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  1. 1. URN 07/1314 Exploring gender differentials in access to business finance – an econometric analysis of survey data December 2006 Final Report Report prepared by Stephen Roper, Nigel Driffield, Vania Sena, Dolores Añon Higon and Jonathan Scott (ESG Associates) for the Small Business Service. Contact details: s.roper@aston.ac.uk
  2. 2. Contents URN 07/1314.............................................................................................................................................1 EXECUTIVE SUMMARY......................................................................................................................4 CHAPTER 1 - AIMS AND OBJECTIVES ..........................................................................................6 1.1 INTRODUCTION ....................................................................................................................................6 1.2 ORGANISATION OF THE REPORT.............................................................................................................6 CHAPTER 2 - A SELECTIVE OVERVIEW OF PREVIOUS EVIDENCE.....................................8 2.1 INTRODUCTION ....................................................................................................................................8 2.2 ISSUES IN SMALL BUSINESS FINANCE......................................................................................................8 2.3 FINANCING START-UP – THE EVIDENCE ON GENDER DIFFERENCES................................................................9 2.4 KEY THEMES ...................................................................................................................................11 CHAPTER 3 - ACCESS TO FINANCE AND BUSINESS START-UP...........................................13 3.1 INTRODUCTION...................................................................................................................................13 3.2 EVIDENCE FROM GEM 2004..............................................................................................................15 3.2.1 Descriptive Analysis..............................................................................................................15 3.2.2 Perceived financial barriers to business start-up ................................................................18 3.2.3 Business Start-up...................................................................................................................18 3.2.4 Reasons for perceived financial barriers .............................................................................19 3.3 EVIDENCE FROM THE HSE .................................................................................................................21 3.3.1 Descriptive Analysis .............................................................................................................21 3.3.2 Start-up and the Financial Sourcing Decision......................................................................24 3.3.3 Start-up and Difficulty Obtaining Finance...........................................................................24 3.3.4 Key Points.............................................................................................................................25 3.4 CONCLUDING REMARKS .....................................................................................................................25 CHAPTER 4 - ACCESS TO FINANCE BY ESTABLISHED FIRMS............................................26 4.1 INTRODUCTION ..................................................................................................................................26 4.2 EVIDENCE FROM THE ASBS...............................................................................................................28 4.2.1 Supply Side Models ..............................................................................................................28 4.2.2 The Demand for finance .......................................................................................................31 4.2.3 Concluding Points ................................................................................................................31 4.3 EVIDENCE FROM THE UKSMEF 2004 DATABASE................................................................................32 4.3.1 Descriptive Analysis..............................................................................................................32 4.3.2. Modelling the “Supply Side”: Barriers to Finance.............................................................34 4.3.3 Modelling the Demand for Finance across different groups................................................35 4.3.4 Summary of Key Points.........................................................................................................36 4.4 CONCLUSIONS FROM THE FIRM BASED ANALYSIS......................................................................................36 CHAPTER 5 – CONCLUSIONS..........................................................................................................38 5.1 INTRODUCTION ..................................................................................................................................38 5.2 GENDER EFFECTS ON ACCESSING FINANCE AND ITS IMPACT ON BUSINESS START-UP ......................................38 5.3 GENDER EFFECTS ON ACCESS TO FINANCE BY EXISTING FIRMS....................................................................39 5.4 ISSUES FOR FUTURE RESEARCH ............................................................................................................40 ANNEX 1: MODELLING ACCESS TO FINANCE AND BUSINESS START-UP ......................41 A1.1 INTRODUCTION ...............................................................................................................................41 A1.2 MODELLING WITH GEM 2004........................................................................................................41 A1.3 MODELLING WITH THE HSE.............................................................................................................44 ANNEX 2: MODELLING THE ACCESS TO FINANCE BY ESTABLISHED FIRMS...............47 A2.1 INTRODUCTION ...............................................................................................................................47 A2.2 MODELLING USING THE ASBS.......................................................................................................47 ............................................................................................................................................................64 A2.3 MODELLING USING THE UKSMEF 2004.........................................................................................65 ANNEX 3: VARIABLE DEFINITIONS AND CONSTRUCTION .................................................70 A3.1 GEM 2004..................................................................................................................................70 A3.2 HSE 2003 ..................................................................................................................................72 2
  3. 3. A3.3 ANNUAL SMALL BUSINESS SURVEY 2003 AND 2004..........................................................................73 A3.4 VARIABLES USED IN THE UKSMEF 2004 ANALYSIS........................................................................74 REFERENCES.......................................................................................................................................76 3
  4. 4. Executive Summary Background 1 The availability of finance for business start-up and expansion has attracted much attention over recent years and stimulated the development of a number of focussed policy initiatives. A particular focus of recent initiatives has been to try and support women’s enterprise given consistent evidence from the Global Enterprise Monitor (GEM) studies and other sources about lower levels of involvement in enterprise among women (Section 1.1). 2 Previous research in this area has emphasised the complexity of the issues relating to business finance and particularly the difficulty of trying to isolate and characterise any specific gender effects. In this study we use an econometric approach to analyse gender differences in access to finance in four pre-existing databases used by the Small Business Service (Section 1.1). 3 Two of these databases – the Global Entrepreneurship Monitor 2004 and Household Survey for Entrepreneurship (HSE) 2003 – are surveys of individuals and provide evidence on the role of financial constraints on business start-up. The two other surveys – the Annual Small Business Survey (ASBS) 2003 and 2004) and the UK Survey of SME Finances (UKSMEF) 2004 – provide information on access to finance by existing businesses. Each of these surveys covers the whole of the UK with the exception of the Household Survey of Entrepreneurship which covers England only (Section 1.1). Gender effects on accessing finance and business start-up 4 Both the GEM 2004 data and the HSE provide evidence of a negative, gender- specific, finance effect which would tend to reduce start-up rates among women. The GEM dataset suggests that women are around 7.5 percentage points more likely to perceive financial barriers to business start-up than men. This in turn works to reduce start-up rates for women by 1.7-3.8 percentage points depending on the start-up indicator being used. Being a woman has an additional direct effect on each of our start-up indicators, that is not linked to financial barriers (Section 3.2) 5 Because of the structure of the data, our HSE analysis focuses on a narrower group of the population than our GEM analysis and relates specifically to those classified as either ‘Thinkers’ and ‘Doers’, i.e. those who are either engaged in or thinking about undertaking some enterprise activity. Within this group we find no evidence that women face any increased difficulty in obtaining start-up finance. We do find evidence, however, that women are less likely to seek external finance for business start-up. (Section 3.3) 6 Taking these points together suggests that women in the general population perceive stronger financial barriers to business start-up than men, and this may be discouraging them from seeking external financial support for business start-up. We find no evidence, however, that where women do seek finance for start-up they are any less likely to obtain it than men. This is suggestive of a dominant 4
