) reported that the significance of several ‘conventional’ explanatory variables declined sharply upon controlling for fixed-effects and state dependence. Hence findings based on simple cross-section models can generate misleading inferences and should be treated with caution. An important implication is that entrepreneurship researchers should whenever possible move away from conducting cross-section surveys and collect panel data instead. Of course, data limitations mean that this is not always possible. And panels can suffer from problems of their own, including sample attrition, whereby individuals drop out of the panel non-randomly, e.g. because they move away from the location where the survey takes place, or because they close their businesses. Parker and Belghitar (2006) discuss this issue in the context of the Panel Study of Entrepreneurial Dynamics (PSED) panel design and propose Heckman selectivity corrections to deal with possible bias caused by sample attrition.

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The use of binary dependent variables gives rise to another problem with fixed effects, since this specification necessarily eliminates from the sample all individuals who do not change occupational status over the sample period – including the numerous interesting cases of ‘survivors’ who remain in business. This is because individuals who never change occupation have fixed zit = zi observations which are perfectly explained by their fixed effect αi . One solution is to use random-effects models instead (for example, see Hochguertel, 2005a). 3.6

Entrepreneurial duration models

Binary choice and hazard models are the most widely used techniques for quantifying the effects of individual- and firm-specific characteristics on the duration of spells in entrepreneurship. Binary choice models Consider again the binary choice models described in section 3.2. These models condition a binary variable zi on a vector of explanatory variables, Wi , where i indexes an individual observation. Suppose one now defines zi as equal to one if individual i remains in entrepreneurship after some time interval has elapsed, and zero if i has left entrepreneurship by this time. Alternatively, in applications where i indexes a particular venture from a sample of ventures, zi can be defined as equal to one if the venture is still in business and zero if the venture has exited the market. In both cases the marginal effects of an explanatory variable on the probability of survival are still calculated using (3.32) in note 2. In a variant of the binary model, Cooper et al. (1992) proposed the following ‘threshold’ specification to link financial performance of an entrepreneur with their exit or continuation decision. Define a variable z to take the value 1 for continuation if π ≥ π, where π is a minimum acceptable profit threshold in entrepreneurship. Hence z = 0 if π < π. Write π = β1
X + u1 and π = β2
X + u2 . A two-step procedure first estimates the simple probit model z = β3
X + u3 , where β3 = (β1 − β2 )/σ , where σ is the standard deviation of uˆ 3 . Second, estimate β1 from the π model, and infer β2 and its standard errors from the β1 and β3 estimates. This approach, which uncovers the determinants of both performance and thresholds, is valid under the assumption that cov(u1 , u2 ) = 0. Although they are widely used to model exit outcomes, binary choice models suffer from a serious drawback. They effectively assume that z = 1 cases, whether defined as entrepreneurs or ventures, survive when, in fact, they have only been observed not yet to fail. Statisticians refer to this as ‘right censoring’, a feature which should be incorporated explicitly in the likelihood function. By not doing so, binary models when applied to duration analysis are prone to bias. A superior approach is to use hazard models. Hazard models As well as addressing right-censoring problems, hazard

models (also referred to as ‘survival’ and ‘event history’ models) provide a direct way

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of identifying the factors that determine how long (rather than whether) individuals remain in entrepreneurship, or how long ventures remain in the market. More generally, these methods can also be used to model business entry as well as other events in the entrepreneurial process (see e.g. Sørensen, 2007). For expositional clarity, consider the case of an individual remaining in entrepreneurship. At each discrete point in time t there is a probability (or hazard) that individual i, who has been observed in entrepreneurship for ai periods up to t, leaves entrepreneurship. The Cox proportional hazard model is described by

Hi (t) = H0 (t) exp[β
Xi (t)],

(3.21)

where H0 (t) is the so-called ‘baseline hazard’ at t; Xi is a vector of characteristics for individual i; and β is a vector of parameters to be estimated. This is called a ‘single risk’ hazard model, because there is only a single risk: that of leaving entrepreneurship. The single-risk model can be estimated without placing restrictions on the form of the baseline hazard in (3.21).10 Many applications report exponentiated values of the coefficients, called ‘hazard ratios’. Ratios greater (resp. less) than 1 indicate positive (resp. negative) impacts on duration: see Beckman, Burton and O’Reilly (2007) for an example in the context of the hazard of founding teams accessing venture capital finance. An ‘event’may take several forms. For example, individuals may leave entrepreneurship for either paid employment or unemployment. We might be interested to discover, for example, whether entrepreneurs who exit into unemployment have different survival characteristics from those who exit into paid employment. If so, a ‘competing risks’ model is needed.11 As an illustration of the usefulness of the distinction between single- and competing-risk formulations, Taylor (1999) distinguished between voluntary exit from self-employment into paid employment and involuntary exit in the form of bankruptcy. This distinction turned out to be an important one. For example, while limited personal wealth was associated with higher bankruptcy rates among British males, it had no effect on voluntary exits. Hazard models have been widely used in applied entrepreneurship research, especially in analyses of the survival of new ventures (see chapter 14). They continue to be enriched and extended. For example, a useful recent development incorporates fixed effects to control for unobserved heterogeneity (Strotmann, 2007). 3.7 Appendices 3.7.1 The reduced form of the two-equation simultaneous equation model Write W1 ≡ [, 1 ] and W2 ≡ [, 2 ], with parameter vectors β1 ≡ [β1ξ , β1⊥ ] and β2 ≡ [β2ξ , β2⊥ ]. Then the reduced form for the model is ∗ z1i =


i (β1ξ + γ1 β2ξ ) + 
1i β1⊥ + 
2i (γ1 β2⊥ ) ∗ + v1i 1 − γ1 γ2

∗ = Vi
1 + v1i

(3.22) (3.23)

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∗ z2i =


i (β2ξ + γ2 β1ξ ) + 
2i β2⊥ + 
1i (γ2 β1⊥ ) ∗ + v2i 1 − γ1 γ2

∗ = Vi
2 + v2i

(3.24) (3.25)

where ∗ v1i :=

v1i + γ1 v2i 1 − γ1 γ2

and

∗ v2i :=

v2i + γ2 v1i 1 − γ1 γ2

(3.26)

are reduced form error terms and Vi := [1, i , 1i , 2i ]. 3.7.2

Fraser and Greene’s (2006) heteroscedastic probit model Suppose that an entrepreneur’s income at t is given by ytE = θ + t , where θ is the entrepreneur’s true but unknown ability, and t ∼ IIN (0, σ 2 ) is random noise. Entrepreneurs do not observe θ and t separately, but only their sum, which they use to learn about θ. They do so by Bayesian updating. An entrepreneur’s initial idiosyncratic prior belief about θ when they enter at t = 0 is assumed to be N (µ0 , σ02 ). With a non-pecuniary benefit in entrepreneurship of ψ, individuals choose to participate in entrepreneurship if

Et−1 (ytE + ψ − yP ) > 0,

(3.27)

where yP is certain income in paid employment, assumed for simplicity to be constant over time. To analyse occupational choice, one must derive the posterior distribution of ytE . Under Bayesian updating, this turns out to be N [Et−1 (θ ), Vt−1 (θ )], where Et−1 (θ) = W (t − 1)Et−2 (θ) + [1 − W (t − 1)]θˆ (t − 1) Vt−1 (θ) = σ02

t−1

W (i),

(3.28) (3.29)

i=0

where θˆ (t − 1) := (t − 1)−1  W (i) :=

t−1  (θ + i ) i=1

i + 2  i−1 σ02 h=0 W (h) σ 1

−1

1  i−1 2

σ0

h=0 W (h)

,

i≥1

W (0) = 1 Since ytE = θ + t , we can write Et−1 (ytE + ψ − yP ) = ytE + ψ − yP + B(t − 1), where B(t − 1) = [Et−1 (θ) − θ] is the entrepreneur’s optimistic bias. Clearly, learning implies plim B(t) = 0, i.e. entrepreneurs’ bias disappears with experience. This is an empirically testable prediction of the model.

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Finally, since the posterior distribution is normal, and its mean (3.28) and variance (3.29) are known, (3.27) implies that the latent decision to participate in entrepreneurship is   E + ψ − y P + B(t − 1) y z∗ =  t . (3.30)  1/2 σ0 t−1 i=0 W (i) This is like a probit expression, but with the difference that it is heteroscedastic (since the denominator of (3.30) is time-varying). Also, because limt→∞ W (t) = 0, the variance is decreasing in the entrepreneur’s experience. Thus Bayesian learning implies a heteroscedastic binary probit model of occupational choice, in which the variance is conditioned (negatively) on the entrepreneur’s experience in entrepreneurship. The likelihood function for this model is  zi  1−zi   n

β
Xi β
Xi L=  , 1 −  exp{γ
Mi } exp{γ
Mi } i=1

where X are the variables deemed to affect the mean, and M are the variables deemed to affect the variance. Notes 1. ML estimation of probit maximises the likelihood function L=

n

1−zi  (β
Wi /σ )zi 1 − (β
Wi /σ ) .

(3.31)

i=1

Non-linear methods are needed to maximise (3.31) to estimate the β parameters (up to a scalar transformation since σ is unknown). It is standard to normalise σ 2 to unity without loss of generality. 2. The effects of changes in the kth explanatory variable of Wi , i.e. Wik , on the probability of entrepreneurship are given by ⎧ for the linear probability model ⎨ βk ∂ Pr(zi = 1) βk Pr(zi = 1)[1 − Pr(zi = 1)] for the logit model (3.32) := ⎩ ∂Wik βk φ(β
Wi ) for the probit model where φ(·) is the density function of the standard normal distribution. In practice, software packages usually evaluate these effects at sample means of the variables. 3. The probit model with relative incomes is sometimes referred to in the literature as the ‘structural probit model’ (including in the previous version of this book). However, it is not actually a structural model in the strict econometric sense mentioned above, so this terminology should probably be discouraged in future. 4. As before, define zi as an indicator variable, equal to 1 if individual i is an entrepreneur, and 0 otherwise. Evidently ∗ , z ∗ ) ≥ 0] = (β
W , β
W ; ρ), Pr(zi = 1) = Pr[min(z1i 2i 1 1i 2 2i

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Selection and analogous to (3.31), the likelihood function is L=

n

 1−zi (β1
W1i , β2
W2i ; ρ)zi 1 − (β1
W1i , β2
W2i ; ρ) .

i=1

5. In terms of the first part of the chapter appendix, researchers should be careful when adopting the conventional approach of interpreting the coefficients in single-equation estimations of (3.23) and (3.25) as structural rather than reduced form parameters. To see how misleading this can be, suppose that γ1 γ2 > 1 (an empirically relevant case: Parker, 2008a). Because the denominators of both 1 and 2 are 1 − γ1 γ2 (see (3.22) and (3.24) above), the structural and reduced form parameters can take systematically opposite signs, leading to precisely the wrong (structural) interpretations. 6. This involves estimating the reduced forms (3.23) and (3.25) by single-equation probit ML at the first stage, and substituting the predicted latent values obtained from these estimates in place ∗ and z ∗ in (3.17) and (3.18). Estimation of the latter by ML generates consistent estimates of z2i 1i of all of the parameters, but requires a correction to the parameter variance-covariance matrix owing to the use of ‘generated regressors’ (see Maddala, 1983, pp. 246–7). 7. A different econometric model is needed to analyse the proportion of total available work hours ∗% an individual spends in entrepreneurship, denoted by h% E . Let hE be a latent variable relating the desired proportion of work hours in entrepreneurship to the regressors X . Consider the model ⎧ ⎪ ⎨ 1 h% = h∗% iE iE ⎪ ⎩ 0

if if if

h∗% iE ≥ 1 0 < h∗% iE < 1 h∗% ≤ 0 iE

(3.33)


h∗% iE = β Xi + ui .

This model can be estimated by double-limit tobit maximum likelihood (see Vijverberg, 1986, for details). However, a limitation of this model is its omission of relative occupational returns from the set of explanatory variables, X . Incorporating this extension into (3.33) complicates the estimation substantially. 8. Parker (1996), Cowling and Mitchell (1997), Parker and Robson (2004) and Bruce and Mohsin (2006). 9. The likelihood function for the probit model with fixed effects and state dependence is L=

n T



1−zit [(β
Wit + γ zit−1 + αi )/σ ]zit 1 − [(β
Wit + γ zit−1 + αi )/σ ] , (3.34)

i=1 t=1

where the αi are the individual fixed-effects; γ is a parameter measuring the importance of state dependence; and Wit are individual- and time-varying control variables. 10. The probability of an individual i’s spell being completed by time t + 1 given that i was still an entrepreneur at t is Li (t) = Pr[ai < t + 1|ai ≥ t] = F[γ (t) + β
Xi (t)], where F[·] is the cumulative distribution function of the Extreme Value distribution; and γ (t) is a set of dummy variables, one for each t, which captures time dependence. To estimate the parameters γ (t) and β, let di be the observed duration of i in entrepreneurship. This is either time completed or time censored (by the end of the sample): define ζi = 1 if the time is completed

Empirical methods in entrepreneurship research and ζi = 0 if it is censored. Then the log-likelihood function is ⎫ ⎧ di n ⎨ d i −1 ⎬   ln L = ln[1 − Li (t)] . ln[1 − Li (t)] + ζi ln Li (di ) + (1 − ζi ) ζi ⎭ ⎩ i=1

t=1

105

(3.35)

t=1

Equation (3.35) is maximised with respect to β and the γ s to obtain ML estimates. The method is implemented in many standard software packages. 11. Such a model posits a separate hazard function for each of the destinations, whose log likelihoods are given by (3.35) where ζi = 1 now denotes exit into the given destination, and ζi = 0 applies for other outcomes. The sum of the log likelihoods over all possible destinations gives the total log likelihood to be maximised.

4

Evidence about the determinants of entrepreneurship

Chapter 2 discussed several theories of entrepreneurship, the role of entrepreneurs and the factors influencing individuals to participate in entrepreneurship. All of these theories treat the decision to participate in entrepreneurship as an occupational choice. The present chapter considers a wide variety of empirical variables bearing on the decision to choose entrepreneurship. The focus of the chapter can be summarised by the following equation:   z ∗ = z π − w, Xhuc , Xsoc , Xrisk , Xpsy , Xdem , Xind , Xmac , Xemp ,

(4.1)

where z ∗ is the latent (unobserved) preference to be an entrepreneur, whose observed counterpart is usually a binary variable taking the value 1 for individuals who are entrepreneurs, and 0 for those who are not (see the previous chapter). The arguments of this equation are treated sequentially in the course of the present chapter. Thus the term π − w denotes (expected) profits in entrepreneurship relative to wage income in paid employment, and captures the relative financial return to entrepreneurship: one expects a positive effect on the propensity for entrepreneurship, z ∗ . The vector Xhuc includes variables measuring human capital; Xsoc measures social capital; and Xrisk measures risk factors, such as individuals’risk aversion or the degree of market risk. The vectors Xpsy and Xdem contain psychological and demographic characteristics, respectively; Xind and Xmac include industry-specific and macroeconomic factors; and Xemp includes characteristics of employers, relevant to employees contemplating a switch into entrepreneurship. Chapter 3 discussed the appropriate multivariate empirical methods for estimating a binary equation like (4.1), together with various extensions. The present chapter presents a comprehensive overview of results based on these estimation methods. Univariate differences between entrepreneurs and non-entrepreneurs, e.g. t tests of differences in location, are still sometimes cited and used as a basis for inference in some non-economics research (especially psychological factors: see section 4.4 below). But, as noted in the previous chapter, and repeated here for emphasis, univariate tests are prone to problems of misleading inference when elements of (4.1) are correlated among themselves (see Greene, 2003). Hence this chapter will devote little attention to results based on them. 106

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Equations like (4.1), or more commonly special cases of it in which only some of the explanatory variables listed above are present, are used to help answer two fundamental questions in entrepreneurship research: (i) Who becomes an entrepreneur and why? (ii) What are the influences of particular personal characteristics and environmental factors? The promise of (4.1) is that it can examine different theories of entrepreneurship from a variety of disciplines, including economics, sociology, psychology, and business and management. This assists research efforts to build the new multi-disciplinary field called ‘entrepreneurship’. To this end, section 4.1 first reviews the evidence about the impact of pecuniary and non-pecuniary incentives to be an entrepreneur. Sections 4.2 and 4.3 explore the roles of human and social capital, respectively. Section 4.4 focuses on risk attitudes and conditions, Xrisk , and psychological characteristics, Xpsy , including over-optimism and other cognitive biases. Section 4.5 treats demographic and industry characteristics, while section 4.6 summarises evidence about the broader macroeconomic factors that affect entrepreneurship, including economic development, technological change, unemployment and regional factors. Up until this point, ‘entrepreneurs’ are taken to be those people who have successfully started a venture. Section 4.7 discusses whether any of the empirical findings differ for those individuals actively engaged in the process of start-up, i.e. ‘nascent entrepreneurs’. Section 4.8 then explores the role of spin-offs and employer characteristics, while section 4.9 supplies a brief conclusion. For convenient reference, the main empirical results of the chapter are summarised in Table 4.1. Results relating to psychological factors (other than risk attitudes) have been omitted because their diversity makes them difficult to summarise in this way. I refer only to published research results based on multivariate analysis, excluding studies citing simple bivariate correlations, since these are vulnerable to the most severe form of omitted variable bias. Positive and negative entries in Table 4.1 refer to measured effects on entrepreneurship that are significantly positive or negative; the zero entries refer to effects that are too imprecisely estimated to reach standard levels of statistical significance (usually 5 per cent). I now discuss the various determinants of entrepreneurship in detail. 4.1

Pecuniary and non-pecuniary incentives

The first argument of (4.1) is relative earnings. The idea here is that individuals become entrepreneurs in order to make more money. However, money is not the only or even necessarily the most important incentive for entrepreneurs. Schumpeter in particular highlighted the importance of non-economic motivations, writing of the ‘will to found a private kingdom. . .to conquer: the impulse to fight, to prove oneself superior to others, to succeed for the sake, not of the fruits of success, but of success itself. . . Finally there is the joy of creating, of getting things done, or simply of exercising one’s energy and ingenuity’ (1934, pp. 93–4). Further to this point, consider Table 4.2, which summarises reasons for being self-employed cited by respondents of the UK’s spring 2000 LFS (Labour Force

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Table 4.1. Summary of determinants of entrepreneurship Explanatory variable 1. Income differential 2. Age 3. Experience 4. Education 5. Risk aversion 6. Married / working spouse 7. Number of children 8. Ill health / disability 9. Entrepreneur parent 10. Technological progress 11. Unemployment Cross-section Time series 12. Urban location 13. Immigrationa 14. Interest ratesb 15. Personal wealthc 16. Personal income tax ratesd

No. +

No. −

No. 0

8 83 24 69 0 52 16 5 40 4

2 6 1 21 11 9 2 4 2 4

4 14 2 27 3 8 3 6 2 2

22 33 7 5 1 40 12

14 5 7 1 9 2 5

18 2 4 0 3 4 1

Notes: +, − and 0 denote significantly positive, significantly negative and zero (insignificant) coefficients, respectively. Only multivariate studies (i.e. those including controls for other explanatory variables) are included; descriptive studies are excluded. For row 11, panel studies with large N and small T are classified as cross-section; those with large T and small N are classified as time-series. Sources:1 a Based on literature discussed in chapter 5. b Based on literature discussed in chapter 4, section 4.6. c Based on literature discussed in chapter 9. d Based on literature discussed in chapter 17.

Survey). These responses highlight the importance of non-pecuniary factors, especially independence, which is the single most important reason cited by the respondents (see below). The next most important reason cited in Table 4.2 is the nature of the occupation, perhaps reflecting the fact that self-employment is the chief or only mode of employment in some locations or occupations (e.g. forestry and construction). Both men and women give similar responses although women stress independence a little less than men, and family commitments substantially more (see also Hakim, 1989a). However, a caveat to interpreting self-declared motivations is that established entrepreneurs apparently attach much less importance to financial motivations than when they were in the process of starting their ventures (Cassar, 2007). This may be indicative of self-justifying response bias when ventures do not turn out as well as expected. So although researchers sometimes relate transitions into entrepreneurship to self-reported motivations

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Table 4.2. Reasons given for becoming self-employed in the UK (per cent) Reason To be independent Wanted more money Better conditions of work Family commitments Capital, space, equipment opportunities Saw the demand Joined the family business Nature of occupation No jobs available locally Made redundant Other reasons No reason given No. valid responses (’000s) a

All

Men

Women

31 13 5 7

33 15 6 2

25 7 3 21

12 8 6 22 3 9 15 3

12 9 6 21 3 11 14 4

11 8 7 23 2 3 18 3

2, 960

2, 156

804

Note: Columns do not sum to 100 per cent because respondents can give up to four reasons. a Imputed percentages based on all those who gave a valid response to the ‘reasons for becoming self-employed’ questions. Source: LFS (2000).

(e.g. Henley, 2007) response bias may make them a misleading guide to actual behaviour. 4.1.1 Pecuniary incentives: relative earnings The most widely used tool for estimating the effects of relative earnings π −w on participation in entrepreneurship is the binary probit model with relative incomes described in chapter 3. Recall from (3.13) of that chapter that if α, ˆ the estimated coefficient on the earnings differential term in the probit model, is positive and significant, a relative earnings advantage in entrepreneurship increases the likelihood of participation in entrepreneurship. It is important to point out that methodologically this approach is superior to analysing the effects of just employee wages on the probability of being an entrepreneur.2 Omitting prospective entrepreneurial profits gives an incomplete account of the opportunity cost affecting individual behaviour, and should be avoided. Taking self-employment as a working definition of entrepreneurship, several British researchers have estimated (3.13). Most have reported positive α estimates. Estimates include 0.37 (Rees and Shah, 1986); 0.04 (Dolton and Makepeace, 1990); 0.09–0.13 (Clark and Drinkwater, 2000); and 0.60 (Taylor, 1996). But only the last two estimates were statistically significant. Subsequent work also provides weak support for the role of financial incentives: for example, Fraser and Greene (2006) reported positive and

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significant effects using data from the 1980s but not from the 1990s. Parker (2003b) obtained mixed results (αˆ took varying signs) using several British data sets from the 1990s; his estimate of α was positive but insignificant for switchers between paid employment and self-employment. Data from other countries also paint a mixed picture: this is reflected in the varied findings documented in the first row of Table 4.1. In contrast, aggregate studies of new business starts or rates of entrepreneurship tend to find fairly uniformly positive effects from average business profits relative to average wages.3 However, aggregate data are ill suited to detecting the importance of individual incentives, since average entrepreneurial income is prone to measurement error and almost certainly endogenous, leading to upward-biased parameter estimates (Parker, Belghitar and Barmby, 2005). These considerations, together with the mixed evidence from individual-level data about the sign of the relationship between relative incomes and entrepreneurial choice, pose something of a puzzle, since they suggest that entrepreneurs do not respond robustly to pecuniary incentives. Perhaps previous researchers have not successfully isolated variables that affect earnings in the two sectors but not occupational choice itself – as is required to identify the econometric model (see chapter 3). And the poor quality of data used in many of these studies, especially relating to reported incomes, might also be partly responsible for the inconclusive results. Alternatively, these results may simply be telling us that pecuniary rewards are not the primary motive for being an entrepreneur (Amit et al., 2000). Participation in entrepreneurship might instead be motivated by lifestyle considerations, for instance as a means of being one’s own boss. These non-pecuniary factors are discussed further below. Another possibility is that entrepreneurs suffer from unrealistic optimism and continue to earn low incomes because they wrongly anticipate high future profits ‘which never come’. While there might be some truth in each of these conjectures, there is so much economic evidence that individuals adjust their behaviour in response to changes in relative prices that it would be puzzling if the same calculus ceased to apply entirely in the realm of entrepreneurship as an occupational choice. Indeed, some economic historians argue that American entrepreneurs have historically been very responsive to incentives, directing their attention to profitable innovations and the satiation of demand (Khan and Sokoloff, 1993). And there is some suggestive evidence that ‘postmaterialism’, i.e. the possession of non-materialistic life goals, is negatively related to entrepreneurship (Uhlaner and Thurik, 2007) – which might be taken to imply that entrepreneurship is largely about the pursuit of material gain. Hence we conclude that more careful research is needed to shed further light on entrepreneurs’ responsiveness to pecuniary incentives.

4.1.2

Desire for independence and job satisfaction It is commonly asserted that an attractive feature of entrepreneurship is independence in the workplace, something that is variously referred to as a desire for autonomy or

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‘being one’s own boss’. This idea can be traced back to Knight (1921), and might explain why individuals remain in entrepreneurship despite the possibility that they earn less there and receive a lower risk-adjusted return on their assets (see chapters 13 and 9, respectively). Below, I review evidence about the desire for independence in entrepreneurship, before asking whether it feeds through into greater happiness among entrepreneurs relative to employees, in terms of both job satisfaction and contentment with work–life balance. Based on an analysis of 1991 BHPS data, Taylor (1996) found that fewer entrepreneurs than paid employees regarded pay and security as important aspects of their job. The proportions were 37 and 32 per cent compared with 48 and 57 per cent, respectively. However, greater proportions of entrepreneurs reported that initiative (51 per cent) and the enjoyment of work itself (57 per cent) were important job aspects, compared with 21 and 41 per cent of employees, respectively. These non-pecuniary factors were significantly associated with being self-employed, even after controlling for observable personal characteristics (see also Burke et al., 2000; and Hundley, 2001b).4 Recall from Table 4.2 that the self-employed claim that independence is a major reason for their occupational choice. Yet it is not clear that entrepreneurship offers much real independence, especially for those working long hours (see chapter 12) or in one of the ‘grey areas’ between paid employment and self-employment (see chapter 1). This might explain why sole proprietors, many of whom enjoy limited autonomy (especially the ‘dependent self-employed’) report lower levels of job satisfaction than entrepreneurs who hire employees – although both groups are more satisfied on average than employees are.5 Moreover, about one-third of GEM respondents in developed countries claim they are ‘necessity’ entrepreneurs, i.e. they become entrepreneurs because they have few other choices. Of course, these responses are all based on declared rather than revealed preferences, so they should be treated with commensurate caution. Nevertheless, the available evidence strongly supports the notion that entrepreneurs regard themselves as more satisfied with their jobs than non-entrepreneurs. For example, Blanchflower and Oswald (1998) reported that approximately 46 per cent of the self-employed claimed they were ‘very satisfied’ with their job, compared with only 29 per cent for employees. Because the self-employed tend to be older on average and are likelier to be married (both factors being associated with happiness: Hundley, 2001b; Benz and Frey, 2004), it is necessary to take account of these other factors when making cross-group comparisons. Several studies which control for job and personal characteristics, including income gained and hours worked, confirm the finding that entrepreneurs are more satisfied with their jobs on average than employees are.6 It is noteworthy that studies which control for socio-demographic and work factors find that higher incomes in entrepreneurship do not explain greater levels of job satisfaction. Nor is greater job satisfaction in entrepreneurship explained by panel-data fixed effects designed to control for unobserved heterogeneity and possible self-selection arising from it.7 Benz and Frey (2008) take this as evidence of ‘procedural utility’: people value not only outcomes, but also the processes leading to outcomes.

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It appears that greater autonomy lies at the heart of entrepreneurs’ higher reported levels of job satisfaction. In a clever ‘natural experiment’, Benz and Frey (2004, 2008) measured self-reported job satisfaction of East Germans before and after the fall of the Berlin Wall in 1989, to explore whether entrepreneurship makes people happier or whether just intrinsically happier people tend to become entrepreneurs. Prior to the fall of the Berlin Wall (which can be regarded as a genuinely exogenous shock), East Germans were prohibited from becoming self-employed. Benz and Frey (2004, 2008) found that individuals who became self-employed after 1989 became significantly more satisfied than East Germans who remained as employees. This suggests that causality flows from entrepreneurship to happiness rather than the other way round. They attributed this to the greater autonomy that is inherent in self-employment. While both employees and entrepreneurs appear to value autonomy and freedom from subordination in a hierarchy, only entrepreneurs can meaningfully practise it.8 Benz and Frey (2004) left aside questions of whether entrepreneurs enjoy greater life satisfaction. Arguably, the flexibility of entrepreneurship makes it especially conducive to balancing work and family role responsibilities, leading to enhanced psychological well-being (Cromie, 1987; Loscocco, 1997). On the other hand, entrepreneurs work longer hours on average than employees do (chapter 12), while bearing direct responsibility for the success and survival of their businesses. That may result in greater work–family conflict for entrepreneurs than for employees. Evidence from the UK, USA and Canada shows that while entrepreneurs are significantly more satisfied on average with their jobs, especially with their flexibility and autonomy at work, they are less satisfied on average with their work hours than employees are, and experience higher levels of work–family conflict and lower levels of satisfaction with their family lives than employees do.9 These drawbacks to entrepreneurship appear to be more pronounced for men than for women. According to Finnish time-use data, the self-employed take significantly less time off on average for sickness or holidays; work under greater time pressure; and supply more work hours in the evenings than employees do (Hyytinen and Ruuskanen, 2007). Although entrepreneurs can and do interrupt their workday somewhat more and spend less time in their primary work location than employees, this might not compensate for these other drawbacks of business ownership. The issue of stress and anxiety in entrepreneurship is receiving growing attention from researchers. It is sometimes claimed that entrepreneurs are likely to suffer from greater average levels of stress, worry and burnout than employees (see e.g. Blanchflower, 2004; Jamal, 2007). On the other hand, entrepreneurs tend to be more autonomous and older on average than employees, and both of these factors are associated with lower levels of (self-reported) stress (Bluedorn and Martin, 2008). Perhaps reflecting these offsetting forces, group differences in satisfaction, stress and work– family conflict levels cease to be significantly different once researchers introduce controls for work and demographic characteristics (Prottas and Thompson, 2006). Note too that while entrepreneurship might not be conducive to reducing work–family conflicts for individuals who prefer to segment these two parts of their lives, it can be ideal for those who prefer to integrate, or ‘blur’, the two.

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To conclude, it appears that for some people, the promise of autonomy can be a more compelling incentive to enter entrepreneurship than financial returns. Autonomy in entrepreneurship yields job satisfaction, but the long hours associated with entrepreneurship can create work–life conflicts which mitigate the accompanying increase in life satisfaction. Just as individuals differ in the way they trade off autonomy with financial returns, so they also differ in their tolerance of stress and the ways they seek to achieve a work–life balance. Only a minority of people seem to be sufficiently fond of autonomy, dedicated enough to work long hours, and able to cope with family conflicts and stress emanating from both work and home environments to become entrepreneurs. Perhaps this explains why so many more individuals claim to be ‘latent’ entrepreneurs than are actual entrepreneurs. 4.2

Human capital

This section analyses three main elements of the human capital vector Xhuc : age, experience and formal education. 4.2.1 Age One might expect older people to be likelier to become entrepreneurs, for the following reasons:

1. The human and physical capital requirements of entrepreneurship are often unavailable to younger workers. For example, older people are more likely to have received inheritances and to have accumulated capital which can be used to overcome borrowing constraints and set up a business (see chapter 9). There might also exist a particular type of human capital which is productive both in managing and in working for others, and which can be acquired most effectively by working initially as an employee. 2. Older individuals might choose entrepreneurship to avoid mandatory retirement provisions sometimes found in paid employment. 3. Older people have had time to build better social and business networks, and to have identified valuable opportunities in entrepreneurship, possibly through learning about the business environment. 4. As their own masters, entrepreneurs often possess greater control over the amount and pace of their work, making it sometimes better suited to older people who have lost their physical stamina, or for workers in poor health or with skills which are obsolete in paid employment. On the other hand (the retirement issue apart – discussed separately in chapter 12), it seems likely that beyond a certain age, entrepreneurship will become less attractive to individuals. Reasons include: 1. Older people are more risk-averse than the young; may have tastes that shift away from enterprise as they age (Lévesque et al., 2002); and are less capable of working the long hours undertaken by entrepreneurs (see chapter 12).

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2. The returns to information gained about suitable job matches diminish with age. Thus, Miller’s (1984) ‘job-shopping’ theory predicts that workers try riskier occupations (like entrepreneurship) when they are younger, since these occupations provide the richest information about workers’ personal job-matching opportunities. 3. Older people’s human capital has a lower discounted value and so provides less of a hedge against risk, so making older individuals less willing to enter a risky occupation like entrepreneurship (Polkovnichenko, 2003). Conversely, if entrepreneurship is risky and requires payment of a sunk entry cost, younger entrepreneurs have more time over which to benefit from the higher returns and over which to amortise the entry cost (Hintermaier and Steinberger, 2005). Taken together, these arguments can be interpreted to suggest that individuals are increasingly likely to become entrepreneurs as they age, up to a certain point, after which the probability of becoming an entrepreneur declines with age (Lévesque and Minniti, 2006). In support of this argument, descriptive studies tend to find that entrepreneurship is concentrated among individuals in mid-career, i.e. between thirty-five and forty-four years of age.10 Recognising this, many empirical studies enter age as an explanatory variable in a quadratic specification, i.e. in levels and squares. The above arguments would lead one to expect the level term to enter a binary choice model of entrepreneurship with a positive coefficient and the squared term to enter with a negative coefficient. Age and experience are not synonymous, yet a common practice (often dictated by data limitations) is to measure ‘experience’ as current age minus school-leaving age. This measure is imperfect because it takes no account of breaks from labour force participation in individuals’ work histories. This may be a particularly salient consideration when analysing female entrepreneurship. It also runs into the problem of conflating cohort and experience effects. For instance, a secular increase in the rate of entrepreneurship over time shows up as a decline in the average age of entrepreneurs, irrespective of any experience effects.11 To separate cohort effects from genuine experience effects, either longitudinal data or accurate measures of years of actual work experience are required (see below). Despite these caveats, most econometric investigations have explored the effects of age on participation in entrepreneurship using cross-section data. Entrepreneurship is most commonly defined in these studies as self-employment or business ownership. Consistent with the theoretical arguments above, by far the most common empirical finding, obtained for a range of countries and time periods, is of a quadratic pattern, with the probability of being or becoming an entrepreneur increasing up to a maximum at some age in the forties or early fifties, before declining thereafter. For simplicity, these findings are reported as positive entries in the second row of Table 4.1. Digging deeper into the data, Evans and Leighton (1989b) report that rates of entry into self-employment do not increase as Americans age, but Americans remain in selfemployment for longer spells as they age. Uusitalo (2001) reports a similar pattern for

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Finland, with a constant entry rate of 1.5 per cent up to age forty, declining thereafter to a constant 1 per cent until retirement. Age may have different effects on the willingness and opportunity to become an entrepreneur. For example, using the bivariate probit model described in chapter 3, van Praag and van Ophem (1995) found that the opportunity to become self-employed is significantly higher for older than for younger Americans. However, older workers are significantly less willing to become self-employed than younger workers (see also Blanchflower et al., 2001). Other work, based on the binary model with unobserved thresholds described in chapter 3, suggests that older entrepreneurs have worse outside options in the labour market, and so are more likely to remain in entrepreneurship once they are there (Cooper et al., 1992). This is of course consistent with Evans and Leighton’s (1989b) empirical findings.

4.2.2 Experience Another component of the human capital vector Xhuc is experience. As intimated earlier, experience captures more informatively than age does the productive impact of post-school training and skill acquisition. Greater experience might promote entrepreneurship for several reasons, some of which are related to age-related arguments reviewed in the previous subsection. But experience also explicitly embodies learning, for example about business opportunities, or how enterprises work in practice. Experience includes training for skills needed to exploit opportunities, such as selling, negotiating, leading, planning, decision-making, problem solving, organising and communicating (Shane, 2003, p. 75). Learning also generates information which reduces uncertainty about the value of exploiting entrepreneurial opportunities (Jovanovic, 1982; Parker, 2007c). The evidence, summarised in the third row of Table 4.1, points to a consistent positive relationship between experience (defined quite broadly) and entrepreneurship. Shane (2003) distinguishes between five types of experience: general business experience; functional experience (e.g. in marketing, product development and management); industry experience; start-up experience; and vicarious experience (e.g. through observing parents, friends and associates in business). One can also distinguish between different types of labour market experience, e.g. the importance of previous experience in entrepreneurship versus experience in paid employment or experience as a manager. The results here are scant but interesting. Evans and Leighton (1989a) estimated that previous self-employment experience had a positive and significant impact on the probability of white male Americans entering self-employment, whereas previous paid employment experience had no effect.12 This result is consistent with Jovanovic’s (1982) theory that entrepreneurs learn about their abilities over time, which they can only do from having engaged in entrepreneurship (see chapter 11). Other forms of business experience also promote entrepreneurship. Illegal experience of entrepreneurship has been associated with legitimate self-employment, both in the USA (experience from drug dealing: Fairlie, 2002) and in Eastern Europe (experience from owning

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a proscribed business before the transition from socialism: Earle and Sakova, 2000; Aidis and van Praag, 2007). It is also noteworthy that individuals with greater past entrepreneurial experience and greater industry experience are more likely to start their own independent venture than to purchase a franchise (Kaufmann, 1999; Williams, 1999). That might be because franchisors furnish franchisees with industry knowledge and sell business models that embody pre-existing business expertise, so individuals with less of both types of experience are likelier to opt for the franchise route. General work experience can promote entrepreneurship if entrepreneurs start businesses related to their former occupations. Entrepreneurs often obtain ideas for new ventures from their previous jobs. For example, 71 per cent of Inc. 500 founders reported that they had replicated or modified an idea they had identified in previous employment, compared with only 4 per cent who claimed they had identified their idea through systematic search (Bhide, 2000). There are good reasons for this: Dunkelberg et al. (1987) observed that the ventures of founders who identified new venture ideas from prior jobs enjoyed higher than average rates of growth. This is suggestive of experience which takes the form of valuable direct knowledge transfers. Diversity of experience might also facilitate entrepreneurship, by conferring a broader set of skills and experience or by exposing potential entrepreneurs to a greater number of novel venture ideas. In which case, one would expect that individuals with more different previous jobs, past employers and job changes are more likely to be entrepreneurs.13 As noted in chapter 2, this has been formalised as the ‘jack-of-all-trades’ (JAT) hypothesis (Lazear, 2005). The hypothesis has been tested directly, by measuring breadth of experience in terms of the number of different work roles in previous jobs, or the number of fields of prior experience (Lazear, 2005; Wagner, 2003a, 2006a). These variables turn out to be significantly and positively associated with entrepreneurship status. Similar results have been recorded in Russia and China (Djankov et al., 2002). And there is evidence that top management teams with more diverse experience enjoy greater success in accessing venture capital and achieving an IPO (Beckman et al., 2007). However, Silva (2007) claims that these JAT results are not robust to the inclusion of panel-data fixed effects, suggesting that unobserved tastes and capabilities rather than diverse experience may simultaneously shape skill-accumulation strategies and choices to enter entrepreneurship. Most entrepreneurs who leave entrepreneurship transition into paid employment rather than unemployment (Cowling et al., 2004). So we can ask whether experience in entrepreneurship has long-term effects on what an individual can earn if they later switch to paid employment. On the one hand, the effect might be positive if entrepreneurship experience augments general human capital; on the other hand, it might be negative if entrepreneurship causes job-specific skills to rust and signals career instability to employers. Empirical investigations of this issue yield mixed and inconclusive results.14 Previous entrepreneurship experience has stronger effects on propensities to try entrepreneurship again in the future, where it is associated with superior sales, profitability and growth performance on average (Westhead and Wright,

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1999; Ucbasaran et al., 2006). These particular findings are suggestive of productive effects of entrepreneurial experience, rather than just a taste for entrepreneurship.

4.2.3

Formal education

One can argue for either a positive or a negative relationship between entrepreneurship and formal education. On one hand, education might improve entrepreneurial judgement by providing people with analytical abilities, information about business opportunities, and an understanding of markets and the entrepreneurial process (Casson, 1995). Formal education is also associated with general search skills, foresight, imagination and computational and communication skills, as well as with specific skills and knowledge needed to run businesses in particular sectors, e.g. engineering, computer science and biochemistry. Even if skills and knowledge gained from formal education are unnecessary for starting a business, in an empirical context they might provide a proxy for social background, ambition and endurance. And there might also be a selection effect at work, if more educated workers select themselves into occupations in which entrepreneurship is more common, such as managerial occupations among professionals (Evans and Leighton, 1989b) or skilled craft jobs among manual workers (Form, 1985). On the other hand, the skills that make entrepreneurs successful are unlikely to be the same as those embodied in formal qualifications (Casson, 2003). In particular, one might hesitate to suggest education as a proxy for managerial ability in entrepreneurship à la Lucas (1978) (see chapter 2). Perhaps even more importantly, education increases the value of the outside option of paid employment, which can make entrepreneurship relatively less attractive to highly educated people at the margin (Le, 1999). Most econometric studies of the effects of education on entrepreneurship utilise cross-section data. In these studies, entrepreneurship is usually measured as selfemployment or business ownership; and educational attainment is usually measured either as years of education completed, or as a set of dummy variables registering the highest qualification achieved by survey respondents. As with age, the majority of studies find a positive relationship between educational attainment and the probability of being or becoming an entrepreneur, though this is far from being a uniform finding, with less than 60 per cent of studies finding a significant positive effect rather than an insignificant or significant negative effect (see row 4 of Table 4.1). These findings relate almost exclusively to developed economies. Developing economies seem to exhibit a different pattern. A meta-analysis of eighty-four journal articles, book chapters and working papers published between 1980 and 2002 conducted by van der Sluis et al. (2005) revealed a greater tendency for educated workers to select paid employment instead of self-employment – although the latter is preferred to farming. This selection effect was found to be stronger for women, urban residents and inhabitants of poor economies where agriculture predominates and literacy rates are low. Most econometric studies conceal some subtleties in the relationship between education and entrepreneurship. First, the quality of education matters. BHPS data indicate

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that former pupils of high-quality British public-sector secondary schools (‘grammar schools’) are significantly more likely to become self-employed than pupils of other schools (Henley, 2004). In the USA, there is evidence that counties operating school voucher programmes have rates of youth entrepreneurship that are approximately one percentage point higher than counties operating traditional public-school delivery programmes (Sobel and King, 2008). Second, reflecting the two fat tails of the entrepreneurial income distribution (chapter 13), there is a bifurcation in the nature of the qualifications held by entrepreneurs compared with non-entrepreneurs, with disproportionate numbers of entrepreneurs being found among the least and most educated groups (Lohmann and Luber, 2004). Third, partly reflecting the occupational and industrial substructure of self-employment, there is growing evidence that vocational qualifications and apprenticeship training rather than purely academic qualifications affect participation in entrepreneurship.15 Fourth, the estimated effects of education on entrepreneurship depend on whether the researcher controls for industry sector and occupational group (Bates, 1995, 1997; Le, 1999). For example, Bates (1995, 1997) reported positive and significant effects of education on the probability of entering selfemployment in skilled services; negative and significant effects on the probability of entering self-employment in construction; and insignificant effects on the probability of entering self-employment in manufacturing and wholesaling. Bates concluded that the overall impact of education on entrepreneurship is obscured by aggregation across dissimilar industries. Fifth, there can be ethnic differences in the relationship between education and entrepreneurship, owing to different cultural traditions. For example, a positive relationship has been found among British whites and a negative relationship among British Indians (Borooah and Hart, 1999). Sixth, there are international differences in the education–entrepreneurship relationship. For example, pooled crosssection time-series micro-data regressions reveal positive effects from education on self-employment choices in the USA but negative effects in the EU (Blanchflower, 2004). The impact of human capital on entrepreneurship propensities can be amplified by externalities. At the regional level, there is evidence that start-ups are more common in locations with higher proportions of highly educated people.16 There are several rationales for a positive externality. As explained in chapter 2, Parker (2005b) proposes one explanation based on the idea that individuals possess imperfect information about their entrepreneurial ability at the time they invest in human capital. Average stocks of human capital in a region provide people with information about the distribution of ability, which informs their occupational choices. Iyigun and Owen (1998, 1999) propose another mechanism in the context of an intergenerational model in which individuals choose between acquiring human capital which is more or less productive in entrepreneurship, but do not take into account the effects of their choices on the production possibilities of the next generation. This leads to the possibility of a dynamically inefficient mix of human capital and occupational choices. For instance, if entrepreneurial experience is the key determinant of future production technologies, there can be too little entrepreneurship, and therefore too little technology,

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leading to skilled wages and human capital stocks which are too low. This strand of research complements the theories about knowledge-based externalities, growth and entrepreneurship reviewed in chapter 2. Finally, entrepreneurship education (which is training and education specifically about entrepreneurship) provides another link between human capital and entrepreneurial entry. Bridge et al. (1998) argue that many individuals do not choose an entrepreneurial career simply because it has never occurred to them. Raising awareness can be done through entrepreneurship education programmes, possibly delivered in schools. Lee and Wong (2006) provide a descriptive overview of entrepreneurship education initiatives, including evidence relating to its efficacy and limitations. 4.3

Social capital

An increasingly widely used construct in empirical models of entrepreneurship is social capital, Xsoc . According to Davidsson and Honig (2003, p. 307), ‘social capital refers to the ability of actors to extract benefits from their social structures, networks and relationships’. Social capital can exist at the country level, for example in the degree of trust in government and other institutions, and at the community level, such as the quality of social networks within the locality. Social networks can involve the extended family, communities and organisational relationships. Abell et al. (2001) argue that social capital confers social legitimacy upon entrepreneurship; reveals information about opportunities, customers, suppliers and competitors; and facilitates access to resources such as cheap labour and capital while providing psychological aid, such as helping entrepreneurs to weather emotional stress and to keep their businesses afloat.17 In principle, social capital might be used to compensate for limited financial or human capital. Social capital can comprise ‘strong’ or ‘weak’ ties. Strong ties come from close relationships such as one’s direct family or close friends, who can leverage support and trust needed for resource acquisition (Brüderl and Preisendörfer, 1998). Weak ties are loose relationships with former business contacts, acquaintances and members of business networks such as trade associations or guilds (Parker, 2008b). A pronounced feature of entrepreneurial networks and start-up teams is homophily, which is the tendency for ‘birds of a feather to flock together’.18 Sociologists argue that an important source of homophily arises from strong ties and dense social networks, which constrain individuals to start ventures with people like themselves. While this can facilitate trust and knowledge sharing, it can also close off sources of diverse information which could benefit the entrepreneur (Kim and Aldrich, 2005). An alternative view is that entrepreneurs are over-optimists who work hard and so prefer to match with other over-optimists. Homophily then arises from free choice as a type of assortative matching outcome (Parker, 2009a). Despite the prevalence of homophilious new venture teams, heterophily apparently endows teams with key organisational and performance advantages. With regard to performance, Eisenhardt and Schoonhoven (1990) observed that semiconductor firms

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Table 4.3. Observations about social capital and implications for entrepreneurship Observation 1 Social capital first rises, then falls with age 2 Social capital declines with expected mobility 3 Social capital is associated with higher returns to skills 4 Social capital is higher among homeowners 5 Social connections decline with social distance 6 Social and human capital investments are complements

Implications for entrepreneurship Another explanation for the empirical observation discussed in section 4.2.1 Entrepreneurs are less mobile than employees As residual claimants, entrepreneurs should obtain more social capital Higher owner-occupier rates among entrepreneurs Entrepreneurship is more common in urban than in rural locations Failure to control for social capital in entrepreneurship research is likely to overstate the role of human capital

with more heterophilious founding teams enjoy faster organisational growth, while in a study of 161 young high-tech firms in Silicon Valley, Beckman et al. (2007) show that diverse prior experience of top management team members is associated with superior access to venture capital financing and a greater likelihood of achieving an IPO. This poses a puzzle about why entrepreneurs prefer to form homophilious teams with lower average performance. Parker (2009a) proposes a solution based on the marriage of two cognitive biases which will be discussed further below: over-optimism and self-serving attribution biases. Glaeser et al. (2002) propose several testable economic propositions about social capital. To the extent that social capital is more productive in entrepreneurship than in paid employment (a debatable point), several of these propositions carry implications for entrepreneurship, summarised in Table 4.3. Some researchers claim that social capital promotes entry into entrepreneurship.19 In fact, there is relatively little hard evidence that it does (hence the absence of an entry for social capital in Table 4.1). Part of the reason is the elusiveness of a compelling empirical operationalisation of the social capital concept (see below). Instead, most research to date has focused on the impact of social capital on entrepreneurial prospects and performance. There is, for example, a general consensus that membership of networks such as clubs and societies, churches, trade associations and other formal and informal entrepreneur networks enhances entrepreneurs’ profits, growth performance and survival prospects.20 By way of illustration, an illuminating study of Gujarati motel owners in Texas found that poorer Gujarati entrepreneurs who owned unbranded motels survived significantly longer if they were located close to successful Gujarati entrepreneurs who owned branded motels (Kalnins and Chung, 2006). Locating close to either co-ethnics running unbranded motels or non-ethnics running branded motels made no significant

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difference to survival, suggesting a direct role for social capital. And high occupational status and relevant industrial work experience have been linked with entrepreneurs’ propensities to use social network ties as opposed to market methods to access start-up capital for high-tech ventures in Singapore and Beijing (Zhang et al., 2008). Several problems beset research on social capital and entrepreneurship. One is the generally poor quality of empirical proxies for social capital, reflecting limited data and a lack of agreement about how to measure this construct. For example, there is still an ongoing debate about the distinction between social ties and social capital (Karlan, 2007).Another limitation arises from correlations between social capital, unobserved ability, human capital and financial capital. To date, relatively few studies have recognised that this can cause endogeneity bias necessitating Instrumental Variable estimation. For example, individuals living in areas with plentiful opportunities might find it easier to form social ties; yet they could feasibly owe their superior economic performance to a more opportunity-rich environment rather than to stronger social networks. As Licht and Siegel put it: ‘improved identification strategies are needed to better delineate the mechanism by which investments in social capital lead to sustainable competitive advantage’ (2006, p. 531). Evidently, much more robust empirical work remains to be done in this area. A start in this direction has been made in the context of micro-finance group-lending schemes in developing countries, in which social ties play a major role. Further discussion of this issue is deferred until chapter 8. 4.4

Risk attitudes, over-optimism and other psychological traits

The economics of entrepreneurship has devoted considerable attention to two particular psychological characteristics: risk attitudes and over-optimism. These are considered in the first two subsections below. The third one considers several other psychological trait variables Xpsy , which have traditionally been more widely used in the psychology and business studies literatures, but which are sometimes also studied by economists. 4.4.1

Risk attitudes and risk Chapter 2 distinguished between risk attitudes – namely risk aversion embodied in individuals’ utility functions – and the actual or perceived level of risk itself. Both of these factors can affect the decision to become an entrepreneur. I will first discuss empirical work which measures risk attitudes and relates them to entrepreneurial choices. This material bears directly on risk-based theories of entrepreneurship, such as that of Kihlstrom and Laffont (1979) discussed in chapter 2. The discussion is followed by a short critique pointing out limitations of this line of research. Finally, I discuss empirical work which measures risk and its effects on entrepreneurial choices. Risk attitudes can be measured in various ways. It is helpful to distinguish between three empirical strategies:

• Univariate comparisons between entrepreneurs and non-entrepreneurs based on hypothetical survey questions about risk preferences;

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• Comparisons based on lifestyle choices deemed indicative of risk aversion; • Econometric estimates of binary choice models of entrepreneurship using hypothetical survey responses to measure risk attitudes. There is a long history of univariate comparisons between entrepreneurs and nonentrepreneurs based on survey questions asking respondents how they would choose between risky hypothetical situations. A famous example is the Choice Dilemma Questionnaire, which in Brockhaus (1980) famously revealed no significant differences between entrepreneurs’ and managers’ risk attitudes. Shaver and Scott (1991) survey the early literature on univariate comparisons and highlight the limitations of studies purporting to show that entrepreneurs are less risk-averse than non-entrepreneurs. Yet a more recent meta-analysis of univariate comparisons by Stewart and Roth (2001) reveals that entrepreneurs appear to be significantly less risk-averse than managers, the difference being greatest for entrepreneurs whose primary goal is venture growth rather than merely income generation. Subsequently, however, Miner and Raju (2004) considered other studies based mainly on the ‘Miner Sentence Completion Scale’ of risk avoidance and disputed Stewart and Roth’s findings; Miner and Raju (2004) conclude by declaring that the univariate evidence on this issue remains inconclusive. The second approach looks for differences between entrepreneurs’ and nonentrepreneurs’ risk attitudes using the revealed preference principle, i.e. by observing whether entrepreneurs adopt riskier lifestyles than non-entrepreneurs. The evidence of this kind is also mixed. Self-employed Scandinavians are apparently less likely to participate in lotteries than employees are (Lindh and Ohlsson, 1996; Uusitalo, 2001); but Dutch entrepreneurs are significantly more willing to take hypothetical gambles than employees (van Praag and Cramer, 2001). Using US SCF (Survey of Consumer Finances) data, Puri and Robinson (2005) find that although nearly twice as many entrepreneurs (32 per cent) claimed they were willing to take above-average hypothetical risks compared with employees (17 per cent), entrepreneurs temper this with stronger family ties (higher marriage rates and more children); good health practices (being less likely to smoke); and longer self-reported financial planning horizons. Puri and Robinson take this as evidence that entrepreneurs are ‘calculated risk-takers’. Brown, Farrell, Harris and Sessions’ (2006) study of UK FES (Family Expenditure Survey) data over 1997–2000 reveal that the self-employed are significantly less likely than employees to purchase financial insurance products, which is consistent with the idea that they are less risk-averse. Most multivariate econometric analyses of binary choice models – the third approach above – utilise survey instruments for risk attitudes based on responses to hypothetical gambles or harm avoidance. They mostly support the hypothesis that entrepreneurs are significantly less risk-averse than the average (see the fifth row of Table 4.1).21 However, in this third as in the first approach, reliance on hypothetical rather than actual risk scenarios is problematic. Entrepreneurs might give ‘risk-loving’ responses because they think interviewers expect this of them. Or there might be reverse causality

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from entrepreneurship to risk attitudes. For instance, Taylor’s (1996) finding from the 1991 BHPS that self-employed respondents are significantly less likely than employees to regard job security as an important job aspect could simply reflect self-employees’ familiarity with working in risky or uncertain conditions, and/or ex post rationalisation of their prior choices. Responding to this objection, Ekelund et al. (2005) argued that reverse causality from entrepreneurship to risk attitudes is unlikely in their sample, since long-established entrepreneurs appear to be as risk-averse as newly minted entrepreneurs. A different measure of risk aversion, ‘fear of failure’, is measured by GEM. Several studies using GEM data claim to find a significant negative association between this variable and the propensity to be a nascent entrepreneur.22 However, it is unclear whether this variable measures risk aversion or something else, like anticipated social stigma, making it a questionable proxy of risk aversion. And interpretative strategies such as inferring risk aversion from decisions of numerous former drug-dealers to become self-employed later in life (Fairlie, 2002) are also problematic. Competing explanations of this observation include a desire for autonomy and a criminal record being a barrier to taking conventional jobs in paid employment (Aidis and van Praag, 2007). Measuring risk aversion using specially designed ‘laboratory’ experimental games might appear to be a more promising empirical strategy. The available evidence of this kind suggests that entrepreneurs are either risk-neutral or mildly risk-averse, and at any event less risk-averse than non-entrepreneurs (Elston et al., 2005). But this approach is also vulnerable to the charge of analysing behaviour in hypothetical scenarios. Perhaps the best way to circumvent these problems in applied research is to use the revealed preference principle to record actual examples of risk-avoiding or riskseeking behaviour among respondents, as per the earlier discussion. This still potentially faces the problem of conflating risk attitudes with over-optimism. There is, for example, some evidence that entrepreneurs frame and interpret the same business stimuli more favourably than non-entrepreneurs do (Palich and Bagby, 1995; Norton and Moore, 2002). Thus researchers could misconstrue adventurous actions based on overoptimistic expectations of outcomes as evidence of greater risk tolerance. In fact, Puri and Robinson (2005) found low correlations between their measures of risk attitudes and over-optimism (see below); and in experimental games of risky entrepreneurial investments, Coelho (2004) also found that the two phenomena appear to be orthogonal. However, over-optimism is not the only factor that can masquerade as risk aversion. Others include over-confidence, causing insufficient subjective uncertainty (Simon and Houghton, 2003); bounded rationality, whereby entrepreneurs do not know what they do not know (Cooper et al., 1995); ‘animal spirits’ (Marchionatti, 1999) and subjective feelings of control (Sarasvathy et al., 1998). Cognitive biases might also be at play, such as a tendency for entrepreneurs to have short-term planning horizons and to engage less in counterfactual thinking (Baron, 2000; Bluedorn and Martin, 2008) – and to be readier than employees to extrapolate from small samples of information when starting new ventures (Simon et al., 2000).

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If it is hard to measure risk attitudes precisely, it has proven no less challenging to measure market risk itself in applied research. Cross-section data is especially ill suited to the measurement of risk, which by definition is a temporal phenomenon. For example, numerous authors, especially in the franchising literature, have measured risk as the cross-section variance of industry sales or profits. However, as Lafontaine and Bhattacharyya (1995) point out, there are at least two serious problems with this approach. It takes no account of variations in ventures’ inputs which also lead to variation in outputs yet which are unrelated to risk; and it is prone to an aggregation problem since the variance of aggregate industry performance also depends on the covariances between different ventures’ performances within an industry which are unrelated to risk. These problems can be expected to be even more pronounced at higher levels of aggregation, such as the entire economy, where others have proposed deviations from trend levels or growth rates (of output or investment) as aggregate measures of risk.23 Instead of the variance of sector sales or profits, Lafontaine and Bhattacharyya (1995) recommend the use of industry discontinuation rates as measures of industry (downside) risk. In fact, this proposal is questionable too, because as will be explained in chapter 14, much discontinuation is voluntary and is not necessarily a response to risk. Arguably, a better approach is to measure temporal variations in profits using individual-level panel-data (Parker, Belghitar and Barmby, 2005). Even this measure, though, has its limitations because it ignores risk in the form of involuntary venture closures – which can also create non-random attrition in the constructed panel-data risk variable. To conclude, it seems that there is not yet a completely ideal way to measure entrepreneurial risk in applied research. This is an important limitation in view of the theoretical importance of risk and risk aversion in the economics of entrepreneurship.

4.4.2

Over-optimism and over-confidence It has long been recognised that people have a tendency to be innately over-optimistic, especially about events which are only partially under their control. As Adam Smith observed, ‘The overweening conceit which the greater part of men have of their abilities is an ancient evil remarked by the philosophers and moralists of all ages. Their absurd presumption in their own good fortune has been less taken notice of [but is], if possible, still more universal. . . The chance of gain is by every man more or less overvalued and the chance of loss by most men undervalued and by scarce any man valued more than it is worth’(1776 [1937, p. 107]). Smith adduced support for this view from contemporary (eighteenth century) examples of economic and social behaviour, including the facts that most residential houses and ships at sea were uninsured; that young people gave little weight to downside risk in their career choices; and that wages appeared to be insufficiently high in certain kinds of dangerous jobs. The psychology literature offers several explanations about why entrepreneurs may be unusually prone to unrealistic over-optimism.24 It turns out that optimism tends to be highest when individuals have emotional commitments to outcomes; when they believe outcomes are under their control; and when there is relatively little hard evidence

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about the likelihood of success of an endeavour. As Coelho et al. (2004) point out, most entrepreneurs’personal wealth is tied up in their businesses, creating obvious emotional commitments; and setting up a new business is uncharted territory and conducive to illusions of control, with little ex ante evidence being available to restrain entrepreneurs’ ‘unchecked fantasising’. These are not the only explanations for over-optimism. Schade and Koellinger (2007) refer to psychological studies suggesting that complex tasks which lack fast and clear feedback are also ripe grounds for optimism. Business scholars also argue that the environments in which entrepreneurs operate are typically so noisy and overloaded with information that cognitive biases like over-optimism may be necessary to cope with them.25 Hence innately over-optimistic people are likely to self-select into entrepreneurship (Busenitz and Barney, 1997). Because one aspect of over-optimism is a cognitive bias entailing excessive focus on one’s own ability at the cost of ignoring competition by others, there is likely to be excessive entry in markets where trading conditions are perceived to be easy (e.g. retail and restaurants), but insufficient entry in others where conditions are perceived to be difficult (Moore et al., 2007). Essentially, many entrepreneurs seem to overlook the fact that markets which they find easy to enter are also easy for others to enter, leading them to underestimate the degree of competition they will face. Psychologists term this type of behaviour ‘reference group neglect’. Evidence from experimental simultaneous market entry games supports these ideas. In these games, participants’ payoffs are designed to decrease with the number of entrants. Subjects choose to participate in the game based on their beliefs about their skill at playing it. Outcomes of these games reveal that entrants seem to believe that the total profit earned by all entrants will be negative but that their own profit will be positive (Camerer and Lovallo, 1999). Bolger et al. (2008) design an experiment in which absolute over-optimism in one’s own abilities, rather than relative over-optimism (i.e. relative to that of other participants), is associated with excess entry – consistent with Moore et al. (2007). One might still wonder though whether there might be a rational basis for optimistic self-selection. As van den Steen (2004) argues, it can be rational for individuals to compete for rewards with high perceived as well as actual probabilities of success, even if the former exceed the latter. In any case, it is important to distinguish between over-optimism and overconfidence. Loosely speaking, over-optimists over-estimate the probability of success and expected returns from a venture, while over-confident people under-estimate the degree of variation of outcomes.26 Despite the fact that over-optimism and overconfidence are distinct concepts, much of the literature confusingly conflates them. At the risk of sounding pedantic, this practice should be discouraged in future. A large body of evidence indicates that although most individuals are over-optimistic, entrepreneurs are even more over-optimistic than average. For example, using a sample of US NFIB (National Federation of Independent Businesses) data, Cooper et al. (1988) reported that 68 per cent of their respondents believed that the odds of their business succeeding were better than for others in the same sector, while only 5 per cent thought

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that they were worse.27 However, while interesting, these findings can be no more than suggestive. While the interviewees displayed greater optimism on average than is objectively merited by statistics about average business performance, it is possible that these beliefs might nevertheless have been warranted in these particular cases. Notably, Cooper et al. (1988) did not compare their expectations with outcomes (for which longitudinal data are needed) or establish whether entrepreneurs are more optimistic than non-entrepreneurs. This task has been attempted by Arabsheibani et al. (2000). Using BHPS panel data over 1990–96 to compare expectations of future prosperity with actual outcomes, Arabsheibani et al. (2000) found that employees and self-employed Britons are both systematically over-optimistic; but that the self-employed are consistently and substantially the most over-optimistic. For example, 4.6 times as many self-employed people forecast an improvement in their prosperity but later experienced deterioration, as forecast a deterioration but later experienced an improvement. For employees the ratio was 2.9. This finding of a pronounced disparity between expectations and outcomes, which is robust to the inclusion of controls for other personal characteristics, has been replicated by subsequent researchers.28 French data show that entrepreneurs who start new firms are more optimistic than those who take over existing firms; optimism is also positively associated with education and a desire for autonomy (Landier and Thesmar, 2008). Landier and Thesmar (2008) further found that over-optimism is negatively related to business performance, as measured by sales, asset and employment growth rates, and returns on assets and equity – suggesting that optimism is not merely an artefact of high risk–high return projects. In contrast, Coelho (2004) and Reid and Smith (2000) report insignificant correlations between measures of over-optimism and performance. It is not just those who actually start or take over a new venture who display over-optimism. According to Astebro’s (2003) study of over one thousand Canadian inventions between 1976 and 1993, the chance of a new innovation reaching the market is only 7 per cent. Of these ‘lucky’ 7 per cent, some 60 per cent realise negative returns, and the average realised return among those who commercialise their inventions is −7 per cent, even ignoring the cost of the inventor’s often enormous efforts. Interestingly, one-half of the inventors persist with their idea even when external advisors recommend abandonment. This suggests that over-optimism is deeply ingrained. In a similar vein, Lowe and Ziedonis’ (2006) analysis of the commercialisation efforts of university inventions reveals that on average entrepreneurs continue with development efforts longer than established firms do. However, while this finding is consistent with over-optimism, it might also be consistent with rational behaviour if there are reasons why new ventures take longer to develop (e.g. lower endowments of expertise and complementary assets). These findings may have important implications for market efficiency. When factor markets are characterised by upward-sloping supply curves, over-optimists may overuse scarce resources in equilibrium and bid up prices that realists must pay. Manove (2000) cites Warren Buffett on this point: ‘It is optimism that is the enemy of the

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rational buyer.’ Even if entrepreneurs have unbiased profit expectations on average, over-optimistic entrepreneurs will drive out realistic entrepreneurs from product markets. This rebuts Friedman’s (1953) long-standing argument that profit-maximising realists will drive over-optimists out of competitive markets in the long run. Realists would make positive profits in the absence of the over-optimists, but may be unable to do so when optimists are present because optimists produce excess output that reduces prices below the break-even price in the industry.29 Over-optimists therefore impose a negative externality on all other entrepreneurs, and the appropriate policy is to discourage entry, rather than to encourage it (de Meza, 2002). A similar policy prescription applies to credit markets, whose efficiency is also generally damaged by the presence of over-optimistic entrepreneurs. This argument is developed further in chapter 7. On the other hand, optimism can confer some advantages on individuals and society. First, over-optimistic entrepreneurs can hold their own against realists by working longer hours, retiring later and saving more, thereby compensating for mistakes induced by their over-optimism (Manove, 2000; Puri and Robinson, 2005). Over-optimistic entrepreneurs exert greater effort because they expect higher returns for any given marginal cost of effort (Landier and Thesmar, 2008; Parker, 2009a). Potentially, this might counteract underprovision of effort owing to moral hazard considerations. Furthermore, entrepreneurs often depend on the efforts of co-founders, employees and suppliers. By working harder, they can increase the marginal productivity of colleagues who then work harder as well, especially in teams where a free-rider incentive would otherwise lead to insufficient effort. Provided they are not too over-optimistic, this externality from other colleagues can even make over-optimistic entrepreneurs net beneficiaries from their over-investment of effort (Gervais and Goldstein, 2006). Overoptimism might confer an additional advantage by signalling high ability to outsiders, such as customers or financiers. On the other hand, realistic entrepreneurs might be better placed to exploit cognitive biases in customers and suppliers than over-optimists, thereby enhancing their business prospects (Bhide, 2000, chap. 3). Second, Bernardo and Welch (2001) claim that over-optimistic entrepreneurs are less likely to imitate their peers and are more likely to explore their environment. While realists can become trapped in an ‘informational cascade’, taking imitative actions that add little or no new information and so perpetuate socially inefficient outcomes (Bikhchandani, Hershleifer and Welch, 1998), bold over-optimistic entrepreneurs might buck the trend by backing their own perceived odds and discounting those of predecessors. This generates valuable informational benefits to the entrepreneurial group, enabling it to thrive in spite of the costs incurred by the entrepreneurs who obtained the information. As Bernardo and Welch put it: ‘Unknowingly, overconfident entrepreneurs behave altruistically, making irrational choices that are to their own detriment but help their groups.’ (Bernardo and Welch, 2001, p. 302). Bernardo and Welch go on to claim that this externality gives the entrepreneur group an evolutionary advantage which helps it to survive. However, this argument is based on the notion of group selection rather than individual selection, which is not in accordance with the received wisdom among evolutionary biologists.

