Determinants of Start-up Firm External Financing Worldwide

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Determinants of Start-up Firm External Financing Worldwide

  1. 1. Determinants of Start-up Firm External Financing Worldwide John R. Nofsinger and Weicheng Wang Washington State University College of Business Pullman, WA 99164-4746 Contact: john_nofsinger@wsu.edu August 2008 Abstract The typical new start-up firm acquires external financing in stages through its development. The later stages of financing (venture capital and initial public offerings) have been frequently examined. The early stages of financing (initial capitalization and angel investing) have rarely been analyzed. This study examines the determinants of the initial start-up financing of entrepreneurial firms in 27 countries. There are information asymmetries and moral hazard problems inherent in the funding of a start-up firm. Institutional investors seem to rely on their abilities to reduce the information asymmetry and the quality of investor protection to reduce the moral hazard. On the other hand, informal investors are also common in initial start-up funding. They tend to use the type of products and entrepreneurial experience in the new firm as a signal of quality to reduce information asymmetries. They also seem to rely more on their connectedness to the entrepreneur through a personal relationship to reduce the moral hazard problem. We thank Kee Chung, Thomas Hellmann, Ken Kim, and Ed Vos for helpful comments as well as seminar participants at the State University of New York at Buffalo and Washington State University.
  2. 2. 1. Introduction Access to external financing is important for entrepreneurship. Throughout the world, there are extensive financing and capital structure studies1 on large corporations and small and medium- sized enterprises (SME).2 In comparison, we know less about the external financing of start-up firms. Further review shows that we know almost nothing about pre-angel firms 3 and their financing. For example, in Denis’s (2004) overview of entrepreneurial finance, the initial start- up capital structure is not even mentioned. Because of severe information asymmetry between initial start-ups and investors, it’s critical for the entrepreneurs to credibly signal their project value. However, we examine the other side of the coin—how different classes of investors respond to the given signals. For the initial start-up firms, external investors can be loosely divided into institutional investors and individual investors. Institutional investors mainly consist of venture capital funds, banks, and government agencies. Traditionally, individual investors only include angel investors, the professional investors with large individual wealth. We argue that individual investors should include another category—‘informal investors’ who are different from angel investors in terms of their personal relations with entrepreneurs and the extent of professionalism in providing financing. They are typically affiliated with entrepreneurs through various social networks such as friends, colleagues, etc., but they are different from other related investors who have family or blood ties with the entrepreneurs. Vos et al. (2007) describe them as being connected to the 1 See Demirgüç-Kunt and Maksimovic (1999) and Jong et al. (2008), or for East Asia see Claessens et al. (2000), for Europe see Bancel and Mittoo (2004) and Kim et al. (2007), and for developing countries see Booth et al. (2001). 2 See Avery et al. (1998), Berger and Udell (1998), and Gregory et al. (2005), and Vos et al. (2007). 3 According to Wong (2002), angel investors start providing financing after at least 10.5 months since the start up of the business. We define the start-up firms less than 6 months old as the initial start-up firms or pre-angel firms. We use these terms interchangeably in this paper.
  3. 3. entrepreneur in the opposite way as public shareholders are separated from the firm. In the next step in financing, angel investors are professional investors who have large individual wealth and specialize in investing profitable startups. There is no need to attract angels with personal relations. We specifically focus on the response of institutional investors and informal investors. The latter investor type is often ignored in entrepreneurial finance due to the data scarcity. In comparison, extant literature extensively examines the behavior of venture capitalists (VCs), banks, and angel investors. Our data provides a unique opportunity to study these informal investors in the context of initial startups. We investigate how institutional investors and informal investors respond to product type (new vs. existing product), production technology (new vs. existing technology), and entrepreneur experience signals. These firm characteristics arguably contain valuable information for assessing the true value of initial startups. However, we hypothesize that different types of external investors may interpret this information in different ways. For example, alternative interpretations of these signals may be: Signal 1a: A new product signals innovation and therefore is associated with better access to external financing; Signal 1b: A new product signals risk and more uncertainty, which leads to worse access to external financing; Signal 2a: Using new technology signals efficiency and therefore better access to external financing; Signal 2b: Using new technology signals difficulty of evaluating the project leading to worse access to external financing; Signal 3a: Entrepreneurial experience signals managing skill and experience and therefore better access to external financing; Signal 3b: Prior experience teaches entrepreneurs how to benefit themselves at the expense of shareholders and therefore worse access to external financing; We argue that due to the different levels of expertise and ability to overcome information asymmetry, informal investors and institutional investors may have different reactions to these 2