  5. 5. demand rather than supply side effect. In either case, however, the effect is similar – that gender differences in access to finance are reducing start-up rates among women (Section 3.4). Gender effects on the access to finance by existing firms 7 Our analysis of the ASBS for 2003 and 2004 and the UKSMEF for 2004 focuses separately on the supply and demand side of the financing relationship for existing businesses (Section 4.1) 8 In terms of the supply side our results are somewhat contradictory with evidence from the ASBS highlighting some negative gender effects but the UKSMEF suggesting that women-led businesses are less likely than men to be discouraged in their search for business finance (Section 4.2) 9 More specifically, the ASBS suggests that women-led businesses are around 2 percentage points more likely to have difficulty raising finance and also 2 percentage points more likely to find it impossible to raise the finance they are seeking. The UKSMEF 2004 data on the other hand suggests that women-led businesses encounter no difference in rejection rates compared to male-led businesses and are less likely to face discouragement when applying for external finance (Section 4.3) 10 In terms of the demand side we see a more consistent picture, however, and find evidence that broadly supports that identified earlier from the GEM and HSE analysis (Section 4.3) 11 The ASBS suggests that women-led businesses are less likely to seek external finance both on a one-off and multiple basis. The UKSMEF provides no support for this general proposition, but does suggest some more specific effects with women-led businesses 17 percentage points less likely to apply for a commercial loan or mortgage from banks or other financial institutions. These demand-side results provide one possible reason for the differences in supply side results suggested by the supply-side analysis (Section 4.4). Acknowledgements We are grateful to the project Steering Committee for their comments and help in progressing this study. Some of the data for this study were provided by the Global Entrepreneurship Monitor UK (GEM UK), which is part of the Global Entrepreneurship Monitor consortium. Names of the members of national teams, the global coordination team, and the financial sponsors are published in the Global Entrepreneurship Monitor 2005 Report, which can be downloaded at www.gemconsortium.org. We thank all the researchers and their financial supporters who made this research possible. Although data used in this work are collected by GEM UK, the analysis and interpretation presented here are the sole responsibility of the authors. 5
  6. 6. Chapter 1 - Aims and Objectives 1.1 Introduction 1.1 The availability of finance for business start-up and expansion has attracted much attention over recent years and stimulated the development of a number of focussed policy initiatives. A particular focus of recent initiatives has been to try to support women’s enterprise given consistent evidence from the Global Entrepreneurship Monitor (GEM) studies and other sources about lower levels of involvement in enterprise among women. 1.2 Previous research in this area has emphasised the complexity of the issues relating to business finance and particularly the difficulty of trying to isolate and characterise any specific gender effects. Is it the case, for example, that lending institutions discriminate either deliberately or unwittingly against entrepreneurs who are women? Or, are women entrepreneurs simply more reluctant to seek business finance? Other factors linked to background or experience may also be important in shaping men’s and women’s access to finance. 1.3 In this study we use an econometric approach to analyse gender differences in access to finance in four pre-existing databases used by the Small Business Service. Two of these databases – the Global Entrepreneurship Monitor 2004 and Household Survey for Entrepreneurship (HSE) 2003 – are surveys of individuals and provide evidence on the role of financial constraints on business start-up. Here there are two main questions. First, how important is gender in shaping individuals access to finance for business start-up? And, secondly, how important are any such financial constraints on subsequent start-up? One important difference between GEM and the HSE is that GEM covers the whole UK while the HSE is England only. The two other surveys (both of which cover the UK) – the Annual Small Business Survey (ASBS) 2003 and 2004 and the UK Survey of SME Finances (UKSMEF) 2004 – provide information on access to finance by existing firms. Here we are interested in whether women-led firms are more likely to experience constraints in obtaining finance, distinguishing between supply and demand side influences. 1.4 Our focus here therefore is not in providing a detailed description of the survey results themselves. This has already been done effectively in the relevant survey reports. Our objective instead is to explore whether adopting an econometric approach to the survey analysis can shed additional light on the relationship between gender and access to finance, taking into account as wide a range of other factors as possible. 1.2 Organisation of the Report 1.5 The remainder of the report is organised as follows. Chapter 2, immediately following this, provides a brief overview of previous studies relating to small
  7. 7. business finance and, more specifically, the role of gender in shaping individuals’ and firms’ access to business finance. 1.6 The main analytical section of the report is then divided into two Chapters. Chapter 3 focuses on the two surveys of individuals and examines the impact of gender on finance for business start-up and then start-up itself. This emphasises both the general significance of gender in terms of perceptions of financial barriers to business start-up, but also the effect of such perceived barriers on start- up (GEM). Other survey data (HSE) emphasises the potential importance of demand side effects, with women generally less willing to seek external finance. 1.7 Chapter 4 then focuses on the demand for finance from existing businesses and investigates whether women-led businesses face particular barriers in accessing business finance. Demand and supply side effects are investigated separately with both surveys suggesting that women-led businesses are often reluctant to seek external finance, an effect which seems particularly strong for commercial loans or mortgages from banks or other financial institutions. Our more general results on barriers to accessing finance for existing women-led businesses are more ambiguous, however, with the ASBS suggesting some difficulties and the UKSMEF suggesting that women-led businesses may actually face less discouragement in their search for external finance. Both results, however, are likely to reflect the reluctance of some women-led businesses to seek bank finance. 1.8 Chapter 5 draws the results of the study together and suggests some possible policy implications and directions for future research and survey development. 7
  8. 8. Chapter 2 - A Selective Overview of Previous Evidence 2.1 Introduction 2.1 In this Chapter we provide a selective review of recent academic and policy related research on access to finance generally for smaller firms and, more specifically, gender differences. Our objective here is not to provide a comprehensive review of the relevant literatures but to highlight the key issues which have emerged from previous studies. These provide the basis for the inclusion of specific variables in the models estimated later in the report and also shape our discussion of access to finance by men and women. 2.2 Section 2.2 reviews some recent literature on small business finance in general, emphasising the range of factors which can influence individuals’ and firms’ access to finance. Section 2.3 focuses more specifically on studies of gender differences in access to finance by start-up businesses and other small firms. Section 2.4 draws out the key themes from the literature review. 2.2 Issues in Small Business Finance 2.3 Financial constraints are of necessity a major issue for small firms and start-up companies but once firms are established it is possible to over-emphasise the importance of financial constraints. The Government’s Policy Action Team (PAT 14) articulated the difficulties faced by some businesses in accessing bank finance – due primarily to their age, experience, track record or business structure. However, access to finance is often over-shadowed by other problems when businesses actually start up, with finance cited as a problem by fewer than two per cent of respondents to the NatWest Small Business Research Team’s quarterly survey. Kotey (1999) is helpful, however, in emphasising that business growth can be constrained and failure can be caused by financing constraints, and that there are both supply and demand side factors involved in shaping small firms’ access to finance. 2.4 On the supply side, Cosh and Hughes (2003) found that loans from banks provide the funding for around two thirds of UK businesses and the largest source for over 25 per cent of firms. By the end of 2004, term lending by banks had grown to nearly £35bn (16 per cent growth in 2004) and overdraft lending had grown to nearly £10bn (9 per cent growth) (British Bankers’ Association 2004). However, Kotey (1999) notes that banks are less likely to lend long-term to SMEs due to risk (which is in itself caused by SMEs “lack[ing] a track record of performance on the basis of which their credit rating could be assessed”) and cost (“administrative costs, potential interest income and to the risk of default”) and on account of collateral unavailability1. 1 More generalised literature includes articles such as those considering lending by ‘de novo’ banks in comparison with incumbent banks in 1987-1994 (Goldberg and White 1998), on relationships between SMEs and banks (Meyer 1998; Jones 2001; Strahan and Weston 1998) and with specific consideration of benefits and costs, including barriers (Bornheim and Herbeck 1998).