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De Meza and Southey (1996) claim that unrealistic optimism explains several wellknown features of entrepreneurship, including persistent business entry despite low incomes and high industry exit rates (see chapters 13 and 14). Over-optimism can also explain why purely self-financed entrepreneurs face higher business failure rates than debt-financed entrepreneurs. By overestimating their prospects of success, optimists prefer maximum self-finance of their projects – unlike realists, who prefer debt finance. But also unlike realists, optimists can tolerate making negative expected returns net of opportunity costs, leading to higher eventual exit rates for this group as their resources run out. The risk of failure is exacerbated by over-optimistic entrepreneurs underestimating the initial resource endowments their ventures require and overestimating their ability to obtain further resources. Over-optimistic entrepreneurs also run the risk of deploying their resources too precipitately and rejecting valuable venture liquidity opportunities by being unwilling to share equity – all making their ventures more prone to failure (Hayward et al., 2006). As a practical suggestion, entrepreneurs are well advised to counter the malign effects of over-optimism by forming relationships with outsiders, such as non-executive board members and professional advisors (Cooper et al., 1988). Outsiders have the objectivity and detachment to counteract unrealistic optimism – hopefully without extinguishing the essential spark of entrepreneurial zeal. Alternatively, Hayward et al. (2006) suggest that entrepreneurs should assemble diverse venture teams in which founders can more easily engage in counterfactual reasoning. However, as noted earlier in this chapter, homophily rather than heterophily generally regulates the composition of new venture teams in practice. Another practical problem is that entrepreneurs often ignore advice that runs counter to their will; and external advisors may also be prone to biases or have their own agendas that are not aligned with the entrepreneur’s interests. Furthermore, in contrast to theoretical work assuming that learning can eliminate entrepreneurial over-optimism (Parker, 2007c), evidence suggests that over-optimism is very persistent (Landier and Thesmar, 2008), something borne out by venture capitalists’ claims that serial entrepreneurs are if anything less able to recognise their own limitations than first-time entrepreneurs are (Wright et al., 1997). The combination of over-optimism with self-attribution bias can in theory explain persistent (and even escalating) over-optimism (Parker, 2009a). Clearly further research is needed on these issues, including the extent to which learning actually mitigates over-optimistic biases. Research is also needed to quantify the externalities caused by over-optimism discussed above, and to determine the appropriate public policy set when over-optimism is present.

4.4.3

Other psychological trait variables More generally, a long-standing research question is whether individuals possess special psychological traits, Xpsy , which predispose them to entrepreneurship. Early studies in this area usually deployed simple univariate statistical comparisons between entrepreneurs and non-entrepreneurs (see Begley and Boyd, 1987, for a representative

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example). More recently, researchers have started to utilise multivariate methods to control for other individual characteristics and so obtain cleaner estimates of the role of psychological traits. In their review of the role of psychological factors in entrepreneurship research, Amit et al. (1993) singled out risk attitudes and three other traits that have attracted substantial research interest: 1. Need for achievement. In one of the first systematic attempts to provide a psychological profile of entrepreneurs, McClelland (1961) posited a ‘need for achievement’ (n-Ach) as the key characteristic of successful entrepreneurs. In McClelland’s words: ‘a society with a generally high level of n-Ach will produce more energetic entrepreneurs who, in turn, produce rapid economic development’ (1961, p. 205). McClelland claimed that entrepreneurs are: proactive and committed to others; like to take personal responsibility for their decisions; prefer decisions involving a moderate amount of risk; desire feedback on their performance; and dislike repetitive, routine work. A corollary of McClelland’s thesis is that the achievement motive can be deliberately inculcated through socialisation and training, although results from such efforts have drawn a mixed response (Chell et al., 1991). It is not clear that n-Ach can be ‘coached’, as has been claimed; and it is questionable whether entrepreneurship is the only vocation in which n-Ach can be expressed (Sexton and Bowman, 1985). 2. Internal locus of control. Another psychological trait is a person’s innate belief that their performance depends largely on their own actions, rather than external factors. Psychologists refer to this as a ‘high internal locus of control’. If entrepreneurship offers greater scope for individuals to exercise their own discretion at work than paid employment does, it follows that those with a high internal locus of control are likelier to become entrepreneurs. A psychological metric known as the Rotter Scale (Rotter, 1982) provides a basis for empirical tests of this hypothesis. The more a survey respondent believes a range of factors to be under her control, as opposed to outside her control, the lower her Rotter score in any given test. Evidence from multivariate binary choice models conditioning on Rotter scores is mixed, mirroring results obtained from univariate studies.30 The reasons for the mixed results are unclear. It could be that a high locus of control is not unique to entrepreneurs, being relevant for successful business managers and other employees too (Sexton and Bowman, 1985). Alternatively, negative empirical findings might reflect the inappropriate use by researchers of locus of control responses measured during respondents’ childhoods. These might understate the importance of this variable, since personalities tend to stabilise only later in life. 3. Tolerance of ambiguity. It is proposed that entrepreneurs have a greater capacity than employees for dealing with environments where the overall framework is illdefined, or ambiguous. Wennekers et al.’s (2005) conjecture that individuals dislike ambiguity, something they refer to as ‘uncertainty avoidance’. In his study of successful US Inc. 500 business owners, Bhide (2000) claimed that self-confidence lies at the root of ambiguity tolerance, and is distinct from risk tolerance.

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The above list of traits is by no means exhaustive. Others have claimed that entrepreneurs: • Exhibit ‘Type A’behaviour, which is characterised by competitiveness, assertiveness, aggression, a striving for achievement, and impatience (Boyd, 1984; Caliendo and Kritikos, 2008); • Possess greater levels of ‘self-efficacy’ and ‘intuition’ than non-entrepreneurs, being more intelligent, perceptive, creative and less likely to identify risks than non-entrepreneurs (Shane, 2003, ch. 5); • Are less likely to exhibit ‘status quo bias’ (e.g. by repeating previous choices) than bankers are (Burmeister and Schade, 2007); • Achieve higher scores for ‘conscientiousness’ and ‘openness to experience’ than managers; lower scores for ‘neuroticism’ and ‘agreeableness’; and similar degrees of ‘extroversion’ (Zhao and Seibert, 2006); • Are misfits or displaced persons who live outside the mainstream of society, finding it hard to accept authority or to work with others, and prone to deviant and criminal behaviour, possibly emanating from a disrupted childhood (Shapero, 1975; Kets de Vries, 1977). Such people establish new enterprises in an act of ‘innovative rebelliousness’ to boost their self-esteem and acquire external approbation. Subsequent research has shown that criminals evince high levels of interest and participation in entrepreneurship (Light and Rosenstein, 1995; Fairlie, 2002), although few such studies control for the damage a criminal record can do for conventional employment prospects. In fact, representative data samples show that more entrepreneurs come from relatively stable work and family backgrounds than from an isolated or deviant fringe. This might reflect the need for entrepreneurs in modern economies to co-operate with others rather than strike out alone as ‘heroic solo entrepreneurs’ (Reynolds and White, 1997; Blanchflower and Oswald, 1998). Taking a biological perspective, one can ask whether psychological traits have a genetic origin. Recent research provides some tantalising clues. White et al. (2006, 2007) collected data from thirty-one male Canadian MBA students declaring significant prior involvement in new venture creation and from seventy-nine other male students declaring no prior start-up experience. White et al. (2006) reported that a physiological characteristic, testosterone, was significantly associated with a questionnaire-based measure of risk tolerance; and that both testosterone and risk tolerance were significantly associated with involvement in new venture creation. In their 2007 follow-up study, White et al. reported that the interaction of testosterone with a family business background was associated with involvement in new venture creation. It seems that individuals with higher testosterone are predisposed towards risky behaviour; and favourable family backgrounds channel this propensity in the particular direction of entrepreneurship. In contrast, less favourable family backgrounds might channel this propensity into socially deviant behaviour. As testosterone is over 80 per cent heritable, and had no independent effects on new venture creation in White et al.’s 2007 study, one possible inference is that entrepreneurs are ‘made as well as born’.

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More generally, scientific research suggests that inherited traits account for approximately 50 per cent of the differences in human personalities, with the rest coming from environmental influences and through people maturing over their life span (Bouchard and Loehlin, 2001; Shirey, 2006). For example, the heritability of IQ is generally estimated at between 50 and 70 per cent, so if IQ is associated with entrepreneurship (as the Dutch evidence of de Wit and van Winden, 1989, and van Praag and Cramer, 2001, suggests), one can perhaps explain intergenerational transmission of entrepreneurship (see below) at least partly in terms of genetic traits. Using a sample of data on monozygotic British twins, Nicolaou et al. (2008) estimate that 48 per cent of the tendency to become self-employed can be attributed to genetic similarities. All of this evidence is consistent with a ‘balanced’ view that entrepreneurs are both born and made. Beginning in the 1980s, there have been growing objections to the whole psychological traits approach. It has still not been decisively established whether there is an ‘essential’ set of entrepreneurial characteristics, and if so what they are. Partly this reflects the small sizes and non-comparability of the samples used in previous research, and the conflicting results obtained from them. Brock and Evans (1986) argued that ‘the scientific validity of these studies, which are seldom based on random samples and often use ambiguous or overly inclusive definitions of an entrepreneur, is open to question’ (1986, n. 9, p. 190). A salient bias could be towards sampling only successful entrepreneurs, leading to the danger that observed traits are confused with entrepreneurial experience (Amit et al., 1993). It may be noteworthy too that psychological factors have a weaker effect on venture growth rates than on participation in entrepreneurship.31 Overall it seems unlikely that such a diverse group of individuals as entrepreneurs are amenable to glib generalisations in terms of their psychological characteristics. Traits are unlikely to be unique to entrepreneurs, raising a demarcation problem; and being unobservable ex ante, they are virtually impossible to separate ex post from luck and other extraneous factors (Amit et al., 1993). As Gartner (1988) put it: ‘the diversity among entrepreneurs is much larger than differences between entrepreneurs and non-entrepreneurs’. There is also, as Kaufmann and Dant have pointed out, ‘a tendency in this literature to personify entrepreneurs as embodiments of all that may be desirable in a business person, and almost deify entrepreneurs in the process’ (1998, pp. 7–8). The conclusion reached by several authors, including this one, is that psychological factors are neither necessary nor sufficient conditions for entrepreneurs or entrepreneurship. Nevertheless, this research programme continues, despite several attacks on it, including Gartner’s (1988) influential argument that it is the behaviours involved in creating new ventures, rather than the personalities of founders, which are fundamental to entrepreneurship. It seems that many people still find it hard to accept that individual psychological differences do not affect entrepreneurial behaviours – including writers in the popular press, practitioners who work with entrepreneurs and entrepreneurs themselves. In response, entrepreneurship researchers in the business studies tradition have shifted their attention to cognitive aspects of entrepreneurial behaviour. However, much of

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this research effort focuses on entrepreneurial intentions and orientations – constructs which are hard to operationalise, veer towards tautology, and which in any case properly fall outside the remit of the economics of entrepreneurship. 4.5

Demographic and industry characteristics

This section discusses the influence of three prominent demographic characteristics, Xdem : marital status, health issues and family background, before turning to the role of industry characteristics, Xind . 4.5.1 Marital status One might expect a disproportionate number of married people to be entrepreneurs compared with single people, for the following reasons:

1. A spouse can help provide start-up capital. 2. Once in business, a spouse can provide labour at below-market rates (for example ‘doing the books’ or taking telephone bookings while their husband or wife is out on jobs). Spouses can make trustworthy workers, being less likely to shirk (Borjas, 1986). And spouses can offer valuable emotional support (Brüderl and Preisendörfer, 1998; Bosma et al., 2004). 3. Spouses can use their own income as insurance against the risky income of their entrepreneur husband or wife. 4. Spouses can share relevant information and knowledge about business ownership and business conditions easily and efficiently. Knowledge spillovers from a spouse who is already in business can therefore increase the likelihood that the other starts up a business of their own (Parker, 2008a). 5. Having a spouse may confer tax advantages, including income sharing to exploit personal tax allowances; introducing the spouse as a ‘sleeping partner’and allocating them a share of the enterprise’s profits; or, if trading through a limited company, providing them with benefits (such as a company car, private medical insurance or payments into a pension scheme). 6. Entrepreneurs are older on average, and older people are more likely to be married. Some of these arguments have prompted several researchers to regard marriage as a form of social capital (e.g. Davidsson and Honig, 2003). On the other hand, married people with children may be unwilling to take the risks associated with entrepreneurship. Cross-section econometric evidence from binary choice models tells a consistent story. People who are, or who become, entrepreneurs are likelier to be, or to have been, married, with dependent children – especially if their spouse is working (see rows 6 and 7 of Table 4.1). This finding appears to hold quite generally, with the possible exception of black Americans (Borjas, 1986) and some ethnic minority English people (Clark and Drinkwater, 2000) – possibly reflecting weaker family structures among these ethnic

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groups. Because most of these findings control for individuals’ ages, they are probably capturing some kind of co-operative factor as in points 1–5 above rather than indirect effects from age itself (point 6). Point 2 receives some specific support from Brüderl and Preisendörfer (1998), whose regression estimates indicated that self-reported levels of emotional support from a spouse improves the survival prospects of new German business ventures. All of points 1 through 6 above are consistent with evidence that being married is associated with higher incomes in entrepreneurship.32 A different but related question is why so many married couples are both entrepreneurs (Brown, Farrell and Sessions, 2006; Parker, 2008a). Numerous studies have shown that having a husband who is an entrepreneur makes a woman significantly more likely to become one. The evidence on this point, which is reviewed in chapter 5, tends to be based on single-equation binary choice models of occupational choice. There is less evidence about whether women entrepreneurs make their husbands more likely to be entrepreneurs as well; and whether these choices are interdependent. Parker (2008a) estimated the simultaneous-equation binary choice model (3.17) and (3.18) in chapter 3 and detected strong positive interdependence in business ownership, for both genders. Parker (2008a) estimated that husbands whose wife is certain of being a business owner have on average a 17 percentage point higher probability of being a business owner than if they are married to a woman who is certain not to be a business owner. The corresponding figure for women is 19 percentage points. This compares with the unconditional probability of being a business owner in the PSID of 13.3 per cent for men and 6.8 per cent for women. These effects are not only large, but also appear to be consistent with knowledge spillovers between spouses. Parker (2008a) found little support for alternative explanations of interdependent occupational choices in entrepreneurship among spouses, including risk diversification, wealth transfers, role-model effects and assortative mating. 4.5.2

Health issues Entrepreneurship is often believed to offer greater flexibility than paid employment in terms of an individual’s discretion over the length, location and scheduling of their work time (Quinn, 1980). To the extent that people with poor health or disabilities need such flexibility, it might be expected that, all else equal, they have higher rates of entrepreneurship. In addition, entrepreneurship may offer a route out of employer discrimination against the disabled. However, entrepreneurship can also be a poor choice for individuals in poor health. Some jobs with high concentrations of self-employment, such as construction, are intrinsically less suited to those with disabilities – not to mention more dangerous, implying that entrepreneurship can also cause disability and ill health. As mentioned earlier in the chapter, work hours and stress are also greater on average in entrepreneurship. The evidence on the effects of health on entrepreneurship participation (in row 8 of Table 4.1) is mixed, mirroring the conflicting theoretical arguments. Some interesting empirical findings suggest that entrepreneurial performance can feed back positively into entrepreneurs’ state of health (Rau et al., 2008). On the other

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hand, long work hours (see chapter 12) and the difficulty of detaching oneself from work in entrepreneurship (‘workaholism’), tend to be negatively related to health outcomes (Rau et al., 2008; Taris et al., 2008). An important policy issue relates to health insurance coverage. Whereas many employees receive subsidised health cover from their employers, entrepreneurs must provide their own. That might explain why self-employed Americans have lower average rates of health insurance coverage than employees do.33 For example, Perry and Rosen (2004) report a coverage rate of 69 per cent for self-employed Americans in 1996, compared with over 81 per cent among employees. This partly reflects high purchase prices of self-employed health insurance, and the more favourable tax treatment of health insurance for employees than the self-employed prior to 2003. Since 2003, self-employed Americans have been able to deduct their entire health premiums as business expenses from their tax liabilities. It might be thought that non-portable employer-based health insurance coverage would discourage employees from leaving their firms to start new ventures. Strikingly, however, the evidence does not support this notion (Holtz-Eakin et al., 1996; Bruce et al., 2000). This is surprising. It is possible that individuals who contemplate becoming entrepreneurs discount health insurance benefits, especially if they are over-optimistic. But we do not know the true reasons.

4.5.3

Family background Entrepreneurship runs in families. Calculations based on 1992 CBO data by Fairlie and Robb (2007a) indicate that over half (51.6 per cent) of all business owners had a self-employed family member prior to starting their business.34 According to Dunn and Holtz-Eakin (2000), a son’s probability of becoming self-employed doubles (from 0.015 to 0.031) when either of his parents is self-employed. According to a collection of papers from eleven nations assembled by Arum and Müller (2004), having a self-employed father raises the odds of a son transitioning into self-employment by a factor of between 1.3 and 2.2; the figure may be as high as 3.0 in France (Colombier and Masclet, 2008). Parental self-employment both increases the fraction of time that offspring spend in self-employment and reduces the age at which they enter it (Dunn and Holtz-Eakin, 2000; Niittykangas and Tervo, 2005). Fairlie and Robb (2007a) propose several reasons why having a parent who is an entrepreneur might increase the probability that a given individual eventually becomes an entrepreneur themselves:

1. Acquisition of general business or managerial experience obtained from proximity to a family-owned business (‘general business human capital’); 2. Acquisition of industry- or firm-specific business experience obtained from proximity to a family-owned business – possibly including business networks (‘specific business human capital’); 3. Inheritance of a business;

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4. Provision of cheap finance by parents to help their offspring to overcome borrowing constraints; 5. Correlated preferences for entrepreneurial activities among family members, perhaps enhanced by favourable family role models. Evidence of strong intergenerational linkages in entrepreneurship between parents and children survives in multivariate settings, where plentiful control variables are used. In such settings, parental (especially fathers’) participation in entrepreneurship is frequently the most important determinant of entrepreneurship among offspring (especially sons). This is true in a wide range of countries, including European nations (Niittykangas and Tervo, 2005; Henley, 2007; Colombier and Masclet, 2008), the USA (Fairlie and Robb, 2007a) and China (Djankov et al., 2006). The balance of evidence is summarised in row 9 of Table 4.1. Instead of describing each study, my strategy below is to first consider what the evidence says about each of the five candidate explanations listed above, before discussing the limitations of empirical tests on this issue. Several studies lend support to explanations 1 and 2. Dunn and Holtz-Eakin (2000) observed that parental experience and parental business success in entrepreneurship (e.g. profitability) almost doubles the probability of a son entering self-employment, even after controlling for the individual’s and parent’s financial capital and the individual’s human capital. The importance of parental experience and success suggests that parents primarily transfer managerial skills to their offspring, rather than merely familiarity with or a taste for entrepreneurship (as suggested by explanation 5). For their part, Fairlie and Robb (2007a) uncovered a significant positive linkage between experience obtained from prior work in a family member’s business and the business owner’s own success, measured in terms of survival rates, employment creation, sales and profits. For example, having worked in a family member’s business reduces the probability of business closure by 0.042; increases the probability of substantial business profits and employment by 0.032 and 0.055 respectively; and increases sales by 40 per cent. In contrast, a dummy variable for merely having a family member who owned a business had a small and statistically insignificant effect. Both findings are consistent with the notion that experience of working in a parent’s venture transfers performance-enhancing general and specific business human capital, rather than intergenerational transfers of preferences or abilities. This evidence provides further support for explanations 1 and 2. So do findings from Kim et al. (2006) that parental business ownership does not affect start-up organising efforts (‘nascent entrepreneurship’), while it does affect actual entrepreneurship. This suggests that early survival in business is enhanced by parental entrepreneurship. However, Fairlie and Robb (2007a) observed that over one-half of small business owners who had a self-employed family member had not worked in that family member’s business before, suggesting that 1 and 2 alone cannot fully explain the data. And Danish evidence shows that parental self-employment significantly increases the likelihood of transitions to self-employment of offspring, regardless of whether the parental self-employment occurred during adolescence or childhood. Even if the parents

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were no longer self-employed by the time their children entered the labour force, the consequences of early exposure were found to persist (Sørensen, 2006).35 Arguably, these findings militate against the ‘social network’ aspect of parental entrepreneurship transmission (explanation 2). There is a clear consensus that explanation 3 does not explain intergenerational entrepreneurship. According to Fairlie and Robb’s (2007a) exploration of 1992 CBO data, only 1.6 per cent of all small businesses are inherited and only 4 per cent of entrepreneurs obtain their businesses through transfers of ownership or gifts. Furthermore, both business heirs and non-heirs benefit in similar ways from having a self-employed parent, implying that business networks inherited from the transfer of a parent’s business are not needed to achieve good financial performance (Lentz and Laband, 1990). Evidence relating to effects of wealth, tastes and role models (explanations 4 and 5) is more opaque. Dunn and Holtz-Eakin (2000) found that self-employed parents are three times wealthier on average than non-self-employed parents. Yet a $10,000 increase in parental assets increases the probability of a son’s annual transition to selfemployment by only 0.0009. This effect seems modest compared with a probability of entry conditional on parental self-employment of 0.031. It is actually unclear whether wealthy entrepreneurial backgrounds chiefly clarify risk-return trade-offs in the minds of children, furnish favourable role models, or provide insurance against failure – all of which could promote entrepreneurship among offspring. Halaby (2003) and Sørensen (2007) observe that the offspring of entrepreneurs are more likely to stress the importance of variety in their jobs and are likelier as well to work for small rather than large firms. These findings accord with the taste transmission idea. Against explanation 4, only 6–8 per cent of business owners borrow capital from family members (Aldrich et al., 1998; Fairlie and Robb, 2007a). It should be recognised that entrepreneurial role models might extend beyond the boundaries of the traditional nuclear family, to include friends and neighbours as well. Several studies suggest that personally knowing someone who has recently set up a business significantly increases the probability of nascent entrepreneurship,36 while Djankov et al. (2006) point out that Chinese entrepreneurs are significantly more likely to have childhood friends who were also entrepreneurs. Further evidence that role models are broad, and not just confined to parents, comes from Stuart and Ding (2006), who show that exposure to colleagues who had previously made the transition to commercial science significantly increases scientists’ hazards of either founding or advising a successful biotechnology company. Other evidence is consistent with several of the proposed explanations, and cannot clearly distinguish between them. For example, having parents who are employers significantly increases the probability of self-employment in Britain, whereas having parents who are sole proprietors does not (Henley, 2004; Taylor, 2004). This could be because a child whose father employs others views entrepreneurship as a higher-status occupation than one whose parent is a sole trader (explanation 5). On the other hand, this finding could alternatively be capturing transfers of greater managerial experience

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associated with employing others (explanation 1), or wealth effects if employers are wealthier than sole proprietors (explanation 4). To summarise so far, it seems that transfers of general and specific human capital play an important, but far from universal, role in shaping the strong intergenerational correlations in entrepreneurship that so many researchers have observed. However, two drawbacks of the current state of research on this topic should give us pause for thought. These are a lack of information about other aspects of parents’ occupations, and clean distinctions between general and specific human capital in the intergenerational transmission process. What we do know about these issues chiefly derives from research into ‘occupational following’, whereby children work in similar trades (e.g. plumbing) to their parents, which tend to be associated with self-employment. In fact, only a minority of entrepreneurs’ sons follow their parents’ occupation; and controlling for parents being in occupations with high self-employment rates does not remove the influence of parental entrepreneurship on the probability that their offspring are entrepreneurs.37 This evidence seems to highlight general rather than specific skill transmission. Sons are significantly more likely to become self-employed if their father is a manager, a professional or a retailer, and significantly less likely to do so if their father is unskilled.38 On the other hand, Meager and Bates (2004) report that parental transmission of selfemployment propensities are important for both skilled and unskilled self-employed parents in Britain, but not for professional self-employed parents, many of whom (e.g. doctors and lawyers) are not ‘real’ entrepreneurs at all. Staying with the human capital composition issue, French evidence indicates that people without self-employed parents obtain more formal human capital than those with self-employed parents (Colombier and Masclet, 2008). This implies that offspring substitute formal for informal human capital, where the latter is derived from working in a family business – consistent with theoretical arguments advanced by Parker and van Praag (2006a). Whatever the reasons for the intergenerational transmission of self-employment propensities, it appears not to take the same form for sons and daughters. Finnish evidence from Niittykangas and Tervo (2005) shows that sons are more likely to follow parents into self-employment than daughters are; but daughters obtain more education than sons. According to Dunn and Holtz-Eakin (2000), fathers’ self-employment experience has a stronger effect on the probability that sons become self-employed than mothers’ self-employment experience does. There is in contrast mixed evidence about the effects of mothers’ self-employment experience on entrepreneurship among their offspring.39 Generally, mothers’ self-employment experience is more important for daughters than for sons; but having two self-employed parents imparts the greatest overall effect. PSED data reveals that sons work more in their parents’ businesses on average than daughters do, although such work experience seems to have little impact on the probability of starting a business of one’s own, and so cannot explain the gender difference in this respect (Aldrich and Kim, 2006). With regard to ethnicity, African-Americans are less likely than white Americans to have self-employed fathers. And those who do have self-employed fathers receive

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smaller impacts on average from parental self-employment on both their own probabilities of becoming entrepreneurs and their performance in entrepreneurship.40 For example, according to 1992 CBO data more than one-half of white business owners had a self-employed family member owner prior to starting their business, compared with approximately one-third of black business owners (Fairlie and Robb, 2007b). Higher rates of marital and family breakdown among blacks (who are 40 per cent less likely to be married than whites) itself no doubt reduces the scope for intergenerational transmission of business ownership for this group. Regarding performance, Fairlie and Robb (2007b) estimated that the lower rates of previous experience of working in a family member’s business among blacks explain between 5.6 and 11.6 per cent of the performance differential between black- and white-owned offspring businesses. In contrast, ethnic differences in simply having a self-employed family member or having inherited a business explain little of the ethnic difference in entrepreneurial performance. More important are ethnic differences in start-up capital, which can explain between 14.5 per cent and 43.2 per cent of ethnic differences in business performance (Fairlie and Robb, 2007b).

4.5.4

Industry characteristics Entrepreneurs are more likely to be found in some industries and occupations than in others. As well as providing some evidence on this point, the discussion below suggests some possible reasons for it. The available evidence is mainly derived from studies which define entrepreneurship in terms of self-employment. The pronounced heterogeneity of entrepreneurs means that one has to exercise caution when discussing industry and occupational dimensions of entrepreneurship. For example, many developed countries in the 1980s and 1990s witnessed pronounced growth in both professional/managerial and low-skill self-employment, at the expense of traditional ‘artisan’self-employment (Müller and Arum, 2004). Nevertheless, several common factors can be elucidated. Entrepreneurs are likelier to enter service rather than manufacturing industries in part because entry barriers and minimum efficient scale are lower in the former than the latter, making sustainable entry easier.41 Entrepreneurs also tend to be found in larger, more profitable and more segmented industries and markets (Hause and Du Rietz, 1984). Entrepreneurs naturally prefer to enter larger markets, but they also quit these markets more frequently owing to stronger competitive pressures (Nocke, 2006). It is scarcely surprising that entrepreneurs prefer to enter industries with lower costs and lower levels of industrial concentration.42 Looking at industry sectors and occupations in greater detail, male entrepreneurs in North America tend to be concentrated in construction, services and retail trades; and in the sales, agriculture, hotels, repairs, craft, managerial and professional occupations. The predominance of service sector self-employment in America has a long history. It pre-dates the recent increases in aggregate self-employment rates in many developed economies and so cannot explain these increases (Aronson, 1991). The role of entrepreneurs in each of the agricultural, transportation, communications and retail

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sectors has steadily declined over time (Steinmetz and Wright, 1989; Aronson, 1991). Similar industry variations and trends in entrepreneurship are observed in the UK and other OECD countries. Certainly international differences in rates of entrepreneurship cannot be explained by appealing to differences in nations’ industry structures (Parker and Robson, 2004; Torrini, 2005). In particular, OECD countries share high concentrations of self-employment in construction, distribution, hotel and repairs, banking and financial services (Loufti, 1992). According to 1991 LFS data, these sectors accounted for 62 per cent of all UK self-employment, although construction in particular is highly cyclical (Harvey, 1995; Meager and Bates, 2004). Agriculture, construction, distribution and finance witness the highest entrepreneurial entry rates in Britain (Taylor, 1996), corresponding to high concentrations of entrepreneurs in both professional and unskilled occupations (Henley, 2004). However, there has been a striking trend decrease in the proportion of self-employees engaged in distribution, hotels and restaurants, from 29.9 per cent in 1984 to 18.7 per cent in 2000 (Meager and Bates, 2004). On the face of it, the last strand of evidence bears out Lucas’ (1978) prediction that small family-owned businesses in these sectors will be progressively replaced by large hospitality and catering chains as capital accumulates in the economy (see chapter 2). To conclude, while entrepreneurship is often concentrated in particular industries, different industry structures can explain neither changes in rates of entrepreneurship over time nor why some places are persistently more entrepreneurial than others.

4.6

Macroeconomic factors

Three broad macroeconomic theories of entrepreneurship and growth were outlined in chapter 2. These were wealth-based, technology-based and knowledge-based theories. Wealth-based theories have attracted an enormous empirical research effort connected to the literature on borrowing constraints, which is reviewed separately in chapter 9. The first two subsections below survey the evidence under the other two headings of technology- and knowledge-based economic development. The third subsection analyses entrepreneurship and the business cycle. Evidence relating to an important macroeconomic policy variable, the interest rate, is also discussed here. The fourth subsection treats a related issue: the well-researched relationship between entrepreneurship and the unemployment rate. The final subsection examines entrepreneurship at an intermediate level of aggregation: the regional level. 4.6.1

Technology as a determinant of entrepreneurship

Improvements in technology are known to drive growth. Such improvements are often measured in terms of their impact on ‘total factor productivity’ (TFP). Growth in TFP is the amount by which output increases as a result of improvements in methods of production, with all inputs unchanged. Blau (1987) was one of the first researchers to explore the role of TFP in entrepreneurship. Blau claimed that changes in TFP ratios

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favouring industries in which self-employment is common was one of the principal causes of rising US self-employment between 1972 and 1982. Blau (1987) went on to suggest that the spread of computers might have been the source of technological change. A problem with this explanation is that self-employment rates have declined steadily in France and Japan over the same period, despite these countries experiencing similar productivity improvements. And personal computers were not widespread even by 1982, the last year of Blau’s sample. Furthermore, Fairlie and Meyer (2000) provide separate evidence questioning the ability of TFP to explain trends in US self-employment rates. More recent evidence points to a stronger link between computers and entrepreneurship, though. Computers are widespread in small businesses, with more than 75 per cent of small US businesses using them in the 1990s according to the 1998 Survey of Small Business Finances (Bitler et al., 2001). Utilising matched CPS and Computer and Internet Usage data from 1997–2001, Fairlie (2006a) showed that computer usage increases the probability that American adults become business owners, not only in the IT sector but across a range of industries. Fairlie (2006a) detected a stronger effect of computer usage on business ownership among women than men. IT might create new opportunities for entrepreneurial firms to outsource goods and services and/or to supply goods and services outsourced by other firms (see chapter 2). Thus evidence that investment in IT is associated with subsequent decreases in the average size of firms is ‘consistent with the hypothesis that IT is facilitating the ‘decoupling’ of existing vertically integrated firms and the supplanting of existing firms by value-adding networks of new, smaller firms . . . the current downsizing of firms, the popularity of outsourcing, and the rise of value-adding partnerships is not simply a management fad, but rather may have a technological and theoretical basis’ (Brynjolfsson et al., 1994, pp. 1640, 1642). More generally, it has been proposed that the relationship between entrepreneurship and exogenous technological change is U-shaped (Acs, Audretsch and Evans, 1994). At early stages of a country’s development, technological change shifts output away from agriculture and small-scale manufacturing towards large-scale manufacturing, with a consequent reduction in self-employment rates. At later stages of development, technology changes again such that manufacturing gives way to services and self-employment rates recover. It is possible to test this hypothesis by regressing cross-country selfemployment rates on measures of value-added in manufacturing and services as a proportion of GNP. However, empirical estimates disagree about the role of these proxy measures (see row 10 of Table 4.1). 4.6.2

Knowledge spillovers and growth Some evidence is consistent with the knowledge spillover theory of entrepreneurship articulated in chapter 2. That theory predicts that entrepreneurs will locate close to sources of knowledge spillovers, forming new organisations in order to exploit them and thereby generating economic growth.

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Specifically, there is evidence that knowledge- and technology-based new ventures tend to locate close to universities and corporate research laboratories.43 For example, Audretsch, Keilbach and Lehmann (2006) observe that publicly held German SMEs are attracted by access to universities’ supplies of graduates and their production of tacit knowledge. At the same time, proximity to universities increases the speed with which new ventures progress from start-ups to stock market listings. Although new ventures undertake negligible amounts of R&D themselves, they appear to be able to generate innovative output by exploiting knowledge spillovers generated by other institutions. Precisely how they do so is less clear, although Almeida and Kogut’s (1997) analysis of patent data from the US semiconductor industry reveals that ideas (and presumably knowledge spillovers) are often spread via the mobility of key scientific personnel. Founders can replicate or modify ideas encountered through previous employment (Bhide, 1994, p. 151). This kind of mobility is facilitated by high-tech firms clustering closely together, especially in industries where new economic knowledge plays a major role, such as biotechnology (Prevenzer, 1997). Mobility can however be hindered by legal systems that enforce post-employment non-compete covenants which restrict workers’ freedom to exploit elsewhere ideas derived from a previous spell of employment. Gilson (1999) partly attributes to laxer enforcement of non-compete provisions in California compared with Massachusetts the greater success of Silicon Valley as a high-tech cluster than Route 128. Some of the knowledge spillover theory’s implications appear to carry through to more aggregated industry and regional settings. Several researchers have found that new firm formation rates are higher in regions where higher proportions of adults hold college degrees and where local densities of firms and personnel are greater.44 This could be suggestive of human capital-based knowledge spillovers. Other evidence suggests that industries and regions with greater investments in knowledge creation (e.g. higher shares of the labour force employed as scientists or engineers) also possess aboveaverage venture start-up rates, especially those where small firms undertake a larger share of the investment.45 Stuart and Sorenson (2003b) report that IPOs of biotechnology firms located contiguous to or within a locality characterised by knowledge creation accelerate venture founding rates in that locality. It is interesting in this regard to speculate about the relationship between innovation and R&D. This is known to be weak or non-existent at the level of the individual firm, but becomes progressively stronger as the level of aggregation rises to that of the industry and nation (Audretsch, 2003). These findings can be reconciled if numerous small firms innovate without conducting R&D but instead exploit external (aggregate) knowledge spillovers, e.g. from universities. It has been observed that industries and patent classes associated with higher levels of university research exhibit higher average start-up rates (Audretsch and Acs, 1994; Shane, 2001). However, new venture creation is known to affect knowledge creation via patent activity (Acs and Varga, 2005) – implying reverse causality and possible endogeneity. Although self-reinforcing feedback from new venture creation to knowledge creation is consistent with a central tenet of the knowledge spillover theory,

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it is troubling that the above findings might simply reflect the existence of greater entrepreneurial opportunities in some industries than in others, which stimulate both patents and new firm entry without there necessarily being a (knowledge spillover) link between them. The knowledge spillover concept has also been criticised by Breschi and Lissoni (2001, p. 976) as being ‘no more than a ‘black box”. Breschi and Lissoni’s (2001) literature survey led them to question the empirical veracity of the knowledge spillover concept, on the grounds that empirical tests of it are indirect (see above) and do not explore precisely the ways that knowledge is actually transferred among agents located in the same geographic area. Breschi and Lissoni (2001) also contend that what appears to be inadvertent knowledge spillovers might actually be well-regulated knowledge flows between universities and firms, or groups of firms, which have more to do with speeding up the development phases of new products and processes than inadvertently providing new innovation opportunities. Consistent with this critique, Bania et al. (1993) detected only a small effect of university research funding on the high-tech startup rate, while Zucker et al. (1998) identified the intellectual capital of star scientists rather than knowledge spillovers as the key factor affecting the location and timing of new biotechnology firms.

4.6.3

Entrepreneurship and the business cycle

In theory, the greater output that entrepreneurs produce in return for bearing greater risk implies that changes in the number of entrepreneurs can have a major impact on output, by amplifying aggregate shocks. That suggests a potentially important macroeconomic role for entrepreneurship. In this vein, Rampini (2004) proposed a risk-based reason why the number of entrepreneurs is likely to be pro-cyclical. When shocks to the economy are favourable, productivity and wealth in entrepreneurship increase, making agents more willing to bear risk (via DARA) and become entrepreneurs. In addition, anticipating greater returns in favourable states, entrepreneurs supply higher levels of effort, reducing moral hazard problems and making lenders more willing to fund risky investment projects. However, when shocks are unfavourable, the opposite process occurs: wealth, investment and entrepreneurship all decline. A dynamic externality inherent in innovation provides another reason why entrepreneurship and aggregate economic activity exhibit similar cycles over time. Radical innovations increase economic activity directly, and frequently indirectly create opportunities for other, subsequent innovations, further increasing opportunities for entrepreneurship and greater economic activity. Because entrepreneurs do not internalise this dynamic externality when making their decisions to innovate and invest, the result is excessive volatility and pro-cyclicality of innovation and economic growth (Barlevy, 2007). US evidence suggests that, at the aggregate level at least, new firm formation is indeed pro-cyclical, with venture formation rates and individual transitions into entrepreneurship rising in booms and falling in recessions.46 However, to set this in perspective,

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pronounced state-dependence (i.e. persistence) in entrepreneurship at the individual level has been observed to dominate temporary or cyclical variations (Henley, 2004). And counter-cyclical forces may be at work too. It has been argued that recessions might have a ‘cleansing’effect, removing low-quality enterprises, and thereby increasing average entrepreneur quality (Caballero and Hammour, 1994). Offsetting this effect, though, wages fall in recessions, causing low-ability marginal types to enter entrepreneurship and reduce the average quality of the entrepreneur pool (Ghatak et al., 2007). This might explain the common emergence of worker co-operatives and other ‘marginal’ enterprises in recessions, which dissolve in economic recoveries when conventional employment opportunities become more readily available (Ben-Ner, 1988; Pérotin, 2006). The interest rate is an important macroeconomic variable which responds to the state of the business cycle, usually in a pro-cyclical manner. When economies are growing above-trend, interest rates tend to rise to control inflation. Obviously, higher interest rates increase the cost of financing and operating a business, leading to lower rates of entrepreneurship. On the other hand, if banks offer long-term loans at fixed interest rates, then entrepreneurship measured at time t might be relatively insensitive to interest rates also measured at time t, depending instead on interest rates at t − 1, t − 2, etc. Overall, though, UK and US time-series evidence suggests that interest rates do have a significant negative effect on entrepreneurship (see row 14 of Table 4.1).

4.6.4 Unemployment There is now an extensive literature on the relationship between entrepreneurship and unemployment. One of the motivations for studying this topic is the policy interest in promoting entrepreneurship as a way of reducing unemployment. There are two channels through which this could occur. First, there is the direct effect of removing a formerly unemployed person from the official unemployment register. Second, there is the indirect effect of eventual job creation by entrepreneurs who hire outside labour. Contrary to the impression given by some authors (e.g. Shane, 2003, chap. 4), there is actually no clear-cut theoretical or empirical relationship between entrepreneurship and unemployment. Unemployment may affect entrepreneurship in two opposite ways: via ‘recession-push’ and ‘prosperity-pull’ effects. According to the ‘recession-push’ hypothesis, unemployment reduces the opportunities of gaining paid employment and the expected gains from job search, which ‘pushes’ people into entrepreneurship. A secondary and complementary effect is that, as firms close down in recessions, the availability and affordability of second-hand capital equipment increases, reducing barriers to entry (Binks and Jennings, 1986). Both effects imply a positive relationship between entrepreneurship and unemployment. According to the ‘prosperity-pull’ hypothesis, on the other hand, at times of high unemployment the products and services of entrepreneurs face a lower market demand. This reduces incomes in entrepreneurship and possibly also the availability of capital, while increasing the risk of bankruptcy. Then individuals are ‘pulled’ out of

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entrepreneurship. At the same time, entrepreneurship may become riskier because if the venture fails, it is less likely that the entrepreneur can fall back on a job in paid employment. Furthermore, at the level of the individual, unemployment could be associated with low levels of human and financial capital needed for successful entry into entrepreneurship. In contrast to the ‘recession-push’ hypothesis, these factors imply a negative relationship between entrepreneurship and unemployment. Empirical estimates of the entrepreneurship/unemployment relationship invariably confound the above two effects, capturing a ‘net’ effect. However, the size and direction of the net effect is still of policy interest. It is helpful to sort the large number of results on this topic by empirical method. Results obtained using cross-section binary choice models are reviewed first, followed by results from time-series and panel-data models. In the cases discussed below, entrepreneurship is measured as self-employment unless otherwise stated. Cross-section evidence In my previous book, I wrote that the bulk of crosssection evidence pointed to a negative relationship between entrepreneurship and unemployment (see Parker, 2004, Table 3.3, row 10, p. 104). It is striking that recent studies have detected more positive than negative relationships – culminating in the ‘muddier’ picture given in row 11 of Table 4.1. It is therefore no longer possible to conclude, as I did in Parker (2004), that ‘overall the econometric evidence from cross-section studies supports the ‘prosperity-pull’ hypothesis’. Why are the newer cross-section findings so different from the older ones? One reason might be that more recent research includes more controls for demand conditions, identifying the opportunity cost mechanism in the ‘recession-push’ argument more precisely. Another possible reason is that more recent research uses data from time periods where chronic unemployment is not observed, so confounding demand effects are more muted. Digging into the data reveals interesting information about the numbers and transitions from unemployment to entrepreneurship, and the impact of worker quality, unemployment experience and unemployment duration on these transitions. The following stylised facts describe transitions from unemployment to self-employment in developed countries:

• Most transitions into self-employment are made from paid employment, reflecting a greater stock of employees than unemployed (Storey, 1982); • Nevertheless, a higher proportion of unemployed than employed people transition to self-employment, being twice as likely to do so according to US evidence (Evans and Leighton, 1989b, 1990);47 • Most unemployed people remain unemployed or transition to paid employment rather than self-employment (Cowling and Taylor, 2001). Of the unemployed, those with unstable work histories (including periods of past unemployment) are significantly more likely to become entrepreneurs.48 In fact, it appears to be a history of job changes, rather than unemployment per se, which increases

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the willingness of workers to become self-employed (van Praag and van Ophem, 1995). One possible interpretation is that many entrepreneurs are former workers who made low-quality job matches in paid employment. Evidence that unemployed people who become entrepreneurs experience a larger drop in their earnings than either the unemployed who return to wage work, or employees who enter entrepreneurship, bears out this idea (see Evans and Leighton, 1989b; Schiller and Crewson, 1997). So does evidence about the duration of unemployment spells, which are related to low-quality job matches and inferior skill sets. This evidence indicates that longer unemployment durations are associated with significantly higher probabilities of transitions into selfemployment.49 For example, Alba-Ramirez (1994) estimates that a 1 per cent increase in unemployment duration increases the probability of a switch to self-employment by 0.15 percentage points in the USA and by 0.17 percentage points in Spain. Cowling and Mitchell (2001) argue that unemployment duration rather than the stock of unemployed is what drives changes in aggregate rates of entrepreneurship. In contrast, there is no reason to believe that employee quits are systematically related to worker quality. According to US evidence, employees who lost their jobs in the previous three years (‘job losers’) are significantly less likely to become entrepreneurs (by 3 percentage points) than ‘non-losers’ – even after controlling for observable characteristics.50 It is useful to distinguish between voluntary quits on one hand and involuntary layoffs and redundancy on the other. The latter are more common for low-skilled workers and are associated with higher rates of entrepreneurship.51 Nevertheless, redundancies actually result in more transitions to paid employment than self-employment; and few new firms set up by redundant workers hire many external workers.52 Finally, there can also be a gender dimension to unemployment–self-employment transitions. Kuhn and Schuetze (2001) report that most of the increase in female Canadian self-employment in the 1980s and 1990s was attributable to an increase in retention rates in self-employment owing to better work prospects for women, whereas for men most of the increase in self-employment was attributable to a decrease in stability in paid employment and inflows from unemployment, owing to deteriorating opportunities for them. Time-series and panel-data evidence In contrast to results from cross-section studies, time-series studies tell a relatively consistent story. As row 11 of Table 4.1 shows, the overwhelming majority of time-series studies uncover significant positive effects of national and regional unemployment rates on national self-employment and new firm formation rates.53 This appears to support the ‘recession-push’ hypothesis. Most researchers estimate a linear relationship between measures of entrepreneurship and unemployment rates, although a few have explored the possibility of non-linearity (Hamilton, 1989; Georgellis and Wall, 2000). Just like cross-section analyses, time-series data have been used to link job layoffs and redundancy payments with regional and national rates of entrepreneurship.54 Paneldata evidence also supports the ‘recession-push’ hypothesis for relatively advantaged

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workers possessing advanced academic qualifications, as well as for people with self-employed parents.55 Two types of panel study can be distinguished: one utilising a relatively large cross-section dimension and one utilising a relatively large time-series dimension.56 Panel-data estimates of the entrepreneurship–unemployment relationship generate mixed results, which vary across countries (Blanchflower, 2000). Unfortunately, significant coefficients are sometimes observed to vanish once other covariates are added to econometric specifications (Henley, 2004; Parker and Robson, 2004). As always, causality is a tricky issue. Thurik et al. (2008) point out that in a dynamic setting, an increase in the unemployment rate is likely to feed into higher rates of entrepreneurship later on (‘recession-push’). Yet higher rates of entrepreneurship are likely to generate more benign economic conditions which serve to reduce subsequent unemployment rates (termed the ‘entrepreneurial effect’). Thurik et al. (2008) embed both of these causal mechanisms in a vector autoregression (VAR) model and detect evidence of both mechanisms using data from twenty-three OECD countries over 1974–2002. The entrepreneurial effect apparently dominates the ‘recession-push’ effect, suggesting that entrepreneurship increases employment growth overall. More will be said on this issue in chapter 11. Conclusion Greater use of cross-section data on transitions into entrepreneurship from unemployment is beginning to generate findings which complement those from time-series data. The upshot is a growing consensus about a zero or positive relationship between unemployment and entrepreneurship. This contrasts with the pre-2000 literature where cross-section studies usually suggested a negative relationship, in contrast to positive relationships identified in time-series studies – and which prompted several researchers to propose sometimes ingenious ways of reconciling these disparate findings.57 It should be borne in mind, however, that the unemployment rate is only an imperfect proxy for the underlying factors inducing workers to enter, or leave, entrepreneurship. Undergraduate macroeconomics students learn that exogenous technological progress need not cause unemployment if wages are sufficiently flexible and workers are sufficiently adaptable to switch from declining sectors into those growing as a direct or indirect result of technological progress. Thus if technological change is the cause of labour-saving changes in production methods, unemployment is only the symptom of slow adjustments in the labour market. If changing costs or technologies make entrepreneurship a more efficient and attractive form of productive organisation, one would expect to see transitions between paid employment and entrepreneurship without necessarily observing any impact via unemployment. So there is really no economic reason why unemployment and entrepreneurship need to be related at all. The extent to which they are related probably reflects rigidities in the economy. These rigidities are likely to diminish over time as governments adjust their tax-benefit systems to make their economies more flexible. Chapter 17 explores some of the policy issues surrounding the entrepreneurship–unemployment nexus.

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Table 4.4. Self-employment rates in the British regions, 1970 and 2000

Region North Yorkshire & Humberside East Midlands East Anglia South East South West West Midlands North West Wales Scotland

1970 rate (%)

2000 rate (%)

Rank in 1970

Rank in 2000

Human capital 2000 (%)

6.02

7.96

8

10

9.1

6.45 7.00 10.11 7.28 11.71 5.74 6.54 10.37 5.68

10.42 10.71 13.35 13.47 14.98 10.29 9.87 12.43 9.31

7 5 3 4 1 9 6 2 10

6 5 3 2 1 7 8 4 9

12.2 13.1 14.5 17.8 14.6 12.9 12.8 12.1 12.9

Note: The self-employment rate is defined as the number of self-employed jobs (male plus female) in a region divided by the region’s labour force. ‘Human capital’ is defined as the percentage of the economically active workforce in a region with a degree-level qualification. Source: Parker (2005b).

Research in the economics of entrepreneurship over the last decade has largely shifted away from the unemployment issue towards other topics. Partly this reflects the end of the era of high unemployment rates in many developed economies, which peaked in the 1980s. But it might also reflect growing recognition among entrepreneurship scholars that there is relatively little more to say about this issue which is either original or exciting (Parker, 2006b, p. 455). 4.6.5

Regional factors

All major economies exhibit regional differences in rates of entrepreneurship. This is true for administrative regions, labour market areas, cities and neighbourhoods. The most entrepreneurial regions within a country can have new firm formation rates which are between two and four times higher than those observed in the least entrepreneurial regions (Reynolds et al., 1994). As Table 4.4 shows for the case of Britain, regional differences are not only pronounced but are also persistent. The Spearman correlation coefficient for Table 4.4’s rank orderings of British self-employment rates in 1970 compared with 2000 is 0.87. Similar patterns have been observed for regional rates of new firm formation in the USA.58 Why are there regional differences in entrepreneurship and why do they persist? I will consider two broad theoretical perspectives below. The first attributes persistent differences in regional rates of entrepreneurship to persistent differences in regional

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characteristics conducive to entrepreneurship. Because this argument has been shown to have only limited explanatory power, recent research has shifted towards explaining a second set of alternative explanations based on externalities and multiple (regional) equilibria. Regions experience different economic conditions, so it is natural to propose these as the causes of regional differences in rates of entrepreneurship. Regions with low levels of demand, poorly educated workers, or high concentrations of capital-intensive industries (which can sustain effective barriers to entry) are less attractive for entrepreneurs to enter. At the other end of the spectrum, with respect to high-tech entrepreneurship venture capitalists’ expertise tends to be concentrated within tight geographic and industry spaces (Sorenson and Stuart, 2001). Previous research shows that the highest new firm formation rates are found in regions with the highest proportions of employment in small firms, despite the risk that colocation enables technologically similar firms to expropriate the ideas of new entrants (Reynolds et al., 1994; Rosenthal and Strange, 2003). Other important determinants of regional new firm formation rates, highlighted in the now classic study of Reynolds et al. (1994), include high rates of in-migration; rapid growth in regional incomes and populations; and high levels of employment specialisation and population densities. These findings have been generally confirmed in subsequent research.59 Interestingly, local government expenditures and assistance programmes appear to have only limited effects on regional firm birth rates (Reynolds et al., 1994). Other factors associated with high levels of regional entrepreneurship include housing wealth (Robson, 1998a); plenty of college drop-outs and well-educated, older citizens (Acs andArmington, 2006; Glaeser, 2007); industrial structures characterised by low capital intensities and factor costs (Fritsch and Falck, 2007); and plentiful access to finance (Naudé et al., 2008). While this approach of identifying different regional characteristics has generated some interesting insights, it suffers from both conceptual and empirical drawbacks. On the conceptual front, less entrepreneurial regions tend to have low stocks of human capital (Table 4.4) and hence wages. Hence one might expect entrepreneurs would seize profitable opportunities by moving from expensive high-entrepreneurship regions to start new firms in low-entrepreneurship regions where there is less competition and a lower cost base. That this does not generally happen implies that some other factors are at work. Indeed, American and Italian entrepreneurs turn out to be less geographically mobile than employees are, especially in more financially developed regions (Michelacci and Silva, 2007).60 One plausible explanation is that local entrepreneurs have greater access to finance than non-locals do, and benefit from ‘home bias’exhibited by investors located in their region (Parwada, 2008). Another is that most entrepreneurs operate businesses whose main customer base is in the immediate or close locality. It is noteworthy that geographical mobility weakens the intergenerational transmission of self-employment status (Niittykangas and Tervo, 2005) – which was identified earlier in this chapter as one of the chief determinants of participation in entrepreneurship. A major drawback of empirical studies linking relative start-up rates to regional conditions is that they typically explain no more than a fraction of actual regional variations

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in entrepreneurship. This signals the existence of omitted explanatory variables. Some of these variables, such as ‘local culture’, social norms and institutional restrictions, might be hard to measure accurately and comprehensively. Recognising these limitations, recent research has sought other explanations for persistent differences in regional rates of entrepreneurship based on the notions of externalities and multiple regional equilibria.61 Externalities can come in several forms: 1. Information spillovers and social ties. Entrepreneurs create information spillovers by signalling information about opportunities and resource requirements to other latent entrepreneurs through social networks (Stuart and Sorenson, 2003a; Rocha and Sternberg, 2005). This information can help to: resolve ambiguities inherent in new venture creation, so reducing uncertainty; advertise role models which increase awareness of perceived net benefits from entrepreneurship; and perpetuate favourable social norms which legitimise entrepreneurship, affecting non-pecuniary aspects of occupational choice (Giannetti and Simonov, 2004). 2. Knowledge spillovers. These tend to be spatially concentrated, partly because the costs of transmitting tacit knowledge increase with distance. Hence entrepreneurs exploit knowledge spillovers locally. This can explain the well-known tendency of spin-offs to locate close to their geographic roots, which in turn might explain industry clustering, without needing to appeal to alternative explanations such as agglomeration economies (Klepper, 2006). 3. Intergenerational transmission. As noted earlier in this chapter, an important influence on entrepreneurship is intergenerational transfers of information, knowledge and attitudes from self-employed parents to their offspring. Also noted earlier is the fact that entrepreneurs tend to be less geographically mobile than entrepreneurs. Hence geographical family immobility can perpetuate regional rates of entrepreneurship. 4. Agglomeration benefits. A geographic concentration of ventures reduces average transaction costs for each entrepreneur while facilitating efficient and rapid transfers of technical knowledge and skills. It also encourages the development of specialised services (e.g. venture capital and corporate legal expertise) which in turn encourages new firms to locate nearby. Some of the entities which service new ventures are themselves new ventures, leading to a regional ‘multiplier’ effect (Johnson and Parker, 1994, 1996; Nyström, 2007). These reasons can explain why different regions can have persistently different rates of entrepreneurship. They also help explain pronounced agglomerations of entrepreneurial activity (‘clusters’) such as Silicon Valley and Boston’s Route 128 in the USA, and Baden-Württemburg and Emilia-Romagna in Europe. These clusters no doubt also benefit from some or all of the following favourable factors: a benign regulatory regime; the proximity of advanced research universities and institutes that are well connected to industry; the availability of a flexible and mobile workforce; mechanisms for maintaining global linkages; access to venture capital; and formal as well

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as informal associations (networks) which leverage social capital and foster collective learning for the whole cluster.62 Conversely other regions can promote deep-rooted attitudes inimical to entrepreneurship, especially in areas dominated by large organisations (Jackson and Rodney, 1994; van Stel and Storey, 2004). However, the relative importance of these factors individually is still not precisely known. As a result, appropriate recommendations for public policy to create successful new clusters remain imperfectly developed. Another possibility is that regional differences in entrepreneurship are the manifestation of multiple equilibria (see chapter 2). In this regard, Parker’s (2005b) model, which links high average levels of regional entrepreneurship with high levels of education, at least appears to be consistent with the evidence (see the final column of Table 4.4 and Georgellis and Wall, 2000). At a more disaggregated level, some research compares urban with regional locations. Urban areas enjoy lower communication and transaction costs with respect to customers and suppliers and are associated with larger, denser markets with higher average incomes – enabling entrepreneurs to reap scale economies and exploit opportunities which might otherwise not be cost-effective. Factors of production, knowledge spillovers and specialised services are also likely to be more abundant in urban areas. On the other hand, inputs such as land and labour can be more expensive in urban areas; and there are often fewer paid employment opportunities in rural areas, increasing the relative attractiveness of new firm creation there. Perhaps reflecting these offsetting effects, estimates from binary choice models reveal a mixed picture about the effects of urban location on entrepreneurship (see row 12 of Table 4.1). Nevertheless, urban ventures appear to generate the highest average entrepreneurial earnings and new firm growth rates.63 There also tend to be more transitions in and out of self-employment in rural than in urban locations, possibly because the scarcity of paid employment opportunities in rural areas makes self-employment an unstable form of ‘employment of last resort’ (Tervo, 2008). Of course, not all urban areas are the same. Lee et al. (2006) argue that urban areas with greater social diversity have lower entry barriers and so attract creative individuals. Duranton and Puga (2001) and Florida (2003) argue that diverse mixes of creative people are more likely to generate new and novel combinations, which are associated with the growth of cities and high-value entrepreneurial innovations. However, the available evidence comprises statistical associations rather than causal effects. Glaeser (2007) argues that local entrepreneurship can explain why some cities grow faster than others (rather than vice versa), with high self-employment rates in cities in 1970 predicting sustained growth in incomes and populations of those cities over the following thirty years. Furthermore, numerous small local firms correlate with subsequent employment growth in a city. Glaeser (2007) concludes that more entrepreneurial cities are more successful cities. In summary, research on the geography of entrepreneurship in general and new venture creation in particular continues to develop. Part of the attractiveness of this topic is the way it connects entrepreneurship to several other prominent issues, including innovation, human capital, spatial structure, growth-enhancing spillovers and public

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policy. But much more research remains to be done in terms of both theory development and the empirical testing of theories. It seems that many hard-to-observe region-specific and individual-specific factors affect regional levels of entrepreneurship. An important but challenging task for empirical researchers is to operationalise precise measures of the various factors at work and to distinguish sharply between their causal effects. 4.7

Nascent entrepreneurship

Do the findings presented so far in this chapter also apply to nascent entrepreneurs (NEs)? As noted in chapter 1, if different factors affect entry compared with survival, then the bulk of studies measuring entrepreneurship in terms of established business owners or self-employees might give a misleading or partial impression of the factors affecting entry into entrepreneurship. This section provides a brief overview of the determinants of nascent entrepreneurship. To anticipate one of the main findings, it turns out that most explanatory variables have similar effects on nascent entrepreneurship as on established entrepreneurship (which have been discussed in earlier sections of this chapter). There are several exceptions, however, which raise some interesting questions about early entry and survival processes for some population groups. This section is divided into two parts. In the first one, characteristics of NEs are discussed and the findings are compared with those from earlier in the chapter and elsewhere in the book. The second part asks what happens to NEs as they develop their new ventures. 4.7.1

Characteristics of nascent entrepreneurs For the most part, human capital variables affect individuals’ propensities to engage in nascent entrepreneurship in a similar way to their engagement in ‘established’ entrepreneurship as discussed earlier in the chapter. This includes education, which has a significant positive effect on propensities to become an NE in some countries (e.g. Sweden and the USA) but more ambiguous effects in others (e.g. Germany).64 Also consistent with evidence from earlier in the chapter, prior business and work experience have generally positive effects on the likelihood of becoming an NE, especially the former.65 Wagner (2006a) shows that diversity of experience is associated with NE status, in line with Lazear’s ‘jack-of-all-trades’ hypothesis. In all developed countries, men are much more likely to become NEs than women are, consistent with other definitions of entrepreneurs (see chapter 6). Also consistent with evidence about ‘established’ entrepreneurs, urban density and population growth rates are positively associated with nascent entrepreneurship.66 And reflecting the mixed effects of unemployment on entrepreneurship, some studies have detected positive effects (e.g. Rotefoss and Kolvereid, 2005), while others have reported negative (Bergmann and Sternberg, 2007) and insignificant (Delmar and Davidsson, 2000) effects. Differences start to creep in when one considers family background variables. In contrast with row 6 of Table 4.1, for example, marital status has more mixed effects on propensities to become an NE.67 And unlike row 9 of Table 4.1, having self-employed

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parents or knowing other entrepreneurs have positive associations with NE status in European and cross-national studies, but not in US studies.68 Other differences between the determinants of nascent and established entrepreneurship relate to the effects of age, wealth and ethnicity. As observed earlier, annual entry rates into self-employment are roughly independent of age (e.g. Evans and Leighton, 1989a). In contrast, the evidence suggests that NE propensities decline with age.69 It is unclear why this is so, although annual household survey data might miss many short-lived and possibly unsuccessful founding efforts made by young NEs, leading to an overly ‘flat’ age-entry profile. Regarding wealth, chapter 9 outlines evidence of an apparently strong positive relationship between this variable and established entrepreneurship (see row 15 of Table 4.1). Interestingly, this relationship tends not to be observed for NEs.70 Taken at face value, these findings of wealth not mattering for NEs might reflect modest initial capital needs at start-up, a well-functioning credit market, and/or bootstrapping by nascent entrepreneurs (Kim et al., 2006). The latter includes reliance on internal funds, borrowing from ‘friends, family and fools’, and low-cost acquisition of material resources (e.g. home-based starts economising on office costs: Harrison et al., 2004). Also, samples of established entrepreneurs are prone to over-sample the ‘successful few’, whom one would expect to be wealthier. This could be an important source of survival bias, casting some doubt on assertions that limited wealth and borrowing constraints hamper start-up efforts. As we will see in chapter 5, self-employment rates of white Americans are some three times greater than those of black Americans. So it is very striking that PSED and GEM data both show black Americans to be nearly twice as likely to engage in nascent entrepreneurship as whites.71 In fact, Parker and Belghitar (2006) show that black Americans are significantly less likely to convert start-up efforts into actual start-ups. Instead they remain NEs for longer than white Americans do. Similar patterns in the more recent PSED II data set show that this is a robust feature of contemporaryAmerican entrepreneurship. It suggests that barriers to black entrepreneurship in America lie not in a lack of interest in starting a business, but in problems of seeing the process through to the successful launch of new ventures. Precisely what these problems are and how they can be overcome are important questions for future research to address. To conclude, broadly similar factors seem to affect selection into NE status as into ‘established’ entrepreneurship. This suggests that, apart from one or two exceptions noted above, hindsight and survival biases pertaining to established entrepreneurs are unlikely to vitiate the main body of findings reported in this chapter, thereby ‘saving the entrepreneurship research community from a need for major reinterpretation of what it thought was known’ (Davidsson, 2006, p. 12).

4.7.2

Venture development paths of nascent entrepreneurs I now ask what explains the hard performance outcomes of NEs (as opposed to ‘soft’ measures such as self-reported declarations of realising intended or expected goals).72 The rationale for ‘intentions’ studies is that intentions translate into actions, including

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business entry. However, these studies rarely discriminate between ‘dreamers’ and ‘doers’; and psychological research as well as longitudinal analyses of follow-through behaviour repeatedly show that intentions are not always related to actual behaviour.73 This might be especially pertinent given that approximately 10 per cent of the PSED sample of NEs claimed to be thinking about starting a business a decade or more prior to the first PSED interview. Broadly speaking, researchers measure venture development in two distinct ways. The first approach records the passing of various activity milestones during the start-up process. The second records whether an NE has started up, abandoned their venture, or is still an NE a year or more after the initial survey. A problem with the first approach is that specific start-up processes can follow almost any sequence – including even claiming to have had a first sale before having thought seriously about starting a business (Carter et al., 1996; Reynolds, 1997). Partly for that reason, studies taking the first approach usually augment measures of process development with hard measures of performance. While the second approach circumvents this problem, different start-up organising efforts are caught at different stages, with over-sampling of efforts which take a long time to gestate (Davidsson, 2006, p. 19). This can usually be dealt with by including a control variable measuring the length of the start-up organising process to date. It is sometimes claimed that written business plans promote the development and survival of new ventures (Delmar and Shane, 2004; Liao and Gartner, 2006). One reason they might do so is that a business plan which establishes a venture as a legal entity ‘legitimises’ the venture in the eyes of outside stakeholders (Delmar and Shane, 2003, 2004). Alternatively, however, rational entrepreneurs with better projects are more likely to invest time and resources in business plans because they are more likely to pay off. Indeed, Bhide (2000) points out that for many entrepreneurs the expected costs of formal planning outweigh the expected benefits. Forty-one per cent of the Inc. 500 founders interviewed by Bhide (2000) started without any business plan at all. According to Newbert (2005) and Parker and Belghitar (2006), business plans do not help predict the venture outcomes of NEs. Based on a review of previous studies, Table 4.5 chronicles what happens to NEs approximately one year after they were first identified as such. Although there is some variation from study to study, between one-third and one-half of nascent entrepreneurs launch their venture within one year. The largest sample of those in the table is drawn from the PSED: according to these data, continuation in nascent entrepreneurship is the modal outcome in the USA (Parker and Belghitar, 2006). This implies that for nearly one-half of NEs, preparing before launching a start-up takes more than the one year proposed by Reynolds and White (1997). In terms of milestones achieved, Carter et al. (1996) claim that American NEs who either start up or abandon their nascent ventures are more similar to each other than are NEs who are still trying. According to Diochon et al. (2003), Canadian NEs abandon because they want to, not because they have to. Alsos and Kolvereid (1998) identified stark differences between outcomes of different types of Norwegian nascent

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Table 4.5. What happens to nascent entrepreneurs Study

Still Nascent

Operating

Gave Up

Total

Country

Carter et al. (1996)a Alsos and Ljunggren (1998) van Gelderen et al. (2001) Diochon et al. (2003)b Parker and Belghitar (2006)

21 (30%) 37 (25%) 89 (27%) 51 (39%) 159 (47%)

34 (48%) 68 (46%) 155 (47%) 45 (34%) 112 (33%)

16 (22%) 43 (29%) 86 (26%) 36 (27%) 69 (20%)

71 148 330 132 340

USA Norway Netherlands Canada USA

Note: Numbers of cases appear as the first cell entries, with sample percentages in parentheses. Data based on destinations of nascent entrepreneurs approximately one year after initial interview, except for a (6–18 months). b Excludes nineteen cases who could not be contacted after the first wave.

entrepreneur, with only 4 per cent of portfolio NEs giving up after one year, compared with 41 per cent of serial and 32 per cent of novice NEs. Parker and Belghitar (2006) argue that the value of waiting plays a key role in predicting NE outcomes. They propose several characteristics which make waiting an optimal strategy. For example, blacks facing discrimination in the credit market (see chapter 5) who cannot borrow enough to launch their ventures immediately might optimally defer launching and wait instead, saving to self-finance well-resourced entry at a later date (see also Parker, 2000). Membership of majority ethnic groups in contrast might be associated with more valuable outside options, promoting quits from NE to paid employment. Other factors in contrast might promote early starts rather than waiting, e.g. if NEs have already received money from customers. Parker and Belghitar (2006) performed a multinomial logit analysis of the three outcomes listed in Table 4.5, measured one year after the initial interview, conditioning on a set of exogenous and objectively verifiable covariates observed at the initial interview. They also corrected for possible attrition bias. Parker and Belghitar (2006) found that NEs are significantly more likely to transition to start-up if they have established credit with suppliers or have already received some money from customers. This is consistent with independent evidence that NEs who pass important ‘milestones’ are more likely to complete the start-up process.74 It is also consistent with evidence that individuals who have become self-employed while preparing their venture for launch are more likely ultimately to start up (van Gelderen et al., 2001; Rotefoss and Kolvereid, 2005). Parker and Belghitar (2006) further reported that transition rates to start-up are higher for NEs who own their own home, are household heads and who are preparing solo ventures (see also Kessler and Frank, 2004, for confirmatory Austrian evidence). In addition, posthigh school education and NE involvement in public- and private-sponsored business assistance programmes significantly increase the probability of transitions to start-ups. Other researchers in contrast have reported insignificant or negative effects on startup propensities from higher levels of education (see Davidsson and Honig, 2003; and Reynolds and White, 1997, chap. 4, respectively).