  4. 4. signals based on different interpretations. Note that this hypothesis has some similarities to the predictions of the Winton and Yerramilli (2008) model of entrepreneurial firm financing. Their model shows that an entrepreneur’s decision to finance with either banks or venture capital depends on their level of monitoring, and the expected risk of the firm. They assume that venture capitalists are better monitors than banks and are therefore better suited to assessing the firm’ situation. If the firm’s uncertainty is low, then the lower cost and lower monitoring of bank financing is optimal. Alternatively, when the uncertainty is high, the entrepreneur chooses venture capital as they are better suited to assessing the risks and providing monitoring. But there are also differences between our story and their model. First, they model entrepreneurial firms at a later financing stage than our sample of firms. Second, while they are modeling the entrepreneur’s financing decision, we are examining financing from the viewpoint of the financiers, not the entrepreneur. Thus, we focus on firm signals that may provide information about the firm’s uncertainty. In addition to signaling, access to external financing also depends on investor protection against opportunistic behaviors of entrepreneurs. Better investor protection will increase the willingness of external investors to provide capital (La Porta, Lopez-de-Silanes, Shleifer and Vishny 1997, hereafter LLSV). In this paper, we argue that investor protection is not only provided by formal laws regarding contract enforcement and property protection, but also by the connectedness of personal relations with entrepreneurs that may provide extra protection. This is especially relevant when informal investors provide startup capital because the entrepreneurs have strong incentives to protect their reputation as a good entrepreneur within their social networks. They can not afford negative rumors about their ethics or managing skills spread throughout their potential population of investors. Such concern will give entrepreneurs 3
  5. 5. incentives to perform well against irresponsible behavior. If we assume investors have the same formal protection within the same country, then the extra protection for informal investors due to their connectedness make them more willing to provide financing to start-up firms, ceteri paribus. We use the survey data from the Global Entrepreneurship Monitor (GEM) implemented in 2003 to explore the response of informal and institutional investors worldwide to informational characteristics. Our sample consists of 1,869 entrepreneurs from 27 countries who have just started up their new business within the past 6 months. We find that investor protection has a significantly positive impact on the access to external financing regardless of the investor type. For informational characteristics, our evidence shows informal investors strongly prefer the startups producing new products. In sharp contrast, institutional investors prefer existing products. Both types of investors are more willing to provide capital if the startups are managed by an experienced entrepreneur. Production technology seems less important in their financing decision. When interacting the quality of investor protection and startup firm characteristics, we find that firm characteristics significantly change the marginal effect of investor protection on the likelihood of obtaining external financing. This effect is different for accessing informal financing relative to institutional financing. This paper is organized as follows. Section 2 reviews the literature in entrepreneurial finance. Section 3 summarizes our sample. Section 4 provides initial evidence for the importance of firm characteristics and investor protection in accessing external financing. Section 5 examines the effects of firm characteristics and investor protection on the likelihood of accessing 4
  6. 6. informal and institutional financing. Their interaction effects are also presented. Section 6 concludes the paper. 2. Literature Review One of the most important factors for entrepreneurial success is the ability to access capital. Entrepreneurs need capital for initial start-up. Over time, new firms need additional capital infusions while the business is developed and expanded. Early stages of capital acquisition typically include angel investors and then venture capital, often leading to corporate investment (Denis, 2004). Some of these firms survive and succeed long enough to conduct an initial public offering (IPO). There is a voluminous literature on many aspects of IPOs. There is a small, but growing literature applying corporate finance theory (primarily agency problems and information asymmetry) to the pre-IPO external financing. Most of this literature investigates the role and impact of venture capitalists on entrepreneurial firms. For example, VC investors appear to be active investors by providing monitoring via frequently visiting the firm (Gorman and Sahlman, 1989), being on the firm’s board of directors (Lerner, 1995), shaping the management team and organizational structure (Kaplan and Strombert, 2001 and Hellman and Puri, 2002), and infusing capital over time through stages (Gompers, 1995). In contrast to the VC literature, Denis (2004) reports that “comparatively little work has been done on angel investors.” Wong (2002) reports that angel financing comes earlier than VC financing and the funds infused are smaller. On average, the entrepreneurial firm is 10.5 months old when they receive angel financing and most have not achieved any revenues. We are not aware of any studies examining the pre-angel, or initial, start-up financing. 5