  9. 9. 2.5 On the demand side, Fraser (2005) reported that some 2.9m SMEs (80 per cent) have used external finance in the last three years and that the main sources of finance for start ups are personal savings (65 per cent), bank loan (10 per cent) and friends/family loan (6 per cent). He also found that approximately 900,000 businesses (24 per cent) use term loans and that obtaining finance is reported as a major problem at start up by some 10 per cent of businesses. This generalised view of the difficulty in obtaining finance for start-up, however, reflects a number of issues relating both to start-ups’ ability to attract finance as well as their willingness to consider different types of business financing. 2.6 Research by Hamilton and Fox (1998) provides insight into the financing preferences of entrepreneurs and: “supports the view that the financing decisions of small firm owners are based on a demand-side pecking order of finance types. The resulting financial structures reflect a desire to minimise intrusion into the firms and are not entirely the consequence of persistent deficiencies in the provision of finance to small firms.” Essentially similar evidence is provided by Howorth (2001) whose evidence suggests that entrepreneurs tend to seek finance first from their own resources, and friends and families, and then from other sources such as banks. Indeed, the money from family and friends is often essential (and often regarded as quasi-equity by the banks) to unlock support from commercial institutions. Thus the issue of entrepreneurs desiring maintenance of the control of their business is also a highly relevant consideration when thinking about barriers to access to bank finance for entrepreneurs. 2.7 More generally, Winker (1999) examined the causes of finance constraints and found these to be influenced by firm age and size. Cressy and Toivanen (2001) also emphasise that, “better borrowers get larger loans and lower interest rates; collateral provision and loan size reduce the interest rate paid … the bank is shown to use qualitative as well as quantitative information in the structuring of loan contracts to small businesses.” A somewhat contrary view is emphasised by Chandler and Hanks (1998), however, who note that: “there is some feeling among scholars that competent founders will find a way of coming up with necessary resources and capital”. 2.3 Financing Start-up – the evidence on gender differences 2.8 A useful starting point here is the review of the literature on women’s entrepreneurship by Carter et al. (2001). They start by reflecting the general tenor of the literature on small business start-up, i.e.: ‘… a preoccupation with start-up permeates the female entrepreneurship literature, but is particularly noticeable within the more descriptive analyses. Within this literature there is a widespread and generally unquestioned acceptance that start-up is more difficult for women. A key debate, however, is whether the barriers encountered by women at start-up have a long-term effect on business performance or whether these constraints dissipate after start up has been successfully negotiated’. (p29) 9
  10. 10. 2.9 The same general point is emphasised by other writers. Marlow and Watson (2004), for example,2 argue that: “female owned enterprises are more likely to be undercapitalised in a variety of forms from the outset, locate in crowded sectors and so under perform over time” Another particularly revealing quotation from other authors is that: “Not only does policy appear to concentrate on areas traditionally associated with men in self employment, but the systems of finance and advice are also firmly oriented towards them, leaving women to face a range of barriers when engaging with self employment” (Warren-Smith and Jackson 2004).3 2.10 More recent reports published by the Small Business Service (SBS) emphasise different aspects of the finance issue. The Annual Survey of Small Business (ASBS) for 2004, for example, suggests that obtaining finance was an obstacle for 15.5 per cent of all small firms but and for 16.2 per cent of women-led enterprises4. The UK Survey of SME Finances (UKSMEF) emphasises another gender related issue, noting that “female-owned businesses pay significantly higher margins on term loans than male-owned businesses (2.9 versus 1.9 percentage points over Base)” (p18). 2.11 Carter et al. (2001) stress, however, that access to finance is only one aspect of the wider set of issues which surround start-up by women. They identify a number of studies, for example, that focus on finance for start-up and 'the social systems that endowed women with a lack of business credibility.' In particular, they quote Hisrich and Brush (1986: 17), who note that there is a perception that women are “not as serious as men about business”: “For a woman entrepreneur who lacks experience in executive management, has had limited financial responsibilities, and proposes a non-proprietary product, the task of persuading a loan officer to lend start-up capital is not an easy one. As a result, a woman must often have her husband co-sign a note, seek a co-owner, or use personal assets or savings. Many women entrepreneurs feel strongly that they have been discriminated against in this financial area”. 2.12 The empirical evidence cited in Carter et al. (2001) on the actual importance of barriers to finance for women is conflicting, however, although there is a general feeling that women may be disadvantaged in their ability to raise start up finance (Schwartz, 1976; Carter and Cannon, 1992; Johnson and Storey, 1993; Koper, 1993; Van Auken et al, 1993; Carter and Rosa, 1998). Carter and Rosa (1998), for example, based on survey of 600 firms, equally split by gender, found that there are: “quantifiable gender differences in certain areas of business financing, although intra-sectoral similarities demonstrate that gender is only one of a number of variables that affect the financing process.” 2.13 Four specific themes emerge from the literature identified by Carter et al (2001) which might provide an explanation for these difficulties: 2 Marlow, S. and Watson, E. (2003) Safety Net or Ties that Bind? Welfare Benefits, Gender and Self Employment, paper presented to the 26th Institute of Small Business Affairs National Small Firms Conference. 3 Warren-Smith, I. and Jackson, C. (2004) Women Creating Wealth Through Rural Enterprise, International Journal of Entrepreneurial Behaviour & Research, vol. 10, no. 10, pp. 369-383. 4 SBS (2004) Annual Small Business Survey: Executive Summary, SBS: Sheffield. 10
  11. 11. 1. Collateral – the financial guarantees required for external financing may be beyond the scope of most women’s personal assets and credit track record (Carter et al 2001 – they refer to Hisrich and Brush, 1986; Riding and Swift, 1990). Verheul and Thurik (2001), for example, focussed on 2,000 entrepreneurial start-ups in 1994 in Holland (25 per cent of which were women) and concluded that women had less capital when starting the business but that there was no difference in the type of capital and that “the proportion of equity and debt capital (bank loans) in the businesses of women entrepreneurs is the same as in those of their male counterparts.” 2. Networks – “finance for the ongoing business may be less readily available for women-owned firms than it is for men-owned enterprises, largely due to women’s inability to penetrate informal financial networks (Olm et al, 1988; Aldrich, 1989; Greene et al, 2001).” (Carter et al 2001) 3. Discrimination – “female entrepreneurs’ relationships with bankers may suffer because of sexual stereotyping and discrimination (Hisrich and Brush, 1986; Buttner and Rosen, 1988, 1989)” (Carter et al 2001). Ennew and McKechnie (1998), for example, suggest that, “discrimination occurs amongst lenders at a more unconscious level”. 4. Financing preferences – it may be that the financial preferences of women and men owner-managers are different. However a recent study, drawing upon the results from a 400-firm telephone survey by Barclays Bank, found that "Gender appears to make little difference to the choice of finance source utilised – most settle for personal savings, but there is little difference across each source" (Irwin and Scott 2006). 2.4 Key Themes 2.14 The review by Carter et al. (2001) and other studies have emphasised the potential importance for women’s start-up rates and business success of access to finance. The quantitative evidence to date, however, on the real impact of financial barriers to start-up is relatively limited and somewhat conflicting. Some key themes do emerge, however, from the literature and these underpin our empirical analysis in subsequent chapters. These are: • Access to finance is a key factor in shaping start-up rates and business development but needs to be seen in the context of other factors which may also be shaping the development of the business. • Both demand and supply-side factors may be influencing perceived financial constraints reflecting individuals’ perceptions and preferences and the types and amounts of finance being sought. • Issues of collateral and background may be important in shaping the willingness of banks or other organisations to lend to individuals or firms, and the preferences of individuals. • Particularly in terms of business start-up there is a strong process dynamic in the financing process, with availability of finance conditioning the probability of business start-up. 11
  12. 12. 2.15 These themes are reflected in our methodological approach which is the subject of Chapter 3. 12
  13. 13. Chapter 3 - Access to Finance and Business Start-up 3.1 Introduction 3.1 In this chapter we consider whether there are gender differences in the perceived or actual financial constraints on individuals, and the impact of these differences on business start-up. Our analysis is based on evidence from two large-scale household surveys intended to represent the overall population of households in the UK: the 2004 Global Entrepreneurship Monitor (GEM) dataset which includes 24,006 respondents and the 2003 Household Survey of Entrepreneurship (HSE) which includes 10,002 respondents in England5. As outlined in Chapter 2, our approach is in two stages: first we consider the factors which determine perceived or actual financial barriers to business start- up, and secondly the impact of this on business start-up itself. 3.2 Comparison between the GEM and HSE results is complicated as both surveys use slightly different questions relating to both potential financial barriers to business start-up as well as the notion of business start-up itself. In the GEM 2004 study, we consider individuals’ perceived financial barriers to business start-up using the responses to a question posed to all respondents: ‘Excluding money from family and friends, would a lack of external funding prevent you from starting up a business?’. This provides a straightforward indication of the perceived lack of business finance and potential psychological or motivational barriers that this might induce to business start-up. In the population of respondents, women were significantly more likely to perceive such financial barriers to business start-up than men (Table 3.1). 3.3 In terms of individuals’ participation in business start-up, GEM 2004 provides three indicators. These are individuals’ responses to: 1. Start-up: Are you, alone or with others, currently trying to start a new business, including any type of self-employment or selling any goods or services to others? 2. Running Business: You are, alone or with others, currently the owner of a business you help manage; or you are self-employed or selling any goods or services to others 3. Expected Start-up: You are, alone or with others, expecting to start a new business, including any type of self-employment, within the next three years 3.4 As with individuals’ perceived financial barriers to business start-up, the most consistent differences of responses here relate to gender, with the proportion of women engaged in each type of business start-up activity significantly lower than that of men (Table 3.1). 5 See the Global Entrepreneurship Monitor UK (2004) report and Small Business Service (2004) for detailed descriptions of the two surveys.