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Although wealth does not play a significant role, NEs who save and self-finance their ventures, especially non-whites, are significantly more likely to continue as NEs (Parker and Belghitar, 2006; van Gelderen et al., 2006). This apparently confirms the value of waiting, which enables the necessary resources for start-up to be acquired. Parker and Belghitar (2006) also found that white American NEs are significantly more likely to quit than other ethnic groups. Similar to Davidsson and Honig (2003), Parker and Belghitar (2006) observed no significant differences in NE outcomes by gender, although others have found males to be more likely to start up than females.75 Davidsson and Honig (2003) estimated that greater human and social capital increases the probability that individuals remain as NEs, while only social capital affects their financial performance. For their part, Parker and Belghitar (2006) found insignificant effects on outcomes from two human capital variables, age and experience; and of their social capital variables only parental self-employment had any effect, serving to increase the probability of continuation as an NE. However, two other studies have found positive effects of industry experience on start-up outcomes (van Gelderen et al., 2001; Rotefoss and Kolvereid, 2005). There is mixed evidence about the effects of business plans and attendance at business education classes on transitions to full start-ups.76 Finally, Koellinger (2008) studied 9,549 NEs from thirty GEM countries over 2002–04 and reported that formal educational attainment, unemployment and a high degree of self-confidence are significantly associated with entrepreneurial innovativeness at the individual level. 4.8

Dependent starts and firm characteristics

This section provides evidence about entrepreneurial ‘spawning’ corresponding to the theories outlined in chapter 2. Salient characteristics of spin-offs are discussed first, followed by aspects of spin-off performance. I will not review evidence about the determinants or performance of corporate ventures or university spin-offs, which are different categories of business venturing with different imperatives.77 A growing body of research links the characteristics and performance of new hightech start-ups to the characteristics and performance of the ‘parent’ firms from which they emerged. Evidence shows that the parent companies of new American VC-backed start-ups tend to be VC-financed themselves and are disproportionately located in hightech clusters such as Silicon Valley or Route 128 in Massachusetts (Gompers et al., 2005; Klepper and Sleeper, 2005). The availability of networks which facilitate entrepreneurship in these clusters might help to explain these findings (for a suggestive descriptive analysis, see Saxenian, 1994). Parent companies which are young or which have slowing growth rates spawn more new ventures than average, as do companies which focus in one market segment rather than several simultaneously (Gompers et al., 2005; Klepper, 2006). The evidence relating to the effects of parent firm size on spawning propensities on the other hand is mixed.78 On the whole, successful parent firms which enjoy long periods of ‘product leadership’ are characterised by higher spin-off rates, and spawn new ventures with above-average survival rates (Klepper, 2006). This suggests that

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spin-offs transfer capabilities from the parent firm to their own organisation (Helfat and Lieberman, 2002). The agency and employee learning theories of spin-offs described in chapter 2 generate different predictions about the markets which spin-offs are likely to enter. An implication of agency theory is that spin-offs can minimise conflicts with their parents by entering (or creating) different markets from those of their parents. In contrast, an implication of learning theories is that spin-offs will utilise their knowledge by entering similar markets to the parent, but differentiated somewhat to reduce the threat of retaliation. The evidence on this issue partially supports the learning perspective. For example, high-tech and high-growth start-ups in the laser industry often develop similar products to parents, rather than entering completely new markets; but the markets targeted by these start-ups tend to be only peripherally related to the core business of their parents, limiting the competitive impact on the latter.79 In other cases (e.g. the hard-disk drive industry) spin-offs have entered new sub-markets before their parents and approximately one-half of them pursued a distinctive innovation that opened up a new sub-market (Christiansen, 1993). Overall, though, the strategy of entering similar markets or selling similar products to their parents is associated with above-average growth performance (Cooper and Bruno, 1977; Feeser and Willard, 1990). Asset complementarity also bears on the question of whether spin-offs co-operate or compete with their parent firms. Examples of co-operation include licensing, strategic alliances and outright acquisitions. Agency theories predict that co-operation is easier: when IPR protection is strong, since that enhances credibility and transparency in negotiation; when the cost of contracting between the two parties is low; and when complementarity in asset ownership between the parties is high. Using a novel data set on the commercialisation strategies of 118 US start-up projects, Gans et al. (2002) found support for these predictions, with IPR protection (measured in terms of patent ownership) and asset complementarity both significantly increasing the probability of the co-operative outcomes listed above. Having a VC relationship is associated with networks which reduce contracting costs, serving to make co-operative outcomes more likely to emerge than competitive ones. Gans et al. (2002) also claimed that innovations pioneered by new ventures in biotechnology more commonly occur in co-operation with incumbents, whereas in the electronics industry they occur more commonly in competition with incumbents.80 Spinoffs outperform other kinds of start-up in several respects, including their survival prospects, speed of product development, employment creation and growth performance.81 As noted earlier in this chapter, spin-off founders can enhance the performance prospects of their ventures by forming large teams which encapsulate broad and deep pools of knowledge.82 Klepper (2001) argues that few spinoffs emerge from unresolved agency conflicts in settings where employees hide discoveries from managers. Instead, most of them are triggered by adverse events at parent firms such as involuntary exits, acquisitions or downsizing imperatives. Employees generally try to work hard for their parent firm

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and usually only resort to an independent start-up when intrapreneurship opportunities are denied to them (Garvin, 1983; Klepper, 2007). However, more data are needed to flesh out the evidence base on this and related issues about spinoffs. 4.9

Conclusion

This chapter presented an extensive body of evidence about the determinants of entrepreneurship. For convenience, a variety of practical definitions of entrepreneurship were used, including self-employment, business ownership and new venture creation. Either consciously or not, most of the studies from which the evidence has been drawn are underpinned by the theories discussed in chapter 2. This is especially true of occupational choice models based on heterogeneous endowments of entrepreneurial ability and risk-taking propensities, which have been operationalised using the binary choice models set out in chapter 3. So, for example, several of the human capital, social capital and psychological variables discussed above can be thought of as proxies for the theoretical constructs of Lucas (1978) and Kihlstrom and Laffont (1979). And empirical studies of entrepreneurial spawning are predicated on various theories of spin-offs presented in chapter 2. We now know a fair amount about the drivers of the entrepreneurial selection process – the focus of this chapter. Table 4.1 demonstrates that a broad consensus has been reached on the impact of many – although not all – of the principal explanatory variables. The most important determinants of entrepreneurship are age, labour market experience, marital status and having a parent who was an entrepreneur. Greater levels of risk and higher interest rates generally have negative effects on participation in entrepreneurship, although market risk and risk attitudes remain poorly measured. More generally, data limitations frequently force researchers to use crude proxies for the theoretical constructs outlined in chapter 2. In future work, finer-grained and more theoretically grounded data are therefore needed to consolidate and extend our knowledge about the individual and environmental determinants of entrepreneurship. Notes 1. References augment the studies cited in Parker (2004) with the following: Borjas (1986); Wong (1986); Macpherson (1988); Evans and Leighton (1989b, 1990); Butler and Herring (1991); Alba-Ramirez (1994); Audretsch and Fritsch (1994a); Brynjolfsson et al. (1994); Davidsson et al. (1994); Guesnier (1994); Robinson and Sexton (1994); Bates (1995); Eisenhauer (1995); Grant (1996); Lindh and Ohlsson (1996); Magnac and Robin (1996); Sanders and Nee (1996); Schiller and Crewson (1997); Garen (1998); Honig (1998); Briscoe et al. (2000); Clain (2000); Delmar and Davidsson (2000); Kangasharju (2000); Taylor (2001); McManus (2000); Armington and Acs (2002); Burke et al. (2002); Carree et al. (2002); Ritsila and Tervo (2002); Taniguchi (2002); Davidsson and Honig (2003); Fairlie and Meyer (2003); Fan and White (2003); Acs and Armington (2004a, 2006);Amossé and Goux (2004);Arum and Müller (2004 - all chapters); Blanchflower (2004); Cowling et al. (2004); Giannetti and Simonov (2004); Guiso et al. (2004); Henley (2004); Perry and Rosen (2004); Stabile (2004); Taylor (2004); Puri and Robinson (2005); Dobrev and Barnett (2005); Brown, Farrell and Sessions (2006); Ekelund et al. (2005); Niittykangas and

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2. 3. 4.

5. 6.

7. 8.

9. 10. 11.

12. 13.

14.

Selection Tervo (2005); Rocha and Sternberg (2005); Beugelsdijk and Noorderhaven (2005); Kanniainen and Vesala (2005); Lunn and Steen (2005); Torrini (2005); Co et al. (2005); Georgellis et al. (2005); van Tubergen (2005); Böheim and Muehlberger (2006); Bruce and Mohsin (2006); Constant and Shachmurove (2006); Constant and Zimmerman (2006); Budig (2006); de Clercq and Arenius (2006); Cowling (2000); Brown, Farrell, Harris and Sessions (2006); Djankov et al. (2006); Fairlie (2006a); Fraser and Greene (2006); van Gelderen et al. (2006); Georgellis and Wall (2006); Grilo and Irigoyen (2006); Hammarstedt (2006); Hundley (2006); Kan and Tsai (2006); Koellinger and Minniti (2006); Mesnard and Ravillion (2006); Mora and Dávila (2006); Tamásy (2006); Tervo (2006); Verheul et al. (2006); Wellington (2006); Wu (2006); Zhang et al. (2006); Andersson and Wadensjoe (2007); Constant et al. (2007); Fairchild (2008); Fritsch and Falck, 2007); Fritsch and Mueller (2007); Glaeser (2007); Goeggel et al. (2007); Mohapatra et al. (2007); Wennekers et al. (2005); White et al. (2007); Colombier and Masclet (2008); Parker (2008a); Thurik et al. (2008). See Eisenhauer (1995), Dobrev and Barnett (2005) and Wu and Knott (2006) for examples of that approach. See Creedy and Johnson (1983), Foti and Vivarelli (1994), Parker (1996), Cowling and Mitchell (1997), Audretsch and Vivarelli (1997), Goedhuys and Sleuwaegen (2000) and Lofstrom (2002). KPMG (1999) observed that ‘lifestyle’motives for self-employment were strongest among founding entrepreneurs and weakest for those operating growing and innovating firms, who were more likely to stress rapid further growth as an objective. However, Burke et al. (2002) claim that entrepreneurs who value being their own boss perform significantly better than those who do not. VandenHeuvel and Wooden (1997), Blanchflower (2004), Ajayi-obe and Parker (2005). See Katz (1993), Blanchflower and Freeman (1994), Blanchflower (2000), Blanchflower et al. (2001), Hundley (2001b), Kawaguchi (2003), Blanchflower (2004), Benz and Frey (2004, 2008), Taylor (2004), Schjoedt and Shaver (2007) and Kawaguchi (2008). Sanfey and Teksoz (2007) report similar findings for people in transition economies. See Benz and Frey (2004, 2008), Taylor (2004) and Kawaguchi (2008). Benz and Frey (2008) also reported that employees of small firms (with smaller hierarchies) enjoy greater job satisfaction than employees of large firms. This might partly explain why small-firm employees are willing to earn less on average than workers in large firms (see chapter 10). An untested prediction of this finding might be that managers at the top of a hierarchy should have similar satisfaction levels to entrepreneurs. See Parasuraman and Simmers (2001), Ajayi-obe and Parker (2005) and Schieman et al. (2006). See Cowling (2000), Reynolds et al. (2002) and Williams (2004) for cross-country evidence. Consistent with this, the median age of MIT alumni who start new ventures has declined from 40 in the 1950s to 30 in the 1990s (Hsu, Roberts and Eesley, 2007). Nevertheless, in the population as a whole, entrepreneurs of both sexes remain older on average than employees (Aronson, 1991). See also Carroll and Mosakowski (1987), van Praag and van Ophem (1995), Quadrini (1999), Lin et al. (2000) and Davidsson and Honig (2003). For supporting evidence, see Evans and Leighton (1989b), Dolton and Makepeace (1990), Blanchflower and Oswald (1990), Taniguchi (2002), Dobrev and Barnett (2005) and Djankov et al. (2006). C.f. Evans and Leighton (1989b), Ferber and Waldfogel (1998), Hamilton (2000), Holtz-Eakin et al. (2000), Williams (2000), Bruce and Schuetze (2004) and Landier (2004). For example, while Evans and Leighton (1989b) and Hamilton (2000) estimated positive effects of self-employment experience on future wages in paid employment, Bruce and Schuetze (2004) and Landier (2004) estimated weak and strong negative effects, respectively. Hyytinen and Rouvinen (2008) also detected negative effects of self-employment experience on employee wages in Europe, which they attributed to self-selection of less able types into and out of self-employment. However,

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15.

16. 17. 18. 19. 20.

21.

22. 23.

24. 25. 26.

27.

28.

29.

30.

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Hyytinen and Rouvinen’s (2008) data sample covers only brief spells, which probably oversample negative selectivity effects. Burke et al. (2000), Knight and McKay (2000), Cramer et al. (2002) and Cowling et al. (2004). In countries like Austria and Germany, advanced vocational qualifications at the tertiary level can be a legal prerequisite for self-employed artisans to trade (Lohmann and Luber, 2004). See Bull and Winter (1991), Guesnier (1994), Audretsch and Fritsch (1994a), Parker (2005b) and Acs and Armington (2006). See also Birley (1985), Sanders and Nee (1996) and Florin et al. (2003). See Bond and Townsend (1996), McPherson et al. (2001), Ruef et al. (2003), Aldrich and Kim (2007) and Parker (2009a). See Abell et al. (2001), Djankov, Quan et al. (2005) and Sine et al. (2007). Honig (1996), Gomez and Santor (2001), Brüderl and Preisendörfer (1998) and Bosma et al. (2004) all reported significant positive effects of these measures of social capital on performance. Foreman-Peck et al. (2006) and Guiso et al. (2004) reported insignificant effects using proxies for social capital of trade associations or business clubs and electoral participation, respectively. See Uusitalo (2001), Cramer et al. (2002), Puri and Robinson (2005), Ekelund et al. (2005), Djankov et al. (2006) and Kan and Tsai (2006). Insignificant effects are reported by Tucker (1988) and Parker (2008a). See e.g. de Clercq and Arenius (2006), Koellinger and Minniti (2006) and Levie (2007). See Ilmakunnas and Kanniainen (2001), Mazzeo (2004), Kanniainen and Vesala (2005), Wu and Knott (2006), Wennberg et al. (2007) and Ghosal (2007). Another strategy is to use aggregate proxy variables such as the number of strikes, the inflation rate or low rates of fixed capital formation as measures of economy-wide risk (Parker, 1996; Robson, 1996; and Cowling and Mitchell, 1997). For references, see de Meza and Southey (1996), Camerer and Lovallo (1999) and Coelho et al. (2004). See Baron (1998), Forbes (2005) and Schade and Koellinger (2007). In terms of chapter 2’s terminology, let F(x) be the actual distribution function of the random variable x, and let G(x) be an individual’s subjective distribution function. Suppose higher values of x are preferred to lower values of x. Rational expectations implies F(x) = G(x), whereas overoptimism implies that G(x) first-order stochastically dominates F(x), e.g. the first central moment of the subjective probability distribution is excessively large. This is because an over-optimistic person overestimates the probability of high-x outcomes and underestimates the probability of low-x outcomes. Overconfidence in contrast implies that G(x) second-order stochastically dominates F(x), e.g. the second moment of the subjective probability distribution is excessively small, perhaps reflecting the individual’s misplaced self-confidence in the accuracy of their beliefs. See also Pinfold (2001) and Koellinger et al. (2007). Cooper et al. (1988) sample was of new business founders who had only recently started up, so survival bias is unlikely to contaminate these results. In contrast, survival bias might vitiate evidence about the accuracy of entrepreneurs’ forecasts about future growth (Ashworth et al., 1998; Cassar and Gibson, 2007), which are in any case imprecise measures of over-optimism. See e.g. Landier and Thesmar (2008) and Puri and Robinson (2005). Puri and Robinson (2005) measured over-optimism as the difference between self-reported life expectancy and statistical life expectancy based on smoking-, age-, education-, race- and gender-specific mortality tables. This might explain why some industries are characterised by persistent negative profits, although an alternative reason might be that potential entrepreneurs confuse the median payoff with the mean payoff (Capone and Capone, 1992). In the economics of entrepreneurship literature, positive effects from Evans and Leighton (1989b) and Schiller and Crewson (1997) contrast with negative effects from van Praag and van Ophem (1995) and Blanchflower and Oswald (1998).

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31. See Begley and Boyd (1987) and the review by Rauch and Frese (2000). Lee and Tsang (2001) provide some dissenting evidence – though rates of growth, n-Ach and locus of control were all self-reported in their study. 32. See Boyd (1991), Robinson and Sexton (1994) and Schiller and Crewson (1997). 33. Gruber and Poterba (1994), Hamilton (2000), Wellington (2001, 2006) and Perry and Rosen (2004). 34. Lentz and Laband (1990) obtained a very similar figure based on NFIB data, of 52.5 per cent. 35. Although see de Wit and van Winden (1989, 1990) for contrary evidence. 36. For GEM studies see de Clercq and Arenius (2006) and Levie (2007). A study using data from rural Spain is Lafuente et al. (2007). 37. Dunn and Holtz-Eakin (2000), Hundley (2006) and Colombier and Masclet (2008). 38. See Evans and Leighton (1989b), Lohmann and Luber (2004) and Niittykangas and Tervo (2005). 39. Contrast positive effects in Borjas and Bronars (1989) and Bernhardt (1994) with negative effects in Laferrère and McEntee (1995) and Lindh and Ohlsson (1996). 40. Fairlie (1999), Hout and Rosen (2000) and Fairlie and Robb (2007b). 41. White (1982), Acs and Audretsch (1989), Bhide (2000), Arauzo-Carod and Segarra-Blasco (2005) and van Stel et al. (2007). 42. See Shane (2003, pp. 125–9; 138–143) for a review of business studies articles which support these (rather obvious) points. 43. For evidence, see Jaffe (1989), Jaffe et al. (1993), Audretsch and Feldman (1996), Audretsch and Stephan (1996) and Audretsch, Keilbach and Lehmann (2006), among others. 44. See Armington and Acs (2002) and Acs and Armington (2004a, 2006) for US evidence and Audretsch and Keilbach (2007b) for German evidence. 45. See Audretsch (1995a), Audretsch, Keilbach and Lehmann (2006) and Audretsch and Keilbach (2007b). 46. Audretsch and Acs (1994), Grant (1996) and Carrasco (1999). 47. The formerly unemployed are also one-and-a-half times as likely to exit self-employment in the first year as former employees; but the entry rate exceeds the exit rate for these workers. For other evidence on the high proportion of unemployed workers transitioning to self-employment, see Storey (1982), Hakim (1988, 1989b), Meager (1992b), Blanchflower and Meyer (1994), Reynolds (1997), Kuhn and Schuetze (2001) and Rissman (2003). 48. Evans and Leighton (1989b), Carrasco (1999), Knight and McKay (2000) and Uusitalo (2001). 49. Evans and Leighton (1989b, 1990), Alba-Ramirez (1994), Cowling and Mitchell (1997) and Moore and Mueller (2002). A more complicated inverse-U-shaped relationship appears in Ritsila and Tervo (2002). 50. Farber (1999). See also Gordus et al. (1981), Carroll and Mosakowski (1987) and Laferrère and McEntee (1995). 51. McManus (2000), Taylor (2001), Moore and Mueller (2002). 52. Johnson (1981), Johnson and Rodger (1983) and McManus (2000). 53. See Meager (1992a, 1994) for a critique based on bias caused by mis-measurement of the dependent variable and Parker (2004, note 34, p. 111) for a response. 54. Storey and Jones (1987), Robson (1991) and Foti and Vivarelli (1994). 55. Tervo and Niittykangas (1994), Niittykangas and Tervo (2005) and Tervo (2006). 56. Examples of the former include Blanchflower (2000), Schuetze (2000), Henley (2004), Niittykangas and Tervo (2005) and Tervo (2006). Examples of the latter include Acs, Audretsch and Evans (1994) and Parker and Robson (2004).

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57. E.g. Hamilton (1989), Storey (1991), Audretsch and Jin (1994) and Ritsila and Tervo (2002). 58. See Parker (2005b), Acs and Armington (2006) and Fritsch and Mueller (2007). Fritsch and Mueller (2007) explain more than one-half of the variance in German regional start-up rates in terms of regional start-up rates from fiftteen years earlier. 59. For evidence based on regional firm formation rates from a variety of countries, see Spilling (1996), Kangashargu (2000), Acs and Armington (2006), Fritsch and Falck (2007), Fritsch and Mueller (2007), Nyström (2007), Naudé et al. (2008) and Tamásy and Le Heron (2008). For evidence based on regional self-employment rates, see Boyd (1990) and Schiller and Crewson (1997). 60. See Blanchflower (2000), Fairlie and Meyer (2003) and Santarelli and Vivarelli (2007) for additional evidence. An exception is Russia, where entrepreneurs are more mobile across jobs and locations than non-entrepreneurs (Djankov et al., 2006). While international tax competition can promote the geographical mobility of entrepreneurs (Honkapohja and Turunen-Red, 2007) to date there is only anecdotal evidence (e.g. of French entrepreneurs living in England) that this is a quantitatively important phenomenon. Stam (2007) discusses other factors which bear on entrepreneurs’ mobility decisions. 61. See, e.g., Bygrave and Minitti (2000), Minniti (2005), Landier (2004) and Parker (2005b). 62. See Feldman (2001), Miller (1984) and Rocha and Sternberg (2005). 63. Evans and Leighton (1989a), Reynolds and White (1997) and Schiller and Crewson (1997). 64. Non-German studies include Davidsson (2006), Kim et al. (2006), Wagner (2006b), de Clercq and Arenius (2006) and Langowitz and Minniti (2007). For Germany, compare Wagner (2004), Mueller (2006b), Wagner and Sternberg (2004) and Bergmann and Sternberg (2007). 65. Delmar and Davidsson (2000), Davidsson and Honig (2003), Kim et al. (2006) and Mueller (2006b). 66. Delmar and Davidsson (2000), Reynolds et al. (2004) and Wagner and Sternberg (2004). 67. Davidsson (2006) concludes this from a review of conflicting evidence including Reynolds (1997), Delmar and Davidsson (2000) and Reynolds et al. (2004). 68. For example, contrast Delmar and Davidsson (2000), Davidsson and Honig (2003), Arenius and Minniti (2005) and Mueller (2006b) with US PSED evidence from Kim et al. (2006). 69. See Reynolds and White (1997), Delmar and Davidsson (2000), Arenius and Minniti (2005), Rotefoss and Kolvereid (2005), Wagner (2006b) and Bergmann and Sternberg (2007). 70. See Reynolds (1997), Delmar and Davidsson (2000), van Gelderen et al. (2001), Reynolds et al. (2004), Arenius and Minniti (2005), Kim et al. (2006) and Mueller (2006b). 71. Reynolds et al. (2004), Kim et al. (2006) and Koellinger and Minniti (2006). 72. See, e.g., Carter et al. (2003) for a survey of business studies articles exploring reasons entrepreneurs cite for starting up. 73. See Katz (1990), Delmar and Davidsson (2000) and Henley (2007). 74. Carter et al. (1996), Davidsson and Honig (2003), Delmar and Shane (2004) and Kessler and Frank (2004). According to Reynolds and White (1997, chap. 4), efforts and investment in start-up organising efforts are greatest for those who ultimately launch their ventures. 75. Reynolds and White (1997, chap. 4), van Gelderen et al. (2001) and Kessler and Frank (2004). 76. Compare Davidsson and Honig (2003), Vivarelli (2004), van Gelderen et al. (2006) and Parker and Belghitar (2006). 77. The interested reader is referred to, e.g. Feldman (2003) and Rosenberg (2003). 78. See Feeser and Willard (1989), Dahlstrand (1997) and Hyytinen and Maliranta (2008). 79. Klepper (2001), Gompers et al. (2005), Klepper and Sleeper (2005).

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80. Interestingly, most of the successful spinoffs in the US disk-drive industry were started by engineers (Christiansen, 1993), while the most successful laser spinoffs were founded by entrepreneurs with backgrounds in marketing and sales, rather than in R&D (Sleeper, 1998; see also Roberts, 1991). 81. Dahlstrand (1997), Klepper (2001) and Koster (2005). 82. Cooper and Bruno (1977), Cooper (1985) and Roberts (1991).

5

Ethnic entrepreneurship and immigration

Entrepreneurship among ethnic minorities and immigrants is an increasingly hot topic. There are several reasons for this. It is widely believed that entrepreneurship offers a route out of poverty for disadvantaged groups and opens up opportunities for economic advancement and assimilation. The value of entrepreneurship for ethnic minorities is enhanced to the extent that they face discrimination. Thus Glazer and Moynihan (1970, p. 36) argue that ‘business is in America the most effective form of social mobility for those who meet prejudice’. Also, minority entrepreneurship can promote economic development and job creation in poor neighbourhoods (Bates, 1993, 2006), although it can also be a source of ethnic tension, as has been observed among Korean-owned businesses located in black communities, for example (Yoon, 1991; Min, 1996). It might be helpful to commence with several ‘stylised facts’ about ethnic entrepreneurship. Most of the extant evidence pertains to the USA and the UK, although it should be borne in mind that the rise of ethnic minority and immigrant entrepreneurship is an international trend, driven largely by demographic changes in both developed and developing economies (Ram and Smallbone, 2003). First, the evidence generally shows that a far higher proportion of whites engage in entrepreneurship than blacks do. For example, self-employment rates of whites are between two and three times higher than those of blacks.1 According to Fairlie and Meyer (2002), this differential has persisted since at least 1910, suggesting that little has changed since Myrdal (1944) first bemoaned the dearth of black-owned businesses in America. Second, many non-black ethnic groups have higher participation rates in entrepreneurship than whites do. For example, Clark and Drinkwater (1998) reported that at the start of the 1990s, Chinese, Pakistanis, Bangladeshis and Indians in Britain had substantially higher self-employment rates (of 26.6, 22.8, 17.8 and 19.6 per cent, respectively) than whites did (12.3 per cent). There is certainly pronounced heterogeneity among ethnic groups in terms of their tendency to become entrepreneurs. For example, 1990 US Census data show that non-rural male self-employment rates varied substantially across sixty ethnic and racial groups, both before and after controlling for age, education, immigrant status and length of time spent in the USA (Fairlie and Meyer, 1996). According to the 1990 US Census, only 4.4 per cent of black males worked 163

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for themselves, compared with 27.9 per cent of Korean-American men. Americans of European origin had self-employment rates close to the US average. Members of ethnic groups from the Middle East and neighbouring countries such as Armenia and Turkey also had high self-employment rates; but Hispanics (other than Cubans) had low self-employment rates. Fairlie and Meyer (1996) also highlighted diversity within the ‘black’ ethnic group, with black Africans and Caribbeans having slightly higher self-employment rates than other black Americans (but still below the US average). This finding, together with similar evidence from the UK, cautions against treating ethnic minorities as a single homogeneous group. Third, trends in entrepreneurship also vary across groups, partly in response to demographic shifts. Latino entrepreneurship in the US is a case in point. According to recent data from the US Census Bureau, Hispanics now represent nearly 14 per cent of the US population, slightly above the population share of blacks. Latinos have similar self-employment rates to blacks, yet the number of Latino enterprises grew by 31 per cent between 1997 and 2002, three times the US national average, albeit from a small base.2 Much of this growth has been in low-value self-employment in services, especially among foreign-born and poorly educated Latinos with modest assets – although the number of larger-scale minority businesses with gross revenues exceeding $1 million per annum has also grown three times faster than the overall group (Bates, 2006). A fourth stylised fact about ethnic entrepreneurship in the USA is that black-owned businesses perform less well on average than white-owned businesses do. This is not the case for American-Asian entrepreneurs, though, who do at least as well as whites on average (Bates, 1997). Consider for example the following findings from US studies: • Black male self-employees earn about one-half of white male self-employees; the corresponding figure for Hispanics is about two-thirds (Borjas and Bronars, 1989; Flota and Mora, 2001). • Black-owned firms are less profitable than white-owned firms. According to 1992 CBO data, only 13.9 per cent of black-owned firms recorded annual profits of $10,000 or more, compared with 30.4 per cent of white-owned firms; and nearly 40 per cent of all black-owned firms recorded negative profits (Fairlie and Robb, 2007b). • Sales of black-owned firms are a small fraction of those of white-owned firms. According to 2002 Survey of Business Owner data, annual sales for these groups averaged $74,018 and $437,870, respectively (Robb and Fairlie, 2007). • Black-owned firms have higher closure rates and hire fewer workers than whiteowned firms do. According to CBO data from 1992, black entrepreneurs hired only 0.63 employees on average, compared with 1.80 for white entrepreneurs. Despite these findings, self-employed black and Hispanic men receive higher mean and median total labour incomes than their wage and salary counterparts. So despite underperformance relative to whites, it is still possible that entrepreneurship can provide a route for relative economic advancement for ethnic minorities (Fairlie, 2004).

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A fifth stylised fact is that some immigrant groups can have higher rates of participation in entrepreneurship than natives, although there is mixed evidence across groups and about their relative financial performance (Schuetze and Antecol, 2006). For example, 1990 US Census micro-data show that the self-employment rate of immigrants was 23 per cent higher than that of natives, although Asian immigrants display marked heterogeneity in this respect, with Korean immigrants having the highest participation rates in entrepreneurship (Fernandez and Kim, 1998; Lunn and Steen, 2005). Overall, the US evidence relating to relative immigrant entrepreneurship rates is mixed.3 In Germany, self-employed immigrants earn 40 per cent more than immigrant employees and 7 per cent more than self-employed natives. This suggests that entrepreneurship may be an attractive option for immigrants in that country (Constant and Zimmermann, 2006). But Swedish evidence points to a major earnings disadvantage for immigrant entrepreneurs (Hjerm, 2004). This chapter explores some of the factors underlying these ‘stylised facts’. Both theoretical models and empirical evidence are discussed. Essentially, two hypotheses have been advanced to explain the observed variations in rates of entrepreneurship between ethnic groups. The first hypothesis relates to discrimination, perpetrated either by employers in the labour market, by banks in the capital market, or by consumers in the product market. The next section treats these issues in turn. The second hypothesis, explored in the section after, posits positive factors which can help make entrepreneurship attractive to members of particular minority groups. Then further evidence on the determinants of ethnic minority entrepreneurship is reviewed, followed by a discussion of theory and evidence relating to entrepreneurship among immigrants. Throughout the chapter, M and NM are used to denote ‘minority’ and ‘non-minority’ values of the variables to which they are attached. In particular, wM and wNM denote minority and non-minority wage rates in paid employment, respectively; while πM and πNM denote minority and non-minority profits in entrepreneurship, respectively. 5.1

Discrimination

5.1.1

Discrimination in the labour market If employers have an exogenous taste for discriminating against members of ethnic minorities, M , what are the implications for ethnic entrepreneurship, and entrepreneurial profits of minority members? Previous researchers have proposed two effects of discrimination by employers against employees of ethnic minority status:4

1. By preventing members of minorities from obtaining jobs in paid employment or by restricting them to relatively low-paid jobs, discrimination increases the attractiveness to them of entrepreneurship. In other words, entrepreneurship can act as an ‘escape route’ from employer discrimination, implying greater participation in entrepreneurship for these individuals. 2. Reflecting lower pay in paid employment, the ratio of minority to non-minority average entrepreneurial profits, πM /πNM , should exceed the ratio of minority to non-minority wages, wM /wNM (Moore, 1983b).

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In fact, point 2 is inconsistent with the evidence about the relative earnings of blacks and Hispanics.5 One reason might be that crowding of M s into entrepreneurship competes down the price they can command for their output and hence their profits, πM . Alternatively, even if the distribution of ability within each ethnic group is identical, employer discrimination reduces πM relative to πNM in a free occupational choice equilibrium.6 Another problem with the employer discrimination hypothesis is that point 1 above is inconsistent with the facts about American and British blacks, who have lower participation rates in entrepreneurship than whites do. Although some ethnic minorities (such as Korean-Americans and British-Asians) have above-average entrepreneurship participation rates, this is an unsatisfactory defence of the employer discrimination hypothesis because it fails to explain why employers discriminate against some ethnic groups but not others. Indeed, Lunn and Steen (2005) argue that substantial heterogeneity of rates of entrepreneurship among Asian-Americans alone is sufficient to cast doubt on the discrimination hypothesis, since one can ask why individuals would discriminate against some Asian groups but not others.7 Finally, it is worth pointing out that when markets are inefficient (a ‘second-best’ outcome), discrimination in paid employment can sometimes actually increase social welfare. Chapter 12 explains that some individuals may engage in ‘rent-seeking’ activities, whereby resources are expropriated from productive entrepreneurs. Suppose that minorities (e.g. Jews in medieval Europe) are debarred from profitable rent-seeking activities (e.g. collecting and enforcing taxes) and are pushed into particular commercial activities instead (e.g. diamond trading). This form of discrimination increases productive entrepreneurship among the minority group, to their benefit; while the nonminority rent-seeking group benefits from having a greater source of income to tax away (Murphy et al., 1991). Of course, in a ‘first-best’ world there would be neither rent-seeking nor discrimination.

5.1.2

Discrimination in the capital market

I now turn to research claiming that lenders in the small business credit market discriminate against ethnic minority entrepreneurs. I commence with a brief overview of some theoretical considerations; go on to outline methods used in empirical research on this issue; and then review the evidence and its implications. It is sometimes argued that lenders practice statistical discrimination against some ethnic groups. Statistical discrimination describes the situation where an ethnic group has different characteristics on average from others, which are then used to adversely screen all members of that group. For example, many UK minority-owned businesses establish themselves in sectors such as retailing, transportation and catering, which have above-average failure rates (Bank of England, 1993). And African Americans possess less personal wealth on average than whites do (one-eleventh, according to Robb and Fairlie, 2007) and hence can offer banks less collateral.8 So even if banks do not discriminate on the basis of ethnicity, bank competition may generate lending rules

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that reward high-collateral and safe-sector start-ups with larger loans – resulting in outcomes that resemble discrimination, since blacks will be observed to receive fewer and/or smaller loans on average. Coate and Tennyson (1992) demonstrate how employer discrimination can spill over into statistical discrimination in the credit market. Consider again the simple occupational choice model outlined in chapter 2. If minorities receive a low outside wage, then more of them will enter entrepreneurship but with a lower average ability (see note 6). Lower ability reduces the probability that an entrepreneurial venture succeeds, which forces lenders operating under conditions of competition to increase the interest rate they charge them in order to recoup their losses. This reduces the profitability of ethnic ventures net of interest repayments. Effectively, lenders observe that higher default rates are positively correlated with ethnicity, and so have incentives to practise statistical discrimination.9 Nevertheless, statistical discrimination is illegal under US federal law. Even if bank profits vary systematically with the ethnicity of the borrower, banks are prohibited from conditioning loan decisions and loan terms on ethnicity (Blanchard et al., 2008). I now turn to empirical issues. Recent empirical research is based on two econometric models. One is a binary choice model (see chapter 3) where the dependent variable takes the value 1 if the individual is denied a loan, and 0 if it is granted. The second is a regression model where the dependent variable is the loan rate of interest. Among the independent variables in both models are one or more dummies for ethnicity. If the coefficient on an ethnic dummy is positive and significant in either case, then this is consistent with racial discrimination, provided the specification omits no explanatory variables which might themselves explain adverse ethnic loan outcomes. Any omitted or unobserved variables that explain actual loan application success or interest rates, but which are correlated with ethnicity, may lead to the incorrect inference that discrimination is present. It is therefore essential that rich and detailed data sets are utilised in applied research, in order to minimise the risk of omitted (or unobserved) variable bias. A data set which satisfies these criteria is the US National Survey of Small Business Finances (NSSBF), which contains numerous variables (including, in recent years, credit ratings). Several authors have exploited it to estimate the extent of discrimination in the US small business credit market. For example, Cavalluzzo and Cavalluzzo (1998) estimated loan denial rates using NSSBF data from the late 1980s. They uncovered evidence of substantially and significantly higher loan denial rates forAfrican-Americans than for white Americans, with no significant differences by gender or between whites and Hispanics. Similar results were obtained by Blanchflower et al. (2003), using richer NSSBF data from 1993 and 1998, which also included controls for credit histories, housing and nonhousing net worth. Blanchflower et al. (2003) estimated that black-owned businesses are about twice as likely to be denied credit as white-owned businesses; and that blacks face interest rates that are about one full percentage point higher than those facing whites. This is a substantially larger difference in loan denial rates than is commonly reported by studies of discrimination in the mortgage loan market. This difference

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might be larger still if discouraged black borrowers – who anticipate discrimination – are also taken into account. A distinct though informal impression of discouraged black borrowers emerges from a descriptive study of 235 small businesses in a Chicago ethnic neighbourhood (Bond and Townsend, 1996).10 Further work by Cavalluzzo et al. (2002) and Cavalluzzo and Wolken (2005) suggests that smaller stocks of personal wealth play a modest role in explaining higher loan rejection rates among Asian-American and Hispanic men compared with whites. Cavalluzzo and Wolken (2005, 2005) observe that self-reported credit histories have greater power in explaining ethnic differences in loan denial rates, as does the market concentration of lenders proximate to the entrepreneur. Market concentration implies weaker inter-bank competition and hence greater scope for lenders to discriminate and still make profits – since one might expect discrimination which trades off profits for indulgence in a Beckerian ‘taste’ for discrimination to be unsupportable under perfect competition. Another interesting finding relates to credit scoring. It has been proposed that credit scoring might increase the objectivity of loan decisions (Berger et al., 2005) and hence reduce discrimination. The fact that signs of credit discrimination persist even after controlling for credit histories and credit-scoring practices must therefore cast doubt on credit scoring as a straightforward solution to (statistical) discrimination. In a recent study, Blanchard et al. (2008) survey the literature on ethnic credit market discrimination and conclude that black-owned firms are at least 20 percentage points more likely to be denied access to credit than are equally qualified white-owned firms (see also Bostic and Lampani, 1999; and Cavalluzzo et al., 2002). According to Blanchard et al. (2008), only a minority of studies have found similar evidence of discrimination against Hispanic- and Asian-owned firms. Using 1993 and 1998 NSSBF data, Blanchard et al. (2008) report statistically significant and substantial discrimination against black-owned and Hispanic-owned businesses in loan approval rates. But they find no ethnic differences in interest rates; and evidence of loan decision discrimination disappears for high net worth black-owned firms. What should we make of these findings, drawn from studies which explain at most one-third of the variation in loan rejection rates; yet use a comprehensive set of covariates which approximate those used by small business loan officers in their loan application procedures; and which find that ethnic groups have substantially higher loan rejection rates – all in apparent contravention of US Federal law? On the one hand, the comprehensiveness of the set of control variables used by researchers, which corresponds to the criteria actually used by banks in actual loan application procedures, makes it hard to avoid the conclusion that discrimination exists in the US market for small business credit. According to Blanchard et al. (2008), the most likely explanation of these findings is statistical discrimination. On the other hand, Cavalluzzo and Wolken (2005) acknowledge the possibility that some omitted variable bias remains, owing in part to lack of data about projected cash flows, which loan officers claim to use in practice. Also, loan officers can validate the accuracy of self-reported responses about loan applicants’ financial conditions, which the researcher typically cannot do. And if researchers use a different model of loan decisions from the bankers,

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any mis-specification of the former may resemble credit market discrimination without any actually being present. Another problem with the research findings discussed above is that they are based on ‘reduced form’ specifications which do not disentangle supply from demand factors (Berger and Udell, 2003, p. 317–18). Acknowledging these problems, Blanchflower et al. (2003) propose that a truly convincing test of discrimination should match loan decisions with actual default rates. Despite the need for caution, the recent evidence does seem to suggest that some ethnic minorities, in particular blacks, find it harder to borrow and to become entrepreneurs in the USA. It also bears out earlier research showing that on average blacks are granted smaller loans for start-ups, and have more limited access to venture capital and trade credit than whites do, even after controlling for characteristics such as education and financial assets.11 An implication of this research is that blacks are both less likely to be able to start businesses and more likely to be under-capitalised and therefore vulnerable to subsequent failure than whites (Robb and Fairlie, 2007). In this regard, a striking finding from Bates’ (1991) analysis of 1982 CBO data is that blacks’ failure rates would have been no different from those of whites if blacks had received the same amounts of external finance. The UK evidence seems to paint a somewhat different picture. There, the main financing difference appears not to be between whites and blacks, but between Asians and Afro-Caribbeans. According to Jones et al. (1994), Asians have a higher probability of obtaining a bank loan than Afro-Caribbeans and whites do, and leverage more funds from banks. These findings cast doubt on the proposition that UK banks are guilty of blanket discrimination, though it does beg the question about why Afro-Caribbeans report greater difficulties in raising bank loans than whites do.12 More recent work by Fraser (2007), which employs a similar econometric approach to Blanchflower et al. (2003) (using fewer covariates) detects no signs of significant racial discrimination in access to loans in the UK loan market. If for whatever reason minorities encounter difficulties with raising bank loans, then they presumably have an incentive to raise capital elsewhere. One possibility is venturecapital funds which specialise in financing minority business enterprises. But recent research shows that, despite earning similar yields to those of ‘mainstream’ venture capital companies, only a tiny number of minority entrepreneurs are served by this type of finance (Bates and Bradford, 2007). Other funding possibilities include (i) family finance, (ii) trade credit, (iii) franchising, and (iv) use of minority-owned banks and Rotating Savings and Credit Associations (Roscas). Regarding (i), Afro-Caribbeans in Britain have below-average access to family finance as well as to formal finance. In partial compensation, British Afro-Caribbeans make the greatest use of formal advice and assistance from third parties (Ram and Smallbone, 2003). There is less information about (ii), although evidence from developing countries points to more limited access among blacks (and women) to trade credit, primarily reflecting more limited social networks (Fafchamps, 2000). More promising is (iii). Franchising appears to be an effective way for blacks to avoid credit restrictions, since banks give preferential treatment to loan applicants wishing to set up franchise

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outlets (Felstead, 1991). But there is as yet little hard evidence that members of ethnic minorities choose franchising in order to escape borrowing constraints. I will say more about franchising among blacks in the next subsection. Finally, concerning (iv), there is a small but growing literature on minority-owned banks and Roscas, which have proven especially popular among Chinese, Japanese and Korean immigrant groups in the USA. Some sociologists claim that Roscas have enabled impecunious immigrants to bootstrap their way to business success.13 For example, in Yoon’s (1991) survey of 199 Korean merchants in minority neighbourhoods in Chicago, 27.6 per cent used loans from Korean Roscas, 27.1 per cent used loans from banks and 34.7 used loans from kin. However, this coverage does not seem to extend to national data sets such as the CBO, where Rosca lending appears to be of marginal importance and is associated with smaller and more failure-prone businesses (Bates, 1997). Involvement in Roscas in the USA seems to have peaked in the early part of the twentieth century, becoming progressively less important over time as ethnic groups have gained greater access to formal credit markets (Besley, 1995). Another problem with the proposition that minority-owned banks can solve capital market discrimination problems is that many ethnic groups (including blacks) have not emulated Asian Roscas – which might have been expected to occur if they were so effective. Closer inspection reveals that Roscas’ value is actually rather dubious. Much Rosca finance is short-term and provided at high interest rates that can exceed 30 per cent per annum. This presumably makes a Rosca a lender of last resort to many borrowers. It is also pertinent that many Roscas are designed primarily to encourage savings rather than business investment by members.

5.1.3

Discrimination in the product market

Another possibility is that NM consumers dislike buying goods and services from M entrepreneurs. This can be expected to reduce the latter’s returns in entrepreneurship, and hence the number of M entrepreneurs. Historically, this type of discrimination might explain the proliferation of black personal service companies which catered specifically for black Americans in response to white consumer discrimination in the first half of the twentieth century (Myrdal, 1944). Evidence from US Census data suggests furthermore that disadvantage continued to shape black American entrepreneurship well into the 1970s (Boyd, 1991). There is evidence though that these consumer patterns started to change in the 1980s. Bates (2003) outlines the post-1970s trend among AfricanAmerican entrepreneurs away from traditional niches such as barbershops and ‘mom and pop’ food stores into new lines of business such as finance, business services and professional services. Borjas and Bronars (1989) studied a model of consumer discrimination in which NM consumers are assumed to have a taste for discrimination against M sellers, while M consumers are indifferent to the race of the seller. Their model generates the following testable predictions. First, the mean income of M sellers will be lower than that of NM sellers, directly reflecting consumer discrimination. As a result, skilled Ms have

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greater incentives to enter paid employment than skilled NMs. Unskilled Ms have greater incentives to enter entrepreneurship than unskilled NMs. Second, NM sellers have a higher return to ability than M sellers. These predictions contrast with those of the employer discrimination model and accord with the evidence about low relative minority entrepreneurial incomes cited at the start of this chapter.14 While the consumer discrimination hypothesis appears useful for understanding ethnic differences in entrepreneurship, it seems less suitable for explaining gender differences. As Aronson (1991) pointed out, women are commonly employed in sales jobs, which would not occur if firms knew that consumers discriminated against them. Another problem with the consumer discrimination hypothesis is that black businesses are relatively common in industries patronised by white customers (Meyer, 1990). One reason could be franchising, since franchisors often discourage attempts by franchisees to differentiate their units (Kaufmann and Lafontaine, 1994) – so reducing consumers’ ability to discriminate. Indeed, Williams (1999) found that black entrepreneurs were more likely than any other ethnic group to become franchisees. Williams also estimated that blacks earned more as franchisees than they would as independent business owners, a finding that is also consistent with Borjas and Bronars’ (1989) discrimination model. If Williams’ findings are true more generally, they suggest that franchising could be a successful way of increasing the level of entrepreneurial activity among blacks. Finally, we note that discrimination might not be the only negative factor inhibiting black entrepreneurship. It has sometimes been argued that slavery perpetuated attitudes which are inimical to entrepreneurship. However, in the early and middle parts of the previous century, southerners dominated African-American entrepreneurship, especially in banking, insurance and publishing – which suggests that there was indeed a black entrepreneurial tradition, despite slavery in the American South (Boyd, 2006). Furthermore, attitudes inculcated by slavery cannot explain the rise of predominantly black entrepreneurs in the northern mass-media entertainment industry catering to racially integrated audiences. 5.2

Positive factors

Discrimination can be regarded as a factor which ‘pushes’ members of ethnic minorities into the escape route of entrepreneurship. Another possibility is that ‘pull’ factors can make entrepreneurship positively attractive to members of minority groups. The following pull factors have been proposed: 5.2.1

Positive expected relative returns in entrepreneurship Positive rewards in entrepreneurship, rather than discrimination in paid employment, may explain high rates of entrepreneurship among some ethnic groups (Bearse, 1984). For example, American and British estimates of the binary choice model with relative incomes (see chapter 3) reveal that relative income differences help explain differences in self-employment rates across ethnic lines (Fairlie and Meyer, 1996; Clark and Drinkwater, 2000).

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Selection

Ethnic enclaves ‘Enclaves’are geographical clusters of ethnic group members who form self-supporting economic communities. Enclaves can provide information networks, sources of credit, ‘protected’ or ‘niche’ markets for the output of ethnic entrepreneurs and a steady supply of workers, possibly drawn from close-knit extended families (Light and Bonacich, 1988). Ethnic minority entrepreneurs may know more about the tastes of ethnic consumers, in such ‘protected markets’as clothing, foodstuffs, religious goods and services (Aldrich et al., 1985). These advantages, and the absence of consumer discrimination by co-ethnics, presumably increase the opportunities and ease with which minority group members can operate a business. Set against this argument, however, is the possibility that the scope for expanding operations into broader markets is more difficult for enclave producers. Also, enclaves can foster intense competition among ethnic entrepreneurs, so limiting entrepreneurial opportunities (Aldrich and Waldinger, 1990) and reducing survival prospects (Bates and Bradford, 1992). In contrast, paid employment incomes may be relatively high in enclaves if ethnic employers do not discriminate against members of their own group. Opportunities for profitable entrepreneurship in enclaves may be limited if ethnic disposable incomes are low and consumer demand is modest, while segregation might generate social problems which are antithetical to successful entrepreneurship and exacerbate unfavourable negative stereotypes about ethnicity among non-ethnics (Fairchild, 2008). The available evidence from a range of countries certainly points to a concentration of ethnic minority entrepreneurs in particular industrial sectors. For example, US Census data from 1980 revealed that 27 per cent of self-employed immigrants worked in the retail sector, compared with 17 per cent of the native-born self-employed (Borjas, 1986). Whereas white self-employed people are concentrated in managerial, technical and professional occupations, their black counterparts tend to be concentrated in manual jobs (Becker, 1984; Boyd, 2006). In contrast, self-employment rates of Koreans are relatively high in all industries (Lunn and Steen, 2005). Even more pronounced ethnic industry concentration patterns are observed in the 1991 British Census, with 90 per cent of Asian self-employed people working in services, and only a tiny minority working in the construction sector (Clark and Drinkwater, 1998). Clark and Drinkwater (1998) also reported that 50 per cent of Indian, Pakistani and Bangladeshi self-employed people work in retail distribution, restaurants and taxi driving, while 80 per cent of the Chinese self-employed work in the restaurant industry. A direct empirical test of the ethnic enclave hypothesis examines whether the proportion of an area’s population belonging to one ethnic group positively affects that group’s rate of entrepreneurship in that area. Borjas (1986) pioneered this approach by including as an explanatory variable in a logit model of entrepreneurship participation the proportion of individuals’ local populations which was Hispanic. Borjas estimated that male Hispanics aged 18–64 were significantly and substantially more likely to be self-employed in areas with large Hispanic populations, whereas no such effect was

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detectable for whites. However, subsequent empirical investigations of this issue have generated mixed results.15 The enclave hypothesis also has problems explaining low black self-employment rates, especially since there is evidence in the USA and the UK of strong black networks, including a loyal black customer base and a practice of blacks hiring other blacks (Aronson, 1991; Jones et al., 1994). In contrast, incidentally, members of other selfemployed minority groups (especially Asians) predominantly hire workers from outside their ethnic group (Aldrich and Waldinger, 1990). Nor is the use of family labour confined to ethnic minorities: it appears to be a common practice among all ethnic groups (Jones et al., 1994). Others have argued that enclaves are not a route to entrepreneurial success, and primarily serve as a fallback for marginal ethnic entrepreneurs. Empirical work along these lines demonstrates that successful Asian-American entrepreneurs predominantly serve non-minority clients; raise finance from conventional lenders; and employ nonminority employees. More generally, the success of ethnic American entrepreneurs appears to be attributable not to ethnic resources but to heavy financial and human capital investments (Bates and Dunham, 1993; Bates, 1997). For example, of Asian immigrant entrepreneurs operating young firms in 1987, 58 per cent were college graduates (compared with 38 per cent of non-minority business owners), with an average start-up capital of $53,550 compared with just $31,939 for non-minority business owners (Bates, 1997). Earlier, Bates (1985) had observed that the most successful minority entrepreneurs were located outside of the ‘traditional’ personal service and retail sectors. The same research drew a contrast with the least successful Asian-owned firms, which relied on social support networks in enclaves; it appears that entrepreneurs with a predominantly ethnic minority clientele who are located in areas with large minority populations have significantly lower survival and profitability rates than the average.

5.2.3

Culture Building on Weber’s (1930) ‘Protestant Work Ethic’ thesis, it is possible that attitudes to entrepreneurship are determined by the culture and religion of particular ethnic groups (Rafiq, 1992). Some prominent figures in Islam and the Sikh religion were businessmen; and some Hindu castes specialise in business activities. In Britain, Clark and Drinkwater (2000) found that, all else equal, Muslims, Hindus and Sikhs had significantly higher probabilities of being self-employed than Christians from ethnic minorities. Related to this, some Asian cultures stress self-sufficiency, thrift and hard work, which might help to explain high British-Asian self-employment rates (Borooah and Hart, 1999). However, most empirical studies find little or no effect from religion on entrepreneurial choices.16 Poor command of the host country’s language might increase the likelihood of ethnic entrepreneurship, by restricting employment opportunities in the formal job market

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without affecting trading opportunities among members of one’s own language group (Bates, 1997). Once again, the evidence on the issue derived from binary choice models is mixed.17 Yet host country language proficiency does appear to be associated with profitability in entrepreneurship.18 Another possibility is that belonging to a minority group may create a feeling of insecurity that encourages a drive for entrepreneurial success (Kilby, 1983; Elkan, 1988). 5.2.4

Role models and inculcation of positive attitudes

An absence of black entrepreneurial role models might help explain the lower rates of entrepreneurship among blacks, as well as their lower average entrepreneurial incomes. A lack of black role models within and outside the immediate family can also explain why Hout and Rosen (2000) found intergenerational links in self-employment to be strong for every American ethnic group except blacks (see also the discussion in the next section). Fewer favourable role models could translate into weaker pro-entrepreneurial attitudes among young blacks. For example, a Gallup survey of over one thousand young Americans detected greater ignorance about how markets work among black than among whites; and fewer black youths knew small-business owners than white youths did (Walstad and Kourilsky, 1998). These authors’findings reflect not only lower entrepreneurship rates among blacks, but also specifically an absence of prominent successful black entrepreneurs. It is noteworthy, for example, that black self-employment incomes are similar to those of whites at the three lower quartiles, but are significantly lower than whites’ incomes at the upper quartile (Hamilton, 2000).

5.3

Further evidence on determinants of ethnic differences in entrepreneurship

As the foregoing discussion has demonstrated, the literature has identified both negative and positive factors that impinge on ethnic entrepreneurship. Most of the negative factors are based on some kind of discrimination. As we saw, however, the role of discrimination has been questioned on both theoretical and empirical grounds. In the USA, for example, Bates (1997) concluded that the substantial human and financial capital inputs by Asian-American entrepreneurs relative to blacks help explain the former’s substantially higher rates of participation and average performance in entrepreneurship. And Fairlie (2006b) drew attention to below-average black transition rates from unemployment to self-employment – which is not what one would expect if black entry into self-employment was driven primarily by disadvantage considerations. Do different personal characteristics, or different returns to common personal characteristics, account for the observed differences in rates of entrepreneurship between ethnic groups? In an attempt to answer this question, Borjas and Bronars (1989) estimated what minority rates of entrepreneurship participation would have been if the coefficients from a self-employment probit based on a white sub-sample (i.e. imposing the same returns to characteristics) were applied to non-whites.19 Using this method, Borjas and Bronars (1989) estimated that blacks and Hispanics would have had the

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same self-employment rates as whites, and that Asians would have had a higher self-employment rate than whites. This implies that unobserved differences in rates of return, rather than differences in observable characteristics, account for most of the ethnic variation in self-employment rates. Similar findings have since been recorded by other researchers. For example, relatively low black self-employment rates in the USA cannot simply be explained by a concentration of blacks in low self-employment industries (Fairlie and Meyer, 2000) or by differences in family background (Hout and Rosen, 2000). Unfortunately, it is unclear whether discrimination, cultural factors, or unobserved characteristics are responsible for these different rates of return to personal characteristics by ethnicity. Further research is needed to dig deeper into this question. Entrepreneurship participation rates conflate entry decisions and survival outcomes. Fairlie (1999) demonstrated the importance of separating these influences in order to identify more precisely the causes of the relatively low African-American selfemployment rate. Using PSID data over 1968–89, Fairlie (1999) reported that black entry rates into self-employment were about one-half those of whites, while black exit rates were about twice those of whites. Using a decomposition analysis, Fairlie found that although some variables helped explain lower black entry rates, about twothirds of the gap was unexplained. Limited asset stocks explained about 15 per cent of the entry differential (see also Fairlie and Robb, 2007b), and the lower incidence of self-employed fathers among blacks was also a significant factor. Although Fairlie (1999) had less success explaining higher black rates of exit from self-employment compared with whites, other studies have highlighted the importance of low levels of initial start-up capital (Bates, 1997; Fairlie and Robb, 2007b). There is an important distinction here between recorded businesses and start-up organising efforts (‘nascent entrepreneurship’). As noted in chapter 4, AfricanAmericans have higher rates of entry into nascent entrepreneurship than white Americans do. There is therefore a striking difference between nascent entrepreneurship data and self-employment data in this respect. This difference can be resolved by recognising that African-American nascent entrepreneurs have higher abandonment rates than white nascent entrepreneurs (Parker and Belghitar, 2006). According to Fairlie (1999), education does not significantly affect either selfemployment entry or exit outcomes for black Americans; and he concluded that the scope for policy intervention to increase black self-employment rates is limited. Even if current black asset levels could somehow be quadrupled, that would reduce the ethnic gap in self-employment entry rates by only 13 per cent (Fairlie, 1999). Perhaps such a policy would have larger effects on enhancing the performance metrics outlined at the start of this chapter (Robb and Fairlie, 2007). Finally, there has been much less research on the determinants of growth of ethnicminority-owned businesses. Discussion of the growth of entrepreneurial ventures is deferred until chapter 11; but it is worth mentioning here a recent study of 350 small African-American-owned ‘gazelles’ by Boston and Boston (2007). Defining a gazelle as a company with an annual employment growth rate over five years of 20 per cent

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or more, Boston and Boston (2007) reported that black-owned gazelles are: less likely than black-owned non-gazelles to compete on the basis of price; more likely to serve regional or national rather than local markets; and have fewer black employees on average. It is also notable that black-owned gazelles are more likely to sell to the government sector, which testifies to the continuing importance of public procurement policies towards American ethnic-minority entrepreneurs. We will say more about this issue in chapter 17.

5.4

Immigration and entrepreneurship

Are immigrants more likely to become entrepreneurs than native-born workers (referred to as ‘natives’ henceforth)? The first subsection below proposes several reasons why they might be, and summarises evidence about immigrants’ rates of participation in entrepreneurship. The second subsection reviews evidence about the determinants of immigrant entrepreneurship. The third subsection discusses the effects of immigration on entrepreneurship. These effects include short-term effects of competition on native entrepreneurs, as well as longer-term implications for immigrant entrepreneurs who return to their country of origin. At the outset, it is important to distinguish between within-country migrants from another region (‘in-migrants’) and migrants from another country altogether (‘immigrants’). The emphasis in this chapter is on immigrants rather than in-migrants, although one study has found little difference between the two groups in terms of nascent entrepreneurship in the UK, suggesting that ‘it is the effect of migrating, not the crossing of international borders, that influence the propensity to engage in new business activity’ (Levie, 2007, p. 164).

5.4.1

Immigrants’ entrepreneurial propensities It has been suggested that immigrants are likelier than natives to be entrepreneurs, for the following reasons:

1. On average, immigrants are better educated and motivated than natives. 2. Immigrants have access to ‘ethnic resources’and social capital (Light, 1984), including a tradition of trading, access to low-paid and trusted workers from the same ethnic group, and access to a ready market of niche products within an ethnic enclave. 3. Some immigrants are ‘sojourners’, who wish to immigrate temporarily in order to accumulate wealth before returning to their homeland. Entrepreneurship may be the most effective means to this end. 4. Immigrants turn to entrepreneurship because of ‘blocked mobility’ in paid employment, owing to language difficulties, discrimination or possession of non-validated foreign qualifications. 5. Immigrants are self-selected risk-takers by virtue of their willingness to leave their homeland to make their way in a foreign country.

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6. Among illegal immigrants, entrepreneurship in the form of self-employment may be a means of escaping detection by the authorities. 7. Immigrants enter industries and occupations that have high rates of entrepreneurship. Empirical studies, which mostly measure entrepreneurship as self-employment, have generated diverse findings about whether immigrants are more or less inclined to entrepreneurship than natives. Both Borjas (1986) and Lofstrom (2002) claimed to find higher self-employment rates among immigrants than natives in the USA, while Brock and Evans (1986) found no such pattern.All of these studies used US Census data. In their review of US studies about whether immigrants have higher self-employment rates or not, Levie and Smallbone (2007) concluded that the evidence is inconclusive. But in a recent comparative study, Schuetze and Antecol (2006) calculated that immigrant self-employment rates are 1 per cent higher than native rates in the USA, 2 per cent higher in Australia and 4 per cent higher in Canada. It has been argued that immigrant rates of entrepreneurship in a ‘host’ country reflect rates of entrepreneurship in a ‘home’ country. Light (1984) emphasised the diversity of self-employment experience among immigrants by ‘home’ country, which he attributed to different traditions of commerce. This theme was taken up by Yuengert (1995), whose analysis of 1980 US Census data indicated that immigrants from countries with relatively high self-employment rates were likelier to become self-employed in the USA. Yuengert (1995) estimated that 55 per cent of the immigrant-native self-employment rate differential was attributable to immigrants having above-average home-country self-employment rates. Although some supporting evidence comes from Hammarstedt (2001), for Sweden, other researchers have obtained contrary evidence (e.g. Fairlie and Meyer, 1996; van Tubergen, 2005). The nature of immigrant entrepreneurship appears to be changing. The labour economics immigration literature emphasises variations in ‘quality’among different cohorts of immigrants. For example, it has been suggested that shifts in US immigration policy which have increasingly prioritised family reunion criteria over human capital skill contributions have been responsible for a decline in immigrant ‘quality’ as measured in terms of earnings. Recent evidence suggests that in the last twenty years, immigrant cohorts to Western countries have become less rather than more likely to engage in entrepreneurship20 and now receive lower incomes there (Mora and Dàvila, 2006). These findings are consistent with the notion of declining quality of immigrants, at least in terms of entrepreneurship. Consequently, when studying this issue it is important to distinguish between ‘assimilation effects’, which capture the extent to which individuals within a given cohort assimilate into the host country, and ‘cohort effects’, which capture the possibility that cohorts differ in quality. To disentangle the two effects, Borjas (1986) proposed a useful decomposition, which is described in the chapter appendix. 5.4.2

The determinants of immigrant entrepreneurship Consider again the list of potential reasons why immigrants might be likelier than natives to become entrepreneurs. For point 1, the evidence shows that the relationship

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between education and entrepreneurship varies across ethnic groups. On the one hand, immigrant Koreans to the USA not only have relatively high self-employment rates (at 12 per cent, more than double that of any other immigrant group), but they also tend to be more educated than average. And a non-US study by Vinogradov and Kolvereid (2007) found that more educated immigrants were significantly more likely to become selfemployed in Norway. But on the other hand, the least-educated Latino immigrants to the USA have the highest entrepreneurial participation rates, in contrast to the experience of Korean-American immigrants.21 Other evidence shows that immigrant business owners tend to obtain most of their finance from personal equity and mainstream lenders, rather than from social resources. Echoing findings from the ethnic entrepreneurship literature reviewed above, immigrant businesses depending on social resources tend to be ‘marginal’and more prone to failure (Bates, 1997).This is contrary to point 2 above.Yet it is also known that immigrants prefer to settle in locations where there are already numerous co-ethnics, as noted by Mora and Dàvila (2006) for Mexican immigrants to the USA. Waldinger et al. (1990) argue that aspiring immigrant entrepreneurs are likely to run ‘marginal’ businesses catering to ethnic or urban poor customers; to sell ethnic products to the general population or unstandardised manufactured goods; and to operate labour-intensive businesses. Another incentive (at least for Mexican-American immigrants) is to turn entrepreneur in order to exploit cross-border trading opportunities. US Census data from 2000 reveal that Mexican immigrants have significantly higher self-employment rates in US locations close to the US–Mexico border than their counterparts in the rest of the USA – and also than non-Hispanic whites in border cities (Mora and Dàvila, 2006). This is so even after controlling for socio-demographic factors, including local unemployment rates. But ‘crowding effects’ have reduced the incomes of such entrepreneurs, apart from those who still primarily operate in Mexico running cross-border businesses. The latter enjoy a significant earnings premium (Mora, 2006). Hence policies restricting trade and labour flows across the US–Mexico border might inadvertently retard Latino entrepreneurship. In summary, neither of the first two hypotheses in the list of potential reasons for immigrant entrepreneurship receives decisive support. Furthermore, contrary to ‘sojourner theory’ (point 3), entrepreneurship is not necessarily a better way of accumulating wealth than paid employment. While some studies claim that immigrants earn more in self-employment than in paid employment (Borjas, 1986; Lofstrom, 2002), the income experiences of immigrants can vary considerably, and sometimes entail disadvantage (Borjas and Bronars, 1989; Portes and Zhou, 1996). An important aspect to this debate appears to be the duration of residence in the host country. Both Brock and Evans (1986) and Lofstrom (2002) estimate that longer self-employment spells in the host country by immigrants eventually reverse initial earnings disadvantage relative to natives. This contrasts with immigrant employees, whose relative earnings disadvantage tends to persist throughout their lifetimes. Another problem for the sojourner theory is that many immigrants ultimately choose to remain in the host country, whatever their original intentions were. Fairlie and Meyer

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(1996) report that immigrants who had been in the USA for over thirty years had higher self-employment rates than immigrants who had been in the USA for less than ten years and who were presumably more likely to be sojourners.22 Immigrant selfemployment rates which increase with length of residence might reflect not only the positive income-duration relationship mentioned above, but also other factors. These might include: (i) greater knowledge of labour markets, tastes of ethnic groups, and institutions within the host country; (ii) accumulation of wealth required for entry into entrepreneurship and growth of a venture; and (iii) greater access to factors of production. At any event, findings that immigrant entrepreneurs have longer periods of residence than non-entrepreneur immigrants (Hjerm, 2004) cast serious doubt on sojourner theory. Point 4 above, which stresses restricted opportunities for immigrants in paid employment, has received somewhat less research attention. There are certainly assertions that disadvantage in the formal labour market pushed Koreans in Atlanta and Chinese in Vancouver into self-employment (Min, 1984; Wong and Ng, 2002). But there is little hard evidence that this operates on a systematic, large-scale basis. By way of indirect evidence, it is noteworthy that Koreans and Cubans are not only remarkably successful immigrant entrepreneur groups in the USA, but also comprise numerous people who were formerly professionals and entrepreneurs in their home countries. It might therefore be less surprising that these groups have done so well in the USA, though their self-perceptions can apparently entail status loss and feelings of failure (Godley, 2006). The importance of language is highlighted by evidence of a German-language income premium to immigrants to Germany of 38 per cent (Constant and Shachmurove, 2006). To the best of my knowledge, research has yet to explore points 5 and 6 above. Point 7 has not received much attention either, although US evidence suggests that self-employed immigrants and natives have similar industry distributions – apart from there being more natives in US construction and professional services, and fewer in retail relative to immigrants (Fairlie and Meyer, 2003). To summarise, the evidence provides relatively little support for the theoretical determinants of immigrant entrepreneurship listed above. This highlights serious limitations in our current understanding of this issue. Nevertheless, empirical research has uncovered several other determinants. A notable study is by van Tubergen (2005), who pooled census data from three ‘classic immigration’ countries (Australia, Canada and the USA) and labour force surveys from fourteen EU countries. The data spanned a range of years from 1980 to 2002 and contained data on 150,000 male immigrants from about 180 origin groups in seventeen destination countries. Van Tubergen (2005) found that immigrants from non-Christian countries of origin have the highest self-employment rates. Higher unemployment rates among natives increase the probability of immigrants being self-employed; and self-employment rates are higher among immigrant groups that are small, highly educated and have a longer settlement history. Constant and Shachmurove (2006) also report that high local unemployment rates are associated with immigrant self-employment – perhaps in response to fewer opportunities in the formal labour market. In most respects, though, the

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determinants of immigrant entrepreneurship appear to be rather similar to those of native entrepreneurship (Constant and Zimmerman, 2006).