  7. 7. Denis (2004) describes how obtaining external financing is made difficult for entrepreneurs because of two fundamental problems; information asymmetry and the moral hazard problem. While the entrepreneur understands the quality of the proposed business, it may be difficult for investors to do so. Alternatively, outside investors and the entrepreneur may disagree about the value. The moral hazard problem recognizes that once substantial external funding is achieved, the entrepreneur may have the incentive to misuse or misallocate those firms to benefit them self. These problems are overcome through investor monitoring (as described above) and contracting. Kaplan and Stromberg (2001) describe how venture capital contracts allocate cash flow rights, voting control, and decision rights. To reduce the information asymmetry and moral hazard problems, investors may evaluate the quality of the business proposal and the ability of the entrepreneur. In other words, the characteristics of the business being created may also impact the external financing opportunity set. Hellman and Puri (2000) find that entrepreneurial firms with innovative products or processes are more likely to get funded by VC investors than imitator firms. Our data allows us to relate the sources of initial start-up financing to whether the firm’s products will be new or not, how experienced is the entrepreneur, and what type of technology is to be utilized. The entrepreneur’s personal characteristics like wealth (Hurst and Lusardi, 2004) and experience (Nanda, 2008) may also impact the external financing opportunity set. For example, Avery et al. (1998) show that the commitment of a small business owner’s personal wealth is important for the firm obtaining external credit and loans. Kaplan et al. (2008) also examine the role and dynamic evolution of alienable assets, business line, and human capital for start-up firms. They find alienable assets and business lines are critical and stable for the success of start-up firms as long as 36 months after an IPO. In comparison, management turnover is substantial, which 6
  8. 8. implies that at the margin, investors in start-ups should place more weight on the business than on the management team. However, at least for the initial period of start-up, the quality of the entrepreneur is also important. Venture capital investors have an advantage over angel investors in overcoming the information asymmetry and moral hazard problems because they have more information about the entrepreneur and firm. By the time VCs gets involved, the newly created firm has demonstrated the viability of the business and the use of previously obtained funds (possibly from angel investors). Angel investors have much less information about either the potential of the business innovation and/or the quality of the entrepreneur. These fundamental problems are even greater at the initial start-up phase. Indeed, these problems may be so great at start-up that much of the institutional financing may not be in the opportunity set. Entrepreneurs may have to turn to informal financing sources at initial start-up. Indeed, Vos et al. (2007) suggests that the entrepreneurs may prefer financing from these connected investors. These informal financing sources are people who are not normally seeking business investments or lending. They probably include an entrepreneur’s relatives, friends, neighbors, and work colleagues. The personal relationships that these people have with the entrepreneur may resolve some of the moral hazard problem. While they may not be able to reduce the information asymmetry, their connectedness to the entrepreneur may allow them to experience less risk as a financier. 3. Data summary We use the survey data from the Global Entrepreneurship Monitor (GEM), which was developed to provide firm creation comparisons among countries, and for some regions within countries. The initial data was assembled as a pretest of five countries in 1998. By 2003, over 40 countries 7
  9. 9. and 3 sub-national regions had been involved in the program. In total, data from 358,274 individuals had been gathered in 138 separate surveys of various adult populations. Due to the annual update of the survey questionnaire, necessary variables related to entrepreneurial financing are only available in the 2003 survey. Therefore, we focus on this survey. There are potential problems of selection bias in using this annual survey because of the limited sample size and incomplete country coverage. We should interpret the results with caution. To verify the consistency of the GEM survey between countries, we summarize the number of respondents choosing positive and negative responses in each country when being asked about their capital sources. The results (Appendix A3) show that GEM has very good consistency in its financing questions across different countries, which rules out the possibility that following empirical results might arise from the systematically different questionnaires. Only persons that, alone or with others, are currently starting (or have started in the past 6 months) a new business were asked about the total investment and personal investment in the firm. In addition, they are also encouraged to indicate whether financing from certain investors4 was available. Appendix Table A1 explains the key variables and corresponding survey questions. The 2003 GEM database contains 8,277 observations for new businesses. After limiting the sample to observations with financing data, the final sample consists of 1,869 startups in 27 countries. The filter criteria can be found in Appendix ‘Filter Criteria to Enter Sample.’ As shown in Table 1, two-thirds of our sample entrepreneurs are male and more than half of them are within the age range of 25 to 44. In addition, 44% of entrepreneurs have at least some college education. The percentage of start-ups providing new products is slightly lower 4 (1) Self saving & income, (2) close family member, (3) work colleague, (4) employer, (5) friends & neighbors, (6) banks & financial institutions, and (7) government program 8