  14. 14. Table 3.1: Perceived Shortages of Finance and Business Start-up Indicators by Gender Women Men per cent per cent Lack of funding preventing starting up a business 64.1 57.3 Involved in business start-up 3.1 5.9 Current business owner 6.7 15.8 Expected business start-up 7.2 11.8 Notes: Figures in bold are significantly different at the 5 per cent level. See the data annex for variable definitions etc. Source: GEM 2004 3.5 The HSE adopts a different approach to both the access to finance for business start-up and the issue of business start-up itself. Here, respondents are divided into three groups according to their level of involvement in entrepreneurial activity with each group being asked different types of questions on the degree of access to external finance and whether they have encountered financial constraints. In the HSE the groups are defined as follows: Thinkers are ‘those who are thinking about becoming entrepreneurs’; Doers are ‘those who are already entrepreneurs through running their own business or by being self- employed’; and Avoiders are ‘those who are neither currently engaged in entrepreneurial activity nor thinking about doing so’. Reflecting the overall level of involvement in entrepreneurial activity from the GEM survey (Table 3.1), 76 per cent of the HSE sample was classified as Avoiders, 11 per cent as Thinkers and 13 per cent as Doers. 3.6 In terms of access to finance the questions asked for each group of HSE respondents were as follows: • For Thinkers: ‘And have you tried to obtain any finance for this new business in the past 12 months?’ and ‘did you have any difficulties in obtaining this finance from the first source you approached?’ • For Doers: ‘In the past year have you tried to obtain finance for your business?’ and ‘Did you have any difficulties in obtaining this finance?’ • For Avoiders: ‘And which two would you say are the biggest barriers to you starting a business or becoming self-employed?’6. 3.7 These questions reflect the access to external finance, as well as the degree of financial constraint Doers and Thinkers experience, along with the impact this may have on business start-up – reflected in individuals’ status as either a Doer or Thinker. This is not true for the Avoiders, however, where the different structure of the HSE survey questions makes comparison with Thinkers and Doers more difficult. In our analysis we therefore focus 6 For Thinkers these were survey questions 13 and 16, for Doers questions 40 and 44 and for Avoiders question 50. 14
  15. 15. primarily on the combined group of Thinkers and Doers and question whether difficulties in accessing finance influence the probability of becoming a Doer rather than a Thinker. Table 3.2: Access to external funding – Thinkers and Doers by gender (Percentage of all Thinkers and Doers) Thinkers Doers Men Women Men Women Did try to obtain external finance ( per cent)? 6.8 4.3 15.6 13.5 Difficulties in obtaining Finance (per cent)? 2.5 1.4 3.8 2.1 Source: HSE 2003 3.8 Table 3.2 summarises the proportions of Thinkers and Doers seeking external finance and experiencing difficulties in accessing this finance (both proportions are expressed relative to the population of all Thinkers or Doers). The proportion of Thinkers trying to get access to external finance is quite small (4-6 per cent) compared to that among the Doers (13-15 per cent). Interestingly, however, a smaller overall proportion of the population of women Thinkers and Doers reported difficulties in obtaining finance than that among men although this may reflect the smaller proportion of women who actually sought finance as well as any difficulties they encountered. 3.9 The rest of this chapter is organised as follows: • Section 3.2 summarises the evidence from the GEM 2004 survey reflecting both individuals’ perceived financial barriers to start-up and the impact of these perceived financial barriers on start-up itself. • Section 3.3 then focuses on the HSE survey, and the impact of gender on access to external finance first and then on the status of individuals as either Doers or Thinkers • Section 3.4 summarises the main results and gives an indication of potential caveats and policy conclusions. Modelling results underlying the key points made in the main text are reported in Annex 1. 3.2 Evidence from GEM 2004 3.2.1 Descriptive Analysis 3.10 As indicated earlier the GEM data provides an indication of the proportion of the UK adult population that perceive financial barriers to business start-up and the impact of this on different aspects of start-up behaviour. In this section we examine the impact of gender on both the perception of financial barriers and start-up. As a prelude to the multivariate analysis, however, it is beneficial 15
  16. 16. to have an understanding of the basic characteristics of the underlying GEM data. Table 3.3 summarises a number of the key characteristics of the sample of GEM respondents, which are representative of the whole working age population, by gender: • There is no significant distinction between the regional composition of the sample of GEM respondents between men and women. Four regions (South East, West Midlands, London and the Eastern region) account for around two-thirds of the national sample. • A higher proportion of men respondents have degrees (36.7 per cent), while ‘A’ levels and GCSEs are more common among women. Lower levels of qualification are equally common. • Women respondents were more likely to be in lower quartiles of the national distribution of household income. • Men respondents were more likely to be working full-time and to be either self-employed or an employer than women. • Finally, men were more likely than women to have received enterprise training and participated in work experience programmes. The suggestion is that in the working age population men responding to the GEM survey were more likely to be highly qualified; more likely to have a stronger financial profile (i.e. are in the upper quartiles of the distribution of household incomes); and more likely to have benefited from relevant working and training experiences than women respondents. Each of these factors is likely to have a positive effect on business start-up aside from any underlying gender differences. 16
  17. 17. Table 3.3: Sample Characteristics: By Gender Women Men per cent per cent A. Home Region South West 9.3 9.5 South East 25.0 25.9 London 21.7 21.7 Eastern 11.4 11.3 Wales 1.7 1.6 West Midlands 11.1 10.4 East Midlands 2.3 2.4 Yorkshire and the Humber 3.3 3.2 North West 5.9 6.0 North East 2.6 2.4 Scotland 5.5 5.2 Northern Ireland 0.2 0.2 B. Highest Educational Level Degree or higher 31.6 36.7 ‘A’ Levels 22.4 20.4 GCSE or equivalent 25.7 22.8 Other vocational quals. 9.1 8.8 No formal qualifications 11.2 11.3 C. National Household Income Distribution Lower quartile 20.5 14.0 2nd quartile 25.3 24.8 3rd quartile 24.6 28.1 4th quartile 29.5 33.1 D. Age Age in years 40.5 40.3 E. Working Status Full-time (30 or more hours) 46.4 78.6 Part-time (8-29 hours) 25.3 6.2 Not working (8 or less hours) 28.3 15.2 F. Employment Status Employee 90.9 80.7 Self-employed 6.0 12.8 Employer 3.1 6.5 G. Enterprise Training and Work Experience Enterprise training at school 11.4 13.7 Enterprise training at college/university 15.8 21.4 Work experience at school 34.1 35.0 Work experience at college/university 12.0 14.0 Notes: Figures in bold are significantly different at the 5 per cent level. See the data annex for variable definitions etc. Source: GEM 2004 17
  18. 18. 3.2.2 Perceived financial barriers to business start-up 3.11 Our main aim in this section is to see whether, when controlling for individuals’ background characteristics, gender influences the perceived financial barriers to business start-up. Our approach is based on a series of probit models of the probability of perceiving financial barriers to business start-up (Table A1.1). Significant coefficients are in bold type in the table and two alternative formulations of the model are presented dropping the insignificant variable ‘Enterprise Training at school’ in the second model. 3.12 Our results here are straightforward, consistent and statistically very strong - even adjusting for a range of background characteristics. Being a woman increases the probability that an individual will perceive financial barriers to business start-up by 7.5 percentage points. 3.13 Our analysis also suggests a number of other factors which prove important in determining the probability that an individual will perceive financial barriers to business start-up. The most consistent effects were: • Respondents in the Eastern region were 3-7 pp less likely to perceive financial barriers to business start-up. No other regional differences were statistically significant. • Individuals with a degree were more likely to perceive financial barriers to business start-up than those with lower level qualifications (by 3-8 pp). • Those in higher income households were less likely to perceive financial barriers to business start-up. • Older individuals were less likely to perceive financial barriers to business start-up. • Those working part-time or not working were less likely to perceive financial barriers to business start-up than the reference group (i.e. those in full-time employment). In general terms therefore the GEM 2004 data provide considerable support for the notion that women may perceive stronger financial barriers to business start-up than men. 3.2.3 Business Start-up 3.14 The aim of this section is to investigate the potential role of perceived financial barriers to business start-up on business start-up itself. If such perceived financial barriers to business start-up are important in influencing business start-up, then the fact that perceived financial barriers to business start-up are concentrated among women may be contributing to lower start-up rates among women. If perceived financial barriers to business start-up are not a factor in shaping business start-up then differential access to finance is likely to be less important in explaining lower start-up rates among women (e.g. Table 3.1). 18