5.4.3

The effects of immigration on entrepreneurship

Two effects of immigration on entrepreneurship are considered below. One is the effect of immigration on native entrepreneurship and entrepreneurial earnings. This can be a politically charged issue in the host country. The second is the effect of immigrant entrepreneurship on the propensity for immigrants to become entrepreneurs when they return to their home country. In theory, the effects of immigration on native entrepreneurship could be either positive or negative, depending on whether immigrants have a greater impact on the supply of entrepreneurship or on the demand for the goods produced by entrepreneurs. In the first case, the effects are negative, since they compete down incomes in entrepreneurship and hence reduce the incentives for natives to become entrepreneurs. In the second case, the effects are positive, since greater demand increases incomes in entrepreneurship and hence strengthens everyone’s incentives to turn entrepreneur. To date, one of the few studies to estimate the effects of immigration on native entrepreneurship is Fairlie and Meyer (2003). Using 1980 and 1990 US Census data, and measuring entrepreneurs as the self-employed, these authors estimated that between 0.37 and 0.85 native men and between 0.09 and 0.19 native women are displaced by each self-employed immigrant.23 The effects of immigration on average native selfemployment earnings were found to be positive, however. The strongest displacement effects were found among less educated native workers, suggesting that self-employed immigrants might be pushing marginal low-income self-employed natives out of selfemployment, thereby increasing average earnings in the native group as a whole. As an illustrative example, Fairlie and Meyer (2003) cite cheap immigrant-owned restaurants driving out cheap native-owned restaurants, while leaving high-end native-owned restaurants unaffected. Turning to the second issue, there is growing evidence that migrants who return to their home country are more likely than average to become entrepreneurs shortly afterwards (whether or not they were entrepreneurs in the host country), and to use savings accumulated abroad as start-up capital.24 For example, Dustman and Kirchkamp (2002) found that one-half of a sample of Turkish emigrants returning from Germany started a micro-enterprise within four years of resettling in Turkey, using money saved while working abroad. Woodruff and Zenteno (2007) emphasise the importance of migration networks for Mexican entrepreneurs. Instrumenting migration networks by the aggregate rate of migration to the USA in an individual’s state of birth in Mexico, Woodruff and Zenteno (2007) report a strong and significant positive effect of migration networks on investment and profits in Mexican enterprises. Finally, it is interesting to speculate on the role of immigration policy with respect to entrepreneurship. Immigration selection is less reliant on skill-based screening criteria in the USA compared with Canada and Australia (Schuetze and Antecol, 2006).

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Yet relative earnings of immigrant entrepreneurs are higher in the USA than in these comparator countries. Arguably, then, immigration policy may have less impact on participation in entrepreneurship and entrepreneurial performance than other factors do, such as the tax and welfare system, or the size of the domestic market. 5.5 Appendix 5.5.1

The Borjas (1986) decomposition Let pˆ t,j denote the predicted probability of entrepreneurship for a representative member of cohort j at time t, and let pˆ t,j+10 be the probability of entrepreneurship at t for a representative member of a cohort who arrived in a host country ten years later (say) than members of j. Then     (5.1) pˆ t, j − pˆ t, j+10 = pˆ t, j − pˆ t−10, j + pˆ t−10, j − pˆ t, j+10

measures the cross-section difference between members of different cohorts at time t. The first term on the RHS of (5.1) measures the ‘within cohort j’change in entrepreneurship probability since year t − 10 (‘the assimilation effect’). The second term measures the change in entrepreneurship probability for immigrants with the same number of years’ experience since immigration (the ‘cross-cohort effect’). Borjas recognised that (5.1) could be biased if changing aggregate labour market conditions altered the attractiveness of entrepreneurship for everyone. To control for secular changes in broader labour market conditions, Borjas suggested decomposing changes in immigrant entrepreneurship relative to native-born entrepreneurs, the latter being denoted by subscript j
:     pˆ t, j − pˆ t, j+10 = pˆ t, j − pˆ t−10, j − pˆ t, j
− pˆ t−10, j
    + pˆ t−10, j − pˆ t, j+10 − pˆ t−10, j
− pˆ t, j
. (5.2) The first term in square brackets is a refined measure of the assimilation effect. It measures the change in entrepreneurship propensities of a given cohort net of the change experienced by a similar native-born cohort. Likewise, the second term measures the cross-cohort effect on entrepreneurship propensities net of economy-wide changes experienced by native-born workers between t − 10 and t. Using a sample of US Census data on male workers, and selecting t = 1980, Borjas reported substantial variation in the magnitude of the two terms of (5.1) and (5.2) across ethnic groups. Yet two common features held for almost all groups. First, the first term on the RHS of (5.2) indicated a strong assimilation effect, suggesting that the relative attractiveness of self-employment increases the longer the individual has been living in the USA. Second, the cross-cohort effect (the second term) usually indicated a greater propensity for more recent immigrant cohorts at that time to choose self-employment relative to earlier cohorts. This is reflected in the higher self-employment rates among more recent immigrants.

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An advantage of Borjas’ decomposition technique is that it separates two important and distinct aspects of immigrant self-employment propensities. But it has yet to be widely adopted by researchers in the field, the occasional exception (e.g. Schuetze and Antecol, 2006) notwithstanding.

Notes 1. See, e.g., Clark and Drinkwater (1998) for evidence from British Census data; and Fairlie (1999) and Robb and Fairlie (2007) for US evidence. 2. See Fairlie (2006b), Lofstrom and Wang (2007) and Robles and Cordero-Guzman (2007). 3. Contrast Light (1984) and Waldinger (1986) with Borjas (1990) and Bates (1994b). 4. Sowell (1981), Moore (1983b) and Metcalf et al. (1996). 5. See Moore (1983b), Borjas and Bronars (1989), Fujii and Hawley (1991) and Clark and Drinkwater (1998). 6. This could occur if entrepreneurs’ profits are an increasing function of entrepreneurial ability x: πj = π(xj ) (j = {M , NM }), with ∂πj /∂x > 0 ∀j. To see how, let the distribution function of x, namely G(x), be the same for each group. Recall that wNM > wM because of employer discrimination. Denote the marginal entrepreneur in each ethnic group, i.e. who is indifferent between paid employment and entrepreneurship, by x˜ NM and x˜ M, respectively. These individuals are defined by the equalities wNM = π(˜xNM )

7.

8.

9.

10.

11. 12. 13.

and

wM = π(˜xM ).

Now wNM > wM ⇒ π(˜xNM ) > π(˜xM ), i.e. the minority marginal entrepreneur is less able, and less well remunerated, than the non-minority marginal entrepreneur. There are also more M than NM entrepreneurs, since 1 − G(˜xM ) > 1 − G(˜xNM ). In contrast, employers do appear to discriminate against individuals with previous criminal convictions, who are significantly more likely to be self-employed: see Fairlie (2002) for evidence from the NLSY (National Longitudinal Survey of Youth). A striking statistic, cited by Robb and Fairlie (2007), is that fully one-half of all black households in America have net worth (measured in terms of housing equity, savings, retirement and mutual funds accounts and other assets) of less than $6,200. As it stands, a higher proportion of M s than NM s is predicted to enter entrepreneurship in the Coate and Tennyson (1992) model – which is contradicted by the empirical evidence for blacks cited earlier. Coate and Tennyson (1992) showed that this prediction can in principle be overturned if entrepreneurial ability is partly determined by human capital investments since, facing employer discrimination, it is rational for Ms to acquire less human capital than NMs. If this reduces M entrepreneurs’ returns πM by more than it reduces wM (see chapter 13), then fewer Ms might choose entrepreneurship than NMs. Bond and Townsend (1996) report scant use of bank finance: in fact, only 11.5 per cent of respondents use it. Half of the respondents in this study used only personal resources, primarily personal savings, to start their businesses; a majority cited ‘lack of need’ as the main reason for not trying to get some kind of loan. Bond and Townsend (1996) concluded that formal sector loans are insufficiently flexible to meet the needs of business borrowers in poor neighbourhoods, with a clear lack of interest from the community, while informal sector funds are insufficient to meet all needs. See Knight and Dorsey (1976), Bates and Bradford (1992), Bates (1997) and Coleman (2005). Jones, McEvoy and Barrett (1994), Bank of England (1999) and Smallbone et al. (2003). Light and Bonacich (1988), Aldrich and Waldinger (1990) and Yoon (1991).

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14. Also consistent with this argument, Borjas and Bronars (1989) estimated significant positive selection into self-employment amongst whites, significant negative selection amongst Hispanics and Asians (see also Flota and Mora, 2001), but zero selection amongst blacks. 15. Some studies offer support for this hypothesis (Boyd, 1990, 1991 – for blacks; Le, 2000; Flota and Mora, 2001; Lofstrom, 2002) but others fail to find significant effects (Borjas and Bronars, 1989; Boyd, 1990 – for Asians; Yuengert, 1995; Razin and Langlois, 1996; Clark and Drinkwater, 1998, 2000, 2002). Fairchild (2008) is a recent study finding a significant negative effect, with both black and white self-employment rates being higher in less segregated areas, though blacks gain more than whites from lesser segregation in this respect. 16. See Pickles and O’Farrell (1987), O’Farrell and Pickles (1989), de Wit and Winden (1989) and de Wit (1993). Exceptions are Carroll and Mosakowski (1987) and Clark and Drinkwater (1998). Cultural factors might deter female ethnic self-employment though. Clark and Drinkwater (2000) found that in Britain, ethnic female self-employment rates were substantially below those of males, except among Chinese people. 17. Some studies find that poor English language skills increase self-employment participation in the USA and the UK (Boyd, 1990 – among Asians but not blacks; Fairlie and Meyer, 1996; Portes and Zhou, 1996; Clark and Drinkwater, 2002) but others find the opposite (Evans, 1989; Flota and Mora, 2001; Lofstrom, 2002). 18. Flota and Mora (2001) provide US evidence; Constant and Zimmermann (2006) provide German evidence. 19. To make this precise, consider the probit equation (3.7), and (suppressing the intercept purely for notational clarity) let βˆNM denote the estimated coefficients of that equation obtained using a purely non-minority data sample. Then for a set of personal characteristics denoted by Wi , the predicted probability of M self-employment given the same returns to characteristics across ethnic groups would be  (βˆ
Wi ) NM pˆ M = , (5.3) n i∈M

20. 21. 22. 23. 24.

where n is the sample size, (·) is the cumulative distribution function of the normal distribution, and where the summation takes place over all persons in the minority group, M . Different versions of (5.3) have been suggested by Clark and Drinkwater (1998), Clark et al. (1998) and Borooah and Hart (1999), but with similar qualitative findings. See Fernandez and Kim (1998) for US evidence; Hammarstedt (2006) for Swedish evidence; and Schuetze and Antecol (2006) for evidence from the USA, Canada and Australia. See Fairlie (2006b), Lofstrom and Wang (2007) and Robles and Cordero-Guzman (2007). For complementary evidence, see Lofstrom (2002), Schuetze and Antecol (2006) and Vinogradov and Kolvereid (2007). Earlier work by the same authors (Fairlie and Meyer, 1996) detected insignificant effects of immigration on numbers of black self-employed natives. See Ilahi (1999), McCormick and Wahba (2001), Dustman and Kirchkamp (2002) and Mesnard (2004) for evidence from Pakistan, Egypt, Turkey and Tunisia, respectively.

6

Female entrepreneurship

An important topic on the entrepreneurship research agenda is gender differences between men and women entrepreneurs. There is growing awareness that, for a variety of reasons, women face different opportunities and constraints in entrepreneurship from men; and that these considerations affect their participation and performance in entrepreneurship. As with ethnic minority entrepreneurship, there has been a concern that women’s prospects in entrepreneurship might be shaped by discrimination. Discrimination in the workplace might promote entrepreneurship among women in an effort to escape it, while discrimination against women by lenders might impede entrepreneurship by restricting access to finance. This chapter has the following structure. The first section describes some basic facts about female entrepreneurship, starting with cross-country evidence about its prevalence and some descriptive findings about gender differences in industry composition and personal characteristics. The second section explains how family factors bear on female entrepreneurship, including marriage, household production and child-rearing. The third section is devoted to the performance of women entrepreneurs, in absolute terms and relative to men. A gender performance gap is identified, and several potential explanations are discussed. The fourth section briefly treats the subject of women and entrepreneurial finance. The final section concludes. 6.1

Some basic facts about female entrepreneurship

Whether entrepreneurship is defined in terms of new venture creation, business ownership or self-employment, a higher proportion of men than women engage in entrepreneurship in all developed economies, despite a recent trend increase in female entrepreneurship in many of them. Among American workers aged twenty-five and over in 2003, for example, 9.2 per cent of women and 15.5 per cent of men were selfemployed. Yet over the period 1975–95, female self-employment grew by 60 per cent compared with 20 per cent for men (Budig, 2006). To put this in perspective, female selfemployment grew from about one-quarter of the US non-agricultural self-employed workforce in 1975 to about one-third by 1990 (Devine, 1994a). According to Aronson (1991) this represents a continuation of a trend increase since at least 1955, when the 184

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female share of the US self-employed workforce stood at only 12 per cent. However, recent CPS data suggests that the growth in the relative female self-employment rate ceased in the 1990s (Wellington, 2006). Women entrepreneurs are also in the minority in Europe, although female selfemployment rates in the EU vary considerably, from just over 20 per cent in the UK, Ireland and Sweden to 40 per cent in Belgium and Portugal (Cowling, 2000). In Italy, women are one-half as likely as men to enter but are significantly more likely to exit self-employment. While Italian men tend to be more likely to enter self-employment following layoff or for career advancement considerations, women are more likely to enter from inactivity or unemployment (Rosti and Chelli, 2005). There is spotty evidence about trends in female entrepreneurship in European countries, though analogous to Wellington’s (2006) American study, Ajayi-obe and Parker (2005) observed stasis in the female self-employment rate in Britain in the 1990s, which has settled at around 7 per cent of the female workforce there. The gender gap in entrepreneurship is less pronounced using alternative definitions of entrepreneurship. For example, Brush (2006, chap 23.3) calculated that 10.6 million of the approximately 22 million firms in the USA are at least 50 per cent owned by women, and claims that women’s participation in entrepreneurship is growing in most countries. Data from the Centre for Women’s Business Research also show that American women formed businesses at twice the national rate over 1997–2002 (Greene et al., 2007). By 1997, female-owned firms employed 23.8 million workers, an increase of 262 per cent over 1987–92 (Coleman, 2007, p. 303). The figures mentioned so far relate to numbers of participants in entrepreneurship. Another salient consideration is work hours, in particular whether women entrepreneurs participate in a part-time or full-time capacity. Evidence shows that American and British self-employed women are more likely to be part-time workers than women employees or men in either employment category. For example, calculations made from the BHPS data set by the present author reveal that females comprise only 16 per cent of the full-time, but as much as 70 per cent of the part-time, self-employed workforce in contemporary Britain. In the USA, self-employed women work either very long or very short working weeks, leading to greater overall dispersion in weekly work hours than is observed for other groups (Devine, 1994a, 1994b; Budig, 2006). Indeed, NLSY data over 1979–98 reveal two apparently distinct groups of women entrepreneurs, with one group working less than fifteen hours per week in their business and the other working more than forty-one hours per week. Budig (2006) argues that the first group of women engage in non-professional self-employment primarily to limit their work hours and juggle family commitments (possibly because non-professional waged jobs tend not to be family-friendly); whereas the second group enters professional selfemployment to advance their careers. These arguments are based on evidence that family factors (especially children) help explain women’s entrance into non-professional self-employment, but (as in the case of males) have little impact on female entry into professional and managerial self-employment. Budig concluded that ‘women entering self-employment in professional occupations are more similar to their male peers in

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self-employment than they are to women entering non-professional self-employment’ (2006, p. 2235). The industry composition of business ventures also has a gender dimension. Women entrepreneurs remain heavily over-represented in a few industry sectors, notably ‘sales’ and ‘other services’ (which include financial, insurance and real estate; professional services; and business services: Bates, 1995). Few self-employed women are found in the construction industry, which remains dominated by males. The concentration of self-employed women in service industries is particularly pronounced, being greater than that of either female employees or either category of males. To the extent that entrepreneurs identify opportunities to start businesses of similar types and in similar industries in which they formerly worked, one might be able to explain some of the concentration of women entrepreneurs in particular industry sectors. But as Aronson (1991) pointed out, the secular growth in services pre-dated the growth in female self-employment and hence cannot explain it. And between 1975 and 1990, growing female entrepreneurship in America was accompanied by few changes in occupational or industrial concentrations. Turning next to human capital, the evidence shows that British self-employed women possess more advanced educational qualifications on average than self-employed men. The exception is the employer group, where men have higher academic educational qualifications on average and women tend to have more vocational qualifications (Cowling and Taylor, 2001). The education qualifications of American nascent entrepreneurs are similar by gender (Kepler and Shane, 2007). On an individual level, the bulk of US studies find that advanced education and skills are associated with female entrepreneurship.1 Consistent with this observation is the pronounced concentration of female paid employees in clerical and administrative jobs which require less advanced qualifications and which yield work experience that is arguably ill-suited to switching into entrepreneurship (Boden, 1996). There are gender differences in other aspects of human capital, too. Descriptive evidence from the business studies literature suggests that male entrepreneurs are more likely than female entrepreneurs to have education and experience with technical and managerial elements (Brush, 1992). The same review showed that male entrepreneurs are more likely than female entrepreneurs to have been employed prior to start-up; they also have more prior work and business experience on average (see also Kepler and Shane, 2007). American women business owners tend to be older than women employees, although this difference narrowed during the 1980s (Devine, 1994a). Other reviews of the business studies literature on female entrepreneurship suggest that female entrepreneurs’ demographic characteristics, business practices and motivations appear to be broadly similar to those of male entrepreneurs. There are in contrast greater differences between genders in relation to choices of industry sector, as noted above; in amounts of start-up capital deployed; in management style; and in sales performance (Birley, 1989; Brush, 1992, 2006). Some of these differences, especially those relating to access to finance and financial performance, will be explored further below.

Female entrepreneurship

6.2

187

Family factors

There are several interrelated family issues which affect female entrepreneurship. They include marriage and household production, and child-rearing. Despite their inter-relatedness, it is convenient to discuss them sequentially. 6.2.1

Marriage and household production It is scarcely possible to understand female entrepreneurship without taking account of the importance of marriage (or cohabitation, which is subsumed under ‘marriage’ hereafter). Self-employed American women are significantly more likely than women employees to be married with a spouse present and to be covered by somebody else’s health insurance.2 To give some illustrative numbers, ‘a much larger proportion of selfemployed women (81 per cent) than of wage-and-salary women (64 per cent) is married, and these proportions are fairly stable on an age-adjusted basis’ (Lombard, 2001, p. 214). The figures are especially stark for self-employed women who employ others (‘job creators’), with nine out of every ten female job creators in Britain being married, predominantly to income-earning husbands. These women have fewer children on average than their sole-trader female counterparts (Cowling and Taylor, 2001). In terms of entry propensities, the presence of a self-employed husband approximately doubles the probability that an American woman switches into self-employment (Bruce, 1999). These findings should be interpreted with caution. Evidence that marriage and children are significantly and positively associated with entrepreneurship among women is sometimes interpreted to mean that women use entrepreneurship primarily to balance work and family commitments, whereas men use it to advance their careers (Carr, 1996). But positive relationships between marriage and entrepreneurship are also observed for men (q.v. Table 4.1 in chapter 4). Chapter 4 outlined several reasons why marriage is associated with entrepreneurship for both men and women. A couple of additional reasons might be germane to women in particular. One relates to the fact that husbands systematically contribute less to household production than women, even when the woman is working, and whether or not they are engaged in paid employment or entrepreneurship.3 With less time to spend on formal work, part-time entrepreneurship can offer married women the flexibility to juggle home and work commitments. Second, to the extent that women end up in low-paying non-professional service sector self-employment, marriage can provide a valuable financial cushion which possibly makes such choices viable. On a related point, explored further in section 6.3 below, men operate larger and more profitable enterprises on average than women do. In which case, marriage might enable women to work as ‘joint entrepreneurs’ in their husband’s business; and husbands might be more tolerant of a wife taking time out for household production than a typical employer. It is less clear what has the greatest impact on a woman’s choice to be an entrepreneur: the presence of a husband of any kind; the presence of a husband who is an entrepreneur; or the presence of a husband who earns a high income. Some studies find that both the presence of a self-employed husband and a husband’s income have separate positive

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effects, with the former being stronger than the latter (Caputo and Dolinsky, 1998; Bruce, 1999). Budig (2006) observed that self-employed women with self-employed husbands tend to be non-professionals in clerical and service occupations, especially childcare, which might offer better opportunities to combine work and family responsibilities. Other researchers however have detected larger effects from incomes than from husbands’participation in entrepreneurship (MacPherson, 1988; Clain, 2000). For example, in Clain’s (2000) analysis of full-time, full-year workers drawn from 1990 US Census data, marital status was significantly associated with self-employment for men, while for women, self-employment was primarily associated with their spouse’s income. It is important not to overlook the possibility that female entrepreneurship can also promote male entrepreneurship, through higher female self-employment incomes (Lombard, 2001) or knowledge spillovers (Parker, 2008a). With the exception of Parker (2008a), the impact of female entrepreneurship on male entrepreneurship has received relatively little attention from researchers. 6.2.2

The impact of children

If externally provided childcare is costly, entrepreneurship might be attractive to women by offering a flexible way of combining work and domestically provided childcare. This is especially true if women can run their businesses from home: ‘time spent in home-based work may have a non-monetary benefit, such as being able to spend more time with one’s children’ (Edwards and Field-Hendrey, 2002, p. 172). The notion that entrepreneurship offers the flexibility to combine childcare and other domestic duties with a work schedule will be referred to hereafter as the ‘flexibility hypothesis’. A crude test of this hypothesis is to look for a positive association between children and female entrepreneurship; more sophisticated tests entail in-depth comparisons of the work and childcare choices of women entrepreneurs and non-entrepreneurs. On the face of it, the evidence would seem to support the crude version of the flexibility hypothesis. There is now compelling evidence that being married and raising children are both strongly associated with self-employment among women.4 Having children under six years old has the greatest impact on the probability that women are self-employed, especially among homeworkers.5 CPS, NLS and NLSY data from the USA all suggest that these effects are most pronounced for highly educated women (Wellington, 2006). The effects of children on male entrepreneurship appear to be weaker. While some studies record that both men and women are likelier to be entrepreneurs the more children they have, others have found little impact from family variables on male selfemployment participation.6 However, the propensity to become self-employed is higher among women with husbands who provide childcare (Caputo and Dolinksy, 1998). Before proceeding any further, a note of caution should be sounded. The number of children, which is commonly used as an independent variable in binary choice models of female entrepreneurship, is likely to be endogenous. We can ask, for example, whether women choose to become entrepreneurs in order to have children more easily,

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or whether having children makes entrepreneurship more attractive to them. Reflecting two-way causality, respondents of the GSS (General Social Survey) have approximately 0.2 to 0.4 more actual and expected children if they are self-employed rather than employees (Broussard et al., 2003). Entrepreneurs might choose to have more children in part to increase the probability of a family member making a good match with running the family firm. This suggests that family structure and entrepreneurship are jointly determined. This is an intriguing possibility which has been insufficiently explored in the literature to date: further research on this issue is called for. Some more detailed evidence is also consistent with the flexibility hypothesis. According to US Census data from 1980, American self-employed mothers are more likely to look after their child (more than 20 per cent did) when in work than their employee counterparts are (less than 2 per cent did). Furthermore, 20 per cent of selfemployed women work from home, compared with just 6 per cent of self-employed men (Carr, 1996). Edwards and Field-Hendrey (2002) report that home-based workers are more likely to choose self-employment (63 per cent) than on-site workers (33 per cent). Apart from joint production of market and household goods and services, home-based self-employment enjoys several advantages over on-site employment, including the absence of employer monitoring and office rental costs. And while the desire for independence and monetary rewards are important for both men and women, British women seem to value the freedom of choosing when they work more than men do (Hakim, 1989a). Interestingly, Wellington (2006) observed no trend in the relationship between child-rearing and female self-employment over the last decades of the twentieth century, contrary to what might be expected if technological change made home-working – an important form of self-employment for women according to Edwards and Field-Hendrey (2002) – more pervasive. Lombard (2001) measured the demand for flexibility in terms of the variability of weekly work hours, relative to a reference point a year before. Lombard (2001) estimated a positive association between a woman’s demand for flexibility and the probability of being self-employed; the association was strongest for women with young children. According to Lombard, relatively low self-employment rates among black women may be associated with their apparent preference for a standard working week. Other findings lend less support to the flexibility hypothesis, however. For example, ECHP data shows that self-employed workers in Europe do not necessarily spend more time with their children than employees do. Indeed, self-employed women actually spend less time caring for children on average than women employees in several European nations (Gustafsson and Kjulin, 1994; Hildebrand and Williams, 2003). This is because self-employment is associated with long work hours as well as flexible work schedules (see chapter 12). 6.3

Performance of women entrepreneurs

This section commences by documenting evidence about the gap in earnings and profitability between men and women entrepreneurs. For the most part these constructs

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are measured in terms of self-employment income, or less commonly business owners’ profits. I then propose several plausible explanations for the performance gap and review the available evidence. Finally, I briefly discuss alternative measures of performance, namely growth and survival.

6.3.1

The gender earnings gap It is now well established that self-employed women earn less on average than selfemployed men do – as well as less than employees of either gender. For example, using 1983 SIPP (Survey of Income and Program Participation) data, Haber et al. (1987) calculated that the ratio of US median full-time female to male self-employment incomes was only 0.30, compared to a ratio of 0.60 in paid employment. Among fulltime, full-year workers in 1990, self-employed women earned 73 per cent of the annual income of women wage-and-salary workers, whereas self-employed men earned 107 per cent of the annual income of their employee counterparts (Devine, 1994a). In the same study, self-employed men were more frequently observed to work in high-paying sectors than women, including executive, administrative, managerial and precision artisan jobs. In contrast, self-employed women were more frequently found in lowerpaying service and retail sectors. These sectors are more competitive and also exhibit lower business survival rates. Earnings in non-professional female self-employment can be very low indeed, especially for women who were not previously employed. For instance, Budig (2006) calculated an average hourly self-employed wage of only $4.08 for young American women over the period 1979–98. Women who do not work or who are in non-professional wage jobs, or who have family commitments, are the likeliest to become non-professional self-employed (Clain, 2000; Budig, 2006). Self-employed women perform somewhat better on average in some other countries, with female/male earnings ratios reaching 87 per cent in the case of Australia (OECD, 1986). But in developing countries, women receive substantially lower average returns than men, especially if they are not employers (e.g. see Honig, 1996, 1998, for Jamaican evidence). The same story is observed when profits and sales turnover are compared across gender lines. Regarding profitability, for example, the average employer-firm owned by a woman generates only 78 per cent of the profits of the comparable business owned by a man (Robb and Wolken, 2002). Women also generate lower sales turnover relative to men – even in same-industry comparisons (Loscocco and Robinson, 1991; Chaganti and Parasuraman, 1996).According to Loscocco and Robinson (1991), women generate only one-quarter of men’s average business receipts. More recent work based on data from the 1998 NSSBF compiled by the US Federal Reserve shows that men-owned businesses are twice as large on average as women-owned firms in terms of sales and assets (Coleman, 2007). Aronson (1991) offers an interesting historical perspective on female relative selfemployment incomes in the USA. In the inter-war and early post-war periods of the twentieth century, self-employed women earned more than women employees did –

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although the comprehensiveness of these data is limited. What is clear, however, is that between 1955 and 1984 the relative earnings position of self-employed women declined steadily relative to their employee counterparts and also relative to self-employed men. It is less clear whether this decline reflects greater part-time participation by selfemployed women or a relative worsening of the average human capital of women choosing self-employment. While the inclusion of incorporated self-employed workers in sample data tends to close the income gap of male self-employees relative to male employees, it makes relatively little difference to the female self-employed– employee income gap, partly because relatively few women run incorporated businesses (Aronson, 1991). For the self-employed generally, it should be borne in mind that self-employment incomes usually omit important dimensions of job remuneration, including health care coverage. Self-employed women have relatively scant job-related health care coverage compared with women employees and men in either self-employment or paid employment (Devine, 1994a). 6.3.2

Explanations of the earnings gap

What explains the gender entrepreneurial earnings gap? Possible explanations include gender differences in: • • • •

Human capital Start-up capital Social capital Preferences, motivations and household production

Consider human capital first. On average, women entrepreneurs possess fewer years of experience than women employees or men in either entrepreneurship or paid employment (Aronson, 1991; Lee and Rendall, 2001). Women entrepreneurs also tend to have more diverse backgrounds than their male counterparts and are more likely to set up a business without having a track record of achievement, vocational training or experience (Watkins and Watkins, 1984). It is notable in this regard that age does not seem to enhance the earnings of self-employed women, in contrast to men (Clain, 2000). Start-up capital appears to be another important consideration. A widespread observation is that women entrepreneurs operate smaller businesses on average, utilising less capital and finance from banks and other lenders than men do.7 For example, Watson’s (2002) analysis of Australian Federal Government data on 13,551 male SME business owners and 875 female SME business owners revealed that women business owners earn similar rates of return on equity and assets to male business owners; but the female business owners invested less to start with, which explains why they ended up with lower absolute income and profits than the male business owners. This suggests that women entrepreneurs might be no less able than males in making returns, but simply invest less to start with. Possible reasons why women entrepreneurs tend to use less start-up capital than men are explored in the next section.

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There is less evidence which relates social capital to the gender earnings gap. What we do know is suggestive only: female entrepreneurs include more women in their social networks whereas male entrepreneurs’ networks are more gender-balanced (Brush, 2006, p. 620). If homophily is a drawback to business performance, homogeneous networks might explain why relatively few women start businesses in the first place, and why they perform less well compared with men (Renzulli et al., 2001). However, this begs the question of why women entrepreneurs form homophilious social networks along gender lines (and whether gender homophily is really a handicap at all). Another open question is why male nascent entrepreneurs appear to examine more ideas and gather more new information when pursuing new start-up opportunities than female nascent entrepreneurs do (Kepler and Shane, 2007). There is much more research on motivations and preferences of women entrepreneurs. As has been noted several times in this book already, self-reported motivations are prone to all sorts of biases, so they should not be taken too literally, especially as less successful entrepreneurs might justify poor ex post performance by claiming they were not driven by a need for success in the first place. Yet it is interesting to observe that among nascent entrepreneurs, who have by definition not yet experienced success or failure, male nascent entrepreneurs and employees rate financial success and innovation as more important motives than women do (Carter et al., 2003; DeMartino and Barbato, 2003). In contrast, women rate more highly the flexibility of work schedules and family considerations – especially women with young children, in contrast to men for whom children did not matter in this respect (Boden, 1999a). Women entrepreneurs also declare stronger motivations in terms of personal interests; a desire for self-fulfilment; and job satisfaction than their male counterparts (Jurik, 1998). Buttner and Moore (1997) examined the motivations of 129 women executives and professionals who left large organisations to become entrepreneurs. Challenge, autonomy and family–work balance were the declared motivations; self-fulfilment and goal achievement ranked higher among these women than profits and business growth. Greater risk aversion among women than men might also explain lower average returns to women entrepreneurs, as they would prefer to occupy positions further down the expected profit/risk frontier than their male counterparts (Watson and Robinson, 2003; Kepler and Shane, 2007). Such women could for example be less likely than men to found businesses which are technologically intensive (Kepler and Shane, 2007). Women seeking to minimise risk might also reject growth strategies which endanger the security of the business and thereby their family’s financial position. In an exploration of this hypothesis, Watson and Robinson (2003) observed that women entrepreneurs in Australia received significantly lower average annual profits than men, but also bore substantially less business-related risk than men. Consistent with finance theory, risk-adjusted returns were similar by gender.8 In fact, the evidence about gender differences in risk attitudes is sparse and inconclusive. While Jianakoplos and Bernasek (1998) adduced survey evidence suggesting that women are more risk-averse than men, experimental evidence (albeit from a small

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sample of students) from Schubert et al. (1999) detected similar levels of risk aversion among men and women. Hundley (2001a) attempted to test some of the competing explanations against each other by applying an Oaxaca decomposition to gender-specific earnings functions.9 Hundley found that the most important explanatory variables were housework, work hours and the number of young children, which together accounted for between 30 per cent and 50 per cent of the American annual self-employment gender earnings differential. This suggests that women earn less than men do because they spend less time managing and developing their businesses. The next most important explanatory variable according to Hundley (2001a) was industrial sector, which accounted for between 9 and 14 per cent of the gender self-employment earnings differential. That reflects the concentration of women in the less lucrative personal services sector, and their under-representation in the more remunerative professional services and construction industries. Differences in capital explained only 3–7 per cent of the differential, and other variables (including experience) seemed to be even less important. Researchers tend to find a positive role for human capital, especially age, schooling and work experience, when trying to explain performance differences among women entrepreneurs (i.e. not relative to men: Schiller and Crewson, 1997; and Coleman, 2007). For example, Coleman (2007) estimated that education and work experience were the main drivers of profitability in women-owned businesses, whereas financial capital was the key variable influencing the profitability of men-owned businesses. This finding is consistent with other evidence that in general human and financial capital may be substitutes for each other (Chandler and Hanks, 1998). There is, on the other hand, mixed evidence about women’s responsiveness to the prospect of financial returns in entrepreneurship. According to various estimates of earnings functions, self-employed American women would earn more than their counterparts in paid employment if they quit and made the switch.10 This suggests that self-employed women have above-average skills (Devine, 1994b). In contrast, Lombard (2001) estimates that women’s self-employment choices are in fact largely explained by their incomes in self-employment relative to what they could have expected to earn in paid employment. Another influence on women’s occupational choice of entrepreneurship might be gender discrimination. If women are discriminated against in paid employment (e.g. because they hit a ‘glass ceiling’), and so earn lower returns to their characteristics there, entrepreneurship can become more attractive. Boden (1999b) explored this possibility using longitudinally matched CPS data. In evidence suggestive of the glass ceiling idea, Boden (1999b) estimated the returns-to-skill component of the gender wage differential and found that it significantly increased the probability that white women would transition from paid employment to self-employment. Some other covariates also helped to explain transitions, including wealth, the presence of young children, education and experience (all with positive effects) and the current wage in paid employment (with a negative effect). But wealth and education also predispose women to contemplate business careers in paid employment. That could merely suggest that both occupations

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appeal to successful women. And other studies show schooling and experience to significantly reduce the probability that women make transitions into self-employment in the USA (Schiller and Crewson, 1997), while lucrative executive jobs in paid employment seem to be preferred to self-employment by highly educated women in Germany (Constant, 2006). Another problem with the notion of a ‘glass ceiling’ in paid employment is that female self-employment participation rates do not vary systematically by job skill level (Devine, 1994b). This is inconsistent with the hypothesis that skilled women choose self-employment to avoid a glass ceiling of limited earnings in self-employment. It also casts doubt on the idea that women use their employment skills as a launching pad for entry into entrepreneurship. However, rigorous tests of the glass ceiling are notoriously hard to devise, as researchers invariably lack objective empirical manifestations of this construct. Simple occupational choice models with labour market discrimination also struggle to explain gender differences in entry rates. To see why, suppose that women receive a lower wage, w(1 − δ) with 0 < δ < 1, than men, who get w. Following Rosti and Chelli (2005), suppose also that: (i) men and women have the same distribution of general ability x, namely G(x); and (ii) entrepreneurship incomes y(x) are increasing in x; and (iii) business survival is an increasing function of x. Then using the simple occupational choice framework of chapter 2, it follows that the average ability and hence survival prospects of female entrepreneurs are less than that of men. However, this simple framework also predicts higher entry rates among women, contrary to the evidence (q.v. above).11 Finally, self-employment seems to be a less effective vehicle for upward earnings mobility for American women than men. Men and women have similar transition rates into self-employment, but men are more likely than women to move up the self-employed earnings distribution (Holtz-Eakin et al., 2000).

6.3.3

Other performance gaps: growth and survival rates

Self-employed women do not under-perform relative to self-employed men merely in terms of their incomes. The same is also true in terms of their turnover, employment creation performance, growth rates and survival prospects.12 For example, Bosma et al. (2004) estimated that male Dutch business founders outperform their female counterparts in terms of survival, profitability and employment creation. Lohmann and Luber (2004) reported that 42 per cent of self-employed German women remain self-employed after five years; for German men the corresponding figure is 63 per cent. A key source of this difference appears to be unstable work histories, which predict lower female but not male survival probabilities. On a similar point, Lee and Rendall (2001) reported that white American women experience shorter spells in self-employment on average than white men, despite working similar numbers of spells. It should be borne in mind that inferior financial performance may reflect founder goals which are less materialistic, rather than constraints due to discrimination, for example.

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Swedish evidence explains low growth rates of women-owned businesses in terms of the small scale of their ventures; their tendency to trade in the service sector; and their reliance on direct sales rather than more lucrative business-to-business sales (Du Rietz and Henrekson, 2000). Yet no significant gender differences have been observed in relation to the performance outcomes of nascent entrepreneurs (Kepler and Shane, 2007), or among long-established business owners, in terms of employment growth (Fischer et al., 1993; Chaganti and Parasuraman, 1996) or business survival rates (Kalleberg and Leicht, 1991; Brüderl and Preisendörfer, 1998). So it appears that gender disadvantage occurs less at the early start-up or mature stages of business development, and more in the intermediate stages. 6.4 Women and entrepreneurial finance

Evidence from a variety of sources shows that men and women entrepreneurs use similar sources of finance, but that women tend to use smaller amounts of external finance on average than men do. For example, Carter and Rosa (1998) estimated that in Britain, women entrepreneurs use one-third less external finance than men on average. Similar evidence has subsequently been obtained for a variety of other countries.13 Kim’s (2006) analysis of US small business loan applications reveals that equally owned mixedgender small businesses occupy an intermediate position in terms of success in obtaining finance compared with women-owned (lowest) and men-owned (highest) businesses – although larger samples from the NSSBF reveal that women-owned American small businesses gain similar access to line-of-credit loans from commercial banks to menowned businesses (Haynes and Haynes, 1999). Access to start-up finance is important because low levels of initial capital may hamper the growth and survival prospects of ventures later on. Some Norwegian evidence bears out this hypothesis (Alsos et al., 2006). However, researchers should be careful to control for the innately limited scale of some opportunities, which can also explain this relationship. It has been widely observed that women entrepreneurs seek and use less formal and informal equity finance than men entrepreneurs. Women own over 35 per cent of US businesses, but receive at most only 5 per cent of venture capital funding (Brush et al., 2003). And only about 10 per cent of business angels in the USA and the UK are women (Amatucci and Sohl, 2007), although their characteristics do not seem to differ by gender (Mason, 2006; Harrison and Mason, 2007). American women entrepreneurs seek angel finance at a lower rate than men entrepreneurs, but with a similar probability of receiving investment; they are more likely to seek, but are less likely to receive, finance from female than from male angels (Becker-Blease and Sohl, 2007). As noted at the start of this chapter, research into gender aspects of entrepreneurial finance is sometimes motivated by a concern about possible gender discrimination by lenders. For example, lenders might perceive women to possess fewer of the characteristics deemed favourable to business success, and so restrict finance for that reason (Buttner and Rosen, 1988). To test this hypothesis, Fay and Williams (1993) conducted

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an interesting experiment by mailing bank loan officers in New Zealand identical loan applications whose sole difference was the gender of the hypothetical applicant in the supporting documentation. Fay and Williams found that significantly more noncollege-educated women than men had loans that were declined or restricted (32 per cent compared with 10 per cent), but there was no difference along gender lines for college-educated loan applicants. A problem with this study, however, is that the sample sizes are very small, being only around fifty for each sex. Evidence based on larger surveys tend to reveal modest or negligible differences in the way that bank loan officers respond to gender in the context of business loan applications (Wilson et al., 2007). Other evidence casts greater doubt on the notion that women face discrimination in the credit market. It is noteworthy that nowadays relatively few men or women entrepreneurs seem to believe that gender discrimination underlies loan decisions; and women business owners report similar levels of satisfaction with their relationships with banks as men do (Buttner and Rosen, 1988; Carter and Rosa, 1998). If discrimination does not explain loan decisions, why do women start businesses with less capital on average than men? Several explanations can be proposed. First, women entrepreneurs tend to run smaller businesses in sectors with fewer market growth opportunities, so it may be optimal for both lenders and entrepreneurs to commence with limited capital (Orser et al., 2006). As noted earlier in the chapter, women entrepreneurs often start businesses in the service and retail sectors, which need less capital and have higher exit rates, making banks reluctant to lend large amounts of finance. Riding and Swift (1990) provide some tentative Canadian evidence suggesting that these factors may be sufficient to explain the bulk of apparent gender inequalities in bank lending. Similarly, once one controls for the size and age of a venture and the particular industry sector it is located in, gender differences in access to and terms of finance (including rates of loan approvals, co-signature requirements, interest rates on loans and lines of credit, and collateral requirements) tend to disappear (Coleman, 2000; Orser et al., 2006). Specifically, Coleman (2000) noted that many women run firms which need little external finance and possess little ‘inside’ (i.e. business-related) collateral, so necessitating the entrepreneur to put up more ‘outside’ (i.e. personal) collateral. This does not reflect discrimination but a simple and understandable desire on behalf of lenders to protect their capital at risk. Furthermore, to the extent that women receive lower wages, have broken job histories and reside in property held in the name of their husband, women entrepreneurs’ access to self-finance is likely to be limited. Yet women entrepreneurs might still demand less external finance than men because they want to run smaller businesses for family and lifestyle reasons. The same outcome can also arise if women are more risk-averse or less financially aware, reflecting their previous industry experience (Carter and Rosa, 1998). As yet, I am unaware of research which distinguishes cleanly between these different explanations of gender loan-size differences. Evidently this is a fruitful topic for future research.

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197

Conclusion

Research on female entrepreneurship continues to expand and develop. Much of the groundwork describing ways that women differ from men in entrepreneurship has now been completed, and our knowledge base has improved considerably over the last twenty years or so. We now know that, relative to their male counterparts, women entrepreneurs tend to operate smaller businesses; use less finance; operate in more competitive industry sectors with lower returns and survival rates; and undertake more household duties in addition to their business activities. Despite some valuable work based on earnings decompositions, it remains unclear precisely what the relative importance of these factors is in explaining different patterns of female participation and performance in entrepreneurship, and much more research remains to be done on this issue. But there is an unmistakable trend away from blaming lower average levels of female entrepreneurial performance simply on gender discrimination. It is striking that relatively little evidence exists of discrimination against women in the credit or labour markets. As each year passes, it seems that there is less talk of discrimination and more discussion of ‘liberal’ or ‘social’ feminist rationales for lower rates of entrepreneurship among women. While the liberal feminist rationale stresses structural barriers for women, which prevent them from gaining experience necessary to access resources, the social feminist rationale emphasises different perspectives, goals and choices specific to women. This author’s view is that a particularly important difference between men and women in entrepreneurship stems from the persistent tendency of women to spend more time in household production and child-rearing activities than men. Until and unless this situation changes, women will continue to opt for forms of entrepreneurship which result in lower average returns compared with men. Given a finite number of hours in the day to run a business, undertake household production and take leisure, women are effectively forced to operate in entrepreneurship at a smaller scale and on a more part-time basis than their male counterparts. This chapter has demonstrated that a labour market perspective is invaluable for analysing female entrepreneurship. Both this and complementary approaches will no doubt be used to push the boundaries of research in the future. Several important questions remain open, although some of them are likely to be difficult to answer. For example, to what extent does a ‘glass ceiling’ in paid employment push women into entrepreneurship? More generally, how many women are being pushed into entrepreneurship as ‘necessity’ rather than ‘opportunity’ entrepreneurs? On a different tack, rather than dwelling on gender differences alone, as much of the literature does, it may be time to deepen the analysis of interactions of gender with other factors, such as skill, ethnicity and family background. Researchers continue to explore whether women entrepreneurs face binding financial constraints, despite the fact that there continues to be little empirical support for this notion. Much less is known, in contrast, about the financial literacy of women entrepreneurs. This is potentially important because in general women are less

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financially literate than men (Lusardi and Mitchell, 2006). Running and growing a new business is a complicated affair, so public policy directed at improving financial literacy might do more to help women succeed in business than policies which try to relax minor or non-existent borrowing constraints. In this regard, improving access to credit for women entrepreneurs is one of the goals of the US Small Business Administration, through targeted loan guarantees. Other policies include gender-targeted federal procurement, training, counselling and mentoring programmes (Brush et al., 2003; and see chapter 17). In the author’s opinion, future researchers should also investigate alternative gender-sensitive policies. In view of the point about long work hours, for example, it is tempting to suggest that female entrepreneurship policies should include a child-care component. But this begs the question about whether such a policy would also be appropriate for women choosing to work in paid employment. It is unclear why policy-makers should treat the two occupations differently in this respect, especially as entrepreneurship seems to provide greater opportunities for flexible work arrangements than paid employment does. Notes 1. See Macpherson (1988), Evans and Leighton (1989a), Connelly (1992), Devine (1994a), Bates (1995), Boden (1996, 1999a), Carr (1996), Wellington (2001) and Edwards and Field-Hendrey (2002). 2. See Devine (1994a, 1994b), Lombard (2001), Taniguchi (2002) and Budig (2006). 3. This finding is based on time-use surveys and responses to household surveys: see Longstreth et al. (1987), Boden (1999a) and Bond and Sales (2001). 4. For US and UK evidence about the importance of marriage for female entrepreneurship, see Devine (1994a, 1994b), Robinson and Sexton (1994), Carr (1996), Schiller and Crewson (1997) and Cowling and Taylor (2001). Caputo and Dolinsky (1998) provide a dissenting view. On the importance of the presence of children for female entrepreneurship, see Macpherson (1988), Evans and Leighton (1989a), Connelly (1992), Boden (1996), Carr (1996), Caputo and Dolinsky (1998), Wellington (2001) and Kepler and Shane (2007). 5. See Connelly (1992), Boden (1996, 1999b), Wellington (2001, 2006) and Edwards and FieldHendrey (2002). 6. Compare Wong (1986), Connelly (1992), Boden (1999a) and Caputo and Dolinksy (1998) with Boden (1996) and Carr (1996). 7. See Aronson (1991), Carter et al. (1997) and Watson (2002). 8. Watson and Robinson (2003) defined risk-adjusted return in terms of Sharpe’s ‘reward to variability’ ratio, i.e., the quotient of mean annual profits and their standard deviation. 9. To see how this works, consider the earnings function ln yij = βj
Xij + uij , where the i subscript denotes an individual and a j subscript denotes gender: j = f indexes females and j = m indexes males. Here y represents annual earnings, X is a vector of explanatory variables and u is a random disturbance term. Consider a particular explanatory variable Xijk . Letting an overbar denote sample means and a hat denote a regression estimate, we can write 
 
X ˆ ˆ ln ym − ln yf = βˆm (6.1) mk − Xfk + βm − βf Xfk . The first term on the RHS of (6.1) is the part of the average earnings difference attributable to explanatory variable Xijk . The other term is the part of the earnings difference that is unexplained

Female entrepreneurship

10. 11.

12.

13.

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by Xijk . Note that one can alternatively write an analogous expression using βˆf
in the first term of the RHS of (6.1). Since the choice is arbitrary, results based on this decomposition method should be quoted for both calculations. Macpherson (1988), Devine (1994b) and Clain (2000). Proof: denote the marginal male (respectively, female) entrepreneur by xˆ m (respectively, xˆ f ), which solves y(ˆxm ) = w (respectively, y(ˆxf ) = w(1 − δ)). Hence for common G(x) it follows that xˆ f < xˆ m and so the average ability and survival chances of female entrepreneurs are less than those of men: E(x|x ≥ xˆ f ) < E(x|x ≥ xˆ m ). But 1 − G(ˆxf ) > 1 − G(ˆxm ) implies that this simple framework predicts higher female entry rates. For evidence relating to developed economies, see Chaganti and Parasuraman (1996), Carter et al. (1997), Schiller and Crewson (1997), Boden and Nucci (2000), Bosma et al. (2004) and Lohmann and Luber (2004). For evidence relating to developing economies in southern Africa, see McPherson (1995, 1996). The same findings also emerge from reviews of the US and UK business studies literature: see Brush (1992) and Rosa et al. (1996). Other evidence relates to the USA (Coleman, 2000, 2007; Kim, 2006); Canada (Orser et al., 2006); the Netherlands (Verheul and Thurik, 2001); and Norway (Alsos et al., 2006).

Part II Financing

7

Debt finance for entrepreneurial ventures

Part I of this book explored various factors that bear on the willingness of individuals to try entrepreneurship. Part II recognises that sometimes individuals have limited opportunities to become entrepreneurs, because of difficulties in raising sufficient finance. Finance is needed to develop prototypes, purchase working capital and marketing services, and defray initial operational and living expenses. Most start-up finance in developed countries is in the form of personal equity (‘selffinance’), i.e. finance supplied by the entrepreneurs themselves. According to the Bank of England (2001), 60 per cent of start-ups in Britain use self-finance. The remaining funds are raised from external sources. According to the Bank, about 60 per cent of external finance is raised through debt-finance contracts (comprising bank overdrafts and term loans) followed by asset-based finance (e.g. leasing: around 20 per cent). A similar picture applies in the USA. Also important is family finance, at around 10 per cent of external finance on average, whereas venture capital (equity finance) tends to play only a very minor role for most entrepreneurs (between 1 and 3 per cent on average). This chapter focuses on the implications for entrepreneurship of raising debt finance. Chapter 8 discusses various other sources of finance, including venture capital. If lenders and entrepreneurs were both perfectly informed about every aspect of new entrepreneurial ventures, and if financial markets were flexible and competitive, then all ventures with positive net present value (NPV) would be funded. Also, it would not matter whether lenders or entrepreneurs undertook the ventures. However, this idealised scenario rarely happens in practice. Many entrepreneurs complain that they are unable to obtain enough funding, or sometimes any funding at all, for what they believe are viable ventures. Such entrepreneurs claim to be ‘credit rationed’. Credit rationing has received considerable attention in policy circles since the publication of influential reports by the Federal Reserve System (in 1958) in the USA, and the ‘Bolton’ and ‘Wilson’ reports (HMSO 1971, 1979) in the UK. These reports contended that there was a general shortage of financial capital to fund new start-ups and to expand existing small businesses. While a subsequent UK government report (HMSO, 1991) contended that problems of access to start-up finance had by then been largely resolved, the opposite conclusion was drawn by a US report on small business finance (SBA, 1996). 203

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Another potential problem is that imperfections in the capital market can result in there being either too few or too many entrepreneurs in equilibrium – judged in terms of the efficient use of the economy’s resources (‘social efficiency’). The present chapter investigates these issues. Along the way, the roles of collateral, loan sizes and lender– borrower relationships will also be discussed. I will leave aside topics such as what lenders and borrowers think about each other; what induces entrepreneurs to apply for loans; and how entrepreneurs can write business plans to improve their chances of successfully obtaining loans. These issues are covered in any number of texts within the business studies literature. For notational brevity, lenders will be referred to simply as ‘banks’ hereafter. This chapter emphasises the importance of asymmetric information in the entrepreneurial credit market. Information is often asymmetric because although entrepreneurs may have accurate information about the quality of their risky proposed ventures and their abilities and commitment to execute them, banks cannot perfectly distinguish the quality of loan applications. Reasons for this include the lack of a track record for new ventures, and prohibitive costs of acquiring reliable information about them. Banks cannot simply ask entrepreneurs to declare truthfully their commitment and ability, because loan applicants lacking these attributes have incentives to overclaim about them. Furthermore, some entrepreneurs may be unwilling to reveal hard information about the potential of new innovative ideas which could be expropriated by others; while investors cannot disclose information about their involvement with other customers who are developing competing products. The problems are compounded because there is no equivalent institution to a credit-rating agency for entrepreneurs. Instead, banks have to rely on their own imperfect screening devices. It will be assumed below that although banks can screen entrepreneurs into groups defined by some observable characteristics, there is invariably also some residual imperfect information which forces them to pool, at least initially, heterogeneous risk types together within each group. Although established corporations also face asymmetric information problems in relation to project finance, there are several reasons to expect these problems to be more pronounced for new entrepreneurial ventures. First, most start-ups have limited funds compared with incumbents, making the absence of finance more critical to their survival prospects. Second, entrepreneurs are more likely than incumbents to develop radical innovations about which little information is available, whereas corporations tend to develop incremental innovations funded from retained profits (Bhide, 2000). In short, these considerations suggest that incentive issues come to the fore in entrepreneurial finance, necessitating the use of sophisticated financial contracting practices. Smith and Smith (2003) point out that the severity of asymmetric information is just one of several reasons why entrepreneurial finance differs from corporate finance. Another difference is that entrepreneurs’ consumption and investment decisions cannot be separated from their financing decisions, as is the case in classical corporate finance. Savings and retained profits are important sources of funds for entrepreneurs, needed in part to signal commitment to their ventures; and entrepreneurs typically

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possess undiversified investment portfolios, making them unusually prone to risk (see chapter 9). Other differences include end-game strategies. Unlike ‘infinitely lived’ corporations, some entrepreneurs plan at the outset, and often at the behest of investors, how they will exit within a finite time to realise value, a process called ‘harvesting’.1 Furthermore, unlike corporations, entrepreneurs often have to buy in outside management expertise; and they rarely separate ownership from control, at least initially. The next section provides some background information about entrepreneurial credit markets, as well as useful terminology used in the remainder of the chapter. The section after presents theoretical arguments for the existence of ‘credit rationing’, ‘redlining’ and ‘under-investment’. This material treats the possible causes of these phenomena; their effects on efficiency and the equilibrium number of entrepreneurs; and the scope for corrective action by policy-makers. The implication of these models is that credit market imperfections cause there to be too few active entrepreneurs for the social good. Theoretical rebuttals of the credit rationing hypothesis are presented in the third section, together with some counter-rebuttals. An important issue discussed here is the possibility that agents can write richer contracts which reveal hidden information and thereby eliminate market imperfections. The fourth section discusses some alternative models, in which over-investment occurs. The implication here is that credit market imperfections can cause there to be too many active entrepreneurs for the social good. The final section discusses other possible sources of inefficiency in entrepreneurial credit markets, and concludes. 7.1

Background and useful terminology

This section has two goals. First, it explains some of the salient aspects of entrepreneurial credit markets. Second, it introduces some useful terminology relating to asymmetric information which is deployed in the rest of the chapter. 7.1.1

Background Below, five salient aspects of entrepreneurial credit markets are explained: ‘collateral’, ‘limited liability’, ‘relationship lending’, ‘credit scoring’ and the optimality of debt as a financial contract.

Collateral is an asset belonging to a borrower that can be seized by a bank if the borrower defaults on a loan extended by the bank. Unlike large established firms which can pledge company assets (‘inside collateral’), most entrepreneurs can only pledge their own personal assets (‘outside collateral’), typically their house. Most models of debt finance assume that banks automatically capture inside assets in the case of default, so explicit analyses of collateral tend to be of the outside sort. In practice, banks typically incur costs when seizing collateral. This friction is built into several models of entrepreneurial finance discussed below. Collateral is widespread. Berger and Udell (1990) reported that nearly 70 per cent of all US commercial and industrial loans are made on a secured basis, while Cressy Collateral

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(1993) found that 95 per cent of UK business overdrafts in excess of £20,000 were fully collateralised. Collateral serves several possible purposes. It can: • Persuade sceptical banks about the worth of ventures (Chan and Kanatos, 1985). • Encourage entrepreneurs to supply optimal amounts of effort (Boot et al., 1991) or to operate less risky ventures (Stiglitz and Weiss, 1992). • Enable entrepreneurs to make credible promises about repayment, since their own assets are at stake (Hart and Moore, 1994, 1998; Hart, 1995). • Enable banks profitably to re-negotiate debt when borrowers default (Bester, 1994). Entrepreneurs operating ventures with nugatory ‘inside collateral’ who know that banks prefer to re-negotiate than try to seize that collateral have incentives to default even when they are successful. Banks can remove this incentive if they have access to defaulters’ outside collateral, allowing the benefits of debt re-negotiation to be realised by both entrepreneurs and banks. • Serve as a screening or signalling device to help able entrepreneurs obtain more favourable borrowing terms (see below). On theoretical grounds, banks are predicted to request more collateral from riskier borrowers. Collateral compensates banks for bearing risk, which can be exacerbated when entrepreneurs are over-optimistic, choose excessively risky investment projects, or engage in opportunistic behaviour.2 On the other hand, if borrowers who post more collateral do so to signal higher ability (see below) they may be rewarded with lower interest repayments. The available evidence from the USA points to a net positive relationship between collateral requirements and project risk, although two recent British studies report negative and U-shaped relationships.3 Entrepreneurial real estate serves both as a productive factor of production and a source of collateral. In the presence of borrowing constraints, a subsidy to entrepreneurial real estate can in principle increase output, employment and welfare (Jin and Zeng, 2007). However, the effectiveness of this policy is undermined if it can be exploited by non-entrepreneurs. And when entrepreneurs are over-optimistic, collateral can actually decrease efficiency. This is because in a perfectly competitive credit market, collateral reduces the competitive interest rate banks charge, encouraging rash investment choices by over-optimistic entrepreneurs who overestimate the probability of success and so undervalue the cost of collateral (Manove and Padilla, 1999). Limited liability Entrepreneurs can obtain some protection from creditors by incorporating their businesses. Incorporation affords the entrepreneur limited liability status. In a legal sense this means that an entrepreneur whose company fails is liable only up to the value of the business assets, i.e. creditors cannot claim their personal wealth. Many credit market models of entrepreneurial finance include the limited liability condition in the form of a lower bound on entrepreneurs’ wealth in the downside state. As a general point, this gives debt contracts less incentivising power, and as we will see below leads directly to the possibility of some kinds of credit rationing.

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In practice, however, even limited liability does not perfectly protect entrepreneurs’ assets. Lenders who anticipate that entrepreneurs will switch funds from the firm to their own personal consumption can require entrepreneurs to secure loans on their own assets at the outset (Berkowitz and White, 2004). This clearly restricts the value of limited liability in practice. So does the threat of future disagreements with independent shareholders if incorporation is motivated by the objective of taking the venture public at a later date (Boot et al., 2006). There is some evidence that entrepreneurs with moderate wealth and capital are the most likely to incorporate, as the costs of incorporating are only worth bearing at a larger scale, while the wealthiest are the least likely to bump up against the limited liability constraint and are also the least risk-averse (Horvath and Woywode, 2005). ‘Relationship lending’ is another important feature of entrepreneurial credit markets. It describes how banks and entrepreneurs can form relationships whose value is reflected in loan terms which vary systematically over time. Relationship lending has the potential to gradually reveal to the bank ‘soft’ information about the enterprise and the entrepreneur. It is sometimes claimed that entrepreneurs rely more on relationship lending to convey messages about their creditworthiness than on the signalling, monitoring, and bonding mechanisms used by larger firms (Petersen and Rajan, 1994; Berger and Udell, 1995). A central question is whether borrowers who have built a relationship with a bank receive more favourable loan terms (e.g. lower interest rates and lower collateral requirements) than new borrowers. They might do so, for example, if lower interest rates provide entrepreneurs with incentives to exert greater effort at the crucial early stages of the relationship (Boot and Thakor, 1994). Much of the available evidence does indeed suggest that repayment terms are more favourable for entrepreneurs in longer bank relationships.4 On the other hand, if banks can lock entrepreneurs into relationships, they can offer low initial interest rates to lure them in, before charging them higher interest rates later on to exploit ‘informational rents’.5 Alternatively, subsequent rounds of finance which make borrowers spread their collateral thinly over a larger aggregate loan size might pose a greater risk for banks, which will also be reflected in interest rates which rise as relationships mature (Hanley and Crook, 2005). Relationship lending

If relationship lending is associated with personal relationships, then credit scoring is associated with the converse. Credit scoring is an automated underwriting technique used by banks to assess small business loan applications. It yields a single quantitative measure, or score, which represents an estimate of the applicant’s creditworthiness with respect to a requested loan. Applications receiving scores above a given threshold receive funding; those with scores below it do not. Credit scoring models usually weight several characteristics strongly, including personal credit history (which is strongly predictive of a venture’s loan repayment prospects) and other personal information such as the borrower’s monthly income, outstanding debt, financial assets, collateral, employment tenure, home-ownership status and cash-flow Credit scoring

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prospects. Large banks tend to make greater use of credit scoring methods than small banks do (Berger and Udell, 2003; Berger et al., 2005). As a cheaper and arguably more accurate screening device than traditional personbased lending methods, it is hypothesised that credit scoring enables banks to: reduce the interest premium on loans; eliminate any credit rationing arising from the cost of processing loans (Thakor and Calloway, 1983); and expand the availability of more risky high-yield credit. Some evidence supports these arguments. For instance, Frame, Srinivasan and Woosley (2001) found that credit scoring is associated with an 8.4 per cent increase in the portfolio share of small-business loans among large US banking organisations. Credit scoring has also been associated with higher loan risk levels and prices (Berger et al., 2005). As well as expanding the volume of credit, credit scoring has the advantage of replacing judgement and discrimination with objective criteria in loan application decisions. As noted in chapter 5, this might be of particular importance for African-American entrepreneurs who struggle to obtain debt finance. Evidence that loan-rejection rates increase at times of high turnover of loan officers suggests that loan officers do indeed use soft information (e.g. assessments of character and information from customers and suppliers) to inform their loan decisions (Scott, 2006). Credit scoring appears to be becoming more widespread. In financially developed economies, the average distance between lenders and small firms is increasing, while their methods of communication are becoming more impersonal (Petersen and Rajan, 2002). This trend appears to be driven by greater lender productivity rather than by the closure of bank branches. It has been argued that ‘ongoing improvements in ICT are reducing information costs and making timely interventions easier, thereby slowly breaking the tyranny of distance…in small business lending’(Petersen and Rajan, 2002, p. 2535). A trend for growing geographical distance between banks and entrepreneurs carries several implications. Quite naturally, it downgrades the importance of relationship lending and individual loan officer input. On the other hand, by continually expanding the size of the available credit market, problems of insufficient credit should be reduced, and concerns about entrepreneurs’ access to credit in response to bank mergers and adverse local shocks to credit availability should ease (Petersen and Rajan, 2002). Nevertheless, it seems likely that geographical proximity will remain an important factor for other types of financial intermediation which involve close monitoring and assistance, such as venture capital (Lerner, 1995). And local financial development is still important in some developed economies. For instance, in Italy a person’s odds of starting a business increase by nearly six per cent if they move from the least financially developed region to the most financially developed one (Guiso et al., 2004). Hence local expertise in screening projects may still be valuable in some places, notwithstanding the broader global trends identified by Petersen and Rajan (2002). Optimality of debt as a financial contract Finally, the common usage by

entrepreneurs of debt as a financial contract (the type of contract exclusively studied in

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this chapter) is an outcome which needs explaining. Several reasons for the optimality of debt finance have been proposed. By ‘optimal’ is commonly meant Pareto optimal. That is, debt is the financial contract which maximises entrepreneurs’ returns without reducing lenders’ returns. Perhaps the best-known rationale for debt finance is the ‘costly state verification’ argument, which recognises that banks incur costs whenever they check entrepreneurs’ actual as opposed to declared performance. With costly state verification, it is efficient for banks not to monitor non-defaulting ventures. By economising on state verification costs, debt becomes the optimal financial contract, by allowing lenders to offer finance more cheaply than costlier alternatives, such as equity.6 A different rationale is that debt finance enjoys the advantage over equity finance of providing partial insurance to entrepreneurs if the latter can renegotiate terms of the financial contract in the bad state (Fender and Sinclair, 2006). In fact, debt turns out to be the optimal contract under very general conditions, including when banks even lack information about the distribution of project returns, entrepreneurs’ types and entrepreneurs’ investment decisions.7 By giving banks all of the project returns if entrepreneurs default, debt contracts can also address moral hazard problems.8 Another argument is that short-term debt contracts can be a good ‘second best’ to contingent contracts (which switch control from optimistic entrepreneurs to realistic investors in bad states) when entrepreneurs over-optimistically overestimate their prospects of success (Landier and Thesmar, 2008). Over-optimists demand debt finance because they do not want to share their high expected returns with financiers as they would have to under an equity contract. If entrepreneurs are predominantly overoptimistic (see chapter 4), then debt finance will prevail over equity for this reason. But even if they are realists, abler types prefer debt finance over equity if they are most likely to succeed and so gain all of the upside returns (net of their fixed debt repayment). Under equity finance, in contrast, entrepreneurs have to share their upside returns. Although this advantage is less compelling for less able types, who have lower probabilities of venture success, these types also have to also request debt finance in order to avoid transmitting an adverse signal about their abilities. Hence in equilibrium every entrepreneur requests debt finance. 7.1.2

Terminology I commence with definitions of some salient concepts discussed in this chapter.