  10. 10. than that of those producing traditional products, which suggests our sample is not dominated by one type of startup. With respect to production technology, most sample startups (84.86%) are using new technology. It is possible that such a pattern arises due to the bias of survey respondents to exaggerate their innovation ability. The High-tech Miracle over the world seems to make entrepreneurship equivalent to new technology. Our sample reflects this point to some extent. The power of production technology in explaining access to external financing might be substantially reduced because of this highly skewed pattern. Slightly less than 90% of them have prior start-up experience. [Insert Table 1 about here] The financing of our sample firms comes from varied sources according to Panel C. Among the seven financing sources asked in the questionnaire, 57% of startups have external financing from friends & neighbors while 40% of startups are self-financed at least partially. The summary provides us initial evidence for the importance of informal financing. Throughout the paper, we define the first two sources (“self saving & income” and “close family member”) as self-financing, the next three (“work colleague,” “employer,” and “friends & neighbors”) as informal financing, the last two (“banks & financial institutions” and “government program”) as institutional financing. We focus on the latter two categories: informal financing and institutional financing. 4. Start-up financing: what matters? A. Product, Technology and Entrepreneurial experience To characterize the access to external financing, we create two variables from the GEM dataset. The external financing ratio (finratio) is calculated as the total investment less self-investment divided by total investment. We characterize this variable as the fraction of start-up capital 9
  11. 11. obtained externally. Next, external financing diversity (extfindiv) is equal to the total number of different external financing sources through which the startup obtained capital. The external financing sources include: (1) work colleague, (2) employer, (3) friends & neighbors, (4) banks & other financial institutions, and (5) government programs. Approximately 40 percent of start up capital comes from external financing through multiple financing sources, as reported in Panel A of Table 2. [Insert Table 2 about here] Panel B of Table 2 provides pairwise comparisons of the external financing ratio and financing diversity for the groups categorized by product type, production technology, and entrepreneur experience. The initial evidence shows that the external financing diversity is significantly higher for those producing a new product or using new technology, while prior start-up experience seems irrelevant. With respect to the external financing ratio, startups using new technology managed by experienced entrepreneurs are financed by a higher percentage of external financing. In general, product type, production technology, and entrepreneurial experience seem important in explaining the difference in external financing. It seems more reasonable to assume that the aggregation of information revealed by these characteristics, rather than individual characteristics, may have more power in explaining the access to external financing. To capture this idea, we create three dummy variables equal to one if the start-up produces a new product, uses a new technology, or is managed by experienced entrepreneurs. They are equal to zero otherwise. We add up these three dummies and call the sum the index of ‘information aggregation,’ which ranges from 0 to 3. We sort the whole sample according to the information aggregation index and evaluate external financing for each index subgroup. For each 10
  12. 12. index subgroup, simple averages of external financing ratio and external financing diversity are calculated.5 [Insert Table 3 about here] Table 3 presents very interesting results. Both variables proxying for the access to external financing are showing a nearly monotonic trend from the lowest information aggregation to the highest aggregation. Due to the construction of aggregation index, we only focus on the lowest and highest subgroups. For the startups producing new product with new technology and managed by experienced entrepreneurs, the firms have access to a much higher percentage of external financing than those producing existing products with existing technology and managed by inexperienced entrepreneurs. The financing diversity also shows a similar monotonic trend. Note that the estimates are significantly different among the four groups. This initial evidence seems to suggest that external investors prefer startups having certain characteristics. As a more formal test, we report Tobit regression results in Table 4. We use Tobit regression because some start-ups do not have external financing at all which implies censoring from the left at zero.6 To control for the different degrees of economic development between these countries, we include GNP per capita in 2003. We also include the ratio of stock market capitalization over GDP to control the different development of each financial system because financial development impacts a firm’s access to external capital (Beck et al., 2004, Harper and McNulty, 2008, and LLSV 1997). Lastly, we include the size (capitalization) of the start-ups to control for the possibility that larger start-ups may need more external financing. 5 We also calculate the size-weighted average of external financing variables, simple and size-weighted average when ownership dummy is excluded. No qualitative changes occur. 6 We only use external financing ratio as the dependent in Tobit regression because the external financing diversity has a narrow discrete range (0-5) which is difficult to model. 11