  19. 19. 3.15 Some significant statistical and econometric issues arise here and these are outlined in Section A1.2. Our preferred models of gender effects on business start-up activity however, are single equation Probit models (Table A1.3) . These suggest three main conclusions in relation to gender and perceived financial barriers. • Women are less likely to be involved in start-up activity, running a business and expected start-up activity than men. Start-up rates for women are reduced 1.7 pp lower than that for men, with expected start-up rates reduced by 3.5 pp (Table 3.6). • Perceived financial barriers to business start-up have a negative effect on business start-up (1.3 pp) and the probability of expected start-up (3.8 pp). The negative finance effect on start-up is around the same size as the direct gender effect. • The probability that an individual is running a business is not significantly influenced by perceived financial barriers to business start-up (Table 3.6). 3.16 Other factors also prove important in increasing business start-up rates with strong and consistently positive effects from: having a background of self employment or as an employer and experiencing enterprise training at college or university. No factors have a consistent negative effect other than being a woman. 3.17 In general therefore the GEM data suggests that gender has both direct and indirect effects on business start-up rates, with the negative effects operating through perceived financial barriers to start-up. Women are more likely to perceive financial barriers to start-up and these are likely to reduce start-up rates. In addition, there is a direct gender effect on start-up rates even allowing for education, location and personal characteristics. 3.2.4 Reasons for perceived financial barriers 3.18 GEM 2004 also provides some information on individuals’ own reasoning for why they did not obtain finance and their justification for this lack of success. This is interesting as it may inform our understanding of why perceived financial barriers are greater among potential women entrepreneurs. Sample sizes here are relatively small, however, as they relate only to a sub-group of those involved in enterprise activity in the survey. It is not possible therefore to model the effects of gender on these perceptions of success (or to identify statistically significant differences) but descriptive data is given in Table 3.4. Table 3.4: Percentage indicating the reasons for their lack of success in obtaining finance Women Men All per cent per cent per cent Not investor ready 15.6 17.1 16.6 Nature of the business 32.0 33.8 33.3 Inadequacies in the 15.7 16.2 16.1 19
  20. 20. business plan Business too small 23.9 31.7 29.1 Fear of debt 21.2 24.3 23.3 Unwillingness to share 17.8 14.0 15.2 ownership Cost of finance too high 34.6 27.0 29.5 Weak management team 8.8 8.7 8.7 3.19 There is a broad similarity between the reasons given by women and men for their lack of success in gaining funding with the nature of the business, business size and the cost of finance predominating. Some more subtle differences are evident however, with women emphasising the cost of finance and males suggesting that business size was a more important factor in their failure to gain financial support. 3.2.5 Summary of key points 3.20 The GEM 2004 data provides a comprehensive database within which the impact of perceived financial barriers to business start-up can be assessed. Our key finding is that being a woman impacts on start-up rates both indirectly and directly. 3.21 First, being a woman increases the probability than an individual will perceive financial barriers to business start-up by 7.5 pp with this, in turn, reducing start-up rates by 1.7-3.8 pp. Being female also has an additional direct effect on each of our start-up indicators (Table A1.3). These results derived from the models allow us to decompose the difference between men’s and women’s start-up rates into a direct ‘gender’ effect, an indirect effect due to the effect of gender on perceived financial barriers and a ‘residual’ or unexplained effect. The relative sizes of these effects then provide an indication of the importance of the overall finance effect. 3.22 Table 3.8 summarises the results for the three start-up indicators considered earlier. In the case of start-up, for example, the start-up rate for males is 2.8 percentage points higher than that for women. Of this difference, the models suggest that 1.7 percentage points can be explained by the direct gender effect with a further 1.0 percentage point being explained by the indirect gender effect due to perceived financial barriers. Here 0.1 percentage point remains of the difference in start-up rates between genders remains unexplained. For the other two business activity indicators the impact of perceived financial barriers on the difference in start-up rates suggested by the models is somewhat smaller (Table 3.5). Table 3.5: Decomposition of differences in start-up rates Running Expected Start-up Business Start-up Start-up rates ( per cent) Women 3.1 6.7 7.2 Men 5.9 15.8 11.8 Difference -2.8 -9.1 -4.6 20
  21. 21. Contribution ( per cent) Direct gender effect -1.7 -1.0 -3.5 Indirect gender effect (via finance) -1.0 -0.1 -0.3 Other factors (residual) -0.1 -8.0 -0.8 Source: GEM 2004, for derivation see text 3.3 Evidence from the HSE 3.23 In this section we turn to the evidence from the Household Survey of Entrepreneurship 2003 (HSE). Here our analysis focuses on whether the probability of engaging in entrepreneurial activity is influenced by individuals’ access to finance, and whether the access to finance itself is influenced by gender. Detailed modelling results are presented in Section A1.3. 3.24 We consider two separate models reflecting different aspects of individual’s access to finance. First, reflecting the general thrust of the GEM analysis presented earlier we consider whether women are more likely to experience difficulties in obtaining finance for business start-up and whether this then influences start-up rates. Unlike the GEM analysis considered earlier, however, the HSE analysis is limited to a smaller sub-group of the population – those defined as Thinkers and Doers, and there is some difference in the question being asked7. In the GEM analysis the question relates to individuals’ general perception of financial barriers for business start-up. In the HSE the question is more specific and relates to whether individuals have actually experienced difficulty in obtaining finance for their business start-up in the last year. This contrast is important as it suggests the difference between perceived financial barriers to business start-up (GEM) and individuals’ actual experience (HSE). 3.25 A second issue considered here is whether gender actually influences individuals’ propensity to seek external finance. Women, for example, may be more reluctant to seek external financial support for their business start-up, and this may in turn influence business start-up rates. Here the question is whether this self-selection mechanism has a gender dimension and whether this then has a significant effect on start-up rates. 3.3.1 Descriptive Analysis 3.26 Before looking at the econometric results it is useful to draw some contrasts between the characteristics of the HSE sample of Thinkers and Doers by gender (Table 3.6): • Overall, male Thinkers appear to be more highly qualified than women Thinkers. For both sexes, the most common qualifications are either a degree or GCSEs. This is also true for Doers, however, among the Doers 7 In some earlier analyses the Thinkers group here has been subdivided into serious and ordinary thinkers. Here these groups are treated together. 21
  22. 22. who are men there is a large proportion that do not hold a formal qualification. • A large proportion of Thinkers are in the 25-44 age bracket; also this distribution does not appear to differ substantially between genders. Doers appear to be concentrated in the 25-64 age brackets and it does not appear there is any difference according to gender. • As for the employment status, a large proportion of men Thinkers appear to be currently in employment while women Thinkers are not. As for the Doers, the picture is more ambiguous with a relatively large proportion of both men and women Doers appear classified as not-employed. • Both men and women Thinkers appear to have some previous experience with self-employment. The proportion of men Doers with some previous experience in self-employment is higher than for women Doers; however, for both sexes, the proportion of Doers without any previous experience is quite high. • The proportion of Thinkers (belonging to both genders) with a positive attitude towards self-employment is large. The same applies to the Doers with the fraction of men Doers being quite substantial. • Thinkers appear to be mostly located in the Northern and Southern regions. The proportion of Thinkers located in the Midlands is small. However, for all the three areas, we can see that the fraction of men Thinkers is usually larger than that of women Thinkers. The same pattern applies to the Doers. Most Doers (men and women) are located in the North and the South of the country. Also, the proportion of men Doers is quite large. • A high proportion of Thinkers and Doers own their own house with higher home ownership proportions among male Thinkers and Doers in the sample. 22