Definition 1 (Type I credit rationing) . Type I credit rationing occurs when some or all loan applicants receive a smaller loan than they desire at the quoted interest rate. Definition 2 (Type II credit rationing) . Type II credit rationing occurs when some randomly selected loan applicants are denied a loan altogether despite being observationally identical to applicants who receive one, and despite being willing to borrow on precisely the same terms; and when banks have such rationing as an optimal equilibrium policy.

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This credit rationing typology follows Keeton (1979). In the case of Type II rationing, a rationed borrower might offer to pay a higher interest rate in order to obtain funds; but the last clause of the definition indicates that this cannot break the rationing outcome. The inability of entrepreneurs to borrow at the interest rate they think is appropriate is in contrast not a valid definition of credit rationing, because it fails to take into account the real-world cost of funds and informational constraints prevailing in the market. The manifestations of Type I and II credit rationing analysed below are ‘equilibrium’ rationing outcomes, in the sense that they can be expected to persist until a favourable shock impacts the credit market. In contrast, ‘temporary’ credit rationing is caused by transient imbalances between the demand for and supply of loans. This case is of less interest because such imbalances will eventually be eliminated as markets adjust. Neither temporary credit rationing, nor equilibrium credit rationing caused by governments fixing interest rates below market clearing levels by diktat (e.g. usury laws), will be discussed in what follows.9 Usury laws have no role in modern deregulated economies. Figure 7.1 illustrates both types of credit rationing. The interest repayment Dm would clear the market for credit, but the actual interest rate is stuck at Dcr < Dm , with an excess demand for funds of LD − LS. In the case of Type I rationing, LS is the equilibrium loan size for an individual entrepreneur: LD is the desired loan size. In the case of Type II rationing, LS is the number of entrepreneurs who obtain a loan: LD is the number of loan applicants. Reasons why the interest rate may not rise above Dcr to Dm to eliminate rationing are explored below. They have their roots in two problems arising from asymmetric information: adverse selection and moral hazard. In the present context, adverse selection is the name given to the situation where the average quality of the borrower pool worsens when the price of funds faced by entrepreneurs (the debt repayment, D) increases. For instance, suppose in the terminology of chapter 2 that entrepreneurs possess different abilities x which are known

Suppl y

Quantit y

DL L

m

SL

e D mand 0 D

cr

D

m

e D tb repa

Figure 7.1 The supply of and demand for loans

yment, D

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to themselves but not to banks. Entrepreneurs cannot credibly signal their ability to banks, because every entrepreneur has an incentive to claim they are of high ability in order to improve their chances of obtaining funds. (That is why this problem is often referred to as one of ‘hidden types’.) Banks do, however, know the average quality of the borrower pool, x. Suppose banks increase D. By increasing the cost of funds it is less profitable to be an entrepreneur. If the ablest entrepreneurs respond by dropping out of the market and deploying their abilities outside entrepreneurship (as in, e.g. case (a) of Figure 2.3, where the expected returns schedule EN shifts downwards by D), while the less able entrepreneurs do not, the average quality of the remaining pool of entrepreneurial borrowers decreases. Then we say that adverse selection occurs. A different possibility is that borrowers do not quit entrepreneurship or the credit market when D increases, but instead devote less effort to their enterprise since the returns to their effort are lower. For example, entrepreneurs might shirk or spend more time on pleasurable but less profitable pursuits, such as solving interesting technical problems at the expense of commercialisation activities. This is known as moral hazard, or alternatively as a ‘hidden action’ problem, since banks cannot directly observe, prevent or contract on actions like effort which are at the discretion of the entrepreneur. The cases just discussed suggest that the danger of adverse selection and moral hazard might give banks pause for thought before increasing the interest rate they charge entrepreneurs. Indeed, the next section will explain how these problems can directly lead to credit rationing, as well as several other forms of inefficiency in the credit market, including redlining and under- or over-investment. I will now formally define these terms. Definition 3 (Redlining) . Redlining occurs when a bank refuses to lend to any loan applicant from a particular group because the bank cannot obtain its required return from these borrowers at any interest rate. Definition 4 (Under-investment) . Under-investment occurs when some socially efficient ventures (i.e. ventures whose expected values are greater than those obtained from employing their resources in their best alternative use) are not undertaken by entrepreneurs. Definition 5 (Over-investment) . Over-investment occurs when some socially inefficient ventures (i.e. ventures whose expected values are less than those obtained from employing their resources in the next best use) are undertaken by entrepreneurs. Redlining is a distinct concept from credit rationing because with redlining no member of a group, whether defined by wealth, location or some other characteristic, can obtain any finance. Credit rationing, in contrast, is the random denial of credit to some, but not all, potential borrowers in a group. Under-investment refers to a situation where too few entrepreneurs are financed for the social good, while over-investment refers to a situation where too many entrepreneurs are financed. Under-investment is distinct from credit rationing and can occur in the absence of credit rationing. Conversely, with over-investment it would be preferable from the standpoint of social efficiency if

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some of the currently active borrowers were to quit entrepreneurship and deploy their resources to alternative, more productive uses. 7.2 Theories of credit rationing and redlining 7.2.1

Type I credit rationing Models of Type I credit rationing have a long pedigree, stretching back to the 1960s. Few of the early models are discussed in the literature nowadays (for a survey see Parker, 2004, chap. 5). One exception is based on the idea that larger loan sizes increase the temptation for entrepreneurs to ‘take the money and run’, which increases default rates and hence banks’ costs of monitoring and seizing collateral. Banks’ best response is to restrict loan sizes, so Type I rationing results.10 Contemporary research tends to focus more on moral hazard as a potential source of Type I credit rationing. For example, if banks, aware that high repayment rates deter entrepreneurs’ effort and hence repayment rates, are unable to reduce interest rates in times of tight credit, their only way of protecting themselves against losses caused by moral hazard may be to restrict loan sizes (Piketty, 1997; Laffont and Martimort, 2002, chap. 4.8.4). Another form of moral hazard emerges because limited liability places a fixed lower bound on entrepreneurs’ downside risk, while reserving to entrepreneurs all of the upside benefits. If venture risks and returns are both functions of the loan size, entrepreneurs optimally request larger loans than banks wish to provide. Larger loans scale up the upside returns which entrepreneurs can capture, as well as the downside risk borne disproportionately by banks. Anticipating this, banks protect themselves by capping loan sizes.11 A formal proof of this result, based on a model by Bernhardt (2000), appears in the first part of the chapter appendix. Although it is possible to propose models with asymmetric information or overoptimism which reverse Type I credit rationing (resulting in entrepreneurs taking larger loans than they would choose under perfect information,12 the available evidence appears to be consistent with Type I credit rationing. According to Parker and van Praag (2006a), Dutch entrepreneurs who were interviewed in the mid-1990s obtained on average only four-fifths of the starting capital they requested. Over one-third of respondents claimed to have faced some degree of Type I credit rationing when they started up. As this was a self-selected sample of surviving ventures, the actual degree of Type I credit rationing might be even higher than this. On the other hand, Parker and van Praag’s (2006a) survey respondents might have exaggerated the difficulties they faced when starting up their ventures. For example, over-optimistic but objectively low-quality entrepreneurs might claim to be credit-rationed even though realistic banks rationally deny them funds (Hillier, 1998). This should caution against placing too much faith in self-reported measures of ‘credit rationing’. 7.2.2 Type II credit rationing, redlining and under-investment The ‘canonical’ model of Type II credit rationing is that of Stiglitz and Weiss (1981), referred to as SW hereafter. This model is based on asymmetric information about the

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values of new ventures. Entrepreneurs are assumed for simplicity to be risk-neutral, to have identical stocks of wealth, and borrow a unit of capital from a competitive riskneutral bank.13 Each entrepreneur operates one investment project with a given risk profile. Entrepreneurs know the risk of their projects but banks do not. It is assumed that there is no credible way for entrepreneurs to convey their private information to banks. As noted in the introduction to the chapter, imperfect information is taken to be residual in the sense that bank screening has already been performed. Although banks do not observe individual entrepreneurs’ types, they do observe the frequency distribution of these types in the population. It is important to note at the outset that without asymmetric information all entrepreneurs with positive NPV investment projects would receive finance from competitive lenders; no entrepreneur with negative NPV projects would be funded. Each active entrepreneur would pay an interest rate which reflected their own riskiness to banks. They would not be pooled with other borrowers. There would be no credit rationing, under-investment or redlining. There are two key features of the SW asymmetric information model. One is that ventures generate the same expected return, but some entrepreneurs operate projects with greater risk than others, in the sense that their project returns are a mean preserving spread (MPS) of other project returns (see chapter 2). The second important feature of SW’s model is that banks offer debt contracts. This assumption, together with limited liability, means that entrepreneurs have bounded downside risk. That is important because although entrepreneurship becomes less attractive for all entrepreneurs as mandated interest repayments D increase, entrepreneurs operating the riskiest projects are the least affected. That is because these entrepreneurs enjoy greater upside risk than entrepreneurs operating safe projects, and are less likely to end up paying D. Hence the first entrepreneurs to drop out of the market as D increases are entrepreneurs operating the safest projects. But these are the best customers from banks’ perspective. There is therefore adverse selection in the credit market. The pool of borrowers becomes riskier as banks increase D, which might reduce banks’ expected profits from doing so. A similar outcome can arise if entrepreneurs can choose the degree of risk of their projects, for then all entrepreneurs would rationally respond to a higher D by selecting riskier projects. The borrower pool once again worsens from banks’ perspective, warranting a higher interest rate and possibly restricted levels of lending. This particular mechanism exemplifies moral hazard, which has qualitatively similar implications to adverse selection. But in this case, restricted lending can cause entrepreneurs to engage in even more moral hazard, reducing lending and hence effort further in a downward spiral. The outcome can be the continued propagation of negative shocks, leading to a credit crunch (Minetti, 2007). There are three major implications of the SW model. One is under-investment. Asymmetric information causes the competitive loan rate to reflect loan applicants’ average riskiness, which may be so high that low-risk entrepreneurs drop out of the market first. If there were perfect information these entrepreneurs would face an interest rate which

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reflected their risk (but not that of riskier borrowers) and that would be sufficient to entice them to become entrepreneurs (de Meza and Webb, 1987, Proposition 5(A)). The under-investment problem can be solved by subsidising interest income, which increases both the equilibrium number of entrepreneurs and social efficiency. A second implication of SW is that Type II credit rationing might occur. As noted above, the worsening pool of borrowers means that increasing the interest rate to satisfy an excess demand for loans might actually reduce bank profits, rather than increasing them as might be expected. Effectively, banks’expected returns may eventually become a decreasing function of the interest rate D, as the pool of entrepreneurial ventures becomes dominated by risky types. This is illustrated in part (a) of Figure 7.2, where Dcr , termed the ‘bank optimal’ interest rate, is the rate that maximises bank expected profits and which therefore holds under competition.14 In this case, the supply of funds also becomes a decreasing function of D above Dcr , since depositors receive the banks’ returns and so supply fewer funds when banks’ average portfolio rate of return ρ decreases. This is illustrated in part (b) of Figure 7.2, which shows how credit rationing can occur if there is a ‘high’demand for funds, and if Dcr < Dm , where Dm is the ‘marketclearing’ interest rate (i.e. which matches supply with demand). Here banks deny credit to L1 − L∗ randomly chosen borrowers who are observationally indistinguishable from those who do receive loans. In effect, credit rationing occurs because banks do not respond to an excess demand for funds by increasing the interest rate. Hence the excess demand persists. A formal derivation of the credit rationing result appears in the second part of the chapter appendix, which also shows why credit rationing can be only a possible, rather than an inevitable, feature of credit markets. Intuitively, if adverse selection effects are very weak, bank expected return functions will not deviate sufficiently from a monotonic increasing function to yield an interior bank-optimal interest rate. With no interior bank-optimal interest rate, there can be no credit rationing in this model. Moral hazard can also generate an interior ‘bank-optimal’ interest rate which is lower than the market-clearing rate – and therefore also credit rationing.As noted above, moral hazard can arise in several ways. For example, entrepreneurs could deliberately select riskier projects in response to a higher interest rate, once again reducing the quality of the borrower pool and discouraging banks from increasing the interest rate to clear the market (Stiglitz and Weiss, 1981). Or entrepreneurs could decrease effort in response to higher repayments which cut their profits, so reducing the value of business assets which banks can claim from failed entrepreneurs.15 Other reasons for a bank-optimal interest rate can also be proposed. First, a higher interest rate which decreases the demand for loans can reduce banks’ ability to exploit scale economies, and might also restrict their ability to leverage their assets profitably. Second, a different form of adverse selection can give rise to an interior bank-optimal interest rate if ‘honest’ entrepreneurs who resist the temptation to default opportunistically are the first to quit the loans market when the interest rate increases (Clemenz, 1986). As we will see in the next section, an alternative source of credit rationing which does not depend on an interior bank-optimal interest rate arises if banks can exploit a willingness of borrowers to expose themselves to the possibility of Type II credit rationing as a tool of efficient financial contracting (Besanko and Thakor, 1987a).

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Banks’ expected portfolio rate of ret

urn, –␳

(a)

0 D

D

cr

(b )

High demand

N umb er of loans, L

L 1

L

L*

S upply

2

Low demand

0 D 2

D

cr

D

m

D

Figure 7.2 Stiglitz and Weiss’ credit rationing model. (a) Banks’ expected returns (b) The market for funds

The third implication of SW’s model is the possibility of redlining. To see this, suppose for expositional clarity that banks can distinguish between three distinct groups of entrepreneurs. The groups are indexed by θ , where θ can be good (g), bad (b) and ‘ok’ (o). Following the logic of Figure 7.2, there is for each borrower group an interior bank optimal interest rate, denoted by Dθ , generating expected returns to banks of ρθ (Dθ ). Banks’ expected return functions for each group are illustrated in Figure 7.3. Let ρ ∗ denote the deposit rate, which must be unique in a competitive deposit market. As Figure 7.3 shows, group g will be fully served, and so will some members of the marginal group o; but a group b generating returns of ρb (Db ) < ρ ∗ cannot make a sufficient return to enable banks to compensate depositors at any interest rate. Hence no member of group b will obtain funds, even though these ventures may have aboveaverage expected social returns. This group is redlined. The only solution to redlining

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␳g (D) ␳* ␳o(D)

␳b (D)

0 D g

D

o

D b

D

Figure 7.3 Redlining

is to somehow induce an outward shift in the supply of funds schedule, since that reduces ρ ∗ . One possibility is universal government lending (Ordover and Weiss, 1981), although that policy is not necessarily welfare-enhancing. Obviously, periods of tight credit or ‘credit crunch’ (as occurred in 2008, for example) are associated with more extensive redlining. SW’s is not the only model of redlining. A different model, by Aghion and Bolton (1997), generates an opposite prediction to SW with respect to the effects of the cost of capital. In SW’s model, an increase in ρ exacerbates redlining, as we have just seen; but in Aghion and Bolton’s (1997) model, where individuals can choose to be borrowers (entrepreneurs) or lenders, borrowers respond to an increase in ρ and redlining by switching out of entrepreneurship and into lending, so reducing the size of the redlined group. Another notable feature of the Aghion–Bolton model is that the number of individuals caught in the redlining trap is predicted to progressively decline as economies accumulate wealth. This suggests that credit constraints are likely to be more prevalent in less-developed than in highly developed economies, and that economies eventually ‘grow out of’ wealth-based redlining.16 Further details about the Aghion–Bolton model can be found in chapter 9. 7.3

Rebuttals of the credit rationing hypothesis and counter-rebuttals

7.3.1 Rebuttals Although it has been hugely influential, SW’s model and other asymmetric information models of credit rationing, redlining and under-investment have been criticised on several grounds. The primary objection to these models concerns their assumption that

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heterogeneous entrepreneurs are pooled together with one financial contract. Instead, it is argued, entrepreneurs and banks have incentives to devise richer financial contracts which separate hidden borrower types. That destroys pooling outcomes on which the credit rationing result (and other types of inefficiency) depends. I will explain this idea first, before moving on to other criticisms, some of which are specific to the SW model. So far, banks have been assumed to specify financial contracts whose only term is the interest rate. As we have seen, the interest rate does not work efficiently because its indirect effects on the quality of loans conflict with its direct price (i.e. quantity) effects. In practice, however, other loan terms (such as collateral) might be available to banks. Entrepreneurs who are better risks from banks’ point of view have incentives to request loan contracts which condition on collateral in order to signal their ability to the banks and thereby separate themselves from more risky types with whom they are pooled (in a ‘pooling equilibrium’). That way, the less risky types can obtain a lower interest rate, reflecting their more favourable borrowing profile (in a ‘separating equilibrium’). Likewise, banks have incentives to offer a menu of different contracts to appeal to different borrower types, in order to steal a competitive advantage over their rivals. That is, banks have incentives to screen heterogeneous entrepreneurs and reveal their hidden types. For expositional ease, much of the discussion below will work with just two hidden entrepreneurial types, a good type g and a bad type b. The precise aspects that make them ‘good’ or ‘bad’ are unimportant here; it suffices merely to think of g as being less prone to default than b. The population shares of the two types will be assumed fixed and known; as before, it is the identities of each individual that cannot be observed by banks. Consider Figure 7.4 for the case of borrowers b and g and two contract terms: the gross interest rate, D, and another ‘bad’ (from borrowers’ perspective), denoted BAD. It might help fix ideas to think of collateral posted by entrepreneurs at the bank’s request as an example of BAD. Indifference curves Jb and Jg in (D, BAD) space show how the different borrowers are prepared to trade off one bad for another. Indifference curves closer to the origin are associated with higher borrower utility. The key point is that the different borrowers have differently sloped indifference curves. Good borrower types g have flatter indifference curves than bad types b because they are more likely to succeed and have to repay the bank D – and hence are more willing to endure more of BAD (e.g. risking more collateral) in return for a lower D. Conversely, bs are less willing to incur the cost of more BAD in return for a lower D because they know they are more likely to default and thereby avoid repaying D but incur the BAD (e.g. lose their collateral). A formal proof of these propositions in the context of collateral, from Bester (1985a), is given in the third part of the chapter appendix.17 Crucially, if Jb and Jg cross only once (the so-called ‘single crossing property’, which certainly holds in Bester’s (1985a) model – see the appendix), then contracts b = (Db , 0) and g = (Dg , BADg ) separate types and are consistent with banks’ ‘isoprofit’ lines (i.e. lines of constant break-even profit) πb and πg . That is, g would prefer a contract (D, BAD), just to the right of g along πg , in preference to b ; but b would

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D b

⌫b

De b t repayment, D

␲b

D

␲g g

⌫g

J

0 BAD

b

J g

g

Another b ad, BAD

Figure 7.4 The use of two-term contracts to separate hidden types

prefer b to that contract. Hence if banks offer these two contracts – and under competition they must offer these contracts18 – then each borrower type will self-select into the contract that maximises their utility, so revealing their type. This equilibrium is incentive compatible, because each type does best under the contract that reveals their type. Masquerading as a different type will result in a lower payoff and so will not be chosen. Able entrepreneurs can signal or be screened using instruments other than collateral. Another BAD they might be willing to bear is foregoing the advantage of limited liability (Chamley, 1983). To see this, suppose that entrepreneurs are risk-averse and can choose between limited and unlimited liability debt contracts. Under symmetric information, all risk-averse entrepreneurs would prefer limited liability because it places a lower bound on their risky returns. But under asymmetric information, high-ability entrepreneurs might be prepared to endure the BAD of unlimited liability in order to signal their higher ability to banks. Entrepreneurs who successfully identify themselves to banks as good risks obtain a lower interest rate (risk premium), giving rise to a partially separating equilibrium like the one in Figure 7.4.19 Indeed, more able entrepreneurs might be willing to offer banks greater personal guarantees, signing away their rights to personal assets beyond the collateral they have pledged. This signal is credible because only able types would believe that they are unlikely to lose their personal assets this way, and ‘put their money where their mouths are’ by risking the consequences. A related possibility open to some ‘international entrepreneurs’ is to cross-list their companies in

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foreign countries where investor protection is stronger (and hence their own protection is weaker) in order to credibly signal their types (Mougin, 2007). Other BAD financial contract terms which convey strong signals about hidden entrepreneur types include short-maturity loans; issuance of convertible preferred stock; and agreements to offer banks forfeiture and anti-dilution provisions (Sahlman, 1990; Diamond, 1993). Able entrepreneurs should also be more willing than less able entrepreneurs to waste resources by requesting inefficiently large (or small) loan sizes in order to signal their types.20 Likewise, they should also be more willing to submit to high interest rates early on in a lending relationship, followed by lower rates later on, because they anticipate a greater probability of their business surviving to the later date when rates are low (Webb, 1991; Boot and Thakor, 1994). However, it should be acknowledged that multi-period lending separation devices can be difficult to implement in practice for reasons relating to imperfect commitment. Furthermore, moral hazard can undermine the viability of long-term credit contracts. For instance, if entrepreneurs learn about their abilities over time, the resulting reduction in uncertainty means they might divert effort into riskier entrepreneurial projects which can undermine the existence of a credit market altogether (Parker, 2007c). Another factor undermining long-term credit contracts is a dynamic inconsistency problem in which entrepreneurs need a stream of finance over several periods. If the borrowing relationship has a clear end, borrowers have an incentive to default in the final period. Anticipating this, banks will not lend in the final period, giving borrowers the incentive to default in the penultimate period. By backward induction, default incentives are peeled back until the mechanism unravels altogether – unless there is sufficient uncertainty about the end date, or if there is well-established progression from one loan tranche to the next. An important consideration is that few entrepreneurs run their businesses to satisfy outside shareholders. Being both owners and managers, entrepreneurs do not separate ownership and control. While this might give entrepreneurs a competitive advantage over corporate competitors whose separation of ownership and control generates agency problems, it turns out that separation of ownership and control can serve a valuable signalling role in credit markets. Banks favour projects where the entrepreneur employs a manager who is paid simply according to whether the project succeeds or fails. These ‘low-powered incentives’remove the incentives for entrepreneurs to take hidden actions which harm lenders. Not separating ownership and control could send an adverse signal such that in equilibrium only ventures in which ownership is separated from control receive finance (Acemoglu, 1998). Somewhat less plausibly, education has also been proposed as an entrepreneurial signalling mechanism in the credit market. According to this argument, gifted entrepreneurs may optimally acquire less education than ordinary individuals if this conveys a signal of strength about their innate entrepreneurial abilities. If innate entrepreneurial ability matters more for business success than formal education, and financiers reward more able entrepreneurs by offering them favourable credit contracts, ordinary people will not find it in their interests to emulate the gifted people, because

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with their abilities they benefit more by taking more schooling and working in paid employment instead (Orzach and Tauman, 2005). Unfortunately, the evidence refutes this theory. Quite apart from the fact that entrepreneurs and non-entrepreneurs choose similar levels of schooling on average, innovating entrepreneurs who take less time to complete their university degree (arguably a direct measure of ability) are more rather than less successful in raising finance (Backes-Gellner and Werner, 2007). Furthermore, better-educated entrepreneurs receive more rather than less credit on average and can access a greater diversity of start-up funds (Parker and van Praag, 2006a; Astebro and Bernhardt, 2003). The foregoing discussion has shown that in principle a richer contract than one based on interest rates alone can separate hidden entrepreneur types. In principle too, type separation removes all information asymmetries and hence the possibility that asymmetric information-induced credit rationing can occur. This analysis readily generalises to more than two hidden entrepreneur types. For example, with three types, three different (D, BAD) contracts can be offered that accomplish separation. However, with two or more dimensions of unobserved borrower heterogeneity, more than two contract terms are needed to separate types. For example, with two dimensions of heterogeneity (e.g. different entrepreneurial abilities and different venture risks), contracts might need to specify an interest rate, collateral and a sub-optimal loan size, say. But the principle remains the same. In the limit, one can imagine banks enriching the menu of contracts with as many contract terms as it takes to reveal all of the hidden information across every dimension. This completes my discussion of signalling and screening criticisms of credit rationing models. The next subsection will explain how these criticisms can be criticised in their turn. The remainder of the present subsection turns instead to other drawbacks of SW and related models. A particular problem with the SW model is that it assumes, rather than derives, debt to be the optimal form of finance. In fact, it can be proved that equity is actually the optimal form of finance in the SW model, where entrepreneurs’ investment projects differ in terms of their risk profiles (Cho, 1986; de Meza and Webb, 1987). But if ventures were to be financed by equity, SW’s competitive equilibrium would no longer exhibit any credit rationing or inefficiency; and banks would randomise rather than fix interest rates as SW assumed (de Meza, 2002). It is therefore necessary to appeal to some factor outside the model which favours debt over equity finance if SW’s results are to remain relevant in a strict sense. That could entail, for example, high costs of writing equity contracts. But this logical inconsistency remains an unsatisfactory feature of SW’s model. A more powerful objection is that, if risk eventually gets resolved in the future, entrepreneurs wanting to operate risky projects would do best by exercising their valuable option to wait before investing. Hence they would become the first to drop out of the market when interest rates increase. Contrary to the SW model, this implies favourable selection in the credit market, ruling out credit rationing (Lensink and Sterken, 2001, 2002). Moreover, Type II credit rationing implies that borrower entrepreneurs’marginal

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costs of funds become infinite. Hence rationed entrepreneurs have an overwhelming incentive to avoid rationing by scaling down the size of their project, decreasing consumption or delaying the project to accumulate wealth for self-finance (Parker, 2000). De Meza and Webb (2006) argue that this makes the conditions for credit rationing very stringent, although credit rationing can still emerge if the values of indivisible projects decay sufficiently quickly with delay. Another criticism of credit rationing models is that other plausible alternative models exist which generate market-clearing outcomes instead (e.g. de Meza and Webb, 1987). There are also grounds for questioning how widespread credit rationing can be in practice when sources of finance other than debt contracts (e.g. trade credit, equity finance and leasing) are available. Evidence about credit rationing is reviewed in chapter 9. 7.3.2

Counter-rebuttals

Proponents of credit rationing have offered several objections to the criticism that sophisticated financial contracting can eliminate the pooling equilibria on which credit rationing depend. First, screening and signalling which lead to separating equilibria are unfeasible if extra contract terms are unavailable. For example, specifying BAD as collateral will be ineffective if borrowers have insufficient collateralisable wealth. Then pooling and credit rationing can emerge again. This may be an important point because it is known, for example, that lack of collateral is one of the major reasons why banks in developed countries refer borrowers to public sector loan guarantee schemes (see chapter 16). Second, BAD contract terms might not be monotonically related to preferences, violating the single-crossing property underlying Figure 7.4. SW pointed out that banks may choose not to request collateral if that reduces requested loan sizes and thereby increases the risk of failure of the ventures in their portfolio owing to under-capitalisation. Furthermore, decreasing absolute risk aversion implies that most collateral will be offered by wealthy borrowers who are most willing to operate the riskiest investment projects – thereby muddying the signal. In both cases, banks can do best by not requiring collateral to deal with the credit rationing problem (Stiglitz and Weiss, 1981, 1992). Other muddy signals emerge if entrepreneurs do not behave fully rationally. For example, if entrepreneurs are over-optimistic, as the evidence in chapter 4 suggests, less able entrepreneurs who do not recognise this may self-select into the ‘wrong’ (i.e. able) group, destroying the value of self-selection contracting tools. Third, if the number of high-risk types is not too numerous, the benefits of screening entrepreneurs might not compensate for the deadweight costs entailed by the seizure of collateral. Banks might then do better by offering pooling contracts without collateral rather than devising separating contracts based on collateral (Mattesini, 1990). More generally, in complex informational environments, signalling equilibria can be infeasible, and scarcely worthwhile for small ventures (Admati and Pfleiderer, 1994; Ravid and Spiegel, 1997). If that is the case, some asymmetric information remains, restoring pooling of at least some entrepreneur types with the associated possibility of credit rationing.

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It should also be noted that whether separating or pooling equilibria emerge also depends on the assumed nature of the game between entrepreneurs and banks, especially the timing of the moves of the game, and the definition of competitive equilibrium (Hellwig, 1987). If there is perfect competition among banks, and if the outcomes of screening or monitoring are public information (i.e. can be exploited by other banks), then the free-rider problem means that each bank has weaker incentives to perform costly screening and thereby resolve asymmetric problems. Then pooling contracts can emerge again.21 When there is imperfect competition among banks, banks can also maximise surplus by means of pooling, rather than separating, contracts (Besanko and Thakor, 1987a). On the face of it, the available evidence appears to lend greater support to the existence of pooling rather than separating equilibria. Actual lending rates tend to be rather insensitive to differences in both ex ante and ex post (e.g. actual performance) measures of ability, with bank small-business lending margins over the base rate typically varying by no more than a few percentage points.22 There is also limited direct evidence of entrepreneurial selection, whether adverse or favourable. Experimental evidence indicates that participants in risky investment games exhibit over-optimism which overwhelms any selection effects. In one such case, as the experimenter increased the opportunity cost of participating in the game (D), there was no systematic tendency for the most or least able to drop out of the game first (Coelho, 2004). On average, those who dropped out were the least optimistic, but no less or more able, than those who continued playing. This result highlights the earlier point about how over-optimism can muddy signals of ability. On the other hand, evidence from developing countries points to the existence of bank-optimal interest rates for some individual loans, where increases in nominal interest rates above 60 per cent are associated with greater default problems and lower portfolio quality (Cull et al., 2007). Even if separation does occur in credit markets, it is theoretically possible for credit rationing itself to serve as a BAD that separates types.23 To see how, suppose entrepreneurs have no collateral, and that BAD is the probability that banks randomly ration borrowers. Then gs are willing to take the risk of being rationed in return for a lower interest rate (since they are more likely to succeed and hence repay their loans) – whereas bs care less about a lower interest rate and more about receiving a loan. In effect, the risk of being credit rationed is the price that gs must pay to signal their types. However, an unrealistic feature of this model is that good risks are rationed, while bad risks are fully funded. An arguably more plausible alternative model, proposed by Stiglitz and Weiss (1983), has banks threatening to ration credit in later periods unless borrowers succeed early on. This induces good behaviour by borrowers and low default rates. But to be credible, banks must be seen to carry out their threat, so in equilibrium some credit must be rationed. In summary, the complication of financial contracts by opponents of credit rationing can be countered by the introduction of new dimensions of unobservable borrower heterogeneity by supporters of credit rationing. It would be desirable to take data to

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the various models to narrow the field of theoretical possibilities by discarding some explanations in favour of others. However, little progress on this front has been made to date. One reason for this is no doubt the tricky practical problem that hidden borrower and venture types are unobservable. To address this problem, the researcher would need to obtain data on hidden characteristics that are somehow unobserved by banks. What the above discussion does reveal is that credit rationing models are sensitive to changes in their assumptions. Some researchers apparently believe that this casts doubt on the relevance of the phenomenon itself (e.g. Hillier and Ibrahimo, 1993). On the other hand, as Clemenz (1986) has argued, it is very unlikely that necessary conditions for credit rationing can ever be found in models that are sufficiently general to remain interesting. Furthermore, the number of possible mechanisms by which credit rationing can arise could instead be regarded as a strength rather than a weakness, because it expands the set of circumstances under which such rationing may occur. Further theoretical refinements of existing models are unlikely to change any of these points. Instead, a more pressing need is for empirical research to address directly the question of whether credit rationing exists, and if so, to what extent. This issue is taken up in chapter 9. 7.4

Over-investment

Implicit in the models discussed so far is a presumption that financing problems are generally associated with too little entrepreneurship. However, other plausible models can be constructed in which the opposite outcome – too much entrepreneurship – arises. De Meza and Webb (1987) (hereafter DW) were the first to propose a model with this property, discussed in the first subsection below. The one after discusses over-optimism as an alternative source of over-investment. 7.4.1

The de Meza and Webb (1987) over-investment model DW utilise several similar assumptions to SW, including the pooling of heterogeneous types with a single credit contract under conditions of asymmetric information. The main difference between the DW and SW models is the assumed structure of project returns. Using the terminology of chapter 2, DW assume that entrepreneurs differ in terms of the probability their venture succeeds (first-order stochastic dominance) rather than in terms of their risk profile (second-order stochastic dominance). Starting with assets B, on which they could earn a gross rate of return ρ outside entrepreneurship, an entrepreneur with ability x whose investment project succeeds with probability p(x) compares the safe return of ρB with expected returns in entrepreneurship, namely p(x)(R − D). Banks would prefer to lend to higher-x borrowers because the probability they repay D from their gross return R is higher. An entrepreneur who fails gets zero returns, and the bank loses its capital in the venture. At the outset, DW point out that all entrepreneurs must invest all of their personal wealth B in their project before applying for loans, in order to avoid transmitting an adverse signal about their type. This is because the most able entrepreneurs are the

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least likely to fail, and so can supply finance to themselves on better terms than they can obtain in the market. Less able entrepreneurs must emulate them in this respect to avoid transmitting an adverse signal about their true abilities which would separate them into less favourable contracts. Full self-finance, a result which has been known since at least Leland and Pyle (1977), does however reduce the welfare of risk-averse borrowers who have to take larger stakes in their own firm than they would wish to without asymmetric information. Some evidence is consistent with the hypothesis of full self-finance (Chandler and Hanks, 1998), though some other research suggests that entrepreneurs tie up only about one-half of their wealth in their business (Gentry and Hubbard, 2001, 2004). Over-optimistic entrepreneurs also offer more self-finance than realists, as one would expect of people who rate their chances of success very highly (Landier and Thesmar, 2008). In terms of the occupational choice model of chapter 2, the marginal entrepreneur x˜ is defined by p(˜x)(R − D) = ρB. Hence if G(x) is the distribution function of ability, a total of 1 − G(˜x) high-ability individuals choose entrepreneurship (recall chapter 2). A key implication of this selection is that the marginal entrepreneur x˜ is of low ability relative to other entrepreneurs (see, e.g., Figure 2.3(b)). As D increases, the least able entrepreneurs leave the market first, improving the quality of the borrower pool, and increasing the average probability of success among the remaining active entrepreneurs, p. Hence p is an increasing function of D. This implies that there is favourable rather than adverse selection. Because banks’ returns are Dp, there can be no credit rationing: banks always have an incentive to increase the interest rate D to remove any excess demand for funds, unlike the case in SW where an interior bank-optimal interest rate did not give them that incentive. Likewise, banks would be forced by competitive pressures to reduce D to the market-clearing rate Dm if there was an excess supply of funds. The impossibility of credit rationing is DW’s first key result. Their second key result is the inevitability of over-investment. This follows because the marginal entrepreneur x˜ generates an expected loss of [p − p(˜x)]D to the bank, i.e. generates expected returns that are less than the opportunity cost of the funds used. In other words, resources would be more efficiently deployed if the least able entrepreneurs cancelled their ventures and became safe investors instead. The reason this does not happen is because under imperfect information the least able are cross-subsidised by the more able. The former consequently find it privately worthwhile to undertake ventures that are socially inefficient. This inefficiency is especially damaging in financial markets characterised by limited liquidity (Vesala, 2007). With over-investment, a tax on interest income can restore the economy to full efficiency. This policy, which probably has minimal adverse work effort disincentive effects, increases the interest rate which must be paid to compensate depositors, and induces the least able entrepreneurs to exit.24 Alternatively, the government can tax entrepreneurs’incomes while promoting policies which favour non-entrepreneurs, such as increasing the minimum wage (Parker, 2003c). In general equilibrium, by removing the least able entrepreneurs from the pool of borrowers and producers this policy can

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allow banks to reduce interest rates to remaining entrepreneurs. At the same time, consumers are willing to pay more in markets where the average quality of goods is higher. Ghatak et al. (2007) refer to a tax on interest income as a ‘trickle up’ policy, since its benefits can work through to benefit even the entrepreneurs who pay the tax via higher interest repayments. In contrast, subsidising credit would attract the least able entrepreneurial types into the market, which would force banks to increase interest rates for every entrepreneur in the pool.

7.4.2 Over-optimism Two influential models of over-optimism and over-investment are de Meza and Southey (1996) and Manove and Padilla (1999). Where these models differ is that de Meza and Southey (1996) assume that banks can distinguish between realistic and over-optimistic borrowers, while Manove and Padilla (1999) assume that banks cannot distinguish between them and pool them together. In the de Meza and Southey (1996) model, over-optimists are the ones to suffer. They remain in entrepreneurship for too long, earning lower incomes and bearing greater risk than they would in paid employment, and committing excessive personal equity to their projects – until they run out of funds and are forced to exit. As a result, over-optimistic entrepreneurs who are denied loans may be better off ex post than those who obtain them. It is noteworthy that over-optimism can actually create or worsen redlining by causing incompetent individuals to apply for start-up funds. Hence subsidies and loan guarantee schemes designed to attract redlined borrowers can decrease rather than enhance social welfare (Coelho et al., 2004). Coelho et al. (2004) shy away from DW’s recommendation of actively taxing entrepreneurship, since they acknowledge the possibility of private and social benefits to entrepreneurship; but they regard proentrepreneurship policies as intervening in ‘the wrong direction’. As they put it, ‘A few good apples may attract all the publicity but this should not divert attention from most of the barrel being bad’ (2004, p. 413). In Manove and Padilla’s (1999) model, on the other hand, competitive banks face asymmetric information about which applicants are over-optimists and which are realists. Over-optimistic entrepreneurs of low ability pass up the chance to apply for smaller investments that would make them a profit, requesting unprofitable larger loan sizes because they incorrectly think they possess high ability. This form of over-investment can be rectified by increasing the interest rate, for example by DW’s interest tax policy. Manove and Padilla (1999) conclude that ‘conservative’ bank lending policies may therefore be justified even in the face of vocal criticism from the small-business lobby. On the other hand, banks do not always get it right, either. As recurring Latin American debt write-offs and the recent crisis in the Western banking system illustrate, banks can also be prone to exuberant over-optimism. Bank over-optimism can be especially damaging to the soundness and stability of the credit market. Suitable capital requirements and prudential regulation are required to counter this problem (Manove and Padilla, 1999).

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Financing

Conclusion

Debt finance for new start-ups has an important bearing on entrepreneurship and public policies towards it. The availability and price of loans, and other contract terms such as collateral and loan sizes, are of primary importance to entrepreneurs who borrow from banks. The theoretical literature reviewed in this chapter showed that when entrepreneurs possess more information about their proposed ventures than banks do, it is possible for efficient contracting between banks and entrepreneurs to break down. Either too much or too little finance, involving too many or too few entrepreneurial ventures, can occur from the standpoint of social efficiency. The view of this author is that de Meza and Webb’s (1987) model of entrepreneurial finance is a particularly important contribution to the debate about entrepreneurial finance. That model challenges the widespread assumption that financing problems necessarily result in too few entrepreneurs. De Meza and Webb show that it is quite possible for there to be too many entrepreneurs in equilibrium, and that a suitable government policy for encouraging entrepreneurship could involve deterring the least able people from borrowing in the credit market. Therefore policy-makers should not automatically equate credit market imperfections with insufficient entrepreneurship, and so should not immediately reach for instruments designed to draw marginal individuals into entrepreneurship (see also Parker, 2007a). My discussion has touched on appropriate policy responses where they exist. These responses are diverse, reflecting the diversity of the models. One should certainly not take any particular policy recommendations too seriously. The models are partial equilibrium in nature, and do not take into account broader effects that need to be analysed in a general equilibrium setting (Hillier and Ibrahimo, 1993). The models also tend to be rather fragile, in the sense that altering some of their assumptions can easily reverse the predicted forms of market failure and the policy implications (Parker, 2002). I will elaborate on this theme of model fragility in the following paragraphs, before highlighting the desirability of future empirical research in this area. The easiest way to demonstrate the fragility of theoretical models of inefficient investment is to combine elements of the DW and SW models together in generalised models. Several generalised models allow entrepreneurs to operate ventures with heterogeneous risks as well as heterogeneous probabilities of success. Perhaps unsurprisingly, generalised models predict that Type II credit rationing, under-investment by some good types and over-investment by some bad types can all occur individually or simultaneously, while the aggregate level of entrepreneurial investment can be greater than or less than what transpires in the ‘first best’ equilibrium under symmetric information (Hillier and Ibrahimo, 1992; de Meza and Webb, 2000). This outcome is intellectually unsatisfying because it replaces concrete (albeit specialised) results with a nebulous demonstration that ‘anything can happen’. A similar problem occurs in a different DW-type model where expected returns in both occupations are increasing in unobserved entrepreneurial ability x, not just entrepreneurship as is assumed in DW and SW (Parker, 2003c). In all of these models, an appropriate policy instrument is income

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taxation or subsidisation of entrepreneurs. More will be said about income taxes and entrepreneurship in chapter 17. As a further example of ambiguity in generalised models, Bernanke and Gertler (1990) added a prior stage to DW’s model in which entrepreneurs must engage in costly search activity to identify a suitable venture. The information gathered from the search is known only by the entrepreneur who conducted the search, and good ventures are pooled with bad ventures at the second stage resulting in over-investment, as in DW. However, pooling diminishes the returns to finding good ventures, which diminishes entrepreneurs’ incentives to search, and so promotes under-investment. Whether the net effect turns out to be under-investment or over-investment is ambiguous. Some efforts have been made to cut through the ambiguity present in generalised models. It was noted earlier in the chapter that when it is costly to verify ex post project returns, debt is the optimal financial contract. But if state verification costs are trivial, equity can coexist with debt. It has recently been demonstrated that if entrepreneurs operate projects with heterogeneous risk profiles and expected returns, and can choose freely between DF and EF, over-investment arises under quite general conditions (Boadway and Keen, 2006). While this solution seems to cut through the Gordian knot of ‘anything can happen’, it is probably of little practical relevance. For reasons outlined in greater detail in the next chapter, most small entrepreneurial ventures are of no interest to equity financiers. Consequently, entrepreneurs can only realistically access one type of financial intermediary: banks. That returns us to the ambiguities of the generalised models based on debt finance alone. In any case, DW’s over-investment result is not robust to alternative generalised models which tie entrepreneurs’ success probabilities to the quality of workers hired by entrepreneurs. More entrepreneurs bid up the wage and attract highquality workers into their firms. But this positive externality is not internalised by the entrepreneurs themselves, implying under-investment in entrepreneurship. Depending on whether this effect or the DW effect is strongest, there can be too many or too few entrepreneurs (Boadway and Sato, 1999). The ensuing policy analysis is complicated. Furthermore, different forms of competition between different types of financial intermediary can contradict Boadway and Keen’s (2006) apparently decisive theoretical result. Gersbach and Uhlig (2007) analyse an economy in which two types of bank compete with each other to supply credit to entrepreneur borrowers. One type is a ‘standard’ bank, as analysed so far, which is good at ex post monitoring but not at ex ante screening. The other is an ‘investment’ bank or credit rating agency, which is good at ex ante screening but not at ex post monitoring. Investment banks which are good at screening cream off the best borrowers at the outset, leaving the worst borrower types to the standard banks. The adverse selection problem this creates forces some standard banks either to leave the market (so reducing the supply of credit) or to increase debt repayments if the market will bear it. But the latter induces moral hazard. In either case, interbank competition causes social inefficiency. The policy recommendation in this model is to reduce competition between the different types of bank. Unfortunately, this is the opposite of what Basel II regulations are deigned to achieve, namely improving

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standard banks’ screening effectiveness which can be expected to intensify competition with investment banks. To conclude, a better basis for policy recommendations would exist if one could sort through the various models and identify the ones with the greatest empirical relevance (Parker, 2002). Future empirical investigation along these lines would be valuable, but likely to encounter major practical difficulties. Perhaps unsurprisingly, therefore, the literature to date has not progressed very far in this direction. Attempting to reject models on the basis of their indirect predictions is also unlikely to be informative, for the simple reason that models can often be modified to better fit the stylised facts when they come into conflict with the original model.25 Data limitations which hamper empirical research also bedevil efforts to measure the extent of Type I and Type II credit rationing. Evidence on these issues, commonly bundled together by applied researchers and referred to simply as ‘credit constraints’, is reviewed in chapter 9. 7.6 Appendices 7.6.1

Bernhardt’s (2000) model of Type I credit rationing

There is a single-period planning horizon, at the start of which an investment project becomes available. Entrepreneurs have the skills to expedite the project but lack the requisite capital, k, which they borrow from a bank. At the end of the period the project pays off pf (k), where p > 0 is a stochastic price with distribution function G(p), whose support is the positive half-line; and where f (·) is a strictly concave production function. Entrepreneurs and lenders are risk-neutral and symmetrically uninformed about realisations of p ex ante. Lenders supply k via standard debt contracts and lend at the competitive gross interest rate r per unit of capital. If an entrepreneur defaults, the lender takes over the project and extracts all the revenues. Entrepreneurs are protected by limited liability (their minimum return is zero) and maximise expected profits, given by max E {max [0, pf (k) − rk]}. k

(7.1)

When choosing k, the entrepreneur being protected by limited liability is concerned only with positive profit realisations, so has the first-order condition
[pfk (k ∗ ) − r] dG(p) = 0, (7.2) p≥p∗

where p∗ is the price at which the entrepreneur just begins to break even: i.e. p∗ f (k ∗ ) − rk ∗ ≡ 0; and where k ∗ denotes the privately optimal capital choice. Here a subscript denotes a derivative. Bernhardt showed that, when there is a positive probability of default, k ∗ is not the same as the efficient level of investment, k e . The first-order condition for k e is


e e pfk (k ) dG(p) ≡ pfk (k ) dG(p) + pfk (k e ) dG(p) = 1. (7.3) p

p≥p∗

p ρ ≡ ρ(D) and differentiate (7.7) to obtain characterised by (D, θ). Write ρ˜ := ρ(θ, ∞ ˜ dρ d θ˜ g(θ) ˜ [1 − F(D, θ )] dG(θ ) . (7.8) + θ (ρ˜ − ρ) =− ˜ dD dD 1 − G(θ˜ ) 1 − G(θ) By inspection, D has two effects on banks’ expected portfolio rate of return. The second term of (7.8) is positive, capturing the positive effect of a higher interest rate on bank expected returns. But the first term is negative (by (7.6)), capturing the adverse selection effect. That is, a higher interest repayment increases the riskiness of banks’ portfolios, leading to lower expected bank returns. If the first term outweighs the second, banks’ expected returns ρ may eventually become a decreasing function of the interest rate D, as illustrated in Part (a) of Figure 7.2. However, if the first term is small in magnitude, ρ(D) increases monotonically, and credit rationing cannot arise. 7.6.3

Bester’s (1985a) screening model Bester (1985a) assumed that all entrepreneurs possess some wealth B, but that it is privately costly for them to convert it into collateral. Suppose that collateral conversion costs are proportional to the amount of collateral posted C, by a factor k > 0. Project returns Rθ for entrepreneur of type θ are stochastic, with distribution function Fθ (R) for type θ ∈ {g, b}, where Fg (R) second-order stochastically dominates (SOSDs) Fb (R), y i.e. 0 [Fb (R) − Fg (R)] dR ≥ 0 ∀y ≥ 0. Entrepreneurs borrow loan size L at interest rate r and post collateral C ≤ B. They pay the collateral conversion cost kC whether they succeed or fail, but lose C to banks as well if they fail. For simplicity there is no limited liability. An entrepreneur θ’s expected profit given credit contract  = (D, C) is therefore

πθ () = E{max[R˜ θ − (1 + r)L − kC, −(1 + k)C]},

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where D = (1 + r)L. On a loan  to entrepreneur θ the bank receives the expected rate of return ρθ () = E{min[(1 + r)L , R˜ θ + C] − L}/L. Entrepreneurs’ preferences depend systematically upon their type. To see how, use πθ () above to obtain the marginal rate of substitution between r and C as σθ () = −

Fθ [(1 + r)L − C] + k . {1 − Fθ [(1 + r)L − C]}L

By SOSD, Fb (R) > Fg (R) which implies σg () > σb (). The ‘single crossing’ property of indifference curves illustrated in Figure 7.4 is satisfied: b has a steeper indifference curve in (r, C) space as in Figure 7.4. Hence separating contracts (g , b ) rule out pooling and therefore credit rationing. Finally, the iso-profit lines in Figure 7.4 can be obtained by using ρθ () above to obtain banks’ indifference curves for loans to type θ entrepreneurs: µθ () = −

Fθ [(1 + r)L − C] . {1 − Fθ [(1 + r)L − C]}L

Note that with k > 0 this is less steep than entrepreneur θ ’s indifference curve at , as illustrated. Notes 1. This process will not be studied explicitly in this book: see Tajnikar et al. (2006) for an overview. 2. See de Meza and Southey (1996), Boot et al. (1991), Bester (1994) and Coco (1999). 3. The US studies include Leeth and Scott (1989), Berger and Udell (1990, 1992, 1995) and Coleman (2000). The UK studies are by Hanley and Crook (2005) and Burke and Hanley (2006). However, none of these studies properly tests the assumption that bank screening based on observable characteristics has already taken place. Instead, the above evidence relates to a pool of ventures with heterogeneous observable characteristics, in which the observably riskiest have to post the most collateral. Therefore this does not rule out the possibility that collateral varies within observable groups in a manner consistent with a signalling mechanism. To convincingly reject the signalling story, evidence would be needed of a positive correlation between risk and collateral among only ventures which appeared to be observably identical to banks. 4. See Petersen and Rajan (1994), Berger and Udell (1995) and Harhoff and Körting (1998). 5. See Greenbaum et al. (1989), Sharpe (1990) and von Thadden (2004) for a correction. 6. See Townsend (1979), Diamond (1984) and Gale and Hellwig (1985). 7. See Admati and Pfleiderer (1994), Ravid and Spiegel (1997) and Povel and Raith (2004). 8. See Laffont and Martimort (2002, chap. 4.8.4) for an explicit discussion of this point. An ongoing research agenda studies optimal contracts in a different class of moral hazard financing problem where entrepreneurs can divert operating cash flows before reporting them to banks. See Biais et al. (2007) for a recent discussion of, and contribution to, this literature. 9. I will not discuss in this chapter macroeconomic implications of credit rationing, as in e.g. Jaffee and Stiglitz (1990, sec. 5), Hillier and Ibrahimo (1993, sec. 6), Acemoglu (2001) and Fender (2005). For example, Acemoglu (2001) links credit market constraints to high levels of unemployment in Europe. 10. See Barro (1976), Keeton (1979), Koskela (1983), Gale and Hellwig (1985) and Gray and Wu (1995).

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11. Clemenz (1986, sec. 5.3), de Meza and Webb (1992) and Bernhardt (2000). 12. E.g. over-optimistic entrepreneurs request excessive loan sizes because they overestimate their future returns (Manove and Padilla, 1999). Or able entrepreneurs trying to separate themselves from less able types request excessively large loan sizes and are imitated by less able types (Boadway et al., 1998). 13. Despite external finance helping entrepreneurs to partially share project risks (Heaton and Lucas, 2004), risk-sharing and risk-aversion do not play a central role in the discussion that follows. Asymmetric information rather than risk aversion is the necessary condition for credit rationing, under-investment and redlining outcomes (Fender and Sinclair, 2006). It might also be thought that banks could refuse to finance entrepreneurs because they are risk-averse. But banks are unlikely to behave in a risk-averse manner, owing to their ability to spread risks over numerous borrowers. In short, risk aversion is not a convincing rationale for credit market inefficiencies. 14. If banks’ expected return function has more than one mode, then SW showed that credit rationing is still possible if the highest mode occurs for an interest rate ≤ Dm , where Dm is the market clearing rate (see below). The other possibility is of two interest rates, where credit at the lower interest rate is rationed, but where all borrowers can obtain funds at the higher interest rate. 15. See Watson (1984), Williamson (1986, 1987) and Ghatak et al. (2001, sec. 6.1). 16. And by increasing the average wage, it has also been argued that economic development can also promote the efficient transfer of family businesses – inherited by untalented scions of successful entrepreneurs – to the talented poor who can afford to buy them (Caselli and Gennaioli, 2005). Another possible source of redlining occurs when creditors are refused ‘absolute priority’ over debtor entrepreneurs’ assets. Anticipating violations of the absolute priority rule (APR), banks expect lower returns from lending to entrepreneurs. They respond by refusing credit altogether to entrepreneurs with little personal equity or large loan size requests (Longhofer, 1997). A culture of APR violations increases the equilibrium cost of finance; a clear policy implication is to enforce APR rules more stringently. 17. For further analysis of collateral in the context of entrepreneurial finance, see Chan and Kanatos (1985), Clemenz (1986), Besanko and Thakor (1987a) and Bester (1987). Coco (2000) surveys the literature. 18. The reason is that, in a competitive equilibrium, a pooling contract, p say, must make zero expected profits. But this involves gs cross-subsidising bs. Hence gs will prefer g to p , and any bank not offering g will lose gs to rivals that do. With the departure of gs, the p contract becomes loss-making, and is driven out of the market by competition. 19. In principle, a continuum of contracts specifying varying degrees of limited liability could be devised to perfectly separate a continuum of types and reveal the entire distribution of entrepreneurial abilities to banks. 20. See Bester (1985b), Besanko and Thakor (1987b), Milde and Riley (1988), Innes (1991, 1992) and Schmidt-Mohr (1997). 21. An open theoretical question is whether banks should share information about their borrowers with competitor banks. On the one hand, information-sharing can improve entrepreneurs’prospects and so mitigate moral hazard practised by ‘trapped’ entrepreneurs (Padilla and Pagano, 1997). This can be expected to enhance banks’ profitability. On the other hand, banks which share information lose informational rents. This relaxes competition among banks for borrowers at early stages of venture financing, which redistributes surplus from talented entrepreneurs to banks, and leads to possible adverse selection in the credit market (Gehrig and Stenbacka, 2007). 22. For UK evidence see Bank of England (1993), Keasy and Watson (1995), Cressy and Toivanen (1997) and Cowling (1998). US evidence reveals similar margins (Berger and Udell, 1992), with smaller firms paying somewhat higher interest rates than larger firms (Hubbard et al., 2002). 23. See Besanko and Thakor (1987a), Smith and Stutzer (1989) and Gale (1990a).

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24. Subsequent contributions have shown that the over-investment result and the interest tax policy are robust to the introduction of costly screening (de Meza and Webb, 1988); variable venture sizes (de Meza and Webb, 1989); risk aversion (de Meza and Webb, 1990); and the addition of ex ante moral hazard à la SW (de Meza and Webb, 1999). However, it should be noted that crosssubsidisation can sometimes increase social welfare, for example if less able types respond to more favourable pooled contract terms by supplying greater effort and so reducing the probability of default, thereby decreasing the pooled interest rate for all borrowers (Vercamman, 2002). 25. For example, de Meza and Webb’s (1987) model predicts a negative relationship between personal wealth and entrepreneurship, in conflict with most evidence on the issue (see chapter 9). But a subsequent paper published in 1999 by these two authors generalised the model by incorporating moral hazard, with the result that a positive relationship between wealth and entrepreneurship emerges. 26. Wette (1983) showed how adverse selection can also occur if banks vary collateral C while keeping D fixed.

8

Venture capital and other sources of finance

Chapter 7 analysed the debt finance of entrepreneurial ventures. While debt is the dominant form of external finance for most start-ups, many entrepreneurs also tap alternative sources of external funding. These include equity finance; informal sources of capital such as friends and relations; micro-finance and credit co-operatives; and trade credit. Sometimes, debt finance is unavailable, especially for entrepreneurs who lack collateral and propose investment projects comprising few tangible assets, high levels of risk and long development periods in which debt claims cannot be serviced. Banks typically do not finance entrepreneurial opportunities of these kinds; equity providers and other types of financiers do. Equity finance comprises both formal and informal venture capital. An extensive literature now documents the role of equity finance in funding high-growth and hightech entrepreneurial ventures. Formal venture capital has been used to finance such wellknown companies as Apple, Google, Amazon, Federal Express and eBay, among others. Equity finance, especially formal venture capital, tends to be ‘narrow but deep’, in the sense that few entrepreneurs use it but those who do can access large sums of funding from it. More widespread, but usually offering much more modest amounts of finance, are informal sources such as friends and family members, as well as micro-finance schemes. Mason (2006) cites survey responses from eighteen of the GEM countries which estimate that 3.4 per cent of adults are informal investors (either ‘business angels’ or friends and family). On average, they provide $196 million per year to new and growing companies, equivalent to 1.1 per cent of the GDP of these countries. Some 50 per cent of this informal investment goes to relatives; 29 per cent to friends and neighbours; 11 per cent to work colleagues; and 8 per cent to strangers. Informal finance is an important source of funding for entrepreneurs, amounting to between 60 and 90 per cent of total venture capital, including capital from institutional sources. The aim of this chapter is to outline the rich array of economic issues underlying these various sources of entrepreneurial finance. The first section briefly outlines the organisational structure of formal venture capital, as well as the size of the venture capital market. The primary aim here is to discuss the economics of this type of entrepreneurial finance, rather than to present an exhaustive description of the structure, 234

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investment process and functioning of the venture capital industry (on this, see Gompers and Lerner, 1999). The second and third sections explain respectively the advantages and limitations of formal venture capital finance for entrepreneurs. The fourth section briefly describes some features of business angels – the providers of informal equity finance. A variety of other informal sources of finance are discussed in the penultimate section; the final section concludes. 8.1 Venture capital and entrepreneurs 8.1.1

Organisational structure

Venture capital has developed as an important intermediary in financial markets, providing capital to firms that might otherwise have difficulty attracting financing. These firms are typically small and young, plagued by high levels of uncertainty and large differences between what entrepreneurs and investors know. Moreover, these firms typically possess few tangible assets and operate in markets that change very rapidly. Venture capital organisations finance these high-risk, potentially high-reward projects, purchasing equity or equity-linked stakes while the firms are still privately held. (Gompers and Lerner, 2001, p. 145)

This section deals with formal venture capital, which the authors of the above extract define as ‘independently managed, dedicated pools of capital that focus on equity or equity-linked investments in privately held, high growth companies’ (Gompers and Lerner, 2003, p. 267). Providers of venture capital are referred to below as ‘venture capitalists’ (VCs). They include private independent venture funds, corporate subsidiaries and special investment schemes. Most professional venture capital funds are organised as venture capital companies which attract funds from outside investors, who are limited partners; the VCs themselves are general partners. VCs frequently manage several entrepreneurial ventures at any one time, and are often actively involved in them to enhance their prospects of success. The discussion that follows focuses mainly on private independent venture funds. Special (public-sector) investment schemes are discussed in chapter 16; another source of equity finance is corporate venture capital, which is discussed below. Gompers and Lerner (1999, 2001) describe the origins of the venture capital industry, and characterise the ‘venture cycle’ of the industry. The venture cycle starts when VCs raise a ‘closed-end’ (i.e. fixed term and fixed size) venture fund. VCs then screen, invest in, monitor and add value to the projects they select. The cycle continues as VCs exit successful deals and return capital to their investors; it is renewed when VCs raise further funds. In return for providing infusions of capital, assistance, advice and expertise (see below), VCs demand a share of entrepreneurs’profits and an exit route that will generate a hefty capital gain on their investment. VC profit shares are negotiated on a projectby-project basis. They tend to vary between 20 and 49.9 per cent (Bovaird, 1990). Inderst and Müller (2004) point out that bargaining power between entrepreneurs and VCs, as reflected in equity shares for example, varies with technological and economic conditions. During times when technology shocks are favourable, entrepreneurs have

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more valuable outside options at the same time as the supply of available funds expands, so bargaining power swings towards entrepreneurs. When valuable entrepreneurial opportunities are less common, however, bargaining power shifts back to the VCs. In fact, entrepreneurs and VCs negotiate not only over equity shares but also over control rights and other contract provisions that protect the VC against agency risks. These include staging of future rounds of financing; staged release of shares to the entrepreneur (‘vesting’); severance payments; and VC oversight including board representation (Sahlman, 1990). VCs often insist on the right to take a seat on the board of directors, and retain control rights, including the ability to appoint managers and remove members of the entrepreneurial team (Bruton et al., 1997). In practice, this often turns out to be an important provision. For example, Hannan et al. (1996) report that on average 10 per cent of CEOs and managers in young high-tech VC-backed firms are replaced in the first twenty months, rising steadily to a replacement rate of 80 per cent after eighty months. Interestingly, opportunistic behaviour by entrepreneurs is not a common reason why VCs dismiss entrepreneurs (Bruton et al., 2000). An important feature of VC contracts is that they often specify options to transfer control rights contingent on performance. These control rights usually change as the venture develops, and favour the VC more the worse is the venture’s performance (Kaplan and Stromberg, 2003). In the presence of entrepreneurial over-optimism, clearsighted VCs have incentives to propose contingent contracts which switch control from over-optimistic entrepreneurs to realistic investors in bad states – which by definition optimists think are unlikely ever to occur (Landier and Thesmar, 2008). Like the price of equity, the allocation of control rights also depends on the balance of bargaining power between VCs and entrepreneurs. Baker and Gompers (2003) measure VC bargaining power in terms of reputation, and furnish evidence that VC reputation is negatively associated with the probability that the original founding entrepreneur remains as CEO. Lerner et al. (2003) show that VCs hold more control rights when market conditions are bad, reflecting their superior bargaining power. According to Lerner et al. (2003), this outcome is not generally optimal because control rights should ideally be allocated to the agent with the greatest ability to influence marginal project returns (which is usually the entrepreneur). The likely consequence is that VC-backed ventures will perform worse when conditions for raising capital are bad. It might seem strange that entrepreneurs would agree to submit to stringent VC terms, such as losing control of their own company. Indeed, one might expect such terms to restrict the supply of entrepreneurs willing to sell equity. In fact Dessi (2005) shows that stringent control rights are economically optimal because they elicit efficient VC monitoring and VC project continuation decisions, while preventing collusion between VCs and entrepreneurs at the expense of other investors. The precise terms of control rights in any given deal do, however, depend on a range of factors in practice (Hellman, 1998b; Kirilenko, 2001). VC equity investments typically last for between three and seven years. VCs usually aim to sell their stake at the end of this period, either through an Initial Public Offering

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(IPO) or, more commonly, a trade sale. In either case, the VC’s return on capital is primarily the capital gain raised from selling (a share of) the business, rather than a regular and ongoing stream of dividends. Hence the development of a viable venture capital market depends on the existence of exit routes which offer investors high returns, including favourable CGT regimes (Black and Gilson, 1998). More will be said about this in chapter 16. There is some disagreement among experts about VCs’ portfolio rates of return and risk profiles. Kerins et al. (2004, p. 387) cite evidence from Venture Economics that over the twenty years to 2001, annual net-of-fee returns to VC funds was 17.7 per cent, compared with 15.6 per cent from the S&P 500 stock index. This estimate was based on a large sample of funds. However, VCs’ returns are highly cyclical over shorter periods (Gompers and Lerner, 1999, 2001). Denis (2004) furnishes an overview of the (surprisingly diverse) empirical evidence about VCs’ average rates of return. Prior to investing, VCs acquire information about proposed new ventures in order to reduce the degree of asymmetric information they face. They do so by first performing a rigorous screening process, followed by a detailed examination of the new ventures and entrepreneurs who make it through the initial screen. This detailed examination is commonly referred to as ‘due diligence’. Due diligence can take anything from six weeks to six months. Even after these exhaustive selection processes, VCs are still left with a substantial amount of asymmetric information and risk if they decide to provide funding. That is why venture capital is also commonly referred to as ‘risk capital’. These considerations have a major bearing on the structure of financial contracts in venture capital, as will be discussed below. VCs commonly leverage specialist industry knowledge and local networks, and often syndicate with other VCs.1 One might expect VCs to keep the best entrepreneurial projects for themselves, reject the bad ones, and to only syndicate intermediate quality proposals, where they can diversify risk while pooling expertise. About 90 per cent of deals in the US venture capital industry are syndicated, compared with only 30 per cent in the less mature German venture capital market.2 Syndication offers several advantages to VCs. It enhances the screening process by increasing the breadth of scrutiny by recognised experts, and prevents competition between investors after investment opportunities are disclosed (Casamatta and Haritchabalet, 2007). Corporate venture capital is prone to similar degrees of risk. However, it comprises a small part of the overall venture capital market and is of limited relevance to most entrepreneurs. Hellmann (1998a) observes that corporate venture capital accounts for less than five per cent of all venture capital financings (though this may be a lower bound on the true size of this sector: see Denis, 2004). Moreover, companies sometimes wish to purchase equity shares in other firms, including potential competitors, for strategic reasons rather than to satisfy a need to raise finance. For example, Mathews (2006) discusses how alliances between an entrepreneurial firm and an established firm can improve the efficiency of both. Alliances often require the entrepreneurial firm to transfer knowledge to the established partner. But that heightens the established firm’s incentives to subsequently exploit the knowledge themselves by entering the

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entrepreneurial firm’s market. The risk of such behaviour can therefore deter knowledge transfers (Alvarez and Barney, 2001). In other cases, though, asymmetric information problems within a corporation can make product development in a start-up company more attractive than internal development in their own laboratories (Dix and Gandelman, 2007). In short, the conflicts of interest that arise between the objectives of the company providing funds, and the one receiving them (especially if they are in the same product line) might explain the limited size of the corporate venture capital sector. There need to be tangible strategic complementarities between companies to overcome this problem, otherwise conventional venture capital financing is preferable (Hellmann, 2002). Indeed, evidence shows that corporate venture capital is concentrated in the ICT sector, where partner firms more routinely synergise novel technologies (Dushnitsky and Lenox, 2006). An alternative solution is for an entrepreneurial firm to sell an equity stake to an established firm, since this dilutes the latter’s incentives to cannibalise the profits derived from the equity stake. But this solution will be less attractive if the sale of equity blunts the entrepreneur’s incentives to supply effort. 8.1.2 Size of the entrepreneurial venture capital market The USA has the longest-established formal venture capital market in the world. In 2001, over $40 billion of venture capital funds were invested there, compared with only $12 billion in Europe (Bottazzi and da Rin, 2005). The US figure was down from its peak of $106 billion in 2000, and dropped further to $30.5 billion in 2007. These numbers illustrate the volatility of venture capital markets. The European market has grown steadily since 1995, with EVCA (European Venture Capital Association) data recording that total investment increased from 24 billion euros in 2001 to 79 billion euros in 2007. Of particular interest is the growing importance of ‘early stage’venture capital investments (‘seed’and ‘early stage’finance), which are arguably the investments most closely identified with individual entrepreneurship. Since the early 1990s about one-third of US venture capital investment has been in early-stage projects. In Europe the corresponding figure is one-eighth. The UK has the largest venture capital market in the European Union, followed by France, Germany and the Netherlands, in that order. Most venture capital investments in Europe and the USA are devoted to buy-outs and late-stage financing deals. It is interesting to speculate why the USAhas historically led Europe in formal venture capital. Some researchers have suggested that Europe suffers from more rigid labour markets, which can deter workers from quitting to become entrepreneurs in VC-backed ventures since they may face greater difficulties rejoining the ranks of employees if they fail (Sahlman, 1990; Jeng and Wells, 2000). It might also be noteworthy that in Europe, banks and governments are prominent sources of venture capital funds, whereas in the USA, private funds predominate. If the latter are more effective commercialisers than the former, one would expect to see a more vigorous and successful US market. Yet another explanation relates to possible differences in cultural values between Europe

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and America. Landier (2001) suggests that in Europe, a ‘stigma of failure’ not only discourages individuals from initiating new starts, but also makes low-risk projects more attractive for those entrepreneurs who do emerge. As noted in chapter 7, the optimal financial contract for low-risk investment projects is debt. In the USA, by contrast, it is claimed that the stigma of failure is less pronounced, encouraging more risky start-ups, for which the optimal financial contract is equity. That could explain why the American venture capital market is larger than its European counterpart. If cultural values such as stigma of failure and attitudes towards success are reinforced by these outcomes, for example by providing negative or positive demonstration effects, these values could become entrenched – making distinct European and American equilibria durable as well as pronounced. Despite the size of the formal venture capital markets in the USA and Europe, relatively few entrepreneurs make use of them. It is an established fact that equity finance (EF) – whether formal or informal – accounts for only a small proportion of external finance for entrepreneurs in most countries. For example, Bates and Bradford (1992) reported that only 2.8 per cent of US small business start-ups obtain EF. Its receipt is positively associated with owners’ education, age, amount of self-finance and track record in business. Business ownership type also matters, with evidence that familyowned SMEs use less EF than non-family-owned SMEs (Wu et al., 2007). A similar picture applies in the UK, where EF accounted for 1.3 per cent of total start-up finance by the end of the 1990s, down from 3 per cent at the start of the decade – despite the strong growth performance of the companies that used it (Bank of England, 2001; and see the next section). The third section of this chapter outlines several reasons why entrepreneurs make only limited use of EF and VCs. 8.2 Advantages of venture capital finance for entrepreneurs

There are two major reasons why entrepreneurs choose venture capital as a source of finance. The first is based on the observation that in addition to funding, VCs provide assistance and advice which adds value to entrepreneurs’ investment projects. The second reason is that equity finance is the optimal financial contract for some entrepreneurs, which VCs specialise in offering. 8.2.1 Value-adding activities by VCs VC involvement often takes the form of advice and assistance which draws on VCs’own experience and contacts. Thus VCs often provide access to investment bankers, lawyers, accountants and consultants. Several recent US studies claim to have detected various additional beneficial effects from venture capital. VCs are thought to add value to entrepreneurs’ projects in three ways: by (1) monitoring, (2) professionalising ventures and (3) certification.