  13. 13. [Insert Table 4 about here] The results related to new technology and prior start-up experiences are not surprising, both of which are positively correlated with the external financing ratio. The coefficient on the new product dummy is somewhat surprising, not only because of its insignificance, but also because of its negative sign. It seems that innovative products are not necessarily helpful to obtain external financing when other variables are controlled for. Our subsequent analysis will show that this result is due to the different responses of informal versus institutional financiers. To this point we have not distinguish between the identities of external investors in this analysis. A more interesting question is how the different classes of investors respond to the start-up characteristics. Do different investors have the same preference? We will study this question in the next section. B. Investor protection In addition to the information asymmetry, another important aspect that determines the access to external financing is investor protection. Given the extent of information asymmetry, if investors believe that their interests will be protected better, they will be more willing to provide capital (LLSV 1997). We argue that strong investor protection laws can offset the information asymmetry problem (especially for institutional investors), thereby encouraging external financing for start-up firms. We will specifically examine this offsetting relation between investor protection and information asymmetry in the following section. Because investor protection is country specific, we first summarize the related country- level variables in Table 5. Based on the legal origin classification from LLSV (1998,1999), our 27 countries are classified into five different origins: common law English origin (10 countries), civil law French origin (8 countries), civil law Germany origin (3 countries), civil law 12
  14. 14. Scandinavian origin (4 countries) and Socialist origin (2 countries). In terms of the number of entrepreneurs, there are 873 of them belonging to countries with English legal origin, 553 to French origin, 208 to Germany origin, 110 to Scandinavian origin and 169 to Socialist origin. [Insert table 5 about here] We characterize the quality of investor protection with three proxy variables: protection of private property, the quality of contract enforcement, and freedom from corruption.7 All of these measures show a very systematic pattern across the five legal origins. The Germany legal origin has the highest ranking in all three aspects. The English legal origin ranks the second in property protection and contract enforcement but falls behind the Scandinavian legal origin in corruption freedom. The French legal origin ranks fourth in contract enforcement and freedom from corruption, but has a slightly higher score in property protection than the Scandinavian legal origin. The Socialist origin has the lowest quality in all aspects. All rankings are generally consistent with the pattern reported by LLSV (1998, 1999). To provide further evidence for the importance of investor protection on entrepreneurial financing, we sort the entire sample into quartiles according to property protection, quality of contract enforcement, and corruption freedom respectively. The bottom quartile includes the countries with the worst protection while the top quartile includes the countries with the best investor protection. For each quartile, we calculate the equal-weighted 8 averages for the two variables measuring access to external financing. [Insert table 6 about here] 7 Property protection : Range: 1-7 where financial assets and wealth are : (1= poorly delineated and not protected by law, 7= clearly delineated and protected by law) Source: The Global Competitive Report 2001-2002; Contract enforcement : range: 1-7 where 7= Judiciary is completely independent from government and/or parties to disputed; government completely neutral among bidders when deciding upon public contracts; 1= opposite. Source: The Global Competitive Report 2001-2002; Corruption freedom: Range: 0-100 where 100 indicates completely free from corruption. Source: Index of Economic Freedom 2003 by The Heritage Foundation. 8 Size-weighted results are qualitatively similar to the equal-weighted average and are therefore not reported. 13
  15. 15. Table 6 shows an almost monotonic trend for the financing variables from the countries with the worse investor protection to those with the best, with very few exceptions. The sorting analysis provides further evidence for the importance of investor protection in determining the ability of entrepreneurs to obtain external financing. We generally find that the start-ups in countries with better protection of private rights, the higher quality of contract enforcement, and less corruption are financed with a higher percentage of external financing and financed through more sources. 5. Institutional financing versus informal financing In previous sections, we have separately examined the impact of firm-level informational characteristics and investor protection on entrepreneurial access to external financing. However, we did not distinguish the investor type when measuring external financing. With our data, we can examine how different investors (institutional versus informal) respond to the same informational characteristics. In addition, we can also examine how the interaction between these characteristics and investor protection affects the likelihood of obtaining financing from different sources. The evidence will shed light on the investing preference of different investor types in financing initial start-ups and how investor protection impacts such preferences. In the context of financing initial start-ups, institutional investors are different from informal investors in their project-picking expertise and needs for protection against entrepreneur expropriation or start-up failure. We argue that institutions have better knowledge about project prospects (or project-picking ability) and can better overcome the information asymmetry problem through this knowledge, but they must rely on formal protection against entrepreneur expropriation, which relies on the quality of the institutional environment. In contrast, informal investors lack the systematic ability to pick promising projects, but their connectedness to the 14