  23. 23. Table 3.6: Basic Characteristics of Thinkers and Doers by Gender Thinkers Doers Men Women Men Women per cent per cent per cent per cent Educational Level Degree 1.75 1.34 2.61 1.55 A level 0.83 0.64 0.96 0.62 GCSE 1.07 0.78 1.43 0.96 Other 0.6 0.25 1.11 0.55 None 0.88 0.46 2.11 0.66 Age Group 16-18 years 0.26 0.16 0.09 0.02 19-24 years 0.61 0.3 0.24 0.11 25-34 years 1.44 1.1 1.25 0.58 35-44 years 1.42 1.09 2.39 1.39 45-54 years 0.89 0.57 2.21 1.27 55-64 years 0.51 0.25 2.04 0.97 Current Employment Status Not Employed 1.76 2 6.61 3.74 Employed 3.37 1.43 1.61 0.6 Previous experience Yes 3.26 2.48 3.72 2.21 No 1.87 0.99 4.5 2.13 Location North East 0.95 0.63 1.43 0.82 Yorks & Humber 0.22 0.2 0.42 0.16 East Midlands 0.33 0.15 0.46 0.2 East 0.15 0.1 0.32 0.2 London 0.72 0.45 0.86 0.44 South East 0.74 0.61 1.33 0.69 South West 0.41 0.21 0.72 0.36 West Midlands 0.33 0.26 0.45 0.26 North West 1.28 0.86 2.23 1.21 Total 5.13 3.47 8.22 4.34 Home ownership Yes 3.68 2.40 7.14 3.90 No 1.45 1.07 1.08 0.44 Total 5.13 3.47 8.22 4.34 3.27 These differences between the characteristics of men and women Thinkers and Doers emphasise the importance of a multivariate approach to modelling potential gender effects on access to finance and business start-up and this is the focus of the next two sections. 23
  24. 24. 3.3.2 Start-up and the Financial Sourcing Decision 3.28 Here we consider whether gender is important in individuals’ decisions whether to seek external funding for business start-up and whether this then affects the start-up decision. Two equations are estimated: the first equation models the self-selection mechanism where we try to understand which factors affect individuals’ decisions about whether to seek external finance; the second equation models the start-up decision and is estimated only on the sample that is selected through the self-selection mechanism (see Table A1.4). 3.29 Generally speaking, the probability of seeking external finance decreases if the individual is a woman. This implies that women tend to self-select themselves out of seeking external finance. Ethnic background also makes a significant difference to the probability of seeking finance with members of the white ethnic group more likely to seek external finance for their start-up activity. These differences in the probability of seeking external finance are important for business start-up, as there is a significant link between the decision to seek external finance and the start-up decision (suggested by the significant correlation coefficient). 3.30 For the subset of individuals that decide to seek external finance the second stage of the model highlights the factors which influence the probability of becoming a Doer rather than a thinker. (Percentage effects are suggested here by the marginal effects in Table A1.4). Prior experience and educational attainment both increase the probability of becoming a doer, with those who are non-employed less likely to become a Doer by around 3.7 pp. A positive attitude towards entrepreneurship also increases the probability of becoming a Doer by around 2.9 pp. In these models, the regional variables are not significant showing that there is no locational effect at work in either the self- selection mechanism or the start-up decision. This is not surprising in the light of the descriptive analysis. 3.3.3 Start-up and Difficulty Obtaining Finance 3.31 Now we consider whether the probability of becoming a Doer is affected by the respondent’s gender and conditioned by the applicant’s probability of experiencing difficulty in obtaining finance (that in turn is affected by an additional set of variables including gender). This involves the estimation of a two-stage model as before: in the first stage we model the respondent’s probability of experiencing difficulty obtaining finance and test whether this is affected by gender, ethnic background, location, education and whether or not (s)he own a house (that can be used as collateral); in the second stage we model the probability of becoming self-employed as a function of gender, the respondent’s foregone wage income (proxied by whether the respondent has a degree8), attitudes towards entrepreneurship, location and previous experience. Detailed results are presented in Table A1.5. 3.32 Neither gender or ethnicity have any significant impact on the probability of facing difficulties obtaining external finance although their interaction (i.e. Women*White) is marginally significant. This is perhaps not surprising, however, given the fact that the proportion of respondents claiming to have 8 The assumption is that the individuals with a degree have a potential for a high income and therefore the opportunity cost of becoming self-employed is higher. 24
  25. 25. been financially constrained is very small. Interestingly, the two significant variables in the first stage equation are the regional variables and the dummy variable on whether or not the respondent owns a house. This last result is as expected: financial constraints are exacerbated by the lack of collateral. In the second stage, the probability of being a Doer is not affected significantly by gender, but is influenced by previous experience and by the individual’s attitude towards entrepreneurship. Marginal effects are generally not significant showing that from this sample we cannot draw conclusions regarding the whole population. 3.3.4 Key Points 3.33 In this part of the report we have estimated two models of self-employment choice and financial constraints using the Household Survey of Entrepreneurship 2003. Our key findings are: • A self-selection mechanism is at work where women decide not to go for external finance (as they may expect to encounter substantial financial constraints) and so implicitly decide not to be self-employed. • Being a woman does not, however, increase the probability that individuals will experience difficulties in obtaining start-up finance. On the contrary these are compounded by the lack of collateral (i.e. not being a home-owner) and by location. 3.4 Concluding Remarks 3.34 Data from the GEM survey and the HSE provide largely complementary perspectives on access to finance by gender and its effect on business start-up. Data from the GEM survey suggest that in the general population women are more likely to perceive finance barriers for business start-up. The HSE data suggests this leads to a decision on the part of women not to seek external finance for business start-up. In both surveys this is also linked to lower start- up rates among women. 3.35 More surprising perhaps is that the HSE data suggest that among those individuals who do seek external finance for start-up, the likelihood of obtaining finance is no different for men and women. In other words, while women in the general population are more likely to perceive greater financial barriers to start-up, and tend to be less likely to seek external finance, the HSE suggests no evidence that such gender barriers to obtaining finance actually exist. Instead, difficulties in actually obtaining finance are much more likely to be linked to a lack of collateral or individual’s location. 3.36 In short therefore, both the HSE and the GEM data suggest that being a woman does have a negative effect on both access to finance and hence business start-up. The finance effect seems more strongly influenced by the demand side than the supply side, however: women are less likely to seek external finance but those who do are no more likely than men to face financial constraints. 25
  26. 26. Chapter 4 - Access to Finance by Established Firms 4.1 Introduction 4.1 In this Chapter we investigate the significance of gender – i.e. women-led firms9– in shaping the ability of existing firms to access external finance using data from the Annual Small Business Survey (2003 and 2004) and the UKSMEF 200410. Our general approach here recognises the fact that when seeking to link women’s leadership to the ability to raise finance, one has to make one or two (not necessarily mutually exclusive) assumptions. Firstly, that any variation in the ability of each gender to raise finance is purely a “supply side” phenomenon. That is, that each group is equally likely to identify a particular source of finance, and see it as desirable, but that the suppliers of that finance are either deliberately or subconsciously more likely to favour a particular gender. The second less restrictive assumption is that there are also “demand side” differences across genders, that is that certain groups are more likely to identify a particular type and source of finance, and subsequently the supply side differences either do or do not apply. This type of effect was evident in Chapter 4 where the evidence from the HSE suggested that women were less likely than men to seek external finance for business start-up. 4.2 With a simple reduced form quantity equation based on whether a business has or has not raised a particular type of finance one faces the well known identification problem, of being unable to distinguish between supply side and demand side effects. However, both the ASBS and UKSMEF ask more qualitative questions from which one can make certain inferences or judgements about the relative supply and demand side effects. Of course, in all of this analysis it is necessary to control for the other factors that will impact on the likelihood of a given business to raise a particular type of finance, and these will be discussed in due course. 