1. Monitoring. There is mounting evidence that VCs are active monitors. Lead VCs visit their portfolios an average of nineteen times a year, despite the costs entailed (Gorman and Sahlman, 1989; Sahlman, 1990). Time spent monitoring and recruiting

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is estimated at about 60 per cent of VCs’available hours, adding up to some 110 hours per annum on average per venture (Gorman and Sahlman, 1989). VCs’representation on boards of directors significantly increases (by 1.75 VCs on average) during times when the need for monitoring is greatest (i.e. when CEOs are replaced), confirming their prominent oversight role (Lerner, 1995). In addition, VCs commonly play a key role in shaping the top management teams of the companies in which they invest, and allocate decision and control rights in a manner that facilitates post-investment monitoring activities (Kaplan and Stromberg, 2001, 2003) 2. Professionalisation. VCs provide a variety of support services to their companies. For example, VC-backed ventures in Silicon Valley are significantly more likely than non-VC-backed ventures to receive help for building their internal organisations, and professionalising along several dimensions: HR policies, recruitment of professional marketing and sales staff, governance structures and the adoption of stock option plans.3 As a result, VC-backed firms raise more funds at IPO and generate superior returns relative to non-VC-backed public offerings (Brav and Gompers, 1997; Bottazzi and da Rin, 2005). This can explain why it is that, although VCs finance only 1 or 2 per cent of all new businesses in the USA, they finance between one-third and one-half of all businesses that achieve IPOs (Gompers and Lerner, 2001). VC-backed enterprises are also likelier than non-VC-backed enterprises to: • Be novel innovators, bringing their products to market quicker (Hellman and Puri, 2000; Kortum and Lerner, 2000); • Start larger and grow faster (Jain and Kini, 1995; Colombo and Grilli, 2005) and • Create more jobs (Belke et al., 2005). Consider, for example, Kortum and Lerner’s (2000) study. Kortum and Lerner recognised that VCs help enterprises to become more innovative; but they acknowledged that there might also be self-selection whereby more innovative firms choose VCs for financing. Kortum and Lerner (2000) instrumented the explanatory variable of VC usage by an exogenous policy regime switch which increased the supply of venture capital. Their findings suggest that a dollar of venture capital is on average three to four times more potent in terms of innovative performance than a dollar of traditional corporate R&D. 3. Certification. The backing of a VC can signal quality and reputation, which can attract additional investors (Amit et al., 1998). Entrepreneurs frequently accept financing offers with lower valuations in order to affiliate with more prominent VCs, consistent with the certification hypothesis (Hsu, 2004). In an important study, Lerner (1999) examined 1,435 new firms over a ten-year period and found that while US Small Business Innovation Research programme awardees located in areas with substantial VC activity grew faster, larger subsidies alone did not lead to better performance – findings which are also consistent with the certification hypothesis.4 Value-adding VC benefits can be substantial, and even more important than the finance itself. Indeed, Gompers and Lerner (2003, p. 277) claim that ‘it is the

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non-monetary aspects of venture capital that are critical to its success’. These benefits might help explain why entrepreneurs are willing to sacrifice ownership, decision and control rights in order to access it. However, entrepreneurs might not receive these benefits in all cases. For example, Bottazzi and da Rin (2002) found that European VCs had little discernible impact on the growth rates, corporate strategies or job creation performances of their portfolio firms. That might point to a quality issue in European venture capital relative to US venture capital. Also, Bottazzi and da Rin (2005) provide some counter-evidence to Belke et al. (2005), finding that venture capital is not associated with growth in post-IPO sales or employment. A further problem, which constitutes a negative manifestation of certification, is ‘grandstanding’, whereby IPOs are launched at earlier dates than entrepreneurs would find ideal, in order to establish the reputations of young VC companies for future fund-raising. If the outcome is under-pricing of IPOs, VCs might therefore not add value on net (Gompers, 1996). This possibility has been tested by comparing the outcomes of formal VCs who might engage in grandstanding with business angels who, lacking an investor fund to satisfy, have the opposite incentives (Chahine et al., 2007). On the other hand, as noted above in the discussion of Kortum and Lerner’s (2000) important study, endogeneity and especially selection biases often bedevil academic studies that claim to measure the impact of VCs on venture performance. These problems are exacerbated by the fact that researchers usually only observe performance for self-selected ventures that receive further stages of funding or which are disposed of profitably (Cressy, 2006c). Overall, the consensus is that the benefits from venture capital can be substantial. VCs’ hands-on role is reflected in their geographical concentration. Being located near entrepreneurs and other VCs facilitates close working relationships and gives VCs better access to possible syndication partners, information about industry trends, and promising developments in university science. It also keeps entrepreneurs competitive and better informed. Somewhat less studied to date has been the role played by the quality of the entrepreneur in value-adding VC activities. In an analysis of VC-backed entrepreneurs over 1986–2000, Gompers et al. (2006) found that although VC-backed serial entrepreneurs comprised only 7–14 per cent of their sample, those serial entrepreneurs who had taken their venture public on a previous occasion enjoyed nearly twice as high a probability of successfully going public in their next venture compared with first-time VC-backed entrepreneurs. But while more experienced VCs significantly increase the probability of success of first-time or failed serial entrepreneurs, they seem to have no such impact on previously successful serial entrepreneurs. Thus it appears that more experienced VCs are better at identifying and helping ‘diamonds in the rough’, but are no better than less experienced VCs at adding value to entrepreneurs who are already proven performers. Although successful VC-backed serial entrepreneurs are rewarded with access to earlier stage financing, and more favourable control provisions than their first-time counterparts, they apparently cannot command higher prices for the equity they sell

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(Kaplan and Stromberg, 2003; Gompers et al., 2006). At the same time, venture capital funds obtain higher returns from investing in serial entrepreneurs than in novices. These findings are consistent with an imperfectly competitive venture capital market, in contrast to many theoretical models of venture capital markets which generally assume competitive conditions. 8.2.2

Equity finance as an optimal financial contract In contrast to debt finance (DF) contracts, which stipulate in advance a given repayment due to a bank that is invariant to gross returns from a successful venture, an equity finance (EF) contract entitles a lender to a stake, or share, of a firm’s profits. The evidence shows that projects receiving EF tend to have particular characteristics that set them apart from those which do not, including a lack of tangible assets; the prospect of years without positive cash flows; and high levels of market and strategic uncertainty (Winton and Yerramilli, 2008). Banks, which mostly issue DF contracts, are typically not interested in these kinds of investments, and require collateral which many high-risk and high-tech projects do not possess (Gompers and Lerner, 1999; Freel, 2007). Indeed, projects with the characteristics listed above tend to be highly concentrated in particular industry sectors, which might explain why venture capital is also concentrated by sector. For example, VentureOne reports that more than three-quarters of venture financings over the 1990s occurred in the IT and healthcare sectors. These sectors account for a large proportion of innovative activity in the US economy. In addition to these points, the financial economics literature has identified several reasons why equity might dominate debt as an optimal financial contract. One of these reasons was explained in chapter 7, namely that when investment projects are ranked by second-order stochastic dominance, EF dominates DF under conditions of asymmetric information (de Meza and Webb, 1987). This argument was made under the assumption of competitive financial markets. But EF can dominate DF in imperfectly competitive markets as well, if entrepreneurs who sell a risky equity claim to a financial investor can thereby incentivise the investor to deny funding to other competitors seeking to enter and compete in the same market (Cestone and White, 2003). Equity can also dominate debt owing to a foreclosure option inherent in DF. Under conditions of asymmetric information, entrepreneurs are fearful of foreclosure by banks, which encourages them to behave more opportunistically and risk failure even though both parties to the DF contract would ideally prefer to continue the project (Trester, 1998). An EF contract can circumvent this problem. An unusual feature of formal venture capital finance, compared with other sources of formal and informal finance, is its use of contracts which mix elements of debt and equity together. This is especially true in the USA, where VCs make greater use of ‘convertible’ contracts than pure equity – in contrast with other developed countries such as Canada, where VCs appear to issue mainly pure equity contracts (Cumming, 2005). As its name suggests, a convertible contract allows VCs to convert the form of their compensation between equity and debt claims, depending on the performance of the venture. For example, convertible debt is a loan with an option for the VC to convert

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the owed sum into equity shares. To see its value, consider a project which starts out modestly but then turns out to enjoy spectacular success. With a convertible contract, a VC has the option to switch from a fixed debt repayment to an equity claim, thereby sharing in the upside return. Using real options logic, uncertainty about venture returns can explain why VCs might prefer convertible contracts to plain equity. Uncertainty also surrounds the eventual exit route for the investment project, namely an IPO or trade sale. Here again, convertibles provide the VC with flexibility to maximise returns depending on the exit route ultimately chosen (Berglöf, 1994; Hellmann, 2006). Other advantages of convertible loans is that they can reduce moral hazard, specifically the inclination of the entrepreneur to choose excessively risky projects (Green, 1984; Dewatripont et al., 2003); and they can induce efficient investment by the entrepreneur in ‘double moral hazard’ settings (Schmidt, 2003). Double moral hazard in this context refers to the situation where both entrepreneurs and VCs have incentives to under-invest in privately costly effort which enhances venture performance. Chapter 16 will provide a formal analysis of this issue, together with a discussion of some appropriate public policy responses. Other financial economists have sought to understand the use of convertible contracts in terms of VCs’ preference for releasing funding to entrepreneurs in stages, rather than all at once. So it is helpful first to understand why VCs utilise staged finance. One reason is that it enables them to gather information and monitor the progress of ventures, giving them the option to abandon unpromising projects relatively cheaply (Bergemann and Hege, 1998). As VCs acquire more information about entrepreneurs and their projects, they can make superior contingent investment decisions compared with a strategy of investing all of the required funds upfront (Li, 2008). The simple threat that a VC can walk away before the funding schedule is completed can also provide entrepreneurs with necessary incentives to conserve capital, hit targets and eschew morally hazardous behaviour – including attempting to renegotiate contracts to create possible hold-up problems for the VC (Neher, 1999; Smirnov and Wait, 2007). If these arguments are valid, one would expect the frequency of re-evaluations and number of financial stages to increase when the VC expects more conflicts with the entrepreneur. The opposite should occur as the company becomes more established or as entrepreneurs’ reputations grow. Evidence from a sample of 794 US companies receiving 2,143 rounds of investment over 1961–92 supports these hypotheses. Gompers (1995) found that as successive stages were reached, the frequency of waiting between rounds for subsequent injections of finance decreased, and the average amounts of funding given to entrepreneurs increased, as did entrepreneurs’ rates of spending (the co-called ‘burn rate’) of funds. As noted above, staged finance can explain the existence of convertible contracts. Consider entrepreneurs who respond to staged finance by engaging in ‘window dressing’ activities. These are activities which exaggerate the short-term performance of the project, in an effort to convince the VC to release the next stage of funds. VCs’ optimal response to window dressing in the presence of staged finance is a convertible

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debt contract (Cornelli and Yosha, 2003). To see why, observe that window dressing can make low-quality projects harder to identify, so reducing the probability of the VC liquidating them. But it also renders high-quality projects easier for the VC to identify, rendering the conversion option valuable to the VC. Entrepreneurs operating promising projects would not wish to trigger the option, which serves as a suitable disincentive to engage in window dressing.5 Finally, one can ask why banks do not offer EF contracts. There appear to be several reasons why they issue DF rather than EF contracts. For a start, until 1999 the Glass–Steagall Act prohibited US banks from owning the equity of non-financial firms (Gompers and Lerner, 1999). But even in cases when banks are allowed to take equity stakes, the general view is that bank loan officers lack the specialised skills and experience to emulate VCs in terms of their value-adding capabilities.6 Banks have a comparative advantage in monitoring borrowers through long-term relationships; but change is very rapid in high-tech industries in particular and requires different skills (Gompers and Lerner, 1999). Nor can banks simply replicate the services of VCs by hiring external consultants (Casamatta, 2003). On the other hand, VCs’ expertise puts them in a better position to steal entrepreneurs’ ideas. Hence the choice between EF and DF may involve a trade-off (Ueda, 2004), and entrepreneurs are more likely to prefer EF the better their intellectual property rights are protected. Taking all of the above arguments together, it is clear that VC-backed finance can offer compelling advantages over alternative sources of finance for particular kinds of entrepreneurial venture. However, there are also good reasons why venture capital is offered to only a small minority of entrepreneurs. These reasons are discussed next. 8.3

Drawbacks of venture capital and equity finance for entrepreneurs

This section consists of two parts. The first explains why only a small minority of entrepreneurs choose to use venture capital. The second briefly considers the possibility of ‘equity rationing’ and under-investment, and suggests that EF might be scarce because its use is (inefficiently) restricted by financiers. 8.3.1

Factors inhibiting the use of equity finance

It is natural to ask why EF accounts for such a small proportion of external finance for most entrepreneurs. The following reasons can be adduced: entrepreneurial unwillingness to cede ownership and control rights; financing costs; agency costs; and information costs. • Entrepreneurial unwillingness to cede ownership and control rights. Most entrepreneurs start off with ideas whose growth potential is not even proven to themselves, let alone to outside VCs. This can make entrepreneurs reluctant to share ownership and control of a business whose future value could be substantial. Entrepreneurs might also be initially unaware of the damage they can inflict on VCs by engaging in moral hazard. But if VCs are aware of this problem, as seems

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likely, they will demand higher equity shares to price in the attendant risk of moral hazard. The danger is that negotiations over equity stakes can fail, with entrepreneurs attributing demands for high VC equity shares to naked greed (hence the epithet ‘vulture capital’). That makes them unwilling to sell the large stakes required by VCs (Hagen and Sannarnes, 2007). Even entrepreneurs who do anticipate growth and moral hazard issues may be reluctant to sell equity and lose complete control over their ventures. According to the ‘pecking-order’ hypothesis, entrepreneurs seek funds in an order that minimises external interference and ownership dilution (Myers and Majluf, 1984). Hence their first preference is for internal finance followed by DF, with EF as a last resort. Evidence supporting the pecking order hypothesis in the context of entrepreneurship appears in Cressy and Olofsson (1997), who reported a widespread preference among Swedish entrepreneurs to sell their business altogether rather than to cede a share of it to an outsider. This is especially pertinent as VCs and business angels often require rates of return of at least 15 per cent on projects and exit routes involving flotation or sale to another company (Kerins et al., 2004). A 15 per cent rate of return exceeds what most ventures can yield, and these exit routes can conflict with entrepreneurs’aspirations of indefinite ownership of a business which they have gone to considerable time and trouble to build themselves. • Financing costs. There are fixed costs of issuing shares and listing on secondary markets where shares can be traded. Most enterprises never grow to a size where these costs are warranted, even for ‘junior’ stock markets such as the US NASDAQ or the UK Alternative Investment Market (AIM). Also, larger deals such as management buy-outs or buy-ins generate greater and more reliable fee income for VCs than start-ups at the seed or early stages. Hence simple cost reasons restrict the viability and availability of EF for many entrepreneurs, especially those starting small-scale ventures. Furthermore, for large as well as small ventures, debt is usually tax-favoured and has prior claim to junior obligations and equity on a corporation’s assets in the event of a liquidation event. • Agency costs. The involvement of outside financiers can cause conflicts of interest with entrepreneurs which reduce the latter’s flexibility, and impose costs on all of the contracting parties. This is probably true to some extent of all financial instruments, but it appears to be especially pronounced for EF. Entrepreneurs have incentives to take perquisites which reduce the outside value of the venture since, unlike DF, EF allows entrepreneurs to share the costs with VCs. In response, VCs monitor the entrepreneur, resulting in both a perceived ‘loss of control’ by the entrepreneurs, and monitoring costs that must be recouped in the form of greater VC equity stakes. If these stakes become sufficiently large, entrepreneurs can be discouraged from seeking EF altogether. DF rather than EF is a natural solution to entrepreneurial moral hazard, since by making the entrepreneur the residual claimant DF furnishes the required high-powered incentives (see also Barzel, 1987). • Information costs. Because entrepreneurs and financiers must co-operate closely once they enter a relationship, each side typically expends a costly search effort.

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High costs of gathering information can increase the price of funds beyond the willingness or ability of entrepreneurs to pay. In addition, full disclosure conditions may compromise confidential information and encourage competitors to appear who bid away the superior returns of the venture to be financed (Campbell, 1979). It might not be possible in these cases for entrepreneurs to erect information barriers to guarantee returns on their innovations (Himmelberg and Petersen, 1994). Furthermore, as was seen in chapter 7, if the least able entrepreneurs anticipate limited returns in good states, they will prefer EF to DF since they have a lower probability of sharing upside returns with VCs while avoiding a certain debt repayment in bad states. In contrast, abler entrepreneurs prefer DF, because they anticipate capturing greater upside returns. Attempting to sell equity can therefore convey a negative signal about an entrepreneur’s ability, so all entrepreneurs are dissuaded from requesting an EF contract.7 For all of the reasons discussed above, entrepreneurs often prefer to utilise DF, for which a cheap, well-established and relatively efficient market is available. Some of the problems with EF outlined above also apply to informal equity providers, but not all, as we will see in section 8.4 below. 8.3.2

Equity rationing, funding gaps and under-investment It is sometimes claimed that there is a ‘funding’ or ‘equity’ gap for EF. The term should be distinguished from ‘equity rationing’. ‘Equity gap’ is commonly used to refer to a mismatch between the fund sizes that interest entrepreneurs on the one hand and VCs or business angels on the other. For example, high fixed costs of screening and monitoring make VCs unwilling to supply the relatively small sums typically required by entrepreneurs. The term ‘equity rationing’ in contrast refers to the problem, analogous to credit rationing, where there is a persistent excess demand for funds which even competitive VCs that face low financing costs are unwilling to satisfy. Part of the motivation for exploring these issues derives from the simple observation that few entrepreneurs use EF in practice. Also, some studies have claimed to find indirect evidence of equity-based rationing by linking constrained access to equity capital with lower R & D expenditures and other forms of company investment (Hao and Jaffe, 1993; Himmelberg and Petersen, 1994). However, these studies were conducted using data on firms from the 1970s and the early 1980s, when the pool of venture capital was smaller than it is now. Even if these firms were genuinely equityrationed, it is not obvious that they would be today, when the supply of funds is so much greater. Hellmann and Stiglitz (2000) proposed a model of equity rationing in which debt and equity providers compete with each other to finance heterogeneous entrepreneurs who possess private information about both their project risks and their returns. Hellmann and Stiglitz’s (2000) model unifies both the Stiglitz-Weiss (SW) and de Meza and Webb (DW) models discussed in chapter 7. Hellmann and Stiglitz (2000) assumed that lenders specialise in either DF or EF, and that entrepreneurs cannot use a mixture of both. They

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then showed that credit and equity rationing may occur individually or simultaneously. The usual culprit of lender return functions which decrease in their own price (chapter 7) accounts for the possibility of rationing in each individual market. As in SW, lenders do not increase the price of funds to clear the market, because good types may exit the market, causing lenders’ expected profits to fall. Hellmann and Stiglitz also obtained the surprising result that competition between the two markets may itself generate the adverse selection that leads to rationing outcomes. The reason is that if many low-risk entrepreneurs switch between debt and equity markets, competition induces lenders in one or both markets to reduce the price of funds below market-clearing levels in order to attract them. Rationing is then needed to break even. However, if only EF was offered in these cases, credit rationing would disappear.8 Greenwald et al. (1984) developed a different model in which the least able entrepreneurs prefer EF and the ablest prefer DF (see above). These authors showed that the adverse signal transmitted by choosing EF can increase the cost of capital sufficiently to deter credit-rationed borrowers from availing themselves of EF altogether. This reinforces the potential importance of the SW credit rationing result, since it rebuts the argument that entrepreneurs who are rationed in the debt market can obtain funds elsewhere, e.g. in the form of EF. Another source of equity rationing is linked to the observation that most VC funds are of the ‘closed end’variety, i.e. have a fixed and predetermined scale. By having ‘shallow pockets’, VCs increase competition for funds among entrepreneurs at the refinancing stage, which improves entrepreneurs’incentives to select projects which not only have a positive NPV at the refinancing stage, but also have returns that are higher than those of competing portfolio projects (Inderst et al., 2007). However, while restricting funds is optimal for VCs, an efficiency loss ensues for society at large because shallow pockets mean that some positive NPV projects are not financed at all. Parallel to the analysis of chapter 7, under-investment is also possible when EF contracts are used. In a classic paper, Myers and Majluf (1984) analysed the problem of an established firm which issues new equity when a valuable investment opportunity appears. Managers of existing enterprises have privileged information about both the company’s assets in place and the value of the new investment opportunity. Suppose that EF is used to finance the new investment; that managers act in the interests of their existing shareholders; and that shareholders do not actively rebalance their portfolios in response to what they learn from the firm’s actions. Then Myers and Majluf (1984) showed that a new share issue can reduce the share price by so much that managers might optimally pass up the new profitable opportunity, implying under-investment. In contrast, the use of internal funds or risk-free DF removes under-investment without reducing the share price – so is preferred to EF by managers. However, these results are sensitive to assumptions about the objectives of managers of the enterprise and the behaviour of shareholders (Noe, 1988). In any case, the applicability of the Myers– Majluf result to most ‘typical’ entrepreneurs is probably limited. More recently, Bergemann and Hege (2006) analysed the financing of an entrepreneurial project under uncertainty about both the time of completion and the

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probability of success. VCs update their beliefs about the probability of success as time goes on. Bergemann and Hege (2006) predict that funding generally stops too early relative to the efficient stopping time. This is another form of under-investment. A different strand of research recognises that the success of complex investment projects often depends on the effort of multiple external agents who provide requisite complementary resources.9 This can create problems of its own. For example, suppose that a series of investors needs to perform a costly evaluation of proposed projects. Then each investor has an incentive to free-ride on the evaluations of the others, leading to the danger of inefficiently protracted delays in assembling resources. Hellmann (2007a) showed that the entrepreneur’s optimal strategy in this problem is to ‘knock on every door’ if resource-providers like to be solicited to go first. If they do not, the entrepreneur should ‘pester’ one resource-provider continually. Overall, the literature on equity rationing appears to be more of theoretical than practical interest. Even in the 1980s, VCs regularly claimed that they had more available funds than attractive projects in which to invest, which is suggestive of an equity gap rather than equity rationing (Dixon, 1991). With the expansion in the size of the venture capital market in the last twenty years, the likelihood of equity rationing has presumably decreased even further. And industry experts regularly argue that limited opportunities rather than limited capital explain the low number of VC-backed start-ups (e.g. Bhide, 2000, p. 162). On the other hand, there is undeniably a pronounced cyclical element to the supply of VC funds, which could be asynchronous with the demand for EF by entrepreneurs. We still await compelling empirical evidence identifying systematic under-investment and equity rationing by VCs, as opposed to occasional mistakes in the evaluation of new venture funding opportunities by these financiers. 8.4

Informal equity finance: business angels

Mason (2006, p. 261) defines business angels as ‘high net worth individuals who invest their own money, along with their time and expertise, directly in unquoted companies in which they have no family connection, in the hope of financial gain’. Angel finance is a long-established source of seed and start-up capital for entrepreneurs. It differs from lending to friends and family members in several respects. In particular, the primary aim of business angels is to make money rather than to leverage personal relationships. The ‘typical’ angel is male, 45–65 years old, well educated but without a PhD. Many are successful cashed-out entrepreneurs. Measuring the size of the informal equity sector is difficult because there is no formal register of informal investors, and for understandable reasons many angels prefer to remain anonymous. This explains why estimates that have been compiled for the USA and the UK exhibit considerable sampling variation. Most estimates suggest that the US informal angel sector is at least as large as the formal equity sector, even though most individual deals are on a smaller scale. Amatucci and Sohl (2007) estimated there were about 225,000 US angels in 2004, investing about $22.5 billion in 48,000 ventures, with an average deal size of $470,000. In contrast, formal VCs invested $21.3 billion

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in 3,000 ventures, with an average deal size of $7.3m. This estimated number of angels is remarkably similar to early estimates compiled by Wetzel (1987) over twenty years ago, although Sohl (2003) obtained somewhat higher estimates of between 300,000 and 350,000 angels in the USA. Sohl (2003) also estimated there are some 20,000– 40,000 angels in the UK, investing between £500,000 and £1 million in 3,000–6,000 companies. According to Mason and Harrison (2000a), the UK’s informal market for start-up and early-stage venture financing is also broadly similar in total value to that of the formal market. The fact that angels’average deal sizes are so much smaller than those of VCs suggests that they serve a different part of the entrepreneurial finance market. Business angels tend to concentrate on early-stage financing in younger companies. Their role usually declines at later stages when more substantial funding is needed. Thus 45 per cent of US angel deals in 2004 focused on seed and early-stage projects, compared with just 6 per cent among VCs (Amatucci and Sohl, 2007). The complementarity between angels and VCs might explain why they frequently share information and collaborate (Mason and Harrison, 2000a, 2000b). Angels can help to close the equity gap between friends and family and other sources of informal finance on one hand, and formal VC on the other. Sohl (2003) estimates that the minimum deal sizes of the latter are about £1m in the UK, and about $5m in the USA. According to Amatucci and Sohl (2007), angels are beginning to redistribute funds away from early project stages towards post-seed stages. If this constitutes a genuine trend, it may have important implications for the availability of finance for the smallest entrepreneurial ventures. On the one hand, it might lead to less access to finance for the smallest projects, while on the other it might help to close the equity gap higher up the value-chain. One factor helping to narrow the difference between angels and VCs is the increasing frequency of syndication in angel finance (Mason, 2006). Mason speculates that this will increase efficiency for several reasons. Angels will be more visible and attractive to clients, enabling them to reap economies of scale in deal flow. Also, larger deal size may fill a vacant equity gap for more valuable projects, since angel syndicates can provide more follow-on finance than single individuals. Syndication also promises greater expertise and hence more value-added activities and professionalisation, leading to greater credibility with VCs, who as noted above are often needed for later-stage ‘follow on’ financing. Business angels share some similarities with VCs. Both types of investor reject most applications for funding, usually because the quality of investment proposals fails to meet a minimum threshold. Furthermore, Amatucci and Sohl (2007) point out that like VCs, angels are often valued by entrepreneurs largely on the basis of their value-adding activities. Partly this reflects the early-stage nature of so many of these deals, where expert guidance and advice is essential to the successful launch of new entrepreneurial ventures. Nevertheless, there are also some important differences between business angels and VCs.According to Wong (2002), angels locate geographically close to the entrepreneurs they finance. More than one-half reportedly operate within half a day’s travel of their

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home (Sohl, 1999; Wong, 2002). Angels also use few of the control mechanisms favoured by formal VCs. For example, unlike VCs, angels are rarely involved in shaping top management teams. Angels also tend to take straight equity (and sometimes issue debt as well as equity); rarely demand board seats; and rarely use stage-financing or demand anti-dilution protection. American and British angels appear to have broadly similar characteristics, although UK investors tend to be less wealthy, investing about one-half of the sums of their US counterparts. UK business angels are also more likely to invest independently rather than in consortia, although similarly to the US eight times as many UK businesses raise finance from business angels as from institutional VC funds (Mason and Harrison, 2000a). Further details about the investment practices of business angels can be found in the recent overviews by Mason (2006) and Amatucci and Sohl (2007).

8.5 8.5.1

Other informal sources of finance Family finance

Families are the most commonly used source of business loans in the USA after banks and other financial institutions. Consider the following evidence drawn from Bates’ (1997) analysis of the US CBO database. Family finance is used by over one-quarter of non-minority-owned businesses, compared with two-thirds who access bank credit. Among some minority groups, however, family finance is used more extensively than bank finance. For example, family finance was used by 41.2 per cent of immigrant Korean and Chinese business owners, compared with 37.4 per cent who used bank finance (Bates, 1997; see also Yoon, 1991). For all groups, family loans are of a smaller average size than bank loans, although family loans remain an important source of funds by value, being worth an average of $35,446 for non-minority owners compared with an average $56,784 for bank loans. There is also a gender difference, with womenowned business owners relying more on family finance than men-owned businesses (Bates, 1997; Haynes and Haynes, 1999). UK evidence tells a similar story, with family loans comprising the largest source of funds after bank loans.10 Family finance accounts for 15–20 per cent of start-up finance by value amongst ethnic-owned businesses in the UK. According to Smallbone et al. (2003), ethnic-minority businesses are significantly more likely to draw on family finance than white-owned businesses are. Take-up rates are 45 per cent compared with 25 per cent, with the highest rates of usage being found among South Asians and the lowest rates of usage by the Afro-Caribbean and Chinese communities. For ethnic British entrepreneurs overall, family finance is a more important source of funding than bank credit. Data from other countries tell a broadly similar story. Knight (1985) reported that the following sources of funds were used by high-tech Canadian firms at the pre-start-up stage: personal savings: 60 per cent; family and friends: 13 per cent; bank loans: 12 per cent; and trade credit: 6 per cent. According to the 2003 GEM report, 31 per cent

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of the funds for start-ups less than three years old across the GEM countries came from family and friends. The importance of families and friends for supplying startup finance appears to be even more pronounced in developing countries, according to evidence from India (Bell, 1990; Kochar, 1997) and Côte d’Ivoire (Goedhuys and Sleuwaegen, 2000). What motivates lending within families? Family members may have private information about borrowers that is unavailable to banks (Casson, 2003); and they may be able to monitor and exert peer pressure on the borrower. For their part, family lenders may be trusted to behave sensitively if the entrepreneur encounters difficult business conditions not of their own making. Family lenders can also serve as loan guarantors to outside lenders (Jones et al., 1994). Furthermore, if the borrower stands to inherit the family lender’s estate, then a family loan effectively becomes a mortgage on his own inheritance. In contrast, banks are usually unwilling to accept the prospect of inheritance as security for a loan (Casson, 2003). Basu and Parker (2001) point out that most family loans tend to be interest-free (Light, 1972). Basu and Parker (2001) argue that family members might supply interest-free loans not only if they are altruistic towards the entrepreneur, but also if they are selfish. The selfish motive for interest-free lending arises if the loan entitles the lender to a sufficiently valuable option to ‘call in the favour’, and turn entrepreneur themselves at a later date. Using a sample of relatively affluent Asian immigrant entrepreneurs, Basu and Parker (2001) claimed to find indirect evidence of both altruistic and selfish family lending motives. Also, they estimated that greater use of family finance is positively associated with an entrepreneur’s age, the number of hours they work in their business and whether they employ their spouse in the venture. Basu and Parker (2001) estimated that family finance is a gross substitute for bank loans. However, a subsequent GEM study has failed to find any evidence that people who had acted as informal investors are any more likely to become entrepreneurs themselves later on (de Clercq and Arenius, 2006). This is consistent with an altruistic but not a selfish motive for within-family lending. Other evidence suggests that family finance is not associated with successful enterprise, being correlated with low profitability and high business failure rates.11 The reasons for this are unclear, although one can speculate that family funds are often limited, and that only marginal businesses might resort to this mode of financing. Evidence from Kenya suggests that business owners who use family finance tend to be younger and run younger firms, perhaps because they lack social capital required to obtain bank credit or loans from informal lenders such as micro-finance schemes (Akoten et al., 2006). There appears to be little appetite for government intervention in this area. Apart from the case of the Dutch government, which has offered tax exemptions for family finance for business start-ups, there are few public policy initiatives in operation. 8.5.2

Micro-finance schemes It is generally agreed that lack of access to credit is one of the principal reasons why citizens of developing countries remain poor (Hermes and Lensink, 2007). Formal credit markets are weak or non-existent in these countries, and few borrowers possess

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meaningful collateral. As a result, valuable entrepreneurial projects go unfunded, thereby hindering economic development. Micro-finance schemes offer a possible solution to this problem. These schemes are invariably non-profit-making organisations which lend small sums to people who are unable to obtain funds from ‘conventional’ banks, usually because they lack collateral. Many such schemes currently operate around the world, mainly in developing countries with under-developed financial sectors. They include the Grameen Bank in Bangladesh, BancoSol in Bolivia and Bank Rayat in Indonesia.12 Perhaps the most famous is the Grameen scheme, founded in 1976 by Muhammad Yunus, an economics professor and winner of the 2006 Nobel Peace Prize. By the start of the present century this scheme, which makes unsecured loans for non-agricultural self-employment activities, served more than 3.2 million borrowers, 95 per cent of whom are women, through over 1,100 branches in 41,000 villages. Its assets exceed $3 billion. These schemes have now been widely copied in many developing countries around the world. Hermes and Lensink (2007, p. F1) cite evidence that the number of microfinance institutions increased from 618 in 1997 to 3,133 in 2005. The growth in the number of customers receiving credit from these institutions likewise increased over this period from 13.5 million to 113.3 million (84 per cent of whom were women). Despite some idiosyncratic variations, micro-finance schemes tend to have several features in common, including direct monitoring of borrowers; stipulation of regular repayment schedules that start immediately after loan disbursement; and the use of nonrefinancing threats to generate high repayment rates from borrowers who would not otherwise receive credit (Armendáriz de Aghion and Morduch, 2000). Hoff and Stiglitz (1990) identify several other direct and indirect methods used by micro-finance schemes to protect themselves against loan defaults. Direct methods include staged investments, with small initial loans followed by larger loans conditional upon satisfactory repayment of earlier loans; and a requirement that borrowers contribute a small percentage of their loaned funds to a savings scheme to insure against ‘covariant’ risk, whereby large-scale shocks such as bad weather hit an entire region and result in mass default. Indirect methods include delegated monitoring, among others (see below). Economists who study micro-finance have tended to focus their attention on group lending schemes with joint liability, in which individuals form into groups and are jointly liable for penalties if one member of the group defaults. The penalty might be the denial of future credit to all group members if one member defaults (as in the Grameen scheme), or group liability for loans if a single member defaults (as in the Bangladesh Rural Advancement Committee scheme). The advantage of joint liability contracts is that they give entrepreneurs incentives to exploit local information and exert pressure to discipline co-members in a manner consistent with the interests of lenders (and, by releasing funds from lenders, therefore also the entrepreneurs). The particular mechanisms involved include: 1. Peer monitoring and mitigation of moral hazard. Group members may be able to monitor each other in a manner unavailable to banks. For example, members may

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know or live near each other, and share information via ‘cross-reporting’, whereby borrowers submit reports on each other at village meetings (as in the Grameen scheme, where cross-reports are collected at the same time as loan repayments are made). In principle, this elicits truthful revelation by borrowers; eliminates collusion by borrowers against lenders; and avoids the need to invoke hefty punishments (Rai and Sjöström, 2003). Furthermore, because under joint liability each member’s payoff depends on whether other members’ ventures succeed, all members have an incentive to monitor other members’ behaviour, and to take remedial action against members who misuse their funds. Joint liability can therefore overcome moral hazard and monitoring problems.13 2. Cheap state verification and repayment enforcement. Group members may be in a better position than banks to learn about partners’ venture outcomes. Then joint liability can encourage them to exert peer pressure to deter partners from defaulting opportunistically in good states. Social ties make members’ behaviour easier to observe by co-members, and enable enforcement of powerful sanctions against malefactors (Besley and Coate, 1995). For example, group members might threaten others with ostracism or some other social sanction if they shirk in a manner that invites default, or if they invest in excessively risky ventures. Also, if group members have lower auditing costs than banks (as seems likely) a group lending scheme may economise on state verification costs. Only if the whole group defaults will a lender incur audit costs, so this arrangement reduces average auditing costs and enhances efficiency. Indeed, if audit costs are too high for banks to be able to offer any individual loan contract, a group lending scheme could facilitate lending where none was possible before. 3. Mitigation of adverse selection. Rather than changing borrowers’ behaviour, as above, joint liability can favourably alter the pool of borrowers. All group members are treated as being in default if any one member of the group does not repay their loan. Hidden entrepreneurial types optimally match together in pairs in a process known as ‘positive assortative matching’. This kind of matching is optimal because although all types prefer to match with the most able types, the joint benefits to the latter are greatest if they match with other able types, since then the probability of a joint default is lowest. Like collateral, joint liability can reveal types and lead to more efficient contracting even when borrowers lack ‘formal’ financial collateral.14 Many micro-finance schemes boast high repayment rates. These have reached 98 per cent (it is claimed) in the case of Grameen, compared with only 50 per cent for conventional bank loans in comparable regions. Repayment rates in Chinese schemes have attained similar levels (Armendáriz de Aghion and Morduch, 2000). While the mechanisms listed above probably do not furnish an exhaustive set of explanations for the high repayment rates of micro-finance schemes,15 some evidence confirms their usefulness, as I now explain. One part of the evidence base identifies the role of peer monitoring and peer pressure. Using data on 137 groups from a group-based lending programme in Guatemala,

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Wydick (1999) found that peer monitoring (associated with shorter distances between group members and also with knowledge that members have of other members’ sales) and a group’s willingness to apply pressure on delinquent members were the salient factors explaining superior borrowing group performance. Paxton et al. (2000) also reported that social pressure within groups is positively related to repayment performance, while Karlan (2007) claimed that through successful monitoring, borrowers know who to punish and who not to punish after a co-member defaults. While 2.8 per cent of Peruvian group lending scheme relationships were observed to deteriorate when there was no default, this figure rose to 12.0 per cent in cases where a member did default (Karlan, 2007). Other evidence supports the notion of selective group formation. In particular, Wenner (1995) reported that repayment rates among twenty-five Costa Rican groups were highest among those which had formal rules about stating how members should behave (see also Zeller, 1998, for further evidence from Madagascan borrower groups on this point). Rules can be interpreted as a proxy for screening of members via local reputations. Sharma and Zeller (1997) claimed that groups that were formed using a self-selection (screening) process have better repayment performance records. The severity of punishments contingent upon default are significantly associated with superior repayment rates. According to Wenner (1995), more geographically isolated groups have higher repayment rates, possibly because lack of alternative sources of finance make successful group repayments imperative. Similarly, Sharma and Zeller (1997) reported superior repayment performance among groups otherwise lacking access to credit. There is also evidence that more severe default penalties and covariant risk increase repayment performance among Thai borrowing groups (Ahlin and Townsend, 2007). An important aspect of the evidence base described so far is the role played by social ties. On the whole, social ties are related to superior repayment performance, as predicted by theory (Zeller, 1998; Wydick, 1999). However, some of these studies measure social ties in terms of engagement with other people in the community. These measures are prone to selection biases if groups are formed in neighbourhoods which have both superior entrepreneurial opportunities and stronger social networks. In the spirit of a ‘natural experiment’, this problem can be avoided, and causality more reliably inferred, by studying borrowing groups with randomly assigned memberships which cut across neighbourhoods. Karlan (2007) followed this strategy using data from FINCAPeru, and found that two measures of social ties (geographical proximity and cultural similarity) were significantly associated with higher repayment rates and savings of members, owing to better monitoring and enforcement of repayments among members with stronger social ties. On the other hand, a study based on data from 140 borrowing groups in Burkina Faso by Paxton et al. (2000) found that group solidarity was more pronounced than peer pressure when groups faced repayment problems. These authors detected a general unwillingness to apply sanctions to colleagues facing repayment problems. This finding raises a serious potential problem: strong social ties might induce borrowers to

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refrain from imposing harsh penalties, effectively leading to collusion against the microfinance lender. Hence social ties do not necessarily improve repayment performance. In line with this possibility, Sharma and Zeller (1997) reported that groups containing lots of relatives were associated with more repayment problems in Bangladesh. And Ahlin and Townsend (2007) reported that co-operation among members, as measured by sharing of resources and joint decision-making, was associated with lower repayment performance among Thai borrowing groups. In fact, the evidence reviewed above is subject to several important limitations (Hermes and Lensink, 2007). Weak linkages of empirical constructs to theory; the use of crude and questionable empirical proxies; and possible endogeneity problems are rife in this literature. In response, there have been efforts to tease out and test some of the key implications of the theories (Ahlin and Townsend, 2007); to utilise data akin to natural experiments (Karlan, 2007); and to apply laboratory experimental methods to shed light on the relationship between social capital and the repayment performance of borrower groups (Cassar et al., 2007). Moving on from the empirical evidence, I now turn to the welfare implications of micro-finance for entrepreneurs. Micro-finance schemes promise several social welfare benefits. First, for the reasons outlined above, they can enhance efficiency and loan repayment rates compared with individual loan contracts (Ghatak, 2000; Lensink et al., 2005). The benefit of higher repayment rates can be recycled to borrowers in the form of lower interest rates and/or larger loan sizes. This can decrease further the severity of asymmetric information problems such as adverse selection (see chapter 7), while directly increasing borrower welfare. Second, ventures can be funded that would otherwise not take place. This can be especially important in poor regions, where selfsufficient entrepreneurship promotes economic development and alleviates poverty – the so-called ‘micro-finance promise’ (Morduch, 1999a). While loans can only ever be part of a solution to poverty, they can certainly assist the process (Morduch, 1999a, p. 1610). Indeed, the evidence suggests that access to micro-finance does contribute to poverty reduction (Khandker, 2005; Azevedo, 2006). Third, micro-finance schemes can carry in their train valuable social development programmes such as vocational training, civic information and information-sharing to members. These have been found to add substantial value to participants’ venture profitability rates and employment generation efforts.16 However, micro-finance schemes can also suffer from drawbacks. First, they can encourage excessive time-consuming and welfare-reducing monitoring by group members (Armendáriz de Aghion, 1999). And the joint liability clause can encourage excessively cautious investment behaviour. Second, there is no guarantee that a scheme will break even; and most micro-finance schemes are dependent on subsidies (Hulme and Mosley, 1996; Morduch, 1999b). Moreover, as Ghatak (2000) warned, joint liability contracts might drive individual loan contracts out of the market, undermining the viability of conventional lending institutions. Third, the transfer of risk from banks to borrowers presumably reduces borrower welfare. Fourth, micro-credit might increase the vulnerability of households to poor financial outcomes and thereby strain family

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relations (see Azevedo, 2006, p. 327). Fifth, group lending schemes can have negative as well as positive effects on repayment rates, since it is possible for borrowers to collude and for whole groups to default in a ‘domino effect’, even though some members would have repaid under individual lending (Besley and Coate, 1995). In some theoretical models, it can be shown that the benefits of micro-finance schemes outweigh the costs (e.g. Stiglitz, 1990). But this is not a general property and it cannot be assumed to hold universally, notwithstanding some recent evidence of substantial pecuniary benefits from Bangladeshi micro-finance schemes.17 For example, McKernan (2002) reported that participation in micro-finance schemes increased monthly self-employment profits by 175 per cent on average. Pitt and Khandker (1998) noted substantial gender differences in Bangladesh, where micro-finance credit has a significantly larger positive effect on households in which women rather than men were the scheme participants. Pitt and Khandker (1998) suggest that this might be indicative of how access to credit unleashes women’s productive skills which, unlike men’s, are held in check by cultural and religious restrictions proscribing formal waged work. Anderson and Baland (2002) advance an alternative and more prosaic view: women have stronger preferences for using money in ways that help family members, whereas men prefer to divert any extra resources towards personal consumption goods such as alcohol and gambling. The discussion so far has centred on the use of micro-finance schemes in developing countries. It is natural to speculate whether these schemes’ success could be replicated in developed countries. Schreiner and Morduch (2001) and Schreiner and Woller (2003) are sceptical, on the grounds that developed countries usually have welldeveloped product and financial markets, reducing the demand for these programmes. Another drawback is that greater anonymity in developed countries makes group lending more difficult there. The force of these objections is reflected in the small number of US schemes; the tiny employment creation resulting from them; and their negligible impact on poverty. For example, Schreiner and Woller (2003) cite research estimating that access to a national programme would at most move one person per thousand from public assistance to micro-enterprise. They concluded wryly that ‘broad access to financial services in the United States is good for micro-enterprises but bad for microenterprise programs’ (Schreiner and Woller, 2003, p. 2006). These authors went on to propose alternative policy recommendations in this area (see also Bates and Servon, 1998). In any case, Armendánriz de Aghion and Morduch (2000) argue that micro-finance lenders in developing countries are moving away from joint lending in favour of individual lending for wealthier entrepreneurs. Thus Grameen of Bangladesh and BancoSol of Bolivia have now adopted individual-based lending as well as group-based lending practices. This increases profitability and hence promotes financial sustainability of micro-finance schemes. However, evidence from 124 micro-finance lenders in 49 countries suggests that the fractions of poor and female borrowers are lower in individual-based than in group-based lending schemes (Cull et al., 2007). Hence greater profitability seems to come at the expense of more limited outreach. This appears to be an enduring trade-off in micro-finance lending.

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Finally, Armendánriz de Aghion and Morduch (2000) claim that it is possible to replicate many beneficial aspects of group lending schemes in ‘conventional’ individual-lending contracts, including direct monitoring, regular repayment schedules and the use of non-refinancing threats. They identified loan sizes of $2,000 as the point at which joint contracts are effectively replaced by individual-lending contracts in Eastern Europe. 8.5.3

Other non-profit-making lending organisations and schemes Non-profit-making (NP) lenders are another alternative source of finance for cashstrapped entrepreneurs. Examples of NP lenders include mutual savings banks, credit unions and co-operative banks. The World Bank and the European Bank for Reconstruction and Development are examples of NP lenders on the international stage. Because NP lenders do not seek to maximise profits, one might wonder (akin to the case of NP entrepreneurs discussed in chapter 2) how an NP lender can survive in competitive markets against for-profit (FP) rival lenders. This is an especially acute question because FP banks should earn zero supernormal profits in the long run under competitive market conditions. One reason might be that FP banks collude monopolistically, though this does not explain why new non-co-operative FP banks do not then enter the market and compete away their rents. Another reason might be that NP lenders are more efficient in some markets than FP lenders are, because they know customers better or specialise in lending to marginal entrepreneurial groups that are redlined by FP lenders. One important rationale for NP lenders is that they can fill a credit gap left by FP lenders. However, as Canning et al., (2003) show, NP lenders might themselves ration credit. Hence this form of financial intermediation cannot be regarded as a panacea for limited market access to entrepreneurial finance. To see the basic intuition for the Canning et al. (2003) result, consider an NP lender which seeks to maximise consumer (i.e. borrower) surplus rather than profits. An NP lender therefore returns any surplus it makes to borrowers in the form of lower interest rates. But to avoid lower interest rates distorting loan volumes requested by borrowers (who will demand a greater volume of loans as a result of the lower interest rate), the NP lender has to ration loan volumes to keep them at their optimal levels. That is, credit rationing allows an NP lender to redistribute the surplus without distorting the volume of activity from the efficient level. This is yet another example of how credit rationing can be used as a tool of efficient contracting (see chapter 7). 8.5.4

Co-operative schemes I now briefly describe some co-operative (‘self-help’) schemes that can also leverage entrepreneurial finance. The following schemes are considered: credit co-operatives and rotating savings and credit associations (Roscas) and mutual guarantee schemes (MGSs). Credit co-operatives and Roscas Credit co-operatives are voluntary groupings of individuals that obtain funds from members and then allocate credit among them.

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They resemble banks by taking deposits from their members while also extending loans. The whole co-operative is liable for the debts of a single member. While the spread of liability dilutes the incentive to perform peer monitoring relative to group lending schemes (GLSs), it does not eliminate it altogether (Banerjee et al., 1994).18 Other features of co-operatives are similar to those of group lending schemes, including the threat of (possibly non-pecuniary) sanctions to discourage opportunistic behaviour by their members. However, given their larger group sizes, co-operatives are more vulnerable to covariant risk. The problem of size also makes banks wary of dealing with co-operatives whose members may collude against them. Rotating savings and credit associations (Roscas) play a similar role to credit cooperatives. Rosca members save on a regular basis and periodically allocate a pot of funds to particular members, either by lot or by bidding. These funds can be used to purchase an indivisible good. This process continues with past winners excluded until everyone has won the pot once (see Besley et al., 1993). While Roscas exist primarily to facilitate purchases of lumpy consumption goods by their members, they can also facilitate the accumulation of capital required for business entry. For example, Dekle and Hamada (2000) observe that Roscas have long provided funds to SMEs in local sectors of the Japanese economy. Although Roscas are no substitute for a wellfunctioning formal credit market, they are probably better than nothing (Besley et al., 1993, 1994). GLSs, credit co-operatives and Roscas tend to be associated with low-income communities. While some of these schemes, especially credit co-operatives, continue to survive alongside formal lenders in developed economies (see, e.g., Scholten, 2000), these schemes tend to decline in importance as economic development occurs, or as the communities involved improve their access to formal credit markets (Besley, 1995). Mutual Guarantee Schemes Mutual Guarantee Schemes (MGSs) are privatesector versions of government-backed loan guarantee schemes (LGSs) (see chapter 16 for details). Like a credit co-operative, an MGS is a voluntary grouping of individuals. But whereas credit co-operatives issue loans directly, an MGS merely guarantees a bank that it will cover a pre-specified fraction of a bank loan made to one of its members in case they default. MGS members pay a fee or save in a fund that provides the scheme’s capital. In practice, MGSs take a variety of organisational structures, although they share several features in common. They tend to be industry-based and located in distinct geographical areas, potentially enabling the exertion of peer pressure to encourage loan repayments. MGSs are widespread in Europe, especially in Italy, where over 800 exist, with over one million member enterprises (Rossi, 1998). The values of guarantees vary from scheme to scheme, but according to Rossi (1998) the typical guarantee is for between 50 and 100 per cent of members’ loans. MGSs are also common in Germany where they are chartered as limited liability companies, with capital provided by the banking system, guilds and chambers of trade. Federal and state governments share the responsibility for guaranteeing up to 70 per cent of loans. The German MGSs claim to

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back loans with lower default rates than conventional bank loans (OECD, 1998). MGSs are less common in the UK and the USA. It is currently unclear whether their existence derives from underlying informational advantages or simply historical legacy effects. The value to banks of a loan guarantee is recycled in the form of lower interest rates charged to MGS members. As with group lending schemes, MGSs probably have an optimal size: small ones are best able to screen loan applications and exert peer pressure, but idiosyncratic risks are spread more widely in larger schemes.19 In practice, MGSs often enhance their effectiveness by pre-screening loan applications, as well as by providing financial advice and encouraging valuable information sharing among members. A self-selection rationale for MGSs can also be proposed based on the analysis of chapter 7. The fee paid by a member into an MGS can be thought of as a BAD, which ‘safe’ borrower types are more willing to pay than ‘risky’ types in return for a lower interest rate. Thus with a range of fees and savings requirements, MGSs can in principle separate types and facilitate efficient contracting. 8.5.5 Trade credit Another potentially valuable source of local information is trade credit (TC). TC comprises loans between firms that are used to purchase materials and goods in process. The duration of TC is the time elapsed between invoicing and payment. This is typically 30–60 days in both developed and developing countries (Fafchamps, 2000). TC is an important source of informal finance to entrepreneurs. According to Acs, Carlsson and Karlsson (1999, table 1.5), the value of TC in the USA in 1995 was $233 billion, compared with $98 billion for bank loans. TC can be an even more valuable source of finance in developing and transition economies, where contract enforcement is weak and formal credit markets are limited. In these circumstances, TC might be capable of mitigating credit rationing (see also Bopaiah, 1998).20 What is the rationale for trade credit? The demand for TC is obvious: delaying payment to suppliers is an important way that small enterprises deal with cash-flow variations, which can aid their survival. Even large firms in developed countries find TC useful. If banks are imperfectly competitive, TC can be cheaper than DF (Emery, 1987). Even when bank finance is cheaper than TC, it can be less attractive because banks hold liquidation rights, making entrepreneurs who fear early liquidation willing to pay a higher price for trade credit (Huyghebaert et al., 2006). In developing countries, TC may emerge as a source of finance of necessity, if formal credit markets are weak or absent. Reasons for the supply of TC are less obvious, although McMillan and Woodruff (1999) summarise two main sets of explanations:

• Explanations based on industrial organisation. If suppliers have market power, TC might enable them to price-discriminate covertly, hiding price cuts from other customers. At the same time, TC can serve as a warranty for product quality, since the delay in payment gives customers time to inspect suppliers’ merchandise. In less favourable economic circumstances, suppliers sometimes have little choice but to offer TC to cash-strapped customers if they want to make a sale.

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• Explanations based on suppliers’ superior information and enforcement. A supplier already engaged in a long-term trading relationship with an entrepreneurial venture often has better information about the latter’s creditworthiness than a bank or finance company does. The day-to-day information flow inherent in exchange means that suppliers are well placed to evaluate their customers’ abilities to pay (Emery, 1984). Furthermore, they can solve incentive problems by threatening to withdraw access to future supplies, a sanction which may be more powerful than banks threatening to withhold future credit (Biais and Gollier, 1997; Petersen and Rajan, 1997). Another consideration is that suppliers are well placed to cheaply repossess and resell their goods in the event that an entrepreneur defaults (Petersen and Rajan, 1997). For all of these reasons, one can expect trade credit to be a useful substitute for formal finance. Some evidence from Vietnam supports the second group of explanations. A supplier’s trust in a customer firm’s willingness to repay is likely to be greater the harder it is for the latter to find an alternative supplier (McMillan and Woodruff, 1999). The threat of being cut off from future trade credit acts as a powerful incentive to repay; the repeated nature of the game and acquisition of reputation bypasses missing legal contract enforcement institutions. McMillan and Woodruff (1999) found that long trading relationships and more intense information-gathering efforts, possibly using social networks, are associated with greater disbursements of TC. Also, Petersen and Rajan (1997) reported that TC in the USA was negatively related to the fraction of customers’ inventories that are finished goods rather than raw supplies, which is consistent with their ‘repossession in default’ rationale. If suppliers have little choice but to offer TC to cash-strapped customers if they want to make a sale, it is possible that firms receive more TC when they receive less DF. On the other hand, the use of TC could convey a favourable signal of creditworthiness to banks, allowing entrepreneurs to use TC to leverage credit that might not otherwise have been forthcoming (Biais and Gollier, 1997). Reflecting these conflicting forces, the evidence on the substitutability or complementarity of TC vis-à-vis DF is mixed. On the one hand, econometric estimates by Rodriguez-Rodriguez (2006) suggest that bank credit and trade credit were substitutes for Canary Islands firms in the 1990s. On the other hand, Cook (1999) claims that TC and DF are complements in the Russian context, while Petersen and Rajan (1997) report that US firms enjoying longer relationships with financial institutions (and which are presumably less credit-constrained) do not receive more TC. Using Vietnamese data, McMillan and Woodruff (1999) detect no relationship between ventures offering TC and receiving a bank loan, although recipients of bank loans receive more TC than average. It seems likely too that this mixed evidence reflects the different economic and social contexts of the very different countries in which these studies were conducted. McMillan and Woodruff (1999) mention a possible drawback of TC and relational contracting more generally. As they put it, ‘exclusion is the corollary of ongoing relationships. Continuing to deal with the customary trading partner might mean refusing

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to deal with new entrants, which could result in some inefficiencies’ (McMillan and Woodruff, 1999, p. 1315). Nevertheless, there is evidence that industries with greater dependence on TC as a source of finance exhibit higher growth rates in countries with relatively weak financial institutions (Fisman and Love, 2003). One possible reason, consistent with the arguments outlined above, is that TC assists entry in industries dependent on external finance (Klapper et al., 2006). 8.6

Conclusion

Debt finance is far from being the only source of entrepreneurial finance. Several other sources of finance were reviewed in this chapter, including venture capital, informal angel finance, family finance, trade credit and various co-operative and not-for-profit lending schemes. This chapter’s treatment of alternative sources of finance has not been exhaustive or complete. For example, I did not discuss explicitly the role of credit cards, leasing arrangements or franchising – despite the possibility that these mechanisms can help eliminate funding gaps caused by limited bank credit (Horvitz, 1984). Leasing can be more economical and less risky for small firms than debt finance, conferring tax advantages and being cheaper than buying capital that will not be utilised intensively (Bowlin, 1984). Likewise, franchisors have been able to finance expansion by requiring franchisees to furnish some or all of the necessary capital (Dant, 1995). Nevertheless, what emerges from the chapter’s discussion is the wide variety of different financing arrangements that are available to budding entrepreneurs. Commentators who express concern about credit rationing sometimes appear to overlook this point. Even in countries where financial markets are poorly developed, and where aspiring entrepreneurs lack even nugatory amounts of collateral, micro-finance schemes have demonstrated the scope to expand financing activities, facilitating the creation of thousands of new ventures and possibly contributing to the alleviation of poverty. Yet the existence of a rich array of financing instruments still does not necessarily mean that entrepreneurs obtain all of the funds they desire in practice. Evidence is needed to evaluate the extent to which entrepreneurs actually face binding borrowing constraints. This subject is explored in the next chapter.

Notes 1. See Cressy (2006cc, chap. 14.5.2), Zacharakis and Eckermann (2007) and Tykvová (2007). 2. See Tykvová (2007). This author goes on to discuss optimal financial contracts for syndicates of VCs. 3. See Sahlman (1990), Hochberg (2002) and Hellman and Puri (2002). 4. Shane (2003, pp. 215–17) describes other kinds of certification, available to both VC- and nonVC-backed firms. 5. Tykvová (2007, sec. 4) discusses other reasons for the use of convertible contracts based on their ability to resolve conflicts between entrepreneurs and VCs at the time of exit from the investment. 6. See e.g. Keuschnigg and Nielsen (2003, 2004a, 2005).

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7. See Ross (1977), Greenwald et al. (1984), de Meza and Webb (1987), Amit et al. (1990) and Innes (1993). 8. Bracoud and Hillier (2000) analyse optimal contracts in a generalisation of Hellman and Stiglitz’s (2000) model, in which expected returns vary among entrepreneurs. 9. Demougin and Fabel (2007) analyse incentive contracts in problems of this sort. 10. See Curran and Blackburn (1993), Metcalf et al. (1996), Basu (1998) and Basu and Parker (2001). 11. Yoon (1991), Bates (1997), Basu (1998). 12. Huppi and Feder (1990), Morduch (1999a) and Azevedo (2006) review the structure, rationale, costs and effects of various micro-finance schemes around the world. Aspects of these schemes have been replicated in poorer rural and inner city areas of developed countries, e.g., MicroBusiness International in the USA, the Calmedow Foundation in Canada, and the ADIE Credit Project for Self-employment in France (Rahman, 1993). 13. Stiglitz (1990), Varian (1990), Banerjee et al. (1994), Armendáriz de Aghion (1999) and Chowdury (2005). 14. See Ghatak (1999, 2000), Ghatak and Guinnane (1999), Armendáriz de Aghion and Gollier (2000), Laffont and N’Guessan (2000) and Gangopadhyay et al. (2005). 15. For example, Barboza and Barreto (2006) propose another, based on peer mentoring, whereby successful group members can actively coach and teach others essential aspects of business management. It is also noteworthy that theoretical models of group lending can relax assumptions of peer information and peer monitoring and still sustain the prediction that micro-credit outperforms individual lending (Daripa, 2008). 16. See Kevane and Wydick (2001), McKernan (2002) and Smith (2002). 17. Ahlin and Jiang (2008) study the long-run effects of micro-credit on economic development. 18. Guinnane (1994) and Ghatak and Guinnane (1999, sec. 3.1) describe the origins of credit cooperatives in Germany in the nineteenth century. Key features of the German system included screening of members (not all were admitted) and project proposals (not all were financed). Banerjee et al. (1994) study the optimal design of a credit co-operative. 19. According to Hughes (1992), there is also a public good character to MGSs, because the founding firms pay the greatest cost in setting up the loan guarantee, which later members can benefit from at lower cost. However, this does not necessarily provide a case for public support, because there is nothing to prevent incumbents devising ways of inducing future members to share the initial costs. 20. Evidence for the role of TC in developing economies can be found in Cook (1999) for Russia; McMillan and Woodruff (1999) for Vietnam, and Fafchamps (2000) for Kenya and Zimbabwe.

9

Wealth and entrepreneurship

This chapter explores the role of entrepreneurs in the accumulation of wealth and aggregate savings. As the first section of this chapter explains, entrepreneurial wealth creation influences aggregate capital accumulation, savings and wealth inequality. Furthermore, personal wealth influences entrepreneurs’decisions about the types of venture to initiate – and whether any are initiated in the first place. Although economic research in the last decade has greatly advanced our understanding of entrepreneurial wealth accumulation, several issues remain unresolved. Among these issues is a major puzzle about entrepreneurs’ asset portfolio decisions, which Moskowitz and Vissing-Jørgenson (2002) call the ‘private equity premium puzzle’. The essence of this puzzle is that entrepreneurs’ private returns are not high enough to fully compensate them for the risk that they bear. In a strictly financial sense, it seems that most entrepreneurs would do better by liquidating their enterprises and investing the proceeds in a balanced portfolio of publicly traded bonds and equities. Similarly, it might be thought that entrepreneurs should reduce ownership stakes in their own ventures and diversify their wealth using the public equity markets. Evidence showing that this strategy is not widely adopted in practice is discussed below, as well as possible reasons for limited entrepreneurial asset diversification. The first three sections of this chapter treat these issues. These sections are essentially descriptive in nature. Issues with greater conceptual content are explored in the remainder of the chapter, starting with the fourth section, which asks whether individuals need personal wealth in order to enter entrepreneurship. If they do, then entrepreneurship might not be a conduit of social and economic mobility for the less well off. This question has been explored intensively in the last two decades. After setting out the theoretical analysis of this topic, the evidence is evaluated. I go on to trace the implications of personal wealth for subsequent entrepreneurial performance. It transpires that the entrepreneurship–wealth relationship is a contentious one. Disagreement continues to rage over the existence of an empirical relationship between entrepreneurship and wealth and what such a relationship actually means. In particular, Evans and Jovanovic (1989), who initiated the debate on this topic, argued that a positive wealth– entrepreneurship relationship can be taken as prima facie evidence of credit constraints which could hinder entrepreneurial entry by worthy but impecunious individuals. But 263

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others have argued that this relationship says little or nothing about borrowing constraints. It has also been argued that even if borrowing constraints exist, entrepreneurs can bypass them by adopting suitable avoidance strategies. The penultimate section of the chapter complements the treatment of ‘Type I’ credit rationing (rationing of loan sizes) underlying the analysis in sections 4–7 of this chapter with a discussion of ‘Type II’ credit rationing (rationing of the number of loans). This material therefore provides an empirical complement to the theoretical analyses of credit rationing given in chapter 7. The conclusion of that chapter was that theory alone cannot determine whether credit rationing exists and how widespread it might be in practice. Hence empirical evidence is needed to reach a judgement about the likely practical relevance of this phenomenon. The available evidence is reviewed here. Throughout the chapter, different sides of the various debates are presented. Brief conclusions are drawn in the closing section. 9.1 The role of entrepreneurs in aggregate wealth accumulation and inequality

Recent research has identified entrepreneurship as a key mechanism driving aggregate wealth accumulation, savings and wealth inequality in modern economies. This research effort has mainly been conducted by economists primarily interested in explaining macroeconomic savings and wealth dynamics in the US economy. The facts linking entrepreneurship with aggregate wealth accumulation are striking. By way of illustration, consider the following findings, drawn from the SCF (Survey of Consumer Finances) database:1 • Entrepreneurs accounted for only 8.7 per cent of US households in 1989, yet held 39 per cent of the nation’s net worth. • About one-half of the richest 5 per cent of American families are business owners, with the income and wealth shares of these families being greater at higher percentiles of the income and wealth distribution. • 81 per cent of individuals in the top 1 per cent of the wealth distribution in 1989 declared that they were either business owners or self-employed, with more business owners than self-employees at the top of the wealth distribution, and more selfemployees than business owners at the bottom. • Although entrepreneurial families hold less wealth in stocks than other similarly wealthy households, they still account for about one-third of all stockholdings. Hence entrepreneurship may have a significant impact on asset prices, as well as on real economic activity. Data from ‘Rich Lists’ tell a similar story. For example, between 60 and 80 per cent of people on the Forbes magazine list of the wealthiest 400 Americans are ‘self-made’ business men and women, while almost all of the rest inherited fortunes which originated in one or more businesses started by one of their parents or grandparents. Only

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three were celebrities. Collectively, this evidence shows that entrepreneurs dominate the ranks of the rich and the super-rich.2 How do entrepreneurs get rich? Consistent with the Forbes list, one reason appears to be dynastic bequests of entrepreneurial wealth. This not only preserves concentrated wealth holdings but also helps perpetuate aggregate wealth inequality across the generations (Cagetti and de Nardi, 2006). A second important factor is that successful entrepreneurs earn and save more than their employee counterparts (Quadrini, 2000; Cagetti and de Nardi, 2006). As well as making entrepreneurs more upwardly mobile in the wealth distribution, entrepreneurial saving drives wealth accumulation at the very top of the wealth distribution.3 Most intriguing is the possibility that entrepreneurial saving drives wealth inequality in society. Simulations suggest that entrepreneurs play an essential role in aggregate wealth accumulation, and their presence makes a major difference to the implied effects on wealth inequality of income tax reform (Meh, 2005). This seems to dominate an offsetting effect whereby entrepreneurs of modest means who introduce paradigm-shifting ‘Schumpeterian’ innovations can reduce inequality by redistributing wealth to themselves from previously successful incumbents (Spencer et al., 2008). In contrast, transfers obtained by entrepreneurs who sell controlling stakes in publicly traded firms to outsiders have little effect on aggregate entrepreneurial wealth accumulation (Klasa, 2007). And while philanthropy by entrepreneurs fulfils a socially useful function (Acs and Phillips, 2002), there is as yet little robust evidence showing it to stimulate entrepreneurship directly, in the way that dynastic family transfers do. Why do entrepreneurs save more than employees? Using a calibrated general equilibrium occupational choice model, Quadrini (2000) attributed higher savings rates by entrepreneurs to four principal factors. One is that wealth is needed to overcome borrowing constraints, enter entrepreneurship and run larger business ventures. Reinvestment of profits is a cheap and ready source of finance that can fuel venture growth. Second, financial intermediation costs drive a wedge between borrowing and lending rates, increasing incentives for entrepreneurs to save. Third, entrepreneurs face greater risk, including that arising from entrepreneurs’ limited asset diversification (Covas, 2006). Hence savings can act as a cushion against risk. Fourth, savings that augment wealth can perpetuate dynastic entrepreneurship, enabling entrepreneurial families to continue accumulating wealth over many decades, and even centuries. Tax reasons might also help explain high savings rates and wealth levels of entrepreneurs. Progressive taxation of labour income encourages owners of incorporated businesses to divert profits from earnings into assets, since dividends are typically more lightly taxed. There can also be tax advantages to self-finance (Kari and Karikallio, 2007). Although there are some dynamic models of entrepreneurial wealth transfers and accumulation,4 none of them explains the size distribution of entrepreneurs’ wealth, or how it compares with the distribution of entrepreneurial incomes. According to estimates compiled by Parker (2003a), older self-employed Britons enjoy above-average wealth holdings, while wealth inequality in this group is modest.