  16. 16. entrepreneurs gives them extra person-specific knowledge and protection, which is especially useful in a weak institutional environment. Thus, we expect to see a different impact of formal investor protection quality on the relations between informational characteristics and access to external financing. To implement the analysis, a logit regression is used for informal financing and institutional financing respectively. The same set of explanatory variables is included for the regressions which include informational characteristics (product type, production technology and entrepreneurial experience), investor protection measurement9 and their interactions. Necessary control variables are also included.10 [Insert table 7 about here] [Insert table 8 about here] The regression results show both similarity and difference. Generally, better investor protections lead to a higher likelihood of obtaining both informal and institutional financing. The coefficients on informational characteristics, however, are showing a quite different pattern. Specificly, informal investors strongly prefer the start-ups producing new products while institutional investors are not only insensitive to the product type, the negative sign on the new product dummy seems to suggest, though insignificantly, that the new products detract from obtaining institutional financing. This reflects how differently institutional investors and informal investors interpret the information signaled by product type. The highly positive response of informal investors is consistent with our common sense about the ‘attractiveness’ of 9 We create a proxy ‘investor protection’ by summing up the scores of property protection, contract enforcement, and freedom from corruption to proxy the quality of investor protection. 10 The firm size, GNP per capita and GDP growth are strongly correlated with the quality of investor protection. To reduce the resulting multicollinearity, we regress firm size, GNP per capita, and GDP growth on the ‘protection of private property’ respectively and use the regression residuals to control the effect of firm size, GNP per capita and GDP growth rate. We also use the residuals from regressing size, GNP per capita, and GDP growth on the created ‘Investor protection’ measurement. The results are all similar and therefore untabulated to save space. 15
  17. 17. a new product. New products, however, may also imply the higher probability of failure and higher uncertainty. Institutional investors seem to have such an interpretation. This difference may also reflect the different expertise of evaluating start-ups. The ‘new technology’ dummy is insignificant in either regression. But opposite signs of its coefficients in the two regressions seem to suggest that informal investors do not like the new technology, possibly because it is harder for individuals to evaluate the real potential of new technology. In contrast, the expertise and more technical knowledge of institutional investors may explain the positive coefficient. Because of the insignificance, such interpretations are not conclusive. Both investor types are more willing to lend money if the start-up is managed by a veteran entrepreneur. The results on the interactive terms are telling us a more interesting story. The coefficient on the interactive term “new product × investor protection” further illustrates the connectedness of the informal investors when facing start-ups producing new products. In Table 7, when the start-ups signal their value by producing a new product, the importance of investor protection is significantly reduced in all specifications, which literally means that informal investors have a greater ability to handle traditional risk because of their connectedness to the entrepreneur. The informational characteristics are significantly offsetting the effect of investor protection on external financing. It also provides indirect evidence for our hypothesis that informal investors may enjoy extra protection due to their personal relations with the entrepreneurs because our investor protection measurement only captures the quality of formal protection. In Table 8, the same interactive terms are associated with totally opposite results. When the start-ups signal their potential by producing a new product, investor protection becomes more important for obtaining institutional financing. This again provides evidence for the ‘dislike’ of institutional investors 16
  18. 18. towards a new product because it may imply higher uncertainty and higher probability of failure. Institutional investors seem to interpret the entrepreneur’s information signals within the context of the legal environment in a much different way than the informal investors. Informal investors and institutional investors are showing similar attitudes towards entrepreneurial experience. The interacting terms between investor protection and entrepreneurial experience show that when an experienced entrepreneur manages the start-up, investor protection becomes less important for both informal and institutional investors. Such results provide some evidence that the experience and ability of entrepreneurs serve as very important signals to reduce the extent of information asymmetry and its consequence on access to external financing. The interactive terms related to new technology are again insignificant. In sum, the above analysis suggests that institutional investors do not prefer to finance the startups producing a new product while informal investors strongly prefer new products. Such different preferences become more evident when investor protection is included. Investor protection becomes less important for the informal investors when a new product will be produced, while institutional investors are more concerned with protection facing the same signal. Both institutional and informal investors prefer to provide capital to the startups managed by the experienced entrepreneurs. Production technology seems irrelevant for access to external financing regardless of investor type. But the opposite signs of the coefficients on new technology are suggesting the different preference of informal investors and institutional investors towards production technology. 6. Conclusions We study the determinants of external financing in firm start-ups in 27 countries. Access to start- up capital is complicated by information asymmetry and moral hazard problems. We conclude 17