4.3 In the ASBS the supply side will be evaluated using the responses to a question: ‘Did you have any difficulties in obtaining this finance from the first source you approached?’ with four possible responses being identified11. The demand side will be evaluated by analysing the answers to two questions, firstly relating to whether the firm had sought to raise finance, and secondly what for, and what type. The latter would include factors such as: ‘Over the next two to three years, do you aim to grow your business?’ And, ‘How much time in a typical week would you say your business spends on paperwork relating to complying with government regulations and taxes (hours)?’ In 9 Defined here as firms in which more than 50 per cent of the directors are women. 10 There were two years of the ASBS, and rather than pool the data, due to the slightly different structures of the surveys, the models were estimated separately for the two years. This is potentially revealing as there was a boost to the ethnicity sampling in 2003 but not in 2004. 11 These were: Yes, was unable to obtain any finance; Yes, obtained some but not all of the finance required; Yes, obtained all the finance required but with some problem; No, had no difficulties in obtaining finance. 26
  27. 27. 2003, of the 8693 respondents to the ASBS, 2330 had sought finance at least once; for ASBS 2004, from a sample of 7505 firms, 1627 had sought finance in the past year. Table 4.1 below analyses the pattern of responses by firms with different leadership groups. This suggests that among women-led firms the proportion of firms obtaining all of the finance they were seeking was marginally lower than that in other groups. Conversely the proportion of women-led firms unable to obtain any finance (12.2 per cent) was also relatively high compared to other sample groups. This suggests the potential for significant gender effects in firms’ ability to access finance. Table 4.1: 2003 analysis of ability of firms to raise finance by gender and ethnicity (ASBS 2003) Yes, was Yes, obtained Yes, obtained No, had no Total unable to some but not all the finance difficulties in obtain any all of the required but obtaining finance finance with some finance required problem per cent per cent per cent per cent Number Leadership Profile Totally Women- 197 led 12.2 10.2 6.1 68.5 Equal numbers of 481 directors 9.4 3.5 6.2 78.0 Women minority 338 among directors 9.8 5.3 8.3 73.7 All directors men 14.9 4.1 7.1 73.9 1283 Total 11.6 4.7 7.0 74.2 2299 Note: The definitions of women-led business are taken from the database variable “smegen” .The dependent variable that is used for the analysis of whether firms reported difficulty in obtaining finance is obtained from ‘Did you have any difficulties in obtaining this finance from the first source you approached?’ Some firms did not provide a detailed response to this question and so the table total (2299) differs from the number of firms which actually sought finance in the survey (2330). Source: ASBS 2003 4.4 Our analysis of the UKSMEF database is based on existing firms attempting to obtain new finance in the last 3 years of trading, rather than at the start up phase12. As in the analysis of the ASBS, our analysis is restricted by the fact that we only observe the responses of those individuals who have been successful in starting a business. Our approach mirrors closely that adopted in the ASBS looking separately at the demand and supply sides. Essentially, the “supply side” will be evaluated using questions in relation to “discouragement” and “denial” (whether an application for finance was denied outright)13. Both variables provide a straightforward indication of barriers and difficulties in obtaining external finance. In addition, we used a constructed variable called “barrier” reflecting whether the business seeking external 12 An alternative approach would have been to base the analysis on businesses in the start-up phase. The number of start-ups in the UKSMEF sample is 149 of a total sample of 2500. This is too small a sample on which to base robust inferences. 13 A number of different measures of discouragement are available in the UKSMEF and are explored below. See the data description in Annex for details. 27
  28. 28. finance experienced either discouragement or denial14. The “demand side” effect will be evaluated by analysing variables which reflect whether businesses actually sought external finance as well as the type of external finance sought (in particular overdrafts, loans, asset-based finance and equity). 4.5 Table 4.2 illustrates SMEs’ external financial needs and restrictions, with a specific distinction for start-ups (business trading for less than 2 years) in terms of gender. Overall, broadly similar proportions of men and women-led businesses were seeking external funding, although some unexpected differences emerge in terms of the barriers to finance. Notably, start-up businesses being run by males seem more likely to face discouragement in applying for funds than those run by women although sample sizes here are relatively small and differences are therefore statistically insignificant. Table 4.2: Financial Needs and Barriers to Finance (per cent of the population) Men Women per cent per cent SME needing Finance 46.2 43.7 Start-ups 73.0 64.7 Discouraged from applying for finance1 4.1 2.0 Start-ups 12.5 3.0 Denied application for finance1 5.7 3.2 Start-ups 3.5 1.0 Notes: 1- Applications for overdraft, term loan, asset-based finance or equity finance. Figures in bold are significantly different at the 5 per cent level. See Annex 1 for variable definitions. Source: UKSMEF 2004 4.2 Evidence from the ASBS 4.2.1 Supply Side Models 4.6 Supply side models were estimated in various forms (see Tables A2.1 and A2.2) and perform well in general terms. In terms of gender, however, the results are conflicting. Women-led businesses were less likely to report financing difficulties based on the 2003 data, although this effect was only significant in one of the three models, but more likely to report financing difficulties based on two of the three models estimated on the 2004 data. In the 2004 models, women-led firms are approximately 2 pp more likely to have difficulty raising finance, and also 2 pp more likely to find it impossible to raise the finance that they are seeking. To set these figures in context, the corresponding figures for Owners/MDs who are over 60 however are 6.5 pp and 1 pp respectively 14 This we compare with the responses to the question: ‘On a scale of 1-10 (where 1 is no problem and 10 is critical problems) how would you rate the severity of the problems faced by your business in the following areas: Finance (by this we mean obtaining sufficient finance and the cost of finance)’. 28
  29. 29. 4.7 The more important variables in explaining the difficulties are firm size, in that the larger firms in the sample report fewer problems in raising finance. The quintile of deprivation is included, though this variable is very sparse, as such it is included for all five categories, with zeros inserted for all missing values to prevent reducing the sample size. This limits what inferences one can make from these variables. 4.8 Both firm size and VAT registration (which to an extent may be capturing the same thing) increase the likelihood of obtaining finance, as does (to a lesser degree) firm age, while being an exporter and being in the second most deprived quintile of the deprivation distribution significantly reduces the likelihood of obtaining finance. VAT registration data were not collected for the 2003 data, so a VAT threshold dummy is used, which may not be capturing the same thing. 4.9 Other variants of these baseline models were estimated reflecting various dimensions of the supply side. These are reported in detail in Annex 2 with a summary of the key points outlined here: (a) Amount Requested 4.10 Tables A2.3 and A2.4 report results dividing the sample by the amount of finance sought. In general, the gender effect appears stronger in the 2004 sample. This analysis is potentially important because it may be, for example, that certain parts of the market for finance work better than others, with responsibility for dealing with small requests devolved to more junior (or more local) decision makers. Based on the 2004 analysis, women-led firms are significantly less likely to obtain finance in the range of £10,000-£100,000. 4.11 Exporting firms are again less likely to obtain finance across all amounts of finance requested– perhaps due to the higher perceived risk of exporting activity. Where firms request the smaller amounts of finance, location in terms of the quintile of deprivation also appear to hamper the ability to raise finance. This may be because decisions regarding smaller amounts of finance are devolved to more local decision makers who are more likely to recognise areas as being deprived. (b) Why finance was being sought 4.12 Tables A2.5 and A2.6 shows a breakdown of why firms sought finance. It is clear from this analysis that obtaining finance for “working capital” is problematic across all businesses, and that there are strong age and business profile effects here. Being registered for VAT for example makes it more likely to receive finance. Based on the 2004 analysis, women-led businesses are less likely than average to be given finance for working capital. Interestingly, these data are skewed to either end of the distribution, with both “impossible to obtain finance” and “no problem” far more prevalent than either of the middle groups. This suggests that many people seeking working capital are not seen by providers of finance as a good bet. The motor trade is an exception here where firms find it easier to raise working capital. These effects are not as strong in the 2003 analysis. 4.13 Across the other types of finance gender appears unimportant, while exporting firms appear far less likely to obtain any type of finance. 29