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9.2 The ‘private equity premium puzzle’

In an influential article, Moskowitz and Vissing-Jørgensen (2002) analysed the portfolio risk-and-return patterns of American entrepreneurs and highlighted the following ‘private equity premium puzzle’. Entrepreneurs tend to invest in undiversified and relatively risky private businesses, yet tolerate an average rate of return which is no greater than what is obtainable from a diversified (and hence less risky) publicly traded portfolio of equities. This is a puzzle because if individuals are risk-averse, they should only be willing to bear the greater risk of entrepreneurship if they are rewarded with a higher return. Entrepreneurs bear some element of undiversified risk because their investment opportunities are not market assets which are equally available to all investors. Some reasons why entrepreneurs hold high shares of their wealth in undiversified businesses are explored in the next section. For now, it is of interest to consider the costs that entrepreneurs bear as a result of the private equity premium puzzle. One way of estimating this cost is by calculating the implicit cost of capital which entrepreneurs face at the time of IPO. According to Kerins et al.’s (2004) analysis of high-tech IPOs, an entrepreneur’s opportunity cost over a one-year holding period is generally between two and four times as high as that of a well-diversified investor. To put this into perspective, for an entrepreneur with 25 per cent of her total wealth invested in her venture, the cost of capital is 40 per cent. If entrepreneurs really are risk-averse, as the evidence suggests (chapter 4), it is desirable to find reasons why entrepreneurs are willing to settle for average returns which do not appear to compensate them properly for the extra risk that they bear. Several explanations have been proposed. First, individuals do not put their human capital at risk when they become entrepreneurs. In particular, future earnings in entrepreneurship or paid employment are unaffected by the risk of the current business. Hence the risk of an individual’s total net worth, which includes the present value of human capital, is much less than that of their financial wealth alone. In a calibration exercise which factors the present value of human capital into estimates of individuals’ total net worth, Polkovnichenko (2003) shows that only small non-pecuniary benefits, amounting to as little as 1.5 per cent of returns, are sufficient to induce individuals to turn entrepreneur despite the greater undiversifiable risk they face in entrepreneurship. For less educated people, with lower present values of human capital, this argument presumably becomes less compelling. On the other hand, less educated people usually have relatively little financial wealth, so the private equity premium puzzle is probably less of an issue for them anyway. For these individuals, the main locus of comparison between entrepreneurship and wage work probably relates to relative wages, as discussed in chapters 2–4. A second potential explanation of the private equity premium puzzle is that entrepreneurship provides additional private benefits which should be capitalised and incorporated into the calculus of relative occupational benefits. An important example of such a benefit is the opportunity to learn about one’s idiosyncratic ability to

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V(B)

Ret urns

R(B)

0 B

Figure 9.1 The convex outer envelope of returns in entrepreneurship, V(B), and paid employment, R(B)

generate business returns (Hintermaier and Steinberger, 2005). Only people who try entrepreneurship are able to realise this benefit, which helps them to make better portfolio decisions not only instantaneously, but also over the rest of their lifetimes. It may be worth accepting a riskier return in the short term in return for this long-term benefit. Third, the value of entrepreneurship when there is an option to quit can be a convex function of wealth. If so, entrepreneurs who can choose a project from a set of projects with common mean returns but different degrees of risk might optimally choose to operate a relatively risky project (Hopenhayn and Vereschagina, 2003). To see why, consider Figure 9.1, which illustrates two concave functions of wealth, B. These are the returns in entrepreneurship as a function of wealth, V (B), and the returns in paid employment, R(B). Individuals’ best choices are given as the ‘outer envelope’ of these functions, illustrated in bold in Figure 9.1. If the two functions cross once, the outer envelope is a convex function. As noted in chapter 2, individuals should choose to operate riskier projects when the return function is convex. So entrepreneurs with wealth around the point of intersection in Figure 9.1, and faced with a choice of projects with different risk profiles, might optimally choose to operate riskier projects. They do not need a risk premium to do so, so there is no private equity premium puzzle to explain. A fourth explanation of the puzzle is based on a preference for skewed returns. A preference of this kind has previously been observed in the context of gambling, where it can explain why risk-averse consumers willingly play lotteries yielding negative expected, but highly positively skewed, returns (Golec and Tamarkin, 1998). The same logic can also be applied to entrepreneurship, as demonstrated in the first part of the appendix to this chapter. It is shown there that individuals might participate in risky entrepreneurship even if expected returns are negative (µ1 < 0, µ2 > 0), as long as they are compensated by a sufficiently large positive skewness of returns, µ3 > 0. It is

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important to note that a preference for skewness is not the mark of an inveterate gambler or a hopeless optimist, but is instead a common trait of a sober-minded risk-averse person with decreasing or constant absolute risk aversion. At the time of writing, research continues into efforts to resolve the private equity premium puzzle. It is unclear at present which (if any) of the above explanations best explains the puzzle. Finding out which does is a valuable research topic, which promises to shed further light on the dynamic structure of entrepreneurial choices. 9.3

Entrepreneurial wealth diversification

The financial portfolios of even very wealthy entrepreneurs are often remarkably undiversified. On average, about 40 per cent of an entrepreneur’s total wealth is tied up in their firm’s equity.5 According to estimates based on the SCF (Survey of Consumer Finances), around 64 per cent of entrepreneurs own their firm in its entirety, with another 10 per cent owning exactly one-half of the equity (Bitler et al., 2005). As noted above, limited asset diversification has been proposed as a possible answer to the entrepreneurial private equity premium puzzle. But this begs the question of why entrepreneurs do not diversify their wealth. Several possible explanations for undiversified asset holdings by entrepreneurs have been proposed. One was noted by Moskowitz and Vissing-Jørgenson (2002) themselves, namely correlated returns from business ownership and the entrepreneur’s human capital, thereby reducing the range of other uses to which their human capital can be put. Asecond set of possibilities relates to work effort by entrepreneurs. In contrast to most paid employment jobs, entrepreneurship offers ample opportunities for flexible working. If entrepreneurs can vary their labour supply relatively freely (see chapter 12), then households can choose riskier financial portfolios (e.g. undiversified entrepreneurship) because flexible labour supply provides an additional hedge against financial risk (Bodie et al., 1992). A different work effort argument focuses on incentive problems arising from diluted ownership stakes. In particular, high ownership stakes might be necessary to provide entrepreneurs with incentives to supply discretionary and non-contractible effort. Without entrepreneurs holding a majority stake in a venture, outside investors would fear being expropriated since entrepreneurs would possess greater incentives to slack. Bitler et al. (2005) adduced some evidence in favour of this hypothesis. Using data from the SCF and NSSBF, Bitler et al. (2005) showed that entrepreneurial effort as measured by weekly hours of work was positively associated with entrepreneurs’ ownership shares; furthermore, effort translated into better performance. As Bitler et al. noted, however, while their findings might explain concentrated ownership shares, they do not explain the puzzle about why these people become entrepreneurs in the first place – another important part of the private equity premium puzzle. A third possible reason for limited entrepreneurial diversification is that almost by definition, small firms are dependent on a limited pool of management expertise, which is a key factor in the success or failure of a business (Hall, 1992). This argument is

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predicated on the notion that entrepreneurs’ business assets are fundamentally illiquid, or indivisible. However, there is a problem with this explanation, because portfolios of continuing entrepreneurs tend to become more rather than less undiversified over time. So this finding does not support the explanation of down-payment constraints when starting a new business. Finally, control issues might make it optimal for entrepreneurs not to diversify their wealth. In addition to a simple non-pecuniary preference for sole control, entrepreneurs might prefer to keep their firm private rather than take it public (and thereby obtain greater access to capital) because they require decision-making autonomy to manage their firm optimally (Boot et al., 2006).

9.4 Wealth and entrepreneurship: theories

The possibility of a relationship between wealth and entrepreneurship is without doubt one of the most heavily researched topics in the economics of entrepreneurship. This relationship is of interest for several reasons, the most prominent being the possibility that it reflects the existence of credit constraints. This section provides an overview of the principal theories underlying the concept of a wealth–entrepreneurship relationship. These theories are based on a variety of models of borrowing constraints in entrepreneurial credit markets. The first subsection discusses the pioneering article of Evans and Jovanovic (1989). This is followed by other important contributions proposed by Banerjee and Newman (1993), Aghion and Bolton (1997) and Newman (2007). The emphasis in this section is on theory; evidence is evaluated in the section which follows. 9.4.1

The Evans and Jovanovic (1989) model Consider a set of individuals who can only borrow up to a multiple γ ≥ 1 of their initial assets, B, to start a business where γ is common to all individuals. Entrepreneurs can therefore operate only capital k ∈ (0, γ B). This rule, which is sometimes also referred to as a ‘limited liability constraint’ (Paulson et al., 2006), corresponds to the case of Type I credit rationing, since banks are willing to extend loans to everyone with some assets, up to some given asset-determined limit, irrespective of the interest rate entrepreneurs are prepared to pay. Although Evans and Jovanovic (1989) (referred to as EJ hereafter) did not explain why banks impose this ‘lending multiple’ policy, wealth-based loan sizes emerge naturally from a moral hazard problem, outlined below, in which entrepreneurs can ‘take the money and run’ (Banerjee and Newman, 1993). EJ assumed that borrowing and production take place in a single period. Entrepreneurs’ incomes y depend on k via the production function y = xk α , where x is managerial ability and α ∈ (0, 1) is a parameter. Constrained borrowers enter entrepreneurship only if their earnings net of capital repayments D = (1 + r)γ B (where r > 0 is the nominal interest rate) exceed their earnings in paid employment, w.

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This occurs if x(γ B)α − (1 + r)γ B > w.

(9.1)

If a sample of individuals with given abilities are drawn at random from the population, then the probability that they are entrepreneurs is an increasing function of assets B, as can be verified by differentiating the LHS of (9.1). This is the first of two predictions of the EJ model: 1. There is a positive relationship between the probability of entering entrepreneurship, and assets prior to entering entrepreneurship. 2. Wealthier entrepreneurs will operate larger enterprises on average than poorer ones and will receive higher incomes. Perhaps counter-intuitively, however, the model predicts that, conditional on wealth, the most constrained entrepreneurs are those with the highest abilities, x.6 It will be seen later in the chapter that actual data are inconsistent with this prediction, at least if x is measured as human capital. To deal with this problem, one could extend the EJ model, perhaps by linking ability or human capital to easier access to supplies of financial capital. This could happen if greater human capital is associated with higher levels of income and savings which banks use as the basis for determining loan sizes, or if human capital serves as a positive signal of productivity which releases funds from lenders (Parker and van Praag, 2006a). Such extensions would render the sign of the theoretical relationship between human capital and borrowing constraints ambiguous (Astebro and Bernhardt, 2003, 2005). It is straightforward to use the EJ framework to demonstrate that an empirical relationship between wealth and participation in entrepreneurship is a sufficient condition for identifying imperfect capital markets. To see this, let output in entrepreneurship be f (k), and let  and φ be the cdf and pdf of the distribution of wages, w. Using the occupational choice model outlined in chapters 2 and 3, the probability that an individual becomes an entrepreneur is z ∗ = [ f (k) − (1 + r)k]. Hence ∂z ∗ /∂k = φ[f
(k) − r]. If capital markets are perfect, f
(k) = r and so z ∗ does not depend on capital. But imperfect capital markets imply f
(k) > r (because constrained borrowers necessarily have k < k ∗ ) – so z ∗ being positively related to k is a sufficient condition to indicate imperfect capital markets. It is not, however, a necessary condition. EJ estimated a simple probit model of entry into self-employment conditioned on assets (in levels and squared) – as well as experience in paid employment, education and assorted personal characteristics. Using NLS (National Longitudinal Survey) data on 1,500 white males over 1978–81 who were wage earners in 1976, EJ reported a positive and significant probit coefficient (p-value = 0.02) on initial assets. This supports prediction 1 above. Also, log self-employment incomes were significantly and positively related to log initial assets, supporting prediction 2. EJ went on to estimate a structural model of occupational choice with borrowing constraints. They estimated γ to be 1.44, and significantly greater than 1; a subsequent estimate by Xu (1998) based on

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more accurate data found γˆ = 2.01. Also, EJ estimated that 94 per cent of individuals likely to start a business faced Type I credit rationing. They claimed that this prevented 1.3 per cent of Americans from trying entrepreneurship. These are large effects, which have encouraged numerous subsequent researchers to explore the empirical robustness of the relationship between entrepreneurial participation and wealth. The empirical findings from these studies are reviewed in the next section of this chapter. The EJ model also implies that the distribution of wealth will influence the amount of entrepreneurship. That is because a mean preserving spread (see chapter 2) in the wealth distribution will make entry easier in the presence of borrowing constraints by increasing the number of people in the right-hand tail with sufficient wealth to selffinance or borrow the funds required for entry (Lindh and Ohlsson, 1998). This can be seen directly from (9.1), since regressively redistributing wealth from those with very low B (who will never enter anyway) to those with a higher B, but who are just unable to enter, increases entry. Using a time-series of Swedish data, Lindh and Ohlsson (1998) went on to claim an empirical link between wealth inequality and the aggregate self-employment rate. However, subsequent theorising suggests that the relationship between wealth inequality and self-employment could be more subtle, being positive when start-up costs are high but turning negative when start-up costs are low. In an empirical application of this idea, Mesnard and Ravillion (2006) obtained estimates of a negative relationship between wealth inequality and self-employment using Tunisian micro-data, a country in which start-up costs are very low on average. 9.4.2

The Banerjee and Newman (1993) model

EJ’s assumption of a wealth-based lending rule can be motivated in several ways. Banerjee and Newman (1993) suggested one rationale, based on the idea that entrepreneurs can ‘take the money and run’. To abstract from unnecessary bankruptcy issues, suppose for simplicity and without loss of generality that project returns are always high enough for entrepreneurs to be able to repay their loans. All entrepreneurs post their entire wealth B as collateral to borrow the amount k, where k > B. In return, they obtain the payoff f (k). The key point is that once they obtain their payoff, entrepreneurs can choose to renege on their repayment (1 + r)k, taking the money f (k) and running away, albeit at the cost of lost collateral, (1+r)B. At the end of the period, the entrepreneur escapes the lender’s efforts to find her with probability 1 − p. If she is caught, with probability p, she consumes all her income and is subject only to a non-monetary punishment c which reduces her utility in a linear fashion. Reneging therefore yields a payoff of f (k) − pc, while repayment yields f (k)+(1+r)(B−k). The entrepreneur will therefore renege whenever (1 + r)B + pc < k(1 + r). Knowing this, lenders will only make loans that satisfy k ≤ B + [pc/(1 + r)].

(9.2)

All loans made in equilibrium will satisfy this constraint, and entrepreneurs who receive loans will never renege. Only wealthy entrepreneurs receive loans, however: (9.2)

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ties loan sizes directly to initial personal wealth, as assumed (but not derived) by EJ. 9.4.3 The Aghion and Bolton (1997) model This outcome of the poor being denied access to credit also emerges from a model proposed by Aghion and Bolton (1997) (referred to as AB hereafter), in which moral hazard problems turn out to be most acute at low wealth levels. Suppose entrepreneurs need a unit of capital to set up a firm. If their wealth is less than unity, i.e. B < 1, they must borrow, owing the bank D(B) per unit borrowed. The project generates a positive payoff Rs with probability p, and the payoff Rf = 0 with probability 1 − p. The entrepreneur can increase p by exerting more effort, but this costs the entrepreneur c(p) = rp2 /2α in utility, where α ∈ (0, 1]. Hence the entrepreneur faces the problem

max{pRs − p(1 − B)D(B) − c(p)} . p

  The solution to this optimisation problem is given by p(B) = α 1 − (1 − B) D(B) Rs . It

turns out that p
(B) > 0, i.e. poorer borrowers have less incentive to supply effort, as they must share a larger fraction of the marginal returns from effort with the bank. As a result, competitive banks optimally cut off credit to (‘redline’) everyone below some critical wealth threshold: only the wealthy can become entrepreneurs.7 Paulson et al. (2006) designed an encompassing empirical framework which nests the EJ and AB models as special cases, and permits the researcher to test which of the models is most congruent with the evidence. The structural model underlying their approach, and the results obtained from estimating it, are outlined in the second part of the appendix to this chapter. 9.4.4

Newman’s (2007) moral hazard model Chapter 2 discussed a moral hazard problem by Newman (2007) in which, counterintuitively, the poorest individuals become entrepreneurs and the wealthiest work for them. As noted there, Newman interpreted this outcome as being problematic for riskattitude-based models of entrepreneurial choice. Taking Newman’s result at face value, borrowing constraints which deny loans to those without wealth will presumably result in no entrepreneurship at all. In fact, modifying the Newman model by adding bankruptcy costs changes its predictions in a more realistic way. Bankruptcy costs mean that the very poorest no longer choose to become entrepreneurs because they can no longer afford to risk failure: their marginal disutility from failure becomes too high in the bad state (Banerji and van Long, 2007). In this case, only individuals with moderate wealth become entrepreneurs. The very richest and poorest choose not to, the former because they must bear an unacceptable level of income risk to be made incentive-compatible in entrepreneurship, and the latter because they cannot afford the bankruptcy cost.

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9.5 Wealth and entrepreneurship: evidence

Following the pioneering work of EJ, many researchers have used cross-sectional and/or longitudinal data to estimate binary choice models of entrepreneurship which include some measure of individuals’ wealth or asset windfalls among the vector of explanatory variables. Most studies report a significant positive relationship between asset endowments and entrepreneurship,8 while others find that home ownership and house prices are linked with new firm formation and self-employment at both the individual and the regional levels.9 In contrast, a handful of studies have detected an insignificant wealth/entrepreneurship relationship.10 The most influential of these studies is Hurst and Lusardi (2004), whose contribution will be analysed in greater detail shortly. Other evidence casts doubt on EJ’s model, too. Researchers have reported a positive relationship between access to capital and human capital (Bates, 1997; Astebro and Bernhardt, 2003, 2005) which, as noted above, contradicts the EJ model. This result has proven robust to allowing human capital to be endogenous (Parker and van Praag, 2006a). A key issue in this literature is the need to recognise and deal with the potential endogeneity of wealth. Endogeneity may have generated spurious estimates of a wealth– entrepreneurship relationship in previous work. One potential source of endogeneity from assets is reverse causality: entrepreneurship makes people wealthy rather than wealth making people become entrepreneurs. In an attempt to overcome this problem, some researchers have analysed the effects of windfalls and inheritances received prior to market entry. Examples of windfalls include lottery wins, redundancy lump sums and favourable exchange rate movements which increase the value of remittances received in developing countries by relatives working overseas.11 Empirical studies generally report positive, significant and substantial effects of windfalls on self-employment status and entry probabilities, with diminishing returns from higher windfall values.12 To give a flavour of the results, Blanchflower and Oswald (1990) reported that a Briton who received £5,000 in 1981 prices was twice as likely to be self-employed in 1981 as an otherwise comparable person who had received nothing. For the USA, Holtz-Eakin et al. (1994a) estimated that a $100,000 inheritance would increase the probability of a transition from paid employment to self-employment by 3.3 percentage points. And Lindh and Ohlsson (1996) estimated that the probability of self-employment in Sweden would increase by 54 per cent if lottery winnings were received, and by 27 per cent following the receipt of an average-sized inheritance. Findings of a positive and significant role for windfalls seem to be robust to several possible objections and criticisms, including entrepreneurs being more willing to gamble on lotteries (the opposite appears to be the case according to Lindh and Ohlsson, 1996 and Uusitalo, 2001); to inheritances being anticipated or taking the form of family businesses; and to industry differences in capital requirements (Bates, 1995). Indeed, true effects from windfalls may be understated to the extent that researchers cannot always measure delayed entries into entrepreneurship following the receipt of a windfall. Entry decisions may take several years to play out. On the other hand, there is some

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evidence that future inheritances predict business formation as well as past inheritances. This implies that inheritance is not a good instrument for capital constraints, as by definition capital-constrained families cannot borrow against future inheritances (Hurst and Lusardi, 2004). Instead, it is possible that inheritances are capturing something other than borrowing constraints, for example a wealthy and successful family background. This raises the possibility that windfall studies, as well as ones based on personal wealth variables, could be vulnerable to endogeneity biases arising from unobserved attributes which make some individuals both more interested in accumulating wealth and in becoming entrepreneurs. If so, it would look as though wealth is correlated with entrepreneurship even when it is not. Some direct evidence of this type of endogeneity appears in Hochguertel (2005a), who estimated the following structural model using a panel of Dutch household data from the 1990s: sit∗ = Xit ’βs + γss sit−1 + γsb ln Bit−1 + αsi + usit ln Bit∗

= Xit ’βb + γbs sit−1 + γbb ln Bit−1 + αbi + ubit  sit =

(9.3) (9.4)

1 if sit∗ > 0 0 otherwise

where s is participation in entrepreneurship (self-employment); B is personal net wealth; the αs are (possibly correlated) fixed effects; and the us are random disturbance terms. If γsb > 0, a positive wealth–entrepreneurship relationship exists, while a positive correlation between the αs means that people who find it attractive to become entrepreneurs also have a preference for acquiring wealth. Hochguertel (2005a) estimated both αs in (9.3) and (9.4) to be statistically significant and strongly positively correlated (corr(αs , αb ) = 0.510, with estimated standard error = 0.066). This suggests correlated unobservable preferences. ML estimates of the γ s are summarised in Table 9.1. According to these estimates, lagged wealth does not affect participation in entrepreneurship, consistent with Hurst and Lusardi (2004) and contradicting the borrowing constraint hypothesis. There is instead clear evidence of reverse causality, with participation in entrepreneurship generating greater wealth (γˆbs > 0). This is also consistent with other US evidence, which shows that following entry, entrepreneurs exhibit high growth rates in wealth and savings, while entrepreneurs who exit tend to dissave (Gentry and Hubbard, 2004). Strong statedependence in entrepreneurship, γˆss , also emerges from the estimates, in accordance with Henley’s (2004) findings based on British BHPS data. These results suggest that researchers need to correct for endogeneity by using robust estimators, such as IV (see chapter 3). Recent studies are beginning to address this problem, using a range of identification strategies. Hurst and Lusardi (2004) were one of the first research teams to instrument wealth, their instrument being house price appreciation.13 Using PSID (Panel Study of Income Dynamics) data, Hurst and Lusardi estimated the relationship between becoming a business owner and wealth to be flat up to the 95th percentile of the wealth distribution, before becoming steep and positive.14 This result suggests that there is no relationship between wealth and entrepreneurship

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Table 9.1. Estimates of a simultaneous equation model of entrepreneurship and wealth

Entrepreneurship at t − 1 Wealth at t − 1

Entrepreneurship at t

Wealth at t

γˆss = 2.762a (0.137) γˆsb = 0.000 (0.052)

γˆbs = 1.036a (0.295) γˆbb = 0.152a (0.009)

Note: Entries are coefficient estimates with estimated standard errors in parentheses beneath; a denotes statistical significance at the 1% level. Source: Hochguertel (2005a).

for most people, and casts doubt on the borrowing constraint hypothesis since it is hard to believe that the rich are liquidity constrained. Hurst and Lusardi’s (2004) findings have been challenged in subsequent research also based on PSID data and enlarged with CPS data to cover more metropolitan areas and a longer time period. Using these richer data, Fairlie and Krashinsky (2006) discovered that the spike at the top of the wealth distribution is associated with wealthy older job losers who face limited job opportunities in paid employment and turn to entrepreneurship instead (q.v. Farber, 1999). Strikingly, when Fairlie and Krashinsky split the sample into job losers and non-job losers, they discovered increasing rates of entry into self-employment for both groups throughout the asset distribution. This suggests that Hurst and Lusardi’s (2004) findings might be an artefact of aggregation across dissimilar groups. Furthermore, Mesnard and Ravillion (2006) used IV to estimate the effects of wealth accumulated abroad by Tunisian émigrés on their propensities to become self-employed once they returned to Tunisia. Mesnard and Ravillion’s identifying instrument was the date of emigration. The reason was a new Tunisian law which made emigration to relatively rich European countries more difficult after 1974. Emigrants who left before 1974 had opportunities to acquire more wealth than those who emigrated afterwards. Since the policy change was exogenous, it should not affect the decision to become self-employed upon eventual return to Tunisia. Mesnard and Ravillion’s IV estimates revealed a positive and significant effect from wealth, with a concave diminishing effect; non-parametric estimation yielded a similar pattern. But while they are interesting, these findings still do not establish the existence of borrowing constraints. Several reasons why they do not are explained in the next section. 9.6 Alternative interpretations of a wealth/entrepreneurship relationship

While findings of a wealth–entrepreneurship relationship are consistent with borrowing constraints, alternative explanations which do not depend on the existence of borrowing constraints can also be proposed: 1. Inherently acquisitive individuals both accumulate assets and prefer entrepreneurship to working in paid employment. As noted in the previous section, this

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3.

4.

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mechanism is associated with a positive relationship between entrepreneurship and wealth, whether or not borrowing constraints exist. Entrepreneurs prefer self-finance to external finance. One reason might be that entrepreneurs regard the terms of external finance to be unreasonable or unprofitable. For example, suppose bank administrative costs drive a wedge between borrowing rates, rb , and saving rates, rs , where rs < rb . If the entrepreneur’s net present value conditioned on the interest rate has the ordering NPV (rb ) < 0 < NPV (rs ) then windfalls which turn borrowers into savers can increase participation in entrepreneurship even in the absence of borrowing constraints. Another reason why self-finance is preferred to external finance might be ‘control aversion’, whereby entrepreneurs do not wish to be dependent upon the decisions of banks (Cressy, 2006b). Consequently, people wait until they have saved (or inherited) enough wealth to enter entrepreneurship without borrowing. Yet all the while banks may have been willing to lend all of the required funds to every loan applicant. Alternatively, consider overoptimistic individuals who propose ventures for financing which banks perceive to be unprofitable at the proposed scale of operation. Banks therefore rationally refuse to extend all of the credit requested by entrepreneurs. The latter then delay entry until they have sufficient wealth, giving rise to a positive wealth–entrepreneurship relationship despite the actual absence of any borrowing constraints. Leisure is a normal good, so wealthier people prefer entrepreneurship because its flexibility enables them to reduce their work hours, in contrast to paid employment where standardised work-week restrictions prevail (Harada and Kijima, 2005). This can happen irrespective of whether or not borrowing constraints exist.15 Apositive association between start-ups and wealth (or windfalls such as inheritances and lottery winnings) might simply reflect the effects of decreasing absolute risk aversion (DARA) rather than borrowing constraints. Consider again the Kihlstrom and Laffont (1979) model outlined in chapter 2. Under DARA, an increase in the wealth of the marginal risk-averse individual makes them more willing to enter risky entrepreneurship, so increasing the aggregate rate of entrepreneurship (Cressy, 2000; Newman, 2007). However, there are several objections to the DARA explanation. In theory, DARA implies that entrepreneurs running larger firms will take more risks and so will have more variable returns. But this is contrary to the evidence (Hopenhayn and Vereshchagina, 2003). And it has been observed that even after controlling for measures of risk preferences, wealth still has a significant positive effect on transitions into self-employment (Kan and Tsai, 2006). Some asymmetric information models have the property that a positive relationship between the number of entrepreneurs and personal wealth is consistent with overinvestment rather than Type I credit rationing (de Meza and Webb, 1999). And if project returns decay with delay in exploiting them, over-investment models also imply that less wealthy entrepreneurs will undertake their projects at a smaller scale and will succeed less often, and may therefore choose ex ante not to undertake them at all (de Meza and Webb, 2006). The implication is once again a positive relationship between wealth and entrepreneurship.

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6. Financially distressed firms face higher interest rates and restricted credit (Harhoff and Körting, 1998). An inheritance or windfall merely relaxes the entrepreneur’s budget constraint and permits them to survive in business a little longer. Conversely, when wealth is plentiful, there is less financial distress and greater participation in entrepreneurship. The resulting competition discourages entry from those with sufficient wealth but poor investment projects – leading to a higher overall business survival rate (Black et al., 1996). 7. In developing economies, wealthy entrepreneurs often enjoy market power. This gives them the most favourable loan terms, inducing them to become entrepreneurs while the less well off do best by lending their capital to the entrepreneurs (Gabszewicz and Laussel, 2006). All of these arguments can explain how wealth promotes entry and persistence in entrepreneurship, despite an absence of borrowing constraints. And even if borrowing constraints do exist, their effects may be limited in scope. There are two major reasons for this, the discussion of which will take up the remainder of this section. One is that most entrepreneurs start ventures which need little or no capital. The other is that even when capital is required but is for some reason unavailable, entrepreneurs can in principle find ‘escape mechanisms’ to circumvent the constraints. There is abundant evidence that lack of capital need not deter entry. According to Meyer (1990), 60 per cent of entrants to entrepreneurship possess no depreciable capital. Borrowing constraints evidently did not prevent these entrepreneurs from entering. Furthermore, Hurst and Lusardi (2004) reported that low-wealth households are no less likely to start up in industries requiring high capital investments. Capital requirements for most business start-ups seem to be modest in any case. Dennis (1998) cites a 1992 survey of Inc.’s 500 fastest-growing firms which found that one-quarter of these firms started up with funds of less than $5,000. Nor need any entrepreneurs denied requisite finance necessarily be passive victims of borrowing constraints. Entrepreneurs have incentives to seek ‘escape routes’ such as finding other sources of funds (Bhide, 2000), or saving (Parker, 2000; Ghatak et al., 2001). The creative use of meeting resource needs without recourse to external finance is known as ‘bootstrapping’. This practice can involve leveraging credit cards and second mortgages; delaying payment to suppliers; and minimising stock and accounts receivable. According to Bhide (2000, p. 15), more than 80 per cent of Inc. 500 founders bootstrapped their ventures with modest funds; their median start-up capital was about $10,000.16 Entrepreneurs are known to have above-average savings rates (Quadrini 1999, 2000). In theory, the prospect of saving to overcome borrowing constraints can incentivise workers to supply high levels of effort, thereby increasing social welfare (Ghatak et al., 2001). So policies which reduce credit market imperfections may reduce welfare by dampening this effect.17 Unfortunately, there is still insufficient evidence to determine whether savings enable entrepreneurs to circumvent borrowing constraints in practice. Some simulation evidence is suggestive, though not decisive. For example,

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Bohàc˘ ek (2006) calibrates a dynamic model which combines a Banerjee–Newman style borrowing constraint with a saving incentive, allowing entrepreneurs to accumulate wealth to escape the constraint. Bohàc˘ ek’s (2006) simulation reveals that this escape mechanism can explain high entrepreneurial savings rates and the pronounced wealth inequality observed in the US economy. However, different calibration models analysed by other researchers can also explain these stylised facts (q.v. section 9.1 above), implying an identification problem with the calibration approach. Some anecdotal evidence also suggests that some people can escape borrowing constraints by saving. This includes Yoon’s (1997) survey of Korean business owners in Chicago and LosAngeles, and Paulson and Townsend’s (2004) analysis of entrepreneurs in Thailand. For instance, Yoon (1997) observed that the respondents to his survey often started as manual, service or sales workers; accumulated capital mainly through personal savings; and then bought the business from the existing owner. Ghatak et al. (2001) cite some other examples of this kind. The notion of saving as an escape mechanism has its own limitations, however. If some individuals are so impatient that they prefer consuming while young instead of saving for the future, they might optimally choose to remain credit-constrained forever (Parker, 2000). This implies that, in some cases, borrowing constraints are better regarded as voluntary than as involuntary. A second caveat to the escape mechanism is that some employees may earn too little to permit them to build up sufficient savings to become entrepreneurs. At very low levels of income and consumption, reducing consumption in order to accumulate assets may be sub-optimal because it can seriously threaten health, production efficiency and longevity (Gersovitz, 1983). While this problem might not be widespread in developed economies, it could be important in some developing countries. Of course, savings are not the only escape mechanisms available to financially constrained entrepreneurs. Another possibility is that entrepreneurs borrow from their employees (Michelacci and Quadrini, 2005). They can do so by launching direct appeals to workers for funds in return for promissory notes; or by offering their workers stock options, whereby firms delay the cash compensation of their employees and effectively borrow from them. Or entrepreneurs can offer workers long-term wage contracts with an increasing profile. In the last of these cases, entrepreneurs effectively borrow from workers while constrained, and then compensate them later as their ventures grow from the proceeds of the borrowing. This does, however, require the entrepreneur to design commitment mechanisms which make long-term rising wage contracts ‘renegotiation proof’. One interesting implication of this line of reasoning is that financially constrained firms are younger and smaller, grow faster and pay lower wages. Another implication is that capital constraints can explain why small entrepreneurial firms have low capital/labour ratios and therefore lower labour productivity (Garmaise, 2008). All of these predictions are consistent with the evidence.18 To conclude, even if entrepreneurial participation is positively related to personal wealth and financial windfalls, this does not prove the existence of borrowing

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constraints. Several alternative explanations are also consistent with the evidence. And it should be remembered that borrowing constraints are problematic only if they prevent positive NPV projects from being undertaken. If they prevent negative NPV projects from going ahead, borrowing constraints can be positively beneficial. In short, the present state of the literature does not allow us to resolve definitively the vexed question of whether borrowing constraints exist, and if so, how widespread they are. Sharper tests, possibly based on bank loan application micro-data matched with detailed borrower surveys, are needed before any firmer conclusions can be drawn.

9.7 Wealth and performance in entrepreneurship

Researchers have explored at least three ways that wealth affects entrepreneurial performance and behaviour. Wealth can affect survival, investment choices and profitability. The arguments for, and evidence relating to, these three effects are now reviewed in turn. 9.7.1 Effects of wealth on venture survival As well as facilitating start-ups, it seems likely that personal wealth can also enhance a new venture’s survival prospects. As Holtz-Eakin et al. put it: ‘if entrepreneurs cannot borrow to attain their profit-maximising levels of capital, then those entrepreneurs who have substantial personal financial resources will be more successful than those who do not’ (1994b, p. 53). These authors argue that unexpected increases in wealth (such as inheritances) will make entrepreneurship more attractive if the alternative is a business operating with a sub-optimal capital stock. But in the absence of borrowing constraints, entrepreneurs will presumably operate their optimal capital stocks, so an inheritance may actually make outside options like paid employment or tax sheltering more attractive, promoting voluntary exits rather than entries. So if it turns out that inheritances are positively associated with company survival, this can be taken as relatively strong evidence of Type I credit rationing. In fact, Holtz-Eakin et al. (1994b) did find that inheritances increase the probability that self-employed Americans remain in self-employment. Similar results have been obtained by other researchers using measures of personal wealth rather than windfalls.19 On the other hand, findings that higher-skilled entrepreneurs are less capitalconstrained than the average have led some researchers to argue that human capital is the real driver of entrepreneurial survival, rather than financial capital (which is related to human capital). For instance, Cressy (1996) claims that failing to control for human capital endows personal (housing) wealth with spurious explanatory power in UK business survival models. However, a growing body of US evidence generally rejects the view that financial inputs are unimportant determinants of survival, even after human capital is controlled for. This evidence will be discussed in chapter 14.

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9.7.2

Effects of wealth on venture investment decisions In an influential article, Fazzari et al. (1988) reported that investment by large US firms is closely related to cash flow, with greater investment sensitivity to cash flow among firms that they considered to be most vulnerable to Type I credit rationing. These findings appear to offer some support to the notion that borrowing is restricted, rendering cash flow an important source of funds. Several authors have subsequently replicated these findings.20 If this argument is true, limited access to capital might have macroeconomic implications as well. Monetary policy could influence the economy through a novel credit channel by affecting the investment decisions of entrepreneurs. Investment-cash flow sensitivity might also carry implications for economic development and export performance. Consider, for the sake of argument, two sectors. One sector is dominated by large capital-intensive firms which can self-finance investments and do not face credit constraints, while the other is dominated by small labour-intensive firms which do face credit constraints. If wealth alleviates credit constraints (Aghion and Bolton, 1997), then it follows that poor economies enjoy a comparative advantage by producing goods in the large-firm sector, while wealthier nations enjoy a comparative advantage by producing goods in the small-firm labour-intensive sector (Wynne, 2005). This might explain why so much of US export trade is labour-intensive, contrary to traditional trade theories which suggest that the USA might be expected to have a comparative advantage in capital-intensive goods. However, echoing the critique presented in the previous section of this chapter, investment sensitivity to cash flow need not reflect borrowing constraints. For example, such sensitivity can also arise if firms learn under uncertainty, as the smallest firms are often the youngest firms, which have the most imprecise beliefs and are therefore more likely to overreact to aggregate shocks affecting cash flow than larger and older firms (Li and Weinberg, 2003; Oliveira and Fortunato, 2006). In any case, as Hubbard (1998) describes it, the problem is that firms desire a loan size L∗ at the market interest rate r, but because of agency costs arising from imperfect information, banks are only willing to supply L∗ at a higher interest rate than r. As Jaffee and Stiglitz (1990, p. 847) point out, this involves ‘price rationing’ rather than credit rationing, since firms could obtain L∗ if they paid a higher interest rate.

9.7.3

Borrowing constraints and profitability Parker and van Praag (2006a) extended the Type I credit rationing model of Bernhardt (2000), outlined in chapter 7, by allowing entrepreneurs to differ from each other in terms of observed and unobserved ability. Unobserved ability is (by definition) undetected by banks which screen loan applicants, so random screening errors mean that some entrepreneurs face tighter constraints than others. Consequently, entrepreneurs make lower profits, π , the more constrained they are. On the other hand, years of schooling, sch, are associated with observed ability, which is assumed to increase entrepreneurs’ profits and to facilitate access to finance. Parker and van Praag (2006a)

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were able to employ a direct measure of capital constraints, con, namely the percentage of initial requested funding which was not granted by lenders: 0 ≤ con ≤ 100. This is an improvement over the indirect measures of borrowing constraints used in previous research, such as wealth, windfalls or start-up capital. Parker and van Praag (2006a) estimated the ‘triangular’ econometric model set out as equations (3.4) through (3.6) in chapter 3. This model recognises the potential endogeneity of capital constraints, con, and years of education, sch. Identifying instruments z1 for sch include father’s education and the number of siblings in the entrepreneur’s family. The identifying instrument z2 for con is the capital intensity of the industry in which an entrepreneur is active. The rationale is that screening errors – which are associated with variations in capital constraints – are more likely to occur in technologically advanced capital-intensive industries with higher proportions of intangible capital.21 Using a sample of Dutch entrepreneurs from 1995, and utilising an IV estimator, Parker and van Praag (2006aa) estimated the rate of return to education in entrepreneurship as 13.7 per cent (βˆs = 0.137). Second, βˆc = − 0.039, implying that a 1 percentage point relaxation of capital constraints increases entrepreneurs’ average business incomes by 3.9 per cent. Third, γˆs = − 1.183, so an extra year of schooling decreases capital constraints by 1.18 percentage points, all else equal. These estimates imply that schooling has an additional indirect effect on entrepreneurial performance, of βˆc γˆs = 0.039 × 1.183 = 0.046. To summarise, credit constraints appear to reduce profitability in entrepreneurship. The dependence of credit constraints on formal education makes the latter an even more effective determinant of entrepreneurial performance than was previously thought. This complements other evidence cited above claiming that access to capital is associated with enhanced entrepreneurial performance. But once again one must caution against rushing to interpret these findings as proof of inefficient capital markets, or arguing that firms which struggle to raise finance require public subsidies. Type I credit rationing in the Parker and van Praag (2006a) model is economically efficient. So failure to obtain finance can in fact amount to the best use of society’s scarce resources.

9.8

Evidence relating to Type II credit rationing

This section reviews evidence relating to outright denials of loans to entrepreneurs (i.e. Type II credit rationing), rather than access to smaller loan sizes than entrepreneurs request. As noted in chapter 7, I will concentrate on tests of equilibrium credit rationing, not ‘temporary’ or disequilibrium credit rationing arising from a temporary excess demand for credit while banks adjust their interest rates. Reflecting the emphasis in published research to date, the evidence discussed below comes from developed economies. The causes and effects of credit rationing in developing countries often simply reflect limited financial development (see, e.g., Levy, 1993; Kochar, 1997). This section has the following structure. First, I explain why several ‘popular’ indicators of credit rationing are in fact no such thing. Second, some influential micro-data

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evidence by Berger and Udell (1992) is reviewed. Third, some miscellaneous additional evidence is considered to round out and conclude the discussion. 9.8.1 What does not constitute evidence of credit rationing

Claims by survey respondents about difficulties of raising capital and the existence of credit rationing should be treated with considerable caution. The fact that one-half of employee survey respondents claim to have seriously considered becoming selfemployed in the past, but blamed insufficient capital as the reason for not making the switch,22 does not necessarily mean that loans were unavailable to these respondents – or that these respondents were ever really very serious about becoming self-employed. Survey responses are prone to self-serving bias because entrepreneurs might blame banks for inherent shortcomings in their loan applications. Asking entrepreneurs what the price of loans should be is equally uninformative, since it is unlikely to reflect the genuine cost of funds. It might be thought that another way of establishing the existence of Type II credit rationing would be to compare survival rates of entrepreneurial ventures funded with government-backed loans that would not have been granted without the government guarantee, with survival rates of ventures that were financed purely privately. If the two survival rates are similar, one might argue that the government intervention alleviated genuine credit rationing. In fact, although there is some evidence that public-sector Loan Guarantee Scheme (LGS)-backed start-ups do have similar survival rates to purely privately funded start-ups, this conclusion does not necessarily follow (see chapter 16). The primary role of an LGS is to help fund entrepreneurs who lack collateral and/or a track record, and so are observably risky to finance. Banks refusing to finance ventures that they expect to yield insufficient returns for the risk borne cannot be construed as rationing credit. The whole point of a loan guarantee is to insure banks against most of the downside risk, turning some marginal investments into attractive lending opportunities. The evidence does suggest that these are indeed the projects that banks typically fund via an LGS (KPMG, 1999). 9.8.2 Berger and Udell’s (1992) approach To avoid the problems identified above, the results described next are based on observed micro-level behaviour rather than on subjective beliefs or co-movements of macroeconomic aggregates. This strategy introduces its own problems, because to identify Type II credit rationing directly, the researcher needs to obtain samples containing a set of ventures which are observationally identical to banks, some of which are given funds and others of which are not. Since such data are unavailable, actual tests of Type II credit rationing tend to be indirect in nature. Indirect tests of Type II credit rationing usually investigate the ‘stickiness’ of commercial loan rates. In the absence of Type II credit rationing, interest rates on commercial loans should adjust freely in response to changes in the supply of and demand for credit. But if credit rationing exists, that is no longer the case, and commercial loan rates can

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exhibit ‘stickiness’. This, at least, can be expected to apply to non-‘commitment loans’, where a commitment loan (CL) is a loan that a bank agrees to allocate to a firm in advance over a specified period should the latter request it. Contractually, a CL cannot be rationed or withdrawn once it has been granted, so it can be regarded as a hedge against the future hazard of Type II credit rationing. A problem with interpreting sticky loan rates as evidence of credit rationing is that alternative explanations for this phenomenon also exist. One is Fried and Howitt’s (1980) implicit contract theory, in which banks and borrowers agree to fix interest rates for long periods of time irrespective of economic conditions. Another is the ‘distressed company’ phenomenon, whereby banks prefer not to increase lending rates in line with base rates if that tips clients into bankruptcy and forces banks to incur bankruptcy costs. Both of these explanations can potentially account for Berger and Udell’s (1992) finding that up to 7 per cent of US commercial loans over 1977–88 charged interest rates at below the (safe) open-market rate. Early studies tested for loan rate stickiness using aggregate time-series data and reported mixed results (Slovin and Sushka, 1983; King, 1986). However, the highly aggregated nature of this research is a serious drawback, because it averages over heterogeneous loan types. Micro-data are to be preferred because of their greater detail and precision. Berger and Udell (1992) conducted an especially thorough analysis of the credit rationing hypothesis using a sample of one million commercial loans in the US between 1977 and 1988. Berger and Udell (1992) detected some loan rate stickiness, consistent with the credit rationing hypothesis. However, CL rates were also sticky, suggesting that reasons other than credit rationing must account for the observed stickiness. And less rather than more stickiness was observed during periods of ‘credit crunch’, which is the opposite of what one would expect from the credit rationing hypothesis. Furthermore, in times of credit crunch or low loan growth rates, the proportion of CLs was observed to decrease. The opposite would be expected to occur under credit rationing. On the basis of this evidence, Berger and Udell concluded that ‘information-based equilibrium credit rationing, if it exists, may be relatively small and economically insignificant’ (1992, p. 1071). They went on to suggest that, even if some borrowers are rationed, others take their place and receive bank loans. 9.8.3

Other evidence

A direct upper bound estimate of the extent of Type II credit rationing is the number of loans rejected by banks for any reason. If loan rejection rates are very low, then credit rationing cannot be very important. Using US data on small company borrowing over 1987–88, Levenson and Willard (2000) estimated that only 2.14 per cent of small firms ultimately failed to obtain the funding they sought. Of course, the actual extent of credit rationing will be lower than this to the extent that some of these loans were observably non-creditworthy and so deserved to be rejected. Levenson and Willard (2000) also reported that the probability of loan denial was negatively related to firm size. Hence the extent of credit rationing by value is even

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less than 2 per cent. UK evidence from Cosh and Hughes (1994) tells a similar story. From a sample of firms surveyed between 1987 and 1989, Cosh and Hughes reported that only 3.2 per cent of firms seeking external finance failed to obtain it. Other evidence sheds further light on the Type II credit rationing phenomenon. Theories of rationing based on adverse selection suggest that bank managers prefer to deny credit to borrowers rather than to raise interest rates, even if rationed borrowers are willing to pay higher rates (see chapter 7). Evidence does show that bank managers are reluctant to raise interest rates above the bank’s ‘standard’ business rate for observably higher-risk projects. But this reluctance appears to be motivated less by concerns about adverse selection or moral hazard, than by anxiety that public relations could be harmed by an image of the bank as a ‘usurer’ (National Economic Research Associates, 1990). It may be noted that the bank managers who were interviewed in this study had no obvious motive for responding to this survey question with self-serving bias.23 On the other hand, even if Type II credit rationing is empirically unimportant, the perception of it might discourage potential entrepreneurs from approaching banks at all. Individuals who do not approach lenders for funds because they fear rejection are called ‘discouraged borrowers’ (Kon and Storey, 2003). Levenson and Willard (2000) estimated that 4.2 per cent of potential entrepreneurs are discouraged borrowers; Cowling (1998) provides a similar estimate for the UK. According to the 1989 SCF, as many as 9 per cent of US business owners stated that they thought of applying for credit but changed their mind because they thought they might be turned down (Cagetti and de Nardi, 2006). The literature on entrepreneurial finance and gender suggests that a higher proportion of discouraged borrowers are women rather than men (Coleman, 2007). There is a rational basis for the discouraged borrower phenomenon. Home ownership and the commitment of personal equity to ventures are known to be strongly linked to successful loan applications. Discouraged borrowers are less likely to own a home or to commit personal equity to their ventures, so they may be correctly anticipating what banks are looking for (Blumberg and Letterie, 2008).24 9.9

Conclusion

The chapter commenced by establishing the importance of wealth for entrepreneurship, and vice-versa. Entrepreneurs play a central role in aggregate wealth accumulation and savings. Savings are used to fund investment, both entrepreneurial and corporate, so they are needed to support economic growth. Research is only just beginning to reveal the scale of entrepreneurs’contributions to these important economic outcomes. Further research is needed in several areas, including empirical studies which can distinguish cleanly between competing explanations for the private equity premium puzzle and limited entrepreneurial wealth diversification. The need for further research is especially urgent in countries outside the United States, where most of the empirical work to date has been conducted. The chapter also analysed the possibility that borrowing constraints cause entrepreneurship and wealth to be related. Because causality between these variables

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could run both ways, and because unobserved characteristics might link wealth acquisition and entrepreneurship without any direct relationship between them, one should not automatically infer the existence of borrowing constraints from empirical studies which find a positive association between them. Personal wealth and windfalls are likely to be endogenous, so researchers should move on from simple probit estimations where wealth or windfalls are used as independent variables serving as proxies for borrowing constraints, and should instead isolate suitable identifying instruments. Better still, researchers should try to measure borrowing constraints more directly (see, e.g. Parker and van Praag, 2006a). Even if convincing instrumental variables for wealth are found, a positive association between wealth and entrepreneurship neither implies that wealth is necessary for entrepreneurial entry nor that credit is rationed. A range of other reasons, which have nothing to do with imperfect capital markets, can also explain this association. It is noteworthy that alternative reasons can also explain why venture survival and investment are related to wealth or internal cash flow. And even if credit rationing exists, it might be efficient, in which case public intervention would be unnecessary and potentially damaging. Although there is some evidence of Type I credit rationing (e.g. Parker and van Praag, 2006a), evidence of Type II credit rationing remains elusive. Periods of credit crunch aside, modern capitalist economies are usually awash with credit to fund entrepreneurs with good ideas, which is not to deny that credit might be withheld from aspiring entrepreneurs with flawed or poorly presented business proposals. Future research should focus on potentially more fruitful lines of enquiry, such as discrimination in entrepreneurial credit markets; the impact of enterprise education on access to finance and subsequent entrepreneurial performance; limited financial literacy among entrepreneurs; and sophisticated modelling of processes of entrepreneurial accumulation and reinvestment. 9.10 Appendices 9.10.1

Preferences for skewness and entrepreneurship Denote wealth by B and suppose returns are given by the random variable ϑ, with density function g(ϑ), whose first three central moments are denoted by µ1 (mean), µ2 (variance) and µ3 (skewness). Then a Taylor expansion gives

U (B + ϑ) =

U (B) U
(B) U

(B) 2 U


(B) 3 + ϑ+ ϑ + ϑ + ... . 0! 1! 2! 3!

So taking expectations and truncating to the third moment yields
∞ E[U (B + ϑ)] = U (B + ϑ)g(ϑ)d ϑ −∞

≈ U (B) + U
(B)µ1 +

U

(B) U


(B) µ2 + µ3 . 2 6

(9.5)

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U (B) can be treated as a constant for present purposes. If decreasing absolute risk aversion (DARA) holds, then U

(B) < 0 < U
(B). But also, crucially, since absolute risk aversion is defined as −U

/U
, DARA implies   d −U

−U
U


+ (U

)2 ≤ 0, = dB U
(U
)2 which holds if U


≥ (U

)2 /U
> 0. Thus U


> 0 under DARA. So by (9.5) agents like odd-numbered moments (mean returns and skewness) and dislike even ones (variance, or risk). Hence individuals might participate in risky entrepreneurship even if expected returns are negative (µ1 < 0, µ2 > 0), as long as they are compensated by a sufficiently large positive skewness of returns, µ3 > 0. A mechanism of this sort might have been observed in the case of the UK’s National Lottery, the popularity of which increased after the reward structure was redesigned to increase the value of the biggest cash prizes while decreasing the values of the smaller cash prizes. 9.10.2

The Paulson et al. (2006) structural model Recall from chapter 3 that the log-likelihood function of the standard probit model is

ln L =

n 

 

zi ln (β
Wi /σ ) + (1 − zi ) 1 − (β
Wi /σ ) .

i=1

˘ denote the probability that an individual is an More generally, one can let Pr(Wi |β) ˘ Then the entrepreneur, which depends on the variables Wi via some parameter set β. likelihood function is ln L =

  ˘ + (1 − zi ) 1 − Pr(Wi |β) ˘ . zi ln Pr(Wi |β)

n  

(9.6)

i=1

The conventional reduced form approach arbitrarily sets Pr =  and assumes a linear ˘ = β
Wi /σ . specification (Wi |β) In contrast, Paulson et al. (2006) pursued a structural approach. The idea here is that a lender specifies for every potential borrower a contract stipulating allocations of consumption, effort, capital and entrepreneurial output, to be received by the borrower:  = (ζ , e, k, q). Lenders observe individuals’ schooling, schi and assets Bi , which conditions their ability xi as described by (9.9) below. Lenders are competitive and break even in equilibrium. Lenders also know the following functional forms for utility, technology and ability: U (ζ , e) =

ζ 1−γ1 eγ2 −κ , 1 − γ1 γ2

κ, γ1 γ2 > 0

Pr(q = x|e, k > 0) = k α e1−α /(1 + k α e1−α ),

00)≥0



subject to (i) lenders breaking even, (ii) the above functional forms, and (iii) adding up constraints on the probabilities. The key part of Paulson et al.’s (2006) approach is to specify additional constraints that reflect either the EJ or the AB representation of borrowing constraints. As noted above, the EJ constraint imposes on  the additional requirement that k ≤ γ B. In contrast, the AB constraint requires that e satisfies an incentive compatibility constraint such that borrowers supply the recommended effort e rather than any alternative effort. Denote the solution to the maximisation of (9.10) subject to the particular set of ˘ in (9.6) above are applicable constraints by Pr∗ (|x, B, sch; k > 0). Then the Pr(Wi |β) calculated as
∞  ∗  ˘ = Pr(Wi |β) Pr (|x, B, sch; k > 0) d φ(). (9.11) −∞

The final step is to use ML to estimate the β˘ that maximises (9.6) subject to (9.11). Evidently, imposing the EJ and AB constraints will yield different solutions of Pr∗ (|x, B, sch; k > 0) and hence different structural probabilities – and therefore different maximised values of ln L. Crucially, likelihood ratio tests can then determine statistically which of the AB or EJ constraints gains most support from the data. For example, if the likelihood suggests that it is admissible to set κ = 0 in (9.7), then moral hazard cannot be a problem; whereas EJ requires γ > 1 to be binding. Paulson et al.’s (2006) results, based on a sample of Thai data from a socioeconomic survey fielded in 1997 to 2,880 households, were as follows. Paulson et al. (2006) rejected the hypothesis that EJ constraints alone can explain the data, and found statistical evidence instead that the dominant source of financial constraints is moral hazard. These authors also obtained reduced form evidence that is consistent with AB but not EJ. AB predicts that greater wealth is associated with less borrowing and more self-finance; EJ predicts the opposite. For wealthier Thai households who declared themselves credit-constrained, net saving is higher – consistent with AB but contradicting EJ. Taken together, these findings cast doubt on the EJ conceptualisation of borrowing constraints, influential though it has been in the literature. Notes 1. See, respectively, Gentry and Hubbard (2004), Quadrini (2000), Cagetti and de Nardi (2006) and Heaton and Lucas (2000).

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2. Nor is the predominance of entrepreneurs at the top of the wealth distribution a new phenomenon.According to Moehling and Steckel (2004), the self-employed in nineteenth-century Massachusetts also held and accumulated a disproportionate share of wealth. 3. Bradford (2003) shows that both black and white entrepreneurs enjoy greater upwards mobility and lower downward mobility in the US wealth distribution than wage-and-salary workers. 4. See Shorrocks (1988), Banerjee and Newman (1993) and Parker (2000). 5. Moskowitz and Vissing-Jørgenson (2002), Gentry and Hubbard (2004) and Bitler et al. (2005). 6. To see why, note that the optimal demand for capital k ∗ maximises xk α − (1 + r)k, yielding k ∗ = [αx/(1 + r)]1/(1−α) – which is increasing in x. 7. To obtain these results, note that bank profits are returned to depositors who receive the rate of return ρ > 0. Hence   D(B) p(B)D(B) = ρ = αD(B) 1 − (1 − B) s . R Solving for D(B), one obtains  D(B) = 1 −

8.

9. 10. 11. 12.

13.

14. 15.

16. 17.

18.

!

4ρ(1 − B) 1− αRs



Rs . 2(1 − B)

(9.12)

Now (9.12) implies (i) D
(B) < 0 so given p(B) = ρ/D(B) we have p
(B) > 0, i.e. effort is increasing in wealth; and (ii) all agents with wealth B < 1 − (αRs )/4ρ will be unable to borrow. For cross-section and panel studies, see Evans and Leighton (1989b), Bernhardt (1994), Bates (1985, 1997), Laferrère and McEntee (1995), van Praag and van Ophem (1995), Taylor (1996), Boden (1999b), Fairlie (1999), Quadrini (1999), Bruce et al. (2000), Dunn and Holtz-Eakin (2000), Johansson (2000), Fairlie and Meyer (2003), Gentry and Hubbard (2004), Henley (2004), Paulson and Townsend (2004) and Nykvist (2008). For time-series studies, see Robson (1991, 1996, 1998a, 1998b), Black et al. (1996) and Cowling and Mitchell (1997). See Wong (1986), Henley (2004) and Shane (2003, p. 152). See Taylor (2001), Uusitalo (2001) and Hurst and Lusardi (2004). See Lindh and Ohlsson (1996), Blanchflower and Oswald (1998) and Yang (2008). See Holtz-Eakin et al. (1994a, 1994b), Lindh and Ohlsson (1996), Blanchflower and Oswald (1998), Taylor (2001), Georgellis et al. (2005) and Yang (2008). According to Georgellis et al. (2005), redundancy and inheritance windfalls have a positive effect on transitions, whereas lottery wins have the reverse effect. Hurst and Lusardi were not the very first, however. To the best of my knowledge, that honour goes to Magnac and Robin (1996), in a study which used French Financial Assets Survey data. Although their instruments lack a convincing rationale, Magnac and Robin (1996) reported that instrumented wealth entered the self-employment specification positively. See Cagetti and de Nardi (2006) for a calibrated multi-period model which provides a theoretical underpinning for this empirical finding. A problem with this explanation, however, is that workers can in fact choose from an array of work hours in paid employment; they do not have to become entrepreneurs to cut their work hours. And the evidence reviewed in chapter 12 shows that entrepreneurs tend to work longer, not shorter, working weeks on average. Winborg and Landström (2000) discuss some bootstrapping strategies. ‘The fact that some unlucky agents suffer the penalty [of borrowing constraints] may, from a social point of view, be less important than the fact that the penalty motivates agents to succeed more often’ (Ghatak et al., 2001, p. 798). See Oi (1983), Brown and Medoff (1989), Oi and Idson (1999) and Garmaise (2008).

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19. See Bates (1990), Black et al. (1996), Taylor (1999), Quadrini (1999) and Bruce et al. (2000). In contrast, Taylor (2001) found no effect of inheritances on UK self-employment survival probabilities. 20. See Hubbard (1998) for a review. Some recent Russian evidence from Hartarska and GonzalesVega (2006) also suggests that firms with cash reserves are significantly more likely to invest than firms which lack them. 21. Indeed, UK evidence shows that entrepreneurs in more technologically intensive industries are more likely to claim they are capital-constrained – though whether these claims are justified is not investigated (Westhead and Storey, 1997). See also Moore (1994) and Oakey (1984) for similar evidence. 22. See Cosh and Hughes (1994), Moore (1994), Blanchflower and Oswald (1998), Guiso (1998), Bratkowski et al. (2000), Blanchflower et al. (2001) and Cagetti and di Nardi (2006). 23. Asking bank loan officers whether they respond to adverse selection or moral hazard is probably superior to asking surviving firms whether they think loan officers take these issues into account when making loan decisions (see Hyytinen and Väänänen, 2006, for an example of the latter approach). 24. De Meza and Webb (2006) provide an alternative interpretation of the observation that borrowers often choose not to return to the credit market following rejection of their loan applications. When unfunded projects decay rapidly, some entrepreneurs who failed to get credit might never reapply because the value of their projects is diminished. This reason is distinct from the entrepreneurs being discouraged.

Part III Performance

10 Entrepreneurship, job creation and innovation

This chapter discusses the role of entrepreneurship in creating new jobs and innovating new products. Both of these topics have their origins in research conducted in the late 1970s and the 1980s, when David Birch first claimed that small new firms acted as the engine of job creation in the economy, and David Audretsch and Zoltan Acs argued that small firms played a disproportionate role in the commercialisation of new innovations. Both topics command widespread interest because they suggest that entrepreneurship directly drives venture performance and economic growth. Much (though not all) of the empirical discussion in this chapter is framed in terms of comparisons between small and large firms. The focus on firm size – which, as noted in chapter 1, does not obviously capture the essence of entrepreneurship – is chiefly a historical legacy. It also reflects data availability. In the discussion that follows, ‘small business’ will merely be assumed to serve as a convenient shorthand for entrepreneurship. However, this focus will be complemented with a discussion about the role of individual entrepreneurs in job creation and innovation. After setting out in the first section some basic facts about entrepreneurs’ decisions to hire external labour, I will present some theory about the labour demand of individual entrepreneurs.1 This paves the way for an analysis of the empirical factors which determine job creation by entrepreneurs. The second section discusses the role of small firms in creating jobs in the broader economy. The third section turns its attention to the role of small firms in aggregate innovation activity. The final section concludes.

10.1

Job creators

10.1.1

Some basic facts about entrepreneurs’ labour demand Despite the attention policy-makers devote to the employment-creation potential of entrepreneurship, relatively few entrepreneurs create any jobs. Measuring entrepreneurship as self-employment, only 20–30 per cent of entrepreneurs in the USA, Canada and the UK employ any external workers. This ‘employer self-employed’ rate seems if anything to have declined in these countries in the 1990s.2 According to Bregger (1996), of the 21 per cent of self-employed Americans who hired any employees in 1995, one-third 293

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Performance

had only one employee, and only one-seventh hired six or more workers. Using BHPS data over 1991–99, Henley (2005) reported that only 6–7 per cent of self-employed Britons create jobs for ten or more people. Transition rates into employer status also appear to be low. Both Carroll et al. (2000) for the USA and Cowling et al. (2004) for the UK observe that only about 7–8 per cent of self-employed sole proprietors hire any other workers over a three- to fouryear period. But sole proprietors are nevertheless more likely to employ other workers than any other labour market group, including new entrants to self-employment from unemployment or paid employment (Cowling et al., 2004). A higher proportion of self-employed people employ others in some of the countries of Continental Europe, reaching 46 per cent in Denmark and 51 per cent in Germany (Cowling, 2003). This might be because these countries lack the low-value sole-trader self-employed workforce which shows up in the left-hand tail of the self-employment income distribution in countries like the UK and the USA (McManus, 2000). According to Estrin et al. (2006), less than one-quarter of self-employees in the transition economies of Poland and Russia are employers, although this fraction reaches about one-half in Hungary and the Czech Republic. Clearly there is considerable heterogeneity in rates of hiring by entrepreneurs across countries.

10.1.2

Theoretical analysis

The theoretical literature on labour demand by entrepreneurs is sparse. To the best of my knowledge, only Jefferson (1997) and Carroll et al. (2000a) have systematically analysed the labour demand choices of entrepreneurs in a theoretical framework. Jefferson (1997) proposed a rather unusual credit rationing model, in which individuals become entrepreneurs if they are non-rationed, but can only work for entrepreneurs or draw unemployment benefit if they are rationed. Rationed workers are unable to verify the quality of projects run by entrepreneurs. Entrepreneurs can always claim to have poor projects, so they can always offer a minimal wage to employees. As a result, in equilibrium rationed individuals will prefer to become unemployed rather than work for entrepreneurs. While this model claims to explain why only a small minority of entrepreneurs hire outside labour, it is clearly unrealistic in several important respects. Carroll et al. (2000a) proposed a different model which distinguishes between two margins: entrepreneurs choose whether to become employers, and if so how many workers to hire. The model is sketched out in the chapter appendix. Carroll et al.’s (2000a) main assumption is that employees are costly to hire, but the entrepreneur’s effort is more productive in ventures where employees are hired. The main result is that a cut in the marginal income tax rate (MTR) has ambiguous effects on an entrepreneur’s decision about whether to become an employer; but if the utility function is sufficiently concave in consumption, cuts in the MTR will have a positive overall effect on the probability of hiring. The latter is precisely what Carroll et al. (2000a) found empirically using US IRS data from 1985 and 1988 – years which span the ‘tax cutting’ Tax Reform Act (TRA) of 1986. Carroll et al.