  19. 19. that product type and entrepreneur experience are very important for overcoming these problems. Such informational characteristics, however, receive different response from different investors. For example, informal investors strongly prefer new products and they become less concerned with investor protection when startups signal that they will produce a ‘new product.’ This may be because their connectedness to the entrepreneur reduces the risk of the investment. In sharp contrast, institutional investors seem to be interpreting the same signal (new product) as having higher uncertainty and higher probability of failure, which leads to less willingness to provide capital. Investor protection becomes more important for the institutional investors when a new product is involved because they need better protection to offset the negative effect imposed by higher uncertainty from the new product. We also find that entrepreneurial experience is helpful to obtain financing from both informal and institutional investors. It will effectively offset the importance of investor protection. Evidence for production technology is quite weak, but the results seem to suggest that informal investors do not like to finance startups using new technology while institutional investors have the opposite attitude. We believe the difference is due to the different levels of expertise in evaluating projects between the two investor types. We also conclude that the institutional environment is very important for access to external financing. High amounts and diversity of external financing are associated with high levels of property rights, contract enforcement, and corruption protection, which all proxy for investor protection. 18
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  23. 23. Table 1 Summary statistics We use the survey data of year 2003 from Global Entrepreneurship Monitor (GEM). Only persons that, alone or with others, are currently starting or have started in the past 6 months a new business were asked about the total investment and personal investment. In addition, they are also encouraged to indicate whether financing from certain investorsa was provided. Appendix Table A1 explains the key variables and corresponding survey questions. The 2003 GEM database contains 8,277 observations for new businesses. After limiting the sample to observations with financing data (see the appendix), the final sample consists of 1,869 startups in 27 countries (or regions). The filter criteria can be found in Appendix ‘Filter Criteria to Enter Sample’. Panel A Entrepreneur characteristics Panel B Firm level characteristics Panel C Financing characteristics N Percent N Percent N Percent Total 1869 Total 1869 100 Total 1869 100 Age New product Financing from 10-17yrs 17 0.91 No 962 51.47 self saving &income 747 39.97 18-24yrs 281 15.03 Yes 907 48.53 close family member 239 12.79 25-34yrs 560 29.96 Ownershipc work colleague 144 7.7 35-44yrs 501 26.81 Single owner 818 44.82 employer 265 14.18 45-54yrs 345 18.46 Multiple owner 1007 55.18 friends & neighbors 1075 57.52 55-64yrs 143 7.65 New technology banks & financial institutions 634 33.92 65-98yrs 22 1.18 No 1586 84.86 government program 206 11.02 Sex Yes 283 15.14 Male 1250 66.88 Start -up experienced Female 619 33.12 No 195 10.65 N USD b Education Yes 1636 89.35 Total Investment 1869 173,542 None 13 0.8 Self Investment 1869 51,400 some secondary( 1-11 yrs) 304 18.79 secondary degree (12 yrs) 485 29.98 post secondary (13-16 yrs) 716 44.25 graduate (17-20 yrs) 100 6.18 a (1) Self saving & income, (2) close family member, (3) work colleague, (4) employer, (5) friends & neighbors, (6) banks & financial institutions, and (7) government program. b c d 1618 1825 1831 22
  24. 24. Table 2 External Financing Ratio and Financing Diversity—firm characteristics External Financing Ratio is calculated as the total investment minus the self-investment divided by the total investment. External Financing Diversity equals the sum of the number of external financing channels including: (1) work colleague (2) employer (3) friends & neighbors (4) banks & other financial institutions (5) government program. T-statistics are provided for group mean comparison. Panel A N Mean Std Min Max External financing ratio 1783 0.41 0.34 0 0.99 External financing diversity 1783 1.24 0.88 0 5 Panel B Group Mean External financing diversity External financing ratio New product yes 1.35 0.41 no 1.15 0.40 yes vs no t-stat 4.65*** 0.32 New technology yes 1.40 0.45 no 1.22 0.40 yes vs. no t-stat 3.06*** 2.32** Prior starting-up Experience Yes 1.24 0.41 No 1.23 0.36 yes vs. no t-stat 0.16 2.00** ***1% significant **5% significant *10% significant 23
  25. 25. Table 3 Sorting by Information Aggregation Three dummy variables are created and equal to one if the start-up produces a new product, uses a new technology, or is managed by experienced entrepreneurs. They are equal to zero otherwise. We add up these three dummies and call their sum the index of ‘information aggregation,’ which ranges from 0 to 3. We sort the whole sample according information aggregation index and evaluate external financing for each index subgroup. For each index subgroup, simple averages calculated. The F-statistic tests whether the four mean financing variables are equal, while the t-statistic tests whether the two extreme group means are equal. Information Aggregation Existing product New product Existing technology New technology No experience Experienced F-stat t-stat 0 1 2 3 Joint Equality µ0 = µ3 Financing ratio 0.33 0.41 0.42 0.46 16.97*** -4.17*** External financing diversity 0.99 1.17 1.32 1.40 7.52*** -3.08*** ***1% significant **5% significant *10% significant 24