  30. 30. (c) By type of finance sought. 4.14 Tables A2.7 and A2.8 show the breakdown by type of finance sought. Several categories from the original questions had to be amalgamated here to obtain sufficient sample size. For the 2004 sample, the difference between the genders appears insignificant. These results, however, highlight the importance of VAT registration across all types of finance. (d) By type of organisation 4.15 Tables A2.9 and A2.10 examine the differences between sole proprietors, partnerships and companies. Sole proprietors in the more deprived areas are less likely to obtain finance, while interestingly women-led companies are less likely than other companies to obtain finance. This difference is, however, significant only at the 15 per cent level. Again VAT registration is important for all types of firms, though firm size is not important for sole proprietors. 4.16 Another approach to the ASBS data is to model the supply side separately for male and women-led businesses and these models are reported in Tables A2.11 and A2.12, again with significant variables in bold. While approximately 12 per cent of women-led firms that applied for finance in 2003 found it impossible to raise money, the corresponding figure for firms with all male directors was just under 15 per cent (Table 4.1). More detailed econometric analysis, standardising for other characteristics, however, suggests that women are disadvantaged in finance markets, particularly where they are also ethnic minority businesses. 4.17 For the 2003 sample, ethnic minority businesses that are women-led report significantly greater problems in raising finance, while the same is true for the 2004 sample. The results for the women-led firms highlight many of the results alluded to. Ethnic minority women-led businesses are less likely to obtain finance, but the same is not true for male businesses. Partnerships of women are more likely to be successful in raising finance than women-led companies, the strongest result for type of firm through this analysis. 4.18 Location is also more important for women than men. The regional dummies in table 4.5 are collectively much more important for the women-led sample than the other sample, and women in Wales and Yorkshire and Humberside are significantly more likely to report problems. It should be stressed however that these results are based on a relatively small sample of firms reporting problems raising finance. For the 2003 sample, among the men-led businesses, firms with some women directors are more likely than the others to encounter problems in raising finance. 4.19 These results also highlight some interesting regional effects. Women-led businesses appear less likely to obtain finance if they are in the South-West of England, and also if they have a degree. They are also more likely to be able to raise finance if they are partnerships or in the least deprived areas, although this effect, while highly significant is very small. 4.20 To summarise, the results of our supply side analysis do suggest the validity of concerns about access to finance of women-led companies. What shows up more strongly is that women in ethnic minority groups are far less likely to 30
  31. 31. raise business finance, even when they have an existing business. This effect is multiplicative; women are far less likely to obtain finance if they are also from ethnic minorities. This also shows that among the women-led group, partnerships are less likely to face problems than companies or sole traders. It is easy to imagine that this is due to the type of sectors inhabited by partnerships, though sectoral differences are captured by dummy variables. 4.2.2 The Demand for finance 4.21 The remainder of this section turns to the differences in the demand for finance. This utilises the information with respect to those firms that have sought finance, as to whether there are differences across gender in seeking finance, rather than in being offered it. The purpose of this is to address the problem that it may be the case that certain groups simply perceive it not to be worthwhile even applying for finance. This reflects the suggestion from the HSE that individual women considering business start-up are less likely to seek external finance than their counterparts who are men. 4.22 The demand side analysis presented in this section is similar in structure to that presented above, focussing on the types of finance sought, where from and how often, rather than the difficulties faced. Again some categories have been amalgamated from the original survey in order to build up large enough samples with sufficient within sample variation. Other variables are included in this analysis to capture further constraints, such as the number of hours per work the owner / MD claims to spend on paperwork. Detailed models are given in Annex 2. (a) Type of finance. 4.23 One reason for carrying out this part of the analysis is that if one looks at finance overall, this included sources such as community development finance, and grants. Both of these are targeted at certain groups, so an analysis of overall finance may mask specific effects. Tables A2.13 and A2.14 illustrate the determinants of the different types of finance sought. More industry variables are included, as industry effects appear more important in the demand side than the supply side, as do regional effects. Gender effects appear to be quite weak in this analysis, though there is some evidence that women-led firms are less likely to seek HP/ factoring finance. Education, and to some extent location in terms of the deprivation measures are however more important than gender or ethnicity. In general terms therefore we find only weak support here for the contention that women-led firms are less likely to seek external finance, this varying by type of firm and by type of finance. (b) Attempted to obtain finance more than once 4.24 Table A2.15 and A2.16 shows the results for an ordered probit, on whether firms have sought finance more than once, once or not at all. There is some evidence here that women-led firms are less likely to seek external finance even allowing for other factors. 4.2.3 Concluding Points 31
  32. 32. 4.25 Our focus in this analysis has been on the situation of an existing firm seeking additional finance. Our results reflect our focus on the supply and demand sides of the financing relationship. Although there are some conflicts between the 2003 and 2004 results, the most consistent picture is suggested by the 2004 models which suggest that, in terms of the supply side, women-led firms are around 2 pp more likely to have difficulty raising finance and also 2 pp more likely to find it impossible to raise the finance they are seeking. In terms of the demand side we also find that women-led firms are less likely to seek external finance both on a one-off and multiple basis. This reflects the situation for individual women noted earlier from the HSE. 4.3 Evidence from the UKSMEF 2004 Database 4.26 In this section we focus on evidence from the UKSMEF 2004. Our approach follows closely that adopted in the ASBS focussing first on the supply side and then exploring women-led firms’ demand for finance. 4.3.1 Descriptive Analysis 4.27 As a prelude to the multivariate analysis of the following sections it is beneficial to have an understanding of the basic characteristics underlying the UKSMEF data, and this is the objective of this section. Table 4.7 summarises a number of key characteristics of the sample by gender. 32
  33. 33. Table 4.7: Sample Characteristics of the UKSMEF 2004 Men Women per cent per cent Region East 9.4 9.9 East Midlands 6.0 5.8 London 21.2 21.0 North East 3.0 2.3 Northern 2.5 4.6 North West 7.5 9.7 Scotland 5.5 4.6 South East 16.4 19.1 South West 8.5 9.4 Wales 4.9 3.7 West Midlands 7.2 4.6 Yorkshire 7.9 5.3 Business age <4 years 13.8 23.1 [4-15] yrs 33.4 32.1 >15 yrs 52.8 44.8 Legal Form Sole trader 65.9 66.5 Partnership 9.7 11.8 Company 24.4 21.7 Business Size <=10 employees 93.2 95.7 >10 employees 6.8 4.3 Export No 89.1 92.9 Exporter 10.9 7.1 Owner Characteristics Previous Experience <16 yrs of experience 35.0 56.3 >=16 yrs of experience 65.0 43.7 Education Attainment Degree or higher 21.5 32.2 "A" levels 8.6 10.4 GCSE or equivalent 13.9 17.5 Other vocational quals. 40.6 23.7 No qualification 15.4 16.2 Notes: Figures in bold are significantly different at the 5 per cent level. See Annex 1 for variable definitions. 4.28 Comparing the group of men and women respondents: • We see no significant distinction between the regional composition of the sample between businesses run by men and women, the exception being the Northern region. Three regions (London, South East and the Eastern region) account for around a half of the national sample. • Women-led businesses were more likely to have been trading for less than 4 years than those which were male-led. • A higher proportion of the women population running an SME are relatively inexperienced (with trading experience of less than 16 years). On 33

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