Entrepreneurship, job creation and innovation

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(2000a) applied data on sole-trader and employer entrepreneurs to the probit model given at the end of the chapter appendix, and estimated that a 10 per cent cut in an entrepreneur’s MTR increases the mean probability they hire workers by about 12 per cent. The implied elasticity of 1.2 suggests that general income tax reductions might be a powerful way of stimulating employment creation – in marked contrast to its apparently limited effects on work hours (chapter 12) or participation in entrepreneurship (chapter 17). An alternative theoretical approach to Carroll et al.’s (2000a) might estimate a labour demand function directly. That can be done if one utilises some simple functional forms for the production and utility functions (Hamermesh, 1993). For example, van Praag and Cramer (2001) and Cowling et al. (2004) both assume a Cobb–Douglas production function which is scaled up by human capital, from which it follows directly that ‘job creators’ are predicted to have greater human capital on average than self-employed sole traders.3 Two limitations of the existing state of theoretical knowledge are over-reliance on the Cobb–Douglas production function (which is restrictive in several important respects4 ), and limited analysis of the number of employees hired by job creators. Data limitations meant that Carroll et al. (2000a) could only estimate the responsiveness of the wage bill, wl, to changes in the MTR.5 Carroll et al. (2000a) estimated that the median wage bill is significantly negatively related to the MTR, with an estimated elasticity (with respect to one minus the MTR) of 0.37.

10.1.3

Evidence Empirical research on the characteristics of job creators is rather limited in scope. Table 10.1 summarises research findings from several dedicated analyses of this issue. Possibly reflecting the small number of studies and the relatively homogeneous nature of the samples used, there is strong agreement about the effects of various factors on the propensity of entrepreneurs to hire outside labour. Human capital, in the form of education, age and experience, all have uniformly positive effects. One source of disagreement is between the two BHPS studies of Cowling et al. (2004) and Henley (2005). Whereas the former found that academic qualifications were not important determinants of job-creator status (in contrast to vocational qualifications), the latter reported that entrepreneurs with university degrees were significantly more likely to be job creators. Henley (2005) used both a superior data sample and an econometric model which fully exploits the longitudinal nature of the data, so his results should probably be preferred. Also noteworthy is that van Praag and Cramer (2001) qualified a positive impact from education by recording negative marginal effects from having an arts degree. Burke et al. (2002) reported a significant positive effect from professional training on job-creator status. Cowling et al. (2004) observed that the ranks of job creators are disproportionately filled by men; this gender difference is confirmed in other studies. Women entrepreneurs are most likely to hire workers if they run their business full-time (Burke et al., 2002).

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Table 10.1. Characteristics of job creators Variable

Effect

Evidence

Education

+

Age Experience Male

+ + +

Parent self-employed or manager Wealth

+ + +

Industry conditions

+

Burke et al. (2000, 2002); van Praag and Cramer (2001); Cowling et al. (2004); Henley (2005); Fairlie and Robb (2007a) Mata (1996); Cowling et al. (2004); Henley (2005) Burke et al. (2002) Van Praag and Cramer (2001); Cowling et al. (2004); Henley (2005) Van Praag and Cramer (2001); Cowling et al. (2004); Henley (2005) Burke et al. (2002); Cowling et al. (2004); Henley (2005); Fairlie and Robb (2007a) See text

Notes: All are UK studies apart from van Praag and Cramer (Netherlands), Mata (Portugal) and Fairlie and Robb (USA). All studies define entrepreneurship in terms of self-employment or business ownership, and estimate a binary choice model (see chapter 3).

The greater incidence of part-time working among women business owners might therefore explain part of the gender gap in job-creator status. Having a parent who was self-employed or a manager also increases the likelihood of job-creator status. According to Henley’s (2005) estimates, a self-employed person is over one-third more likely to employ one or two employees if they had a parent who employed others. And they are three-quarters more likely to employ ten or more employees than someone without a parent who employed others. Wealth and inheritances are also associated with job-creator status, although these variables are likely to be endogenous (see chapter 9). According to Curran and Burrows (1989), male employer self-employees in Britain are more likely to own their home (89.3 per cent) than male own-account self-employees (75.6 per cent), who in turn are more likely to own their home than male employees (68.2 per cent). Finally, industry conditions and the state of the local economy can also be important factors. For example, using a sample of data on 766 Portuguese firms, Mata (1996) reported that the log employment size of new firms was significantly larger in more turbulent industries and in industries with greater minimum efficient scale. More generally, job creators are most commonly found in professional and managerial occupations or in the distribution, hotels and restaurant sector. They are significantly less likely to be found in the construction industry (Curran and Burrows, 1989). Henley (2005) reported that local unemployment rates are significantly positively associated with self-employed job creation: this suggests that employers might exploit slacker labour markets by hiring more workers – consistent with the neoclassical theory of labour demand (Hamermesh, 1993).

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297

Job creation by small firms

The publication of the influential Birch Report in the USA (Birch, 1979) stimulated a wave of research on the job-creation performance of small firms. While entrepreneurs rather than small firms are the primary focus of this book, many small firms are owned and managed by entrepreneurs, so it is appropriate to review this literature. Below, I will discuss evidence about the relative job-creation performance of small firms as a group, rather than on the determinants of employment growth in representative samples of small firms. The latter topic will be treated separately in chapter 11. Birch (1979) claimed that, between 1969 and 1976, small firms employing fewer than twenty workers generated 66 per cent of all new US jobs, and firms with fewer than a hundred employees accounted for 82 per cent of net job gains. The implication is that the small-firm sector is the primary engine of job creation in the economy. Subsequent researchers have confirmed these findings for the USA and other countries.6 Acs and Audretsch (1993) highlighted a distinct and consistent shift away from employment in large firms and towards small enterprises in the 1980s in every major Western economy. Others have challenged these claims, however. An early rebuff came from Armington and Odle (1982), whose study of employment changes over 1978–80 revealed much more modest small-business job-creation rates than Birch (1979). But as Kirchhoff and Greene (1998) pointed out, Armington and Odle’s findings might have merely reflected the unusually subdued macroeconomic conditions prevailing between 1978 and 1980. Weightier criticisms of Birch’s thesis came in the late 1980s and the 1990s. In particular, Davis and Haltiwanger (1992) and Davis et al. (1996a, 1996b) argued that the ‘conventional wisdom about the job-creating powers of small businesses rests on statistical fallacies and misleading interpretations of the data’ (1996a, p. 57). These were said to include: 1. The ‘size distribution fallacy’, whereby static size distributions of numbers employed by firm size are used to draw inferences about dynamic changes in employment shares. Measures such as ‘the net change in employment by small firms as a proportion of the net change in total employment’ are biased when firms move between size categories. They can also be misleading if the denominator of this ratio is very small over a particular period, exaggerating the scale of employment growth by small firms. 2. The ‘regression fallacy’, whereby transitory size shocks bias the relationship between employment growth and firm size. Transitory shocks imply that businesses which have recently contracted will be measured as small and will be observed to grow subsequently, while businesses that have recently expanded will be measured as large and will contract subsequently.7 3. Poor quality US micro-data, distorting the true employment creation–firm size relationship (see the previous point).

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4. Confusion between net and gross job creation, since small firms destroy many jobs as well as creating them. 5. Sample selection bias, whereby small firms with poor job creation records die and leave the sample, biasing upwards measured small firm job creation rates. 6. Disguised job creation: some ‘small firm’ job creation may actually be done by large firms in disguise, as incumbents often expand by creating additional small establishments (Armington and Odle, 1982). As Hamermesh puts it: ‘The locus of expansion is the small plant; but the locus of decision-making is the average-sized firm’ (1993, p. 157). On the other hand, job-creation performance by small firms may be understated if growing firms undergo changes of ownership, such as acquisition, which subtract from employment growth attributable to small firms by recording a false death, and add to employment growth attributable to large firms by recording a false birth (Kirchhoff and Greene, 1998). Large firms often acquire small ones, while in counterpoint to point 1 above, small firms which are successful grow into large ones, adding to apparent job creation by ‘large’ firms. Another issue which muddies the waters is subcontracting. For example, some firms subcontract the manufacture and distribution of their products, which can create additional indirect employment that goes unrecorded – or is attributed to other firms which gain the manufacturing and distribution contracts. The sizes of the contractors’ businesses determine whether the new jobs are attributed to ‘small’ or ‘large’ firms. While the researcher interested in gauging the role of small firms in job creation can rarely do much about the quality of available data, they should always strive to fix the other problems listed above. Most researchers now eschew simplistic static size distribution comparisons, and measure net rather than gross job-creation rates. Many also circumvent the regression fallacy by measuring a firm’s growth rate relative to a sample period average size rather than relative to initial size. Recognising these points, and using a panel of data on US manufacturing plants over 1972–88, Davis et al. (1996a, 1996b) claimed, in contrast to Birch, that larger plants and firms create (and destroy) most manufacturing jobs. In addition, Davis et al. found no clear relationship between rates of net job creation and employer size. However, despite some corroborative evidence from Wagner (1995), most other researchers who have made similar corrections have re-asserted a negative cross-section relationship between firm size and net employment creation.8 Some of the disagreement between these findings may be attributable to the exclusion by Davis et al. (1996a, 1996a) and Wagner (1995) of service-sector firms from their samples. This point is especially pertinent because most new jobs created in the USA in the 1990s were in the service sector. Thus Bednarzik (2000) showed that small ventures employing fewer than twenty employees played a much greater role in job creation in services in the USA than they did in manufacturing. In short, although the precise scale of the contribution by small firms to employment creation is still disputed, the OECD (1998) could justly claim that there is now ‘general agreement’ that the share of jobs accounted for by small firms has steadily increased since the early 1970s in most developed economies.

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In many ways, though, the debate has now moved on. There is declining interest in the issue of job creation by small firms, and growing interest in the issue of job creation by new firms. New firms are widely believed to better represent the notion of entrepreneurship than small firms (and there are of course no ‘regression to the mean’ problems when firms are categorised by age). In contrast to the contentious evidence relating to size, that relating to age is rather clear-cut. For example, using US Census Bureau Longitudinal Business Database (LBD) data, Haltiwanger (2006) showed that net employment creation rates decrease monotonically in business age; and few systematic relationships between employment creation and firm size emerge after controlling for business age. About 60 per cent of the job creation from young firms comes from entry and about one-half of their job destruction comes from exit. The corresponding rates for mature firms are about one-half of these. Acs and Armington (2006) provide corroborating evidence: establishments less than two years old account for almost 100 per cent of net job growth, while all other age classes of establishments shed employment on average. In the words of Haltiwanger and Krizan (1999, p. 94): ‘for employment growth, it looks as though the more important factor is age and not size. Put differently, most small establishments are new. Thus, the role of small business in job creation may simply reflect the role of births and in turn young establishments.’ A word of caution is in order, however. The above findings might be a particularly American phenomenon. More modest effects on job creation from new firms have been observed in Europe, where new firm formation rates are lower on average (Bednarzik, 2000). Even if new and small firms create more jobs than large incumbents, one can question the quality of those jobs. Evidence consistently shows that the jobs created by new and small ventures are of lower average quality than those created by larger incumbents. Small firms in particular tend to employ higher proportions of part-time workers, freelancers and home workers. Their employees tend to be less educated on average, receive lower levels of training, and work longer hours with a greater risk of accidents and major injuries (Storey, 1994a; Hasle and Limborg, 2006). At the same time they enjoy lower job tenure than their counterparts in larger firms (Brown et al., 1990). One reason for the last finding is no doubt that new and small enterprises have higher closure rates than their older and larger counterparts (see chapter 14). Small firms also pay lower wages and fringe benefits (e.g. health insurance, maternity leave and retirement benefits) than large firms do.9 Brown et al. (1990) reported that workers in large companies earn over 30 per cent more on average than comparable workers in small firms. This finding appears to hold across industries and countries. It is consistent with simple efficiency wage models, although an alternative explanation is that small firms are simply less efficient in terms of total factor productivity (Alvarez and Crespi, 2003). In fact, it turns out to be difficult to explain the large-firm wage premium empirically using ‘standard’ explanatory variables (Troske, 1999). Nevertheless, the size–wage premium is an important phenomenon in the present context. When Baldwin (1998) adjusted raw measures of employment to take account of higher wages in larger

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firms, he found that small manufacturers in Canada no longer outperformed their large counterparts in terms of growth. New firms also pay lower wages than otherwise comparable incumbents, including those of a similar size (Brown and Medoff, 2003). Brixy et al. (2007) estimated the wage gap between new firms and incumbents to be 8 per cent in Germany over 1997– 2001, though the gap diminished as the surviving new firms matured. Troske (1999) had greater success explaining the incumbent–wage premium (over young firms) than the size–wage premium. The key explanatory variables were the experience, tenure, education and demographic characteristics of the employees, as well as occupation type. The ‘new firm wage discount’ raises the interesting question of how new venture founders can attract talented employees away from incumbents, where they are better remunerated. Stock options which entitle employees to a share of future profits are one possible explanation (Caselli and Gennaioli, 2005). The value of these options does not usually show up in current wages in new start-ups. To set this last point in context, it should be noted that entrepreneurs use productivityrelated pay schemes less frequently than owners of large firms do (Cowling, 2001). Perhaps a more important attractor is the higher job satisfaction enjoyed by employees of small firms compared with large ones, at least in Europe (Clark and Oswald, 1996; Benz and Frey, 2004, 2008). As noted in chapter 4, a plausible reason for higher job satisfaction in small firms is that employees in small firms are less subordinated to complex hierarchies than employees in large firms. From a policy perspective, the objection that new firms create lower-quality jobs than large ones do should not be overdone. Policy-makers cannot simply choose between creating high-quality jobs in large incumbent firms on one hand and low-quality jobs in new ventures on the other. If anything, policy-makers face instead a choice between promoting the creation of new jobs in small firms (some of which may eventually become large employers), and promoting no job creation at all. This realisation no doubt underpins public policy efforts to encourage entrepreneurship and the creation of new firms, the above facts about differential job quality notwithstanding. I will return to the issue of public policy towards entrepreneurship in the closing chapters of this book. 10.3

Innovation by small firms

Innovation, deemed by Schumpeter to be a central aspect of entrepreneurship, is another major reason for policy interest in entrepreneurship. It is known that industries with high rates of entry by small firms tend to enjoy high rates of productivity growth and innovation (Geroski and Pomroy, 1990; Cosh et al., 1999). Furthermore, more innovative new entrants tend to enjoy superior post-entry performance (Vivarelli and Audretsch, 1998; Arrighetti and Vivarelli, 1999). Josef Schumpeter believed that large incumbents would ultimately come to dominate the innovation process by exploiting their economies of scale. Schumpeter predicted that the vast majority of R&D and innovations would eventually come to be conducted

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Table 10.2. Innovation and entrepreneurship Large firm innovation advantages

Small firm innovation advantages

1. Scale economies - spread high fixed R&D costs over larger output - generate economies of scope - free limited managerial attention 2. Innovation is less risky - size and market power - diversification of product lines

1. Diseconomies of scale - less bureaucracy in small firms - shorter lines of communication - greater agility and lower agency costs

3. Larger firms can afford to spend more on R&D

2. Greater incentives to innovate - easier to incentivise agents in small firms - easier to overcome entry barriers and competition 3. Diminishing marginal returns to R&D; entrepreneurs can exploit knowledge spillovers

4. ‘Efficiency effect’ favours large incumbents

4. ‘Replacement effect’ favours small entrants

by large firms, while small firms would merely become the repositories of low-level imitation: ‘relics of a bygone age’. Subsequently this view has been challenged on both theoretical and empirical grounds. Both theory and evidence are now considered in turn. 10.3.1 Theoretical arguments The first column of Table 10.2 lists several reasons why large firms might possess advantages at innovation over small firms. The second column lists some counterarguments. As Table 10.2 shows, there are four major areas of theoretical disagreement about the role small firms are likely to play in innovative activity. The first major difference relates to economies of scale, the basis of Schumpeter’s predictions mentioned above. A modern articulation of this view is found in Klepper (1996), in which early innovators rapidly achieve scale, providing greater incentives to implement incremental process innovations than their smaller rivals. Klepper’s key insight is that large firms can spread the costs of developing new innovations over a massive scale, so that innovations which yield cost reductions of a given percentage rate yield greater absolute profit margins in larger firms. These forces enable large firms to reduce costs and expand scale even more. This self-reinforcing process forces out smaller rivals and chokes off entry, eventually culminating in highly concentrated, oligopolistic market structures. Hence innovation interacts with scale to entrench large firm competitive advantage. Scale economies in production might also provide ‘economies of scope’, thereby increasing profits from innovation (Acs and Audretsch, 2003). And senior managers of large firms can more easily delegate operational tasks to more junior managers, freeing up precious managerial attention needed to identify new ideas and innovations (Gifford, 1998).

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On the other hand, large firms can be prone to diseconomies of scale, potentially making innovation more difficult. Large firms can suffer from bureaucratic inertia, which is antithetical to innovation (Link and Rees, 1990; Freeman and Engel, 2007). Indeed, it has been claimed that incumbents are not only relatively poor at pioneering radical innovations, but also struggle to develop incremental ones (Henderson and Clark, 1990). It could be that managers in existing firms fail to spot new opportunities because they follow internal routines which blind them to new trends in the market. If they act quickly, entrepreneurs located outside large firms might therefore be well placed to develop innovative ideas untrammelled by conventional corporate thinking (Pavitt et al., 1987; Freeman and Engel, 2007). There also tend to be shorter lines of communication in small firms (Fielden et al., 2000). This facilitates easier sharing of information, by promoting tighter linkages between work colleagues and fostering greater trust. There can also be greater ease of technology diffusion between networks of small firms, especially those involved in clusters which generate opportunities for cross-organisational learning (Morgan, 1997). And new, small ventures might be more agile and responsive to opportunities created by changing demand and demographic patterns (Bannock, 1981). The second set of entries in Table 10.2 recognises that incentives to innovate are also likely to differ by firm size. Large diversified firms can not only use existing marketing channels to sell innovative products to numerous customers quickly and easily, but can also spread the risks of innovation, giving them greater incentives to develop radical innovations. On the other hand, large bureaucratic firms might find it costly to overcome agency problems and to provide the high-powered incentives required to motivate their workers to develop radical innovations (Holmstrom, 1989). Facing sufficiently high costs to incentivise effort in these cases, large firms may optimally pass over uncertain new technology development in favour of more routine ones, despite the more modest average returns of the latter (Bhide, 2000). Furthermore, incremental changes in corporations most easily satisfy objective external processes of scrutiny. And small firms may have to innovate in order to overcome entry barriers and to cope with retaliatory conduct by incumbents (Acs and Audretsch, 1989). This argument highlights important strategic and industry dimensions of small firm innovation activity. The third row in Table 10.2 draws on evidence that larger firms tend to perform more R&D (Cohen and Klepper, 1992). Larger firms usually have easier access to finance, through re-invested profits and bank loans, to fund expensive innovation. Yet there can be diminishing returns to R&D, which weigh on large firms more than small ones. That attenuates the large firm R&D advantage (Acs and Audretsch, 1991). Small firms can also compensate for limited direct R&D spending by exploiting knowledge spillovers which leak out of larger organisations (see below). Finally, Table 10.2 contrasts the so-called ‘efficiency’ effect with the ‘replacement’ effect. The efficiency effect recognises that incumbents have greater incentives to innovate and retain monopoly profits than new entrants. The latter can at best obtain duopoly profits by exploiting an innovation opportunity in the same market (Gilbert and Newbery, 1982). The urge to preserve market share driven by monopoly profit

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incentives might explain why corporate giants in the IT world like Intel, Microsoft and Cisco continually innovate, sometimes radically. The replacement effect, on the other hand, recognises that large incumbents might be unwilling to destroy their monopoly rents by engaging in paradigm-shifting innovations which render existing products obsolete (Arrow, 1962). That is, by definition, not a problem for new entrants. If the efficiency effect is strong enough, monopoly positions in product markets will tend to last, which might explain persistent innovation by incumbents in some industries, such as IT products (Klepper, 1996; Klepper and Simons, 2000a). But in sectors open to drastic innovations, the replacement effect is more likely to dominate (Reinganum, 1983), entailing a strong tendency to entry.

10.3.2

Evidence about innovation

The evidence relating to the role of large versus small firms, and radical versus incremental innovation, speaks to two issues. One concerns whether small firms are more or less innovative than large firms, and the other concerns whether large firms are responsible for a disproportionate number of incremental innovations while small firms are associated with more radical ones. An important preliminary question is how to measure innovation. Researchers typically utilise one or more of the following measures: R&D, the number of patents, the impact of innovations, and the commercialisation of inventions. The first two measures are skewed in favour of large firms and suffer from numerous methodological limitations (Acs and Audretsch, 2003). Among these, R&D is an input rather than an output measure; and many patents never lead to useful innovations in practice. The last two measures are perhaps the most informative, although it is important to use objective data rather than subjective self-assessments. Aggregate cross-industry evidence paints a mixed picture about the contributions of small and large firms to aggregate levels of innovative activity. Large firms certainly perform the bulk of R&D spending and patenting activity,10 though consistent with the right-hand side entry of row 3, Table 10.2, small firms enjoy greater marginal growth benefits than large firms from additional R&D spending (van Praag and Versloot, 2007). As noted above, though, R&D is an input, not an output. In terms of innovative output, smaller and younger firms appear to enjoy pronounced advantages over their larger and older counterparts, at least in some industries.11 For example, according to Scherer (1991), in the 1980s ‘small’ US firms (defined as those with fewer than 500 employees) created 322 innovations per million employees compared with 225 per million in large firms. In a similar vein, Acs and Audretsch (1990, chap. 2) reviewed four databases measuring technological innovation on the basis of their peer-reviewed ‘importance’. These authors estimated that small firms contribute around 2.4 times as many innovations per employee as large firms. The SBA (2003) estimates that small firms represent one-third of the most prolific patenting companies that have registered fifteen or more US patents. Small firms’ patents are twice as closely linked to scientific research as those of large firms, being more ‘high-tech and leading-edge’. Moreover,

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their patents are on average more highly cited than those of large firms. In particular, new ventures hold more highly cited patents in the biotechnology industry than incumbent firms, in both the USA and France (Gittelman, 2006). Reflecting these findings, many famous radical innovations originated in small rather than large firms. Examples include the airplane, FM radio, the zipper and the personal computer, among many others (Baumol, 2007). This evidence supports the contention that small firms specialise in radical innovations while large firms focus on incremental ones.12 Further evidence of this kind comes from Prusa and Schmitz’s (1991) analysis of the software industry, in which new firms provided the majority of ‘category opening’ products in the 1980s, developing six times as many of these products in absolute terms as large firms. Prusa and Schmitz (1991) observed that existing firms had a comparative advantage in incremental improvements within existing product categories, consistent with the ‘replacement effect’ argument that incumbents avoid introducing competencedestroying technologies.13 Further buttressing this point, Audretsch (2003) observed greater displacement of incumbents by entrants in innovative industries, suggesting that new firms have a ‘more pronounced Schumpeterian creative destruction’ innovation impact compared with incumbents. On the other hand, large firms have produced many path-breaking innovations too (Acs and Gifford, 1996). A study of consumer durables and office products in the US concluded that, in these sectors at least, incumbents and large organisations introduced the majority of radical product innovations over the last sixty years of the previous century (Chandy and Tellis, 2000). Furthermore, King and Tucci (2002) chronicle how experienced incumbents successfully rode each new technological wave that hit the hard-disk-drive industry in the USA. Incumbents may not have been the first ones into the market, but their survival rates exceeded those of new entrants. Indeed, later entry by incumbents does not necessarily point to failure or irrationality (Berchicci and Tucci, 2006; Bayus and Agarwal, 2007). Baum et al. (1995) report that the shift from analogue to digital technology in the facsimile transmission business enhanced the competitiveness of incumbents and reduced the new firm formation rate. These findings are all broadly consistent with Klepper’s (1996) evolutionary perspective in which early innovators continue to grow by exploiting ever-increasing incentives to innovate, owing to ever-increasing economies of scale. The evidence from a range of US industries, including tyres, autos, penicillin and TVs, seems to confirm the innovative advantage of large, long-established incumbents. There is greater disagreement among researchers about whether start-ups are more or less likely than established firms to commercialise inventions generated by university departments. Mansfield (1991) claimed that small firms have an advantage over large firms in this regard, but Lowe and Ziedonis (2006, p. 180) detected few such differences between start-ups and established firms. Both sets of authors agree, however, that small start-ups bring commercial applications based on academic research quicker to market than large firms do. On the negative side, entrepreneurs continue to pursue unsuccessful commercialisations for longer than established firms, perhaps because of over-optimism, and thereby can end up destroying value (van Praag and Versloot, 2007).

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Building on work by Jaffe (1989), Acs, Audretsch and Feldman (1994) argue that new small ventures exploit knowledge which spills over from universities and large companies. Based on estimates derived from a simple econometric model,14 Acs, Audretsch and Feldman (1994) claim that knowledge spillovers are more decisive in promoting innovative activity of small firms than of large corporations. Other evidence is consistent with these findings.15 An analysis of Canadian biotech firms in the 1990s shows that entrants are attracted to incumbents’ R&D resources within a 500-metre radius, but not outside this radius (Aharonson et al., 2007). This suggests the existence either of agglomeration benefits (access to pools of labour or specialised inputs) or knowledge spillover externalities, which dissipate rapidly with distance. Aharonson et al. (2007) also observe that entry rates are significantly higher if technologically similar incumbents and firms with university alliances are situated close by. In their commanding survey of the evidence base on innovation, technological change and small firms, Acs and Audretsch (2003) conclude that economies bestowed through geographical proximity and spatial clusters might be more important for producing innovative output than ‘traditional’ scale economies, at least in some industries. In absolute terms, while it might be true that large firms have been the most important sources of innovations in the US economy, small firms have bucked the trend in several industries, including computers, process control instruments and biotechnology (Acs and Audretsch, 1990; Gittelman, 2006). That is, small firm innovative advantage in the USA tends to be in different industries to those where large firms have an innovative advantage (Acs and Audretsch, 1988; Prevenzer, 1997). As a generalisation, large firms have a comparative advantage in exploiting efficient internal ‘routinised regimes’ to develop innovations (including radical ones) in capital-intensive, concentrated and unionised industries which produce differentiated products (Acs and Audretsch, 1991). Precisely this outcome has been observed in manufacturing, for example (Tether et al., 1997; Craggs and Jones, 1998). In contrast, small firms have a comparative advantage in exploiting their ‘entrepreneurial regimes’ in industries where human capital and skilled labour are important productive factors.16 This nuanced picture does not provide unambiguous support for Schumpeter’s prediction of ever-increasing concentration of innovation in large firms. As always in discussions about the contributions made by entrepreneurs, it is important to retain a sense of perspective. At the level of the individual entrepreneur, most start-ups are in mundane non-innovative trades such as hairdressing and car mechanic businesses (Storey, 1994a). Real innovation appears to be confined to a small handful of businesses run by a few talented, visionary and determined entrepreneurs. 10.4

Conclusion

This chapter reviewed the literature relating to two important contributions made by entrepreneurs: job creation and innovation. Both topics have been extensively analysed through the lens of small versus large firms; rather less analysis has been performed at the level of the individual entrepreneur. More research is needed into the innovative

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performance of entrepreneurial firms at the micro-level, especially research which is tied into the local and national economic and policy context. A good exemplar in this regard, which unifies data on institutions, individuals and the regional context, is Zucker et al. (1998). These authors found that although major universities and federal research support made important contributions to the sudden appearance and rapid growth of the US biotechnology industry, the growth and location of ‘intellectual human capital’ (embodied in star scientists) were the principal determinants of the growth and location of the industry. Interestingly, venture capital availability did not seem to play a driving role. With regard to the theme of the first part of this chapter, more theory and evidence is also needed about job creators and the factors which determine the number of workers they hire. Future research could usefully estimate econometric models of labour demand. These have been extensively analysed for samples of established firms, but scarcely at all for samples of entrepreneurial firms. Pursuit of this research agenda will require high-quality data on factor prices, factor quantities and production costs. This agenda is worth pursuing because of its relevance to several pressing public policy issues. For example, it would be desirable to understand how government labour market regulations bear on individual entrepreneurs’ hiring decisions. Some discussion of this question framed at the aggregate level, and at the interface between innovation, public policy and entrepreneurship, appears in the last two chapters of this book.

10.5 Appendix 10.5.1

Carroll et al.’s (2000a) model of labour demand and taxation Let ζh and ζn denote consumption and eh and en denote effort, where the h and n subscripts denote ‘hiring’ and ‘non-hiring’ status of an entrepreneur, respectively. Also, let π(w, eh ) denote the indirect profit function of an employer entrepreneur, where w is the market wage paid to employees. This function is decreasing in its first argument and increasing in its second argument. Also, let Ben denote the profits of non-hiring entrepreneurs, where B > 0 is a constant. With a common marginal tax rate of τ , consumption possibilities for both types of entrepreneur are

ζh = (1 − τ )π(w, eh )

and

ζn = (1 − τ )Ben

respectively. Letting l denote the number of hired workers, the indirect profit function π ∗ is obtained by solving the first-order condition ∂F(e, l)/∂l − w = 0 from the profit function π = F(e, l) − wl to get the labour demand function l ∗ = l(e, w), which is put back into π to obtain π ∗ = π(w, e). If the utility function is separable in consumption and leisure, one can write Vh = u(ζh ) − µ

and

Vn = u(ζn ),

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where µ > 0 is a fixed cost of hiring workers. Then using the envelope theorem, it follows directly that ∂(Vh − Vn ) = u
(ζh )π(w, eh ) − u
(ζn )Ben . ∂(1 − τ )

(10.1)

Eq. (10.1) can be expanded in a Taylor series around en to obtain ∂(Vh − Vn ) = u
(ζn )[π(w, en ) − Ben )] ∂(1 − τ )

" ∂F[e, l(e)] "" + u (ζn )(1 + σ )(eh − en ) , " ∂e e=en


(10.2)

where σ is the elasticity of the marginal utility of consumption. The first term on the RHS of (10.2) is the utility of additional net income of being an employer relative to a sole trader, holding effort constant over the two modes. The second term captures the fact that because effort is generally different in the two modes there are additional effects on consumption and marginal utility. To sign (10.2), assume that effort is more productive in the employer mode than in the sole-trader mode. Then π(w, eh ) − Ben > 0 and also eh∗ > en∗ . It therefore follows directly that if σ > −1, (10.2) is positive. So cutting income taxes would increase the propensity of entrepreneurs to hire labour (i.e. switch from n to h status). In their empirical work, Carroll et al. (2000a) estimated the probit equation

Pr[l88 > 0] =  ξ0 + ξ1  ln(1 − τ ) + ξ2 [l85  ln(1 − τ )] + ξ3 l85 + γ
X . (10.3) The dependent variable in (10.3) is the probability that a given entrepreneur is an employer in 1988;  ln(1 − τ ) := ln(1 − τ88 ) − ln(1 − τ85 ) is the change in the log of one minus the marginal tax rate resulting from the 1986 TRA; and l85 is a dummy variable taking the value one if the entrepreneur employed labour in 1985 and zero otherwise. The interaction term allows the response to changes in taxes to vary by employer status. Carroll et al.’s estimates of ξ1 and ξ2 are used to calculate the income tax/labour hiring elasticity discussed in the text. Notes 1. This material draws from and updates Parker (2006b). 2. See Carroll et al. (2000a), Kuhn and Schuetze (2001) and Moralee (1998), respectively. 3. The Cobb–Douglas production function is F(K, l) = δK α (1 + l)β , α, β > 0, where δ > 0 is a measure of human capital, K is physical capital and l is hired labour. It follows from this specification that ‘job creators’, i.e. those entrepreneurs for whom l ∗ > 0, have greater human capital δ than sole traders for whom l ∗ = 0. 4. The Cobb–Douglas forces the elasticity of substitution between capital and labour to be unity, and imposes an infinite elasticity of substitution between own labour supply and outside labour. 5. Letting e denote the entrepreneur’s own effort, Carroll et al. (2000a) observed that ∂l ∂l ∂e = . ∂(1 − τ ) ∂e ∂(1 − τ )

(10.4)

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To estimate responsiveness of wl, multiply both sides of (10.4) by w, which is assumed to be constant in competitive markets. The sign of the second of the derivatives on the RHS of (10.4) is positive, but the sign of the first derivative is ambiguous, depending on whether outside and inside labour effort are complements or substitutes. 6. See Leonard (1986), Dunne et al. (1989a, 1989b), Brown et al. (1990), Acs and Audretsch (1993), OECD (1994a), Birch et al. (1997), Baldwin (1998), SBA (2001) and Acs and Mueller (2008). According to SBA (2001), small firms account for about 75 per cent of net new jobs created each year in the USA. A similar proportion of new jobs were created in the self-employment sector in Canada in the 1990s (Lin et al., 2000). 7. To see this, let Ht∗ be true employment size at time t, and Ht be observed size: Ht = Ht∗ +vt , where ∗ + u , where u is a transitory vt is a measurement error with variance σv2 . Suppose Ht∗ = Ht−1 t t shock that is independent of vt . Then the true average conditional change in employment size is zero: ∗ ) = 0. E(Ht∗ |Ht−1

However, were the conditional change in employment to be estimated using actual data, which are subject to measurement error and transitory shocks, one would erroneously find E(Ht |Ht−1 ) = −[σv2 /(σH2 ∗ + σv2 )]Ht−1 < 0, implying a spurious negative relationship between firm growth and size. 8. See e.g. Baldwin and Picot (1995), Konings (1995), Hart and Oulton (1996) and Davidsson et al. (1998). The role of small firms in job creation is if anything even more pronounced in transition economies, as large state-owned incumbents have shed jobs en masse (Konings et al., 1996). 9. See Brown and Medoff (1989), Brown et al. (1990), Ringuedè (1998), Oi and Idson (1999) and Wunnava and Ewing (2000). 10. Cohen and Klepper (1992), Almeida and Kogut (1997) and Sørensen and Stuart (2000). 11. See Scherer (1980, pp. 407–38; 1984; 1991), Acs and Audretsch (1988), Audretsch (1991), Cohen and Klepper (1996) and Klepper (1996). 12. Also consistent with these arguments is differences in the sources of innovation by firm size: ‘Entrepreneurs commercialise innovations to a larger extent, but score lower on the adoption of innovations than their [larger] counterparts’ (van Praag and Versloot, 2007, p. 377). 13. For similar evidence from the US cement, minicomputer and airline industries, see Tushman and Anderson (1986). 14. Acs, Audretsch and Feldman (1994) estimated a ‘knowledge production function’ of the form Iik = β0k + β1k ln RDi + β2k URi + β3k ln(UR.GC)ik + uik , where i indexes firms and k the type of firm (small – under 500 employees – or large); I is the number of innovations in 1982; URi is expenditure on university research which may be accessible to firm i; and RD is industry R&D that may be accessible to i. GC is a measure of geographical propinquity of university and industrial research: this captures spillover effects. Acs, Audretsch and Feldman (1994) estimated this function by tobit and found that βˆ1 was largest for large firms while βˆ2 was largest for small firms. Also, βˆ3 was positive but insignificant for large firms (= 0.033 with t-statistic 0.687) but positive and significant for small firms (= 0.111 with t = 1.965). 15. For other evidence that the presence of external knowledge sources (e.g. large R&D-intensive firms and universities) in regions increases the innovative output of firms located in those regions, see Jaffe (1989), Jaffe et al. (1993), Feldman (1994), Audretsch and Feldman (1996), SBA

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(2002b), Audretsch and Lehmann (2006) and Aharonson et al. (2007). According to Stephan (1996) and Audretsch and Lehmann (2006), proximity to universities which excel in social (rather than natural) science outputs is associated with greater density (and subsequent IPO performance) of start-ups. 16. Pavitt et al. (1987); Acs and Audretsch (1987a, 1987b, 1988, 1991); Marvel and Lumpkin (2007).

11 Entrepreneurship and growth

Once a venture has obtained finance and made the transition to start-up, what determines whether it grows and survives, or stagnates and exits? These issues are examined in the present chapter and in chapter 14. There is evidently more to successful entrepreneurship than simply deciding whether or not to start a new firm. Many entrants create little value and do not last long, while others grow to a considerable size and end up creating substantial wealth. Clearly growth plays a central role in any discussion of value-creating entrepreneurship. For their part, policy-makers appear to be moving away from wholesale efforts to encourage new entrants, in favour of promoting a business climate where growth-orientated businesses can more easily create jobs and wealth for the economy. Most firms start small, so to achieve these laudable goals they have to grow. It is interesting in this respect that a well-known theory of venture growth, Gibrat’s Law, effectively rules out any role for activist public policies. Gibrat’s Law states that growth rates are unrelated to venture size and age, and are effectively random. In fact, the evidence reviewed below suggests that while Gibrat’s Law approximates growth patterns for large firms, it does not do so for small ventures, which tend to grow at a faster rate. After outlining Gibrat’s Law (and some extensions), several theories of entrepreneurial growth are discussed. The second section of this chapter reviews evidence relating to these theories and identifies the salient empirical determinants of venture growth. Three groups of empirical growth determinants are identified: those associated with the founder, those associated with the firm, and those associated with the entrepreneur’s strategic choices. The basic message to emerge from the empirical literature is that while a few measurable factors seem to influence growth, by far the largest source of variation in enterprise growth rates remains unaccounted for. This might explain why Gibrat’s Law, which predicts that growth is random, has proven to be such a useful (if only approximate) representation of venture growth in the literature. The final section of the chapter moves from the level of the individual venture to the more aggregate levels of industries, regions and national economies. The basic question explored here is whether there is a relationship between entrepreneurship and aggregate economic growth. Several useful econometric growth models are introduced 310

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as we proceed throughout the chapter; they are collected together in a table located in the chapter appendix.

11.1 Theories of venture growth

Several theories of growth are discussed in this section. The first one is Gibrat’s Law of Proportionate Effect. The second is Jovanovic’s (1982) industry selection model of entry, learning, investment and exit. In contrast to the ‘atheoretical’ model of Gibrat, Jovanovic’s theory studies growth at the level of the individual firm and goes on to derive properties of industry equilibrium. It generates a rich array of testable propositions. Similar properties are shared by a different strand of theorising linking innovation, growth and shakeouts, which is associated with the work of Steven Klepper. Both Jovanovic’s and Klepper’s writings have had a major influence on how growth is conceptualised, at the level of the individual firm as well as for entire industries. The final part of this section considers some alternative theories of the entrepreneurial growth of new enterprises. 11.1.1

Gibrat’s Law Gibrat’s ‘Law of Proportionate Effect’(Gibrat, 1931) is a well-known benchmark model of firm growth. Gibrat’s ‘Law’ states that if there is a fixed number of firms, and if firms’ growth rates are random draws from a normal distribution which are independent of firm size and previous draws, then the distribution of firm sizes will be lognormal with a variance that increases over time. An econometric specification of Gibrat’s Law appears in the first row of Table 11.2 in the chapter appendix. Early studies found some empirical support for lognormal firm size distributions and for the assumption that venture growth rates are roughly independent of firm size (see e.g. Hart and Prais, 1956; Simon and Bonini, 1958). More recent studies (Audretsch et al., 2004; Petrunia, 2008) emphatically reject Gibrat’s Law. Three assumptions underlying Gibrat’s Law appear especially questionable. One is that the population of firms is fixed. In practice, of course, firms are born and die, so this assumption cannot be tenable in a strict sense. A second assumption is that each firm faces a draw from a common distribution of random shocks. But recent evidence, discussed below, suggests that the variance of firm growth rates is higher for small than for large firms. Third, Gibrat’s Law assumes that mean growth rates are the same for all firms and for all time. Again, recent evidence reviewed below refutes this claim, showing that smaller firms tend to have higher growth rates than larger firms. In the light of these problems several refinements to Gibrat’s Law have been proposed (see Ijiri and Simon, 1977). One contribution modifies the Law to allow for idiosyncratic firm-specific components of growth and for growth rates which depend on firm size.1 Another modification adds non-linear age, a, and size, q, effects to the model (see (11.3) in Table 11.2). This permits testing of the Jovanovic (1982) model described next.

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11.1.2

Jovanovic’s (1982) model of industry selection In a seminal paper, Jovanovic (1982) developed a model in which entrepreneurs have imperfect information about their innate abilities, which can only be learned by trying entrepreneurship. Jovanovic’s model predicts that the ventures of able and/or lucky entrepreneurs survive and grow, while those of less able and/or unlucky entrepreneurs shrink and exit. It derives endogenously the entry, growth and exit behaviour of a cohort of new ventures within an optimising framework. As will be seen later, the predictions of the model turn out to be consistent with a large body of empirical evidence about new firm dynamics. The Jovanovic model shares several features in common with some of the models of entrepreneurship discussed in chapter 2. As in Lucas (1978), individuals have heterogeneous abilities in entrepreneurship, x; but unlike Lucas, entrepreneurs do not know their abilities when they start their ventures. As in the Kihlstrom and Laffont (1979) model, entrepreneurs face a risky trading environment; but unlike Kihlstrom and Laffont, all individuals are assumed to be risk-neutral rather than risk-averse. Finally, like Dixit and Rob (1994) and Parker (1997aa), the Jovanovic model is dynamic. But in contrast to those models, there are no switching costs and entrepreneurs learn by doing. In particular, entrepreneurs learn about their idiosyncratic x, about which they are initially uncertain. For simplicity, Jovanovic assumed that all entrepreneurs start with the same prior belief about their ability, so they all enter at the same scale of operation. Entrepreneurs use a Bayesian updating rule to adjust their beliefs about their ability as information comes in. Although relatively able entrepreneurs are more likely than less able entrepreneurs to receive information which signals high ability, this is not guaranteed to occur. Thus able entrepreneurs can receive an unlucky sequence of bad draws, and erroneously come to believe that they possess low ability. Conversely, less able entrepreneurs can receive a lucky sequence of good draws, and erroneously come to believe that they possess high ability. Beliefs matter because they influence entrepreneurs’ output decisions. Entrepreneurs increase output if they infer high ability and decrease output if they infer the opposite. As time goes on, unrepresentative good and bad random draws should cancel out, and innate ability is increasingly likely to shine through. As in all occupational choice models, individuals do not have to be entrepreneurs. Entrepreneurs who believe they are able remain in entrepreneurship, and increase their output in accordance with their perceived ability. Entrepreneurs who come to believe they possess low ability exit and take a valuable outside option instead. All exits are voluntary; entrepreneurs quit when the expected value of continuation in entrepreneurship falls below some positive threshold given by the outside option. Hence there are no involuntary bankruptcies. Jovanovic’s model generates the following implications for the distribution of ability, venture growth rates, exits, industry concentration, profits and output:

J.1 Entrepreneurs who run young firms have had less time to accumulate information about their true abilities. Therefore the level and variability of growth rates are

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J.2

J.3

J.4

J.5

J.6 J.7

J.8

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highest among younger and smaller firms. Growth rates are lowest among mature surviving firms. Firms that exit never return to the market, in part because an exiting entrepreneur never obtains any more information that could change their terminal belief about their innate ability. Results J.1 and J.2 imply that (cohort) industry concentration increases over time, since initially all firms of a given cohort were the same size, at which point industrial concentration was at a minimum. Entrepreneurs who remain in business indefinitely eventually learn their true ability, whereas entrepreneurs who exit possess relatively imprecise estimates of their true abilities. The last clause of J.4 implies that some entrepreneurs who exit might have true entrepreneurial abilities which would have made entrepreneurship optimal for them. Unfortunately, these entrepreneurs received ‘unlucky’ draws from the distribution of shocks, and incorrectly (but understandably) interpreted these draws as evidence of low ability, prompting their decision to exit. The greater the noise in the learning process, the more of these ‘efficient but unlucky’closures there will be. Surviving firms are larger and older than both firms which failed and new entrants. Larger, more efficient firms earn profits as a reward for their exceptional ability. For each cohort of firms, average profits increase as the industry matures. The distribution of profits resembles the distribution of ability. The more dispersed the latter, the more dispersed are firm sizes and profitability rates. Entry occurs in every period.

As will be seen below, Jovanovic’s predictions relating to firm growth rates (J.1) receive strong empirical support. Additional predictions relating to survival will be discussed in chapter 14. At the risk of ending this section on a negative note, however, I shall conclude by mentioning a few less satisfactory aspects of the model. First, it is debatable whether in practice average cohort firm size and industrial concentration increase continually over time (in a first-order stochastic sense), as suggested by J.3 and J.6. One can think of many practical reasons, absent from Jovanovic’s model, why firms might face barriers to growth, including regulatory constraints and tax disincentives. Also, entrepreneurs have only finite attention to apportion between maintaining current projects and evaluating and adopting new ones. The opportunity cost of neglecting profitable current projects might deter firms from seeking growth and so place bounds on firms’ sizes (Oi, 1983; Gifford, 1998). While some entrepreneurs might be able to release scarce resources to exploit profitable new opportunities by closing or transferring viable existing businesses to less able entrepreneurs (Holmes and Schmitz, 1990), this is not always feasible. A second questionable feature of the model is that entrepreneurs who exit never return and ‘try their luck again’ (J.2). As noted in the appendix to chapter 1, between 20 and 30 per cent of entrepreneurs at any given point in time are ‘serial entrepreneurs’. Many entrepreneurs who have run an unsuccessful venture the first time around are keen to

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try again (Stam et al., 2008). In fact, re-entry by failed entrepreneurs can be sustained in a modification of Jovanovic’s model by replacing the assumption of an inexhaustible supply of potential entrepreneurs with an alternative assumption stipulating a limited supply (Brock and Evans, 1986, pp. 60–3). Yet the possibility remains that entrepreneurs want to re-enter because their unsuccessful entrepreneurial experience nevertheless enabled them to perform ‘active’ learning (which enhances their abilities and hence future expected returns in entrepreneurship) rather than the ‘passive’learning envisaged by Jovanovic (Stam et al., 2008). More will be said about the distinction between active and passive learning in chapter 12. Third, the next section notes that entry eventually ceases in the mature stages of most industry life cycles. This refutes prediction J.8. Fourth, Jovanovic’s model is not the only theory to generate several of the predictions listed above, although it is economical in generating so many predictions from such a simple theoretical framework. Below I briefly consider several other theories which have also influenced our thinking about the entrepreneurial growth process at the levels of both the individual firm and the industry.

11.1.3

Innovation, growth and shakeouts Klepper (1996) and Klepper and Simons (2000a) offer an alternative theory of the growth of enterprises. These authors analyse the evolution of innovative industries by building on ‘stage’models of product life cycles and the theory of shakeouts, articulated in Gort and Klepper (1982) and Klepper and Graddy (1990). Unlike Jovanovic (1982), Klepper’s (1996) theory has surviving entrepreneurs not learning more, but innovating more. Suppose that owing to some innate differences in entrepreneurial talent or access to resources, some entrepreneurs can innovate while others can only imitate. Costly R&D is needed to generate innovations. Innovations reduce average production costs and increase output and profits for the innovators. Larger ventures optimally undertake the most R&D because they can spread the lower resulting average costs over a larger scale of output. The greater output and lower average costs increase competitive pressure on the imitators. Next period, all imitator firms which can survive the growing competitive pressure catch up and adopt the innovation themselves. Average costs therefore fall across the board and aggregate output rises. For a given industry demand, greater output decreases the market-clearing price. This process continues over time with further rounds of innovation, imitation, rising output and falling prices. An interesting set of predictions about industry structure follows if prices decrease by more each period than average production costs decline following the initiation and imitation of last period’s innovations:

• Innovation promotes ongoing entry and growth. But price–cost margins shrink over time, eventually choking off entry by imitators, and ultimately even entry by new innovators. Entry eventually ceases altogether.

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• There is ongoing exit of high-cost firms and later entrants (which are smaller on average). A shake-out of producers occurs, despite growing industry output (which, for example, reached 20 per cent per annum in the early years of the auto industry). • The entrepreneurs who were the earliest innovators grow rapidly and ultimately end up dominating the industry. These innovators have lower average costs and so are always larger than imitators who entered at the same time. Earlier entrants have a lower hazard of exit at older ages. Thus size, age, growth, innovation and survival are all interrelated. Innovation drives growth, which increases the gains (and hence incentives) from further innovation. The pressure of competition then leaves the fastest-growing and most innovative ventures to dominate the market, eventually culminating in an oligopoly (Klepper, 2002). Like Jovanovic (1982), this theory also carries important implications for firm exit, which will be discussed in chapter 14. This model takes early entrants to be the innovators. This implies a form of firstmover advantage in entrepreneurial innovation. The reason is exploitation of economies of scale. In fact, Geroski (2003) identified this as just one of four sources of first-mover advantage. Others include monopolisation of scarce inputs; lock-in of consumers who are reluctant to change products once they become accustomed to them; and enhanced brand identity and status from being first to market. According to Geroski (2003), these benefits generally outweigh second-mover advantages such as learning from the mistakes of early movers and free-riding on their efforts. While shakeouts have been observed in several industries, they are not ubiquitous. In both the USA and Germany, for example, the laser industry has evolved differently from traditional manufacturing industries, exhibiting sustained entry and neither shakeouts nor first-mover advantages for early entrants (Buenstorf, 2007). It is not yet clear why this industry has exhibited such different development patterns from autos and other manufacturing industries. 11.1.4

Other theories of growth

Other models also predict that smaller firms will have higher and/or more variable growth rates. For example, Segal and Spivak (1989) analysed an entrepreneur who chooses how much profit to take out of her business and how much to re-invest in it. Re-investment of profits increases output (which is subject to random shocks), enabling small firms to rapidly attain the industry’s minimum efficient scale (Audretsch, 1991). An entrepreneur’s firm fails and closes if output reaches zero, perhaps following an unlucky sequence of successive adverse shocks. In that event, the owner pays an intangible dissolution cost – perhaps the loss of reputation. Now consider an entrepreneur who enters the market with an output that is positive but sufficiently close to the zero boundary to make the prospect of failure a real possibility. Clearly, this entrepreneur has a particularly strong incentive to reinvest profits in order to decrease the risk of costly failure, compared with a manager of a larger enterprise producing output that is further from the boundary. It therefore follows that small firms have higher output growth

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rates than larger firms, since re-investment of profits promotes growth. An interesting by-product of Segal and Spivak’s (1989) model is the prediction that large firms have average growth rates that converge to a constant – consistent with Gibrat’s Law. Another reason why small new firms may grow faster than their older larger counterparts is attributable to sunk costs (Cabral, 1995). There is often an option value of waiting before making sunk cost investments. Consequently new firms will optimally start operations at a small scale and only exercise an expansion investment option if circumstances prove to be favourable. In contrast, large firms optimally invest more initially and therefore have lower subsequent growth rates. In support of this theory, Geroski (1995) reported that most new firms start out with output less than the industry minimum efficient scale (MES). The Industrial Organisation literature has also spawned several other models of optimising behaviour under conditions of risk which give rise to negative relationships between firm age, growth and exit. In an important contribution which diverges from Jovanovic (1982), Ericson and Pakes (1995) supposed that each firm knows the value of a parameter which determines the distribution of its profits. But the value of that parameter responds dynamically to the investments made by the firm and its competitors. Ericson and Pakes (1995) predict that small firms optimally start with a relatively low level of investment, and can never compete with larger firms which continue to invest and scale up output and so entrench a competitive advantage over them. Similarly to Klepper (1996), most entrants die young. Survivors invest, grow and learn until they reach a ‘coasting’ point. Pakes and Ericson (1998) discuss empirical strategies to distinguish their model from that of Jovanovic (1982), and argue that different models may apply in different industries.2

11.2

Evidence about the growth of entrepreneurial ventures

Many new enterprises do not grow. Even among those which do, rapid growth is difficult to achieve and maintain. Most start-ups are imitative businesses in mature industries serving local markets, which necessarily have limited growth potential (Aldrich, 1999). Only about one in seven firms generates sustained profitable growth. It is noteworthy that a mere handful of new firms enjoying spectacular growth rates create the majority of new jobs. In the context of the small firms literature, Storey (1994a) estimated that 5–10 per cent of UK manufacturing businesses which started in the 1970s provided 40–50 per cent of total employment in their cohort ten years later. David Storey refers to these firms as the ‘ten-percenters’. Likewise, only five per cent of American businesses grow their employment by 15 per cent per annum or more over five-year time spans (Zook and Allen, 1999; Barringer et al., 2005). Bhide (2000, p. 13) cites work by Birch and Medoff claiming that 4 per cent of all US firms generate 60 per cent of all new jobs in the US economy. These fast-growing firms are called ‘gazelles’. British data show that most gazelles cannot sustain their rapid growth rates (Parker, Storey and van Witteloostuijn, 2005).

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Understanding the determinants of growth holds out the promise of understanding ongoing value creation by entrepreneurs. However, in any discussion about the growth of entrepreneurial firms, it is imperative to define clearly at the outset how growth is to be measured. The first subsection below considers the relevant issues. The one after summarises evidence from multivariate regression analyses about the relationship between entrepreneurial growth performance gi among a sample of firms indexed by i, and three broad groups of explanatory variables Xi : founder characteristics, firm attributes and entrepreneurial strategies. The ‘standard form’ of these regressions defined over a sample of ventures indexed by i is given in row 4 of Table 11.2. At no point will I discuss so-called ‘stages of growth models’ which are popular in many business studies courses (e.g. Churchill and Lewis, 1983). These simple descriptive constructs assume that ventures develop their organisational structures in a predictable fashion from birth through several pre-defined growth stages until maturity. As several critics have pointed out, they assume an excessive pre-determination and homogeneity of development, and understate the role of the entrepreneur (Bhide, 2000; Davidsson, Achtenhagen and Naldi, 2006). The latter criticism can also be applied to ‘evolutionary’ theories of enterprise development (Nelson and Winter, 1982) which attribute growth to luck and exogenous routines rather than to the purposeful choices of entrepreneurs and other decision-makers. 11.2.1

Definitional and measurement issues Before conducting an empirical analysis, the researcher first needs to address several basic questions about what is meant by ‘growth’. For example, is the researcher interested in tracking an entrepreneur’s growth performance over time, or in following the growth of a particular venture, regardless of the sequence of ownership during its lifetime? Over what time period is growth to be measured? Should it be measured across ventures spanning different industries or should a more restricted and homogeneous sample be chosen? Is the researcher interested only in ‘organic’ growth or is he or she prepared to count growth by acquisitions as well? What type of data (e.g. panel or cross-section) and empirical methods are to be used to estimate the determinants of growth? Should ‘the growth rate’ be defined in relative or absolute terms (see below)? And what quantity is growth measured for: sales, employment or something else? Answers to most of these questions depend upon the context. Rightly or wrongly, and for a plethora of reasons which researchers rarely record, most of the evidence cited below is drawn from studies which analyse the growth rates of a set of ventures each of which could be associated with one or more individual entrepreneurs. These studies typically measure growth between one and five years from the date of first sampling (depending on data availability), and usually sample ventures from a range of industries. Shorter time spans for assessing growth reduce the risk that ventures change so much (e.g. by acquisition) that one is no longer measuring the same entity; but in these cases outcomes can be distorted by transitory idiosyncratic shocks. Also,

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there can be a long time lag before entrepreneurial growth occurs. For example, only one-tenth of new firms between 1976–86 grew in employment terms in their first four years, but over one-half had grown within eight years (Phillips and Kirchhoff, 1989). Recommendations are easier to make regarding some of the other issues. The majority of growth in new and small enterprises is organic, whereas that in established large firms is often acquisition-based (Davidsson, Delmar and Wiklund, 2006). Organic growth is usually regarded as more interesting than growth by acquisition, although surprisingly few empirical studies actually differentiate between them. And despite the superiority of panel over cross-section data, and of multivariate econometric analysis over bivariate comparisons, some studies still report results based on the latter rather than the former. There appears to be greater disagreement in the literature about whether growth should be measured in relative or absolute terms; and which variable should be the subject of growth. Let q(t) be a performance variable (e.g. revenue) and write q(t) = q(t) − q(t − 1). Relative growth (the ‘growth rate’) is q(t) ∂q(t)/∂t ≈ , dt→0 q(t) q(t − 1)

g =  ln q(t) = lim

as can be readily seen by manipulating the growth process q(t) = q(0) exp{gt}.Absolute growth is simply q(t). ‘Small’ firms (i.e. with small values of q(t − 1)) tend to record higher relative growth but lower absolute growth than large firms. It is sometimes argued that relative growth rates overstate the actual growth performance of small relative to large firms, because of the small denominator for small firms. However, the same is true of GDP growth rates for poor developing countries and few objections are made in that context. Furthermore, the use of relative measures is consistent with the theories outlined earlier. Most of the studies cited below use relative rather than absolute measures of growth. Variables whose growth rates are often studied include profitability, revenues (or sales turnover), returns on assets and the number of employees. Each measure has its strengths and weaknesses. Sales growth precedes employment growth in the newest ventures, making it an obvious measure of choice in samples of new firms (Davidsson, Delmar and Wiklund, 2006). And sales growth rather than employment tends to be the indicator of performance that many entrepreneurs use themselves (Barkham, Gudgin et al., 1996; Robson and Bennett, 2000). Moreover, ‘sales’ is probably superior to ‘assets’ as a growth variable because few new ventures have much in the way of tangible assets, especially those in the service sector. On the other hand, policy-makers are usually more interested in employment growth than in sales growth. Davidsson, Delmar and Wiklund (2006) observe that for a broad range of enterprises, employment growth appears to be more strongly correlated with other growth measures than sales growth is. Rather than selecting one growth variable, some researchers use a compendium of measures in an attempt to obtain a more rounded understanding of growth. An alternative strategy is to aggregate them into a portmanteau measure. The latter however can be hard to interpret. With these preliminary remarks in mind, I now discuss the determinants of growth at the level of the individual venture.

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11.2.2

Evidence about determinants of venture growth Storey (1994a) proposes the following three groups of determinants of entrepreneurial venture growth:

I. Founder characteristics II. Firm attributes III. Strategies of the entrepreneur This classification will be used to frame the empirical results which follow. These results are drawn from multivariate econometric analyses of reduced-form growth models. Reduced-form models are prone to the problem that some individual characteristics and firm attributes might affect growth indirectly through hard-to-measure entrepreneurial strategic choices (Wiklund, 2007). This is a type of endogeneity problem, which will be found to recur several times below. Table 11.1 provides a selected summary of prominent findings from the literature. Under I, the discussion in chapter 4 would probably lead one to expect human capital to improve venture growth prospects. As the first panel of Table 11.1 shows, this is broadly (but not uniformly) what emerges from previous research on education and experience. Digging into the education variable, Almus and Nerlinger (1999) reported higher employment growth in new ventures whose founders have degrees in technological subjects and business. With respect to experience, managerial experience appears to have a stronger positive effect on venture growth than previous entrepreneurial experience in the same industry sector – while the effects from founder’s age and previous time spent in self-employment are mixed (Storey, 1994a). Mixed effects have also been found for gender, training, membership of a business network and having a self-employed parent (see the entry in Table 11.1 under ‘social capital’). Several authors have cited positive effects from entrepreneurs having growth motivations at the outset.3 But the evidence about motivations is not uniform. For example, Birley and Westhead (1994) found no such effects. Perhaps growth aspirations are best interpreted in a ‘negative’ way: i.e. one would be surprised if entrepreneurs who did not intend or desire to grow in the future ever did so. It is important in this light to remember that firms can sometimes do better by not growing. For example, some small firms retain flexibility by relying on interdependent networks of external businesses offering specialised services (Saxenian, 1994) – benefits which could be lost or compromised if they attempted to ‘make rather than buy’ by growing internally instead. These considerations might explain why, for example, less than one-half of small firms in the UK regard employment growth as an objective, even in economically buoyant times (Hakim, 1989b). Scase and Goffee (1982) claimed that some entrepreneurs do not hire workers if they suspect this will destroy a personal client-based service; if they believe that hired labour is unreliable; or (among some artisans) if hiring entails spending one’s time as a ‘businessman’ rather than as a ‘craftsman’. Other entrepreneurs fear losing the family atmosphere of a small firm and dislike having to rely on external capital with its perceived (or actual) loss of control.

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Table 11.1. Determinants of entrepreneurial venture growth I. Founder characteristics Variable Education

Effect +

0 Experience

+

Age

?

Male gender Training Social capital

+ 0 ? ?

Growth motivation

? ?

Evidence Cooper et al. (1991, 1994); Westhead (1995); Westhead and Cowling (1995); Reynolds and White (1997); Almus and Nerlinger (1999); Kangasharju and Pekkala (2002); Akoten et al. (2006); Tamásy (2006) Schutjens and Wever (2000); Haber and Reichel (2007); Capelleras and Greene (2008) Cooper et al. (1991, 1994); Kangasharju and Pekkala (2002); Koeller and Lechler (2006) Cf. Storey (1994a); Barkham, Hart and Hanvey (1996); Capelleras and Greene (2008) Cooper et al. (1991) Kalleberg and Leicht (1991); Capelleras and Greene (2008) Cf. Skuras et al. (2005) and Foreman-Peck et al. (2006) Network membership: Cf. Bosma et al. (2004) and Foreman-Peck et al. (2006) Parent se: Cf. Cooper et al. (1991) and Tamásy (2006) Cf. Storey (1994a) with Birley and Westhead (1994) II. Firm attributes

Variable

Effect

Evidence

Initial firm size Firm age Venture team size

+

Limited liability

+

(Reinvested) profits

?

Numerous: see text Numerous: see text Eisenhardt and Schoonhoven (1990) Cooper et al. (1991, 1994); Barkham, Hart and Hanvey (1996); Reynolds and White (1997); Schutjens and Wever (2000); Tamásy (2006); Westhead and Howarth (2006) Koeller and Lechler (2006) Storey (1994a); Almus and Nerlinger (1999) Capelleras and Greene (2008) Cf. Reid (1993) and Koeller and Lechler (2006) III. Strategies of the entrepreneur

Variable Sources of finance Personal savings Formal processes

Effect + + +

Evidence Westhead and Cowling (1995); Capelleras and Greene (2008) Barkham, Gudgin et al. (1996); Koeller and Lechler (2006); Foreman-Peck et al. (2006)

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Table 11.1 (cont.) III. Strategies of the entrepreneur Business plans

+ 0

External assistance

+ ?

Schutjens and Wever (2000); Masuerl and Smit (2000) Orser et al. (2000) Reid and Smith (2000); Tamásy (2006); Haber and Reichel (2007); Capelleras and Greene (2008) Links to external firms: Almus and Nerlinger (1999) Outsourced retail: Koeller and Lechler (2006) Financial and advisory assistance: Cf. Reid and Smith (2000); Haber and Reichel (2007); Capelleras and Greene (2008)

Notes: ‘Effect’ summarises the consensus about the sign of the relationship, with ‘?’ indicating mixed or inconclusive results.

Turning to II, a widely documented finding is that growth rates are decreasing in venture size among ventures of the same age, and are decreasing in venture age among ventures of the same size. These findings have been obtained using several of the specifications outlined in the first part of Table 11.2.4 In addition, younger firms have more variable growth rates (Brock and Evans, 1986; Bates, 1990). These results all support Jovanovic’s (1982) prediction J.1. Occasionally, however, researchers identify data samples in which long-term growth is found to be higher among ventures which commit more resources (including hiring of employees) at the time of launch (e.g. Schutjens and Wever, 2000). This outcome is more likely to occur when the study omits controls for venture quality, since one would expect entrepreneurs to invest more resources in higher-quality ventures. One index of quality is high technology. In this regard, Almus and Nerlinger (1999) reported significantly higher employment growth rates among new technology-based firms (NTBFs) which, unlike non-NTBFs, created jobs on net in Germany over the period 1989–96. One concern is that several of the growth determinants considered so far might be endogenous. This concern deepens when discussing other variables under II such as the size of the founding team, the company’s legal status, and its profitability. As noted in chapter 4, multiple founders may have access to a broader, more heterogeneous pool of skills and experience and may give each other technical and psychological support (Eisenhardt and Schoonhaven, 1990). One would therefore expect venture team size to enhance growth. As the second panel of Table 11.1 reveals, this conjecture is largely borne out by the evidence. But the number of founders is a choice variable and is likely to be positively correlated with the quality of a venture, making this variable endogenous. Although there is some evidence that limited-liability and multiple-establishment ventures (Variyam and Kraybill, 1992) also have higher growth rates than ventures which do not, these variables are also prone to endogeneity. Surveying previous work, Davidsson, Achtenhagen and Naldi (2006) concluded that a link between profitability and growth is still not well established. It is important

322

Performance

to distinguish, however, between trading profits and retained profits. The difference between them comprises directors’remuneration and taxation. Watson (1990) reported a weak relationship between employment growth and trading profits, but a much stronger relationship between employment growth and retained profits. This is consistent with the notion that retained profits are re-invested for expansion. Finally under II, average growth performance varies systematically by industry. For example, using data on 1,053 US entrepreneurs tracked over a three-year period, Cooper et al. (1991) recorded significantly below-average growth rates among ventures located in retail and personal services sectors. Foreman-Peck et al. (2006) reported significantly above-average growth rates for Welsh SMEs located in the financial services sector. Strategy variables (which come under III) are more commonly used by business studies researchers than by economists. Table 11.1 includes a selected set of relatively ‘objective’ determinants of this kind. These include strategies for accessing multiple sources of finance; use of formal information management processes (e.g. computerised accounts); business plans and use of external assistance. Despite occasional claims that business plans and planning in general are associated with superior venture performance (Shane, 2003, p. 223), Table 11.1 shows that the econometric evidence on this issue is mixed. Indeed, Bhide (2000) observed that 41 per cent of the Inc. 500 founders in his sample had no business plan at all at start-up. Bhide speculated that the expected costs of formal planning for these entrepreneurs outweighed the expected benefits. In contrast, other forms of external linkages appear to promote growth, including connections with other firms and outsourcing of product distribution (see Table 11.1). Franchising is widely regarded as a facilitator of rapid growth in industries where opportunities can be codified, especially at early stages of the venture life cycle (Martin, 1988; Michael, 1996). Investors sometimes balk at the size of funding requests from entrepreneurs wanting to ‘get big fast’, but franchisees can help entrepreneurs spread start-up costs and risks in an economical way.5 By making franchisees residual claimants and by including termination clauses, franchisors can reduce agency costs while enabling self-selection of able franchisee managers (Norton, 1988). A franchising strategy can therefore promote growth-oriented entrepreneurship. The findings relating to entrepreneurs’ strategies under III should be interpreted with considerable caution. Unusually promising ventures might adopt particular strategies to promote growth that would be entirely inappropriate for more run-of-the-mill firms (which probably constitute the majority of start-ups). Related to this point, it has been recognised that strategy has to fit the context to be effective (Parker and van Witteloostuijn, 2009). For example, for small NTBFs, the founding team’s technological experience is an important determinant of the success of a product differentiation strategy (Shrader and Siegel, 2007). If fit to context matters more generally, few generalisations about appropriate strategies may be possible. The problem is compounded by the possibility that several aspects of strategy could be important but hard to measure. These considerations might hold the key to explaining why strategy variables are generally found to have only weak effects in venture growth regressions (Storey, 1994a).

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In a lively descriptive account, Bhide (2000) discusses some of the hard-to-measure determinants