  26. 26. Table 4 External Financing and Information In a Tobit regression framework, external financing ratio is regressed on the informational characteristics including a new product, new technology and entrepreneurial experience. In addition, size (log of total investment), log GNP per capita in 2003, and the stock market capitalization to GDP ratio in 2003 are included as control variables. The regressions are censored from the left at zero. (Standard error is below the estimate.) External Financing Ratio 1 2 3 4 Informational Characteristics New product -0.0028 -0.0038 (0.0121) (0.0117) New technology 0.0342** 0.0276* (0.0165) (0.0161) prior starting-up experience 0.0486** 0.0471** (0.0199) (0.0191) Control Variables size 0.0209*** (0.0029) log GNP per capita 0.017** (0.0067) Stkmktcap/GDP 0.0089 (0.0119) Intercept 0.6231* 0.6163* 0.5781* 0.1962*** (0.0084) (0.0066) (0.0189) (0.0586) number of observations 1783 1783 1783 1783 Noncensored value 1177 1177 1177 1177 Log likelihood 173.5 175.6 176.4 226.8 ***1% significant **5% significant *10% significant 25
  27. 27. Table 5 Country summary Institutional environment is characterized in three aspects: protection of private property, quality of contract enforcement, and freedom from corruption. Property rights: 7= financial asset and wealth are clearly delineated and protected by law, 1= poorly delineated and not protected by law. Contract enforcement: 7= Judiciary is completely independent from government and/or parties to disputed; government completely neutral among bidders when deciding upon public contracts, 1= opposite. Corruption Freedom: 0-100 where 100 indicates completely free from corruption. Legal origin classification follows LLSV (1998, 1999). Country Frequency Property Rights Contract Corruption Stock market GNP GDP growth Enforcement Freedom cap/GDP per capita (1990-2001) Belgium 30 5.9 5.41 66 0.8918 19900 3.9 Canada 42 6.2 5.50 89 0.8643 21930 3.1 Hong Kong, China 13 6.4 5.64 79 3.7362 25330 3.8 Ireland 57 6.1 5.71 75 0.4660 22850 7.7 New Zealand 81 5.9 6.05 94 0.3408 13250 3.1 Singapore 29 6.5 5.97 92 1.3496 21500 7.4 South Africa 44 5.3 4.17 48 1.3651 2820 2.1 United Kingdom 239 6.3 5.86 83 1.2049 25120 2.7 United States 271 6.5 5.64 76 1.1689 34280 3.4 Australia 38 6.2 5.86 85 0.5557 6940 3.6 English-origin average 844 6.13 5.58 71.55 1.09 17629.09 3.71 Brazil 54 5 3.97 40 0.4888 23850 2.2 Chile 127 5.6 5.03 75 0.9091 4590 6.3 France 14 6.4 5.69 67 0.6490 22730 1.9 Greece 22 5 4.44 42 0.5028 11430 2.4 Italy 11 6.2 4.55 55 0.3657 19390 1.6 Netherlands 22 6.5 6.09 88 0.8333 24330 2.9 Spain 76 5.9 5.23 70 0.6798 14300 2.7 Venezuela 223 3.8 2.76 28 0.0469 4760 1.5 French-origin average 549 5.55 4.72 58.13 0.56 15672.50 2.69 Germany 154 6.5 5.89 74 0.3645 23560 1.5 Japan 6 6.1 5.23 71 0.6141 35610 1.3 Switzerland 47 6.5 5.97 84 1.9949 38330 1.0 German-origin average 207 6.37 5.70 76.33 0.99 32500.00 1.27 Denmark 27 6.4 6.21 95 0.4663 30600 2.4 Finland 17 6.5 6.35 99 0.9585 23780 2.9 Norway 34 5.9 5.62 86 0.3658 35630 3.5 Sweden 22 5.9 5.87 90 0.7755 25400 2.1 Scandinavian-origin average 100 4.94 4.81 74 0.51 15257.5 2.73 Slovenia 33 4.8 4.50 52 0.2103 9760 2.9 China 136 4.1 3.74 35 0.3507 890 10.0 Socialist-origin average 169 4.45 4.12 29 0.19 3550.00 4.30 26
  28. 28. Table 6 Sorting by Property Rights External Financing Ratio is calculated as the total investment minus the self-investment divided by the total investment. External Financing Diversity = sum of the number of external financing channels including: (1) work colleague (2) employer (3) friends & neighbors (4) banks & other financial institutions (5) government program. Countries are sorted into quartiles of property rights, contract enforcement, and corruption freedom. Equal-weighted averages are reported for each quartile. The F-statistic tests whether the four mean financing variables are equal, while the t-statistic tests whether the two extreme group means are equal. Property right protection 1 (Worst) 2 3 4 (Best) Joint equality F-stat ‘Worst’ vs. ‘Best’ t-stat Financing ratio 0.385 0.397 0.429 0.441 2.98** -2.67*** External financing diversity 0.963 1.314 1.358 1.367 24.84*** -7.54*** Contract enforcement 1 (Worst) 2 3 4 (Best) Joint equality F-stat ‘Worst’ vs. ‘Best’ t-stat Financing ratio 0.386 0.42 0.424 0.434 1.72 -1.78* External financing diversity 0.96 1.34 1.372 1.354 26.02*** -5.68*** Corruption freedom 1 (Worst) 2 3 4 (Best) Joint equality F-stat ‘Worst’ vs. ‘Best’ t-stat Financing ratio 0.384 0.4 0.44 0.444 3.18** -2.17** External financing diversity 0.947 1.32 1.35 1.431 28.05*** -6.91*** ***1% significant **5% significant *10% significant 27
  29. 29. Table 7 Informal Financing These are Logit regressions of the presence of informal financing on informational characteristics (product type, production technology and entrepreneur experience), investor protection and their interactive terms. New product=1 if product is new to all or some consumers, 0 otherwise; New technology =1 if the production technology was not available 1 year ago, zero otherwise; Prior startup experience=1 if entrepreneurs have prior startup experience, 0 otherwise. Investor protection is equal to the sum of protection of private property, quality of contract enforcement and freedom from corruption. The higher the score, the better investors are protected. (Standard error is provided in the parentheses.) 1 2 3 4 New product 0.7663** 0.8876** (0.3823) (0.3961) New technology -0.4195 -0.6083 (0.5495) (0.5669) Prior startup experience 1.1073* 1.0343 (0.6706) (0.6772) Investor protection 0.0178*** 0.0137*** 0.0276*** 0.0297*** (0.0030) (0.0025) (0.0079) (0.0082) New product × Investor protection -0.0087* -0.0105** (0.0047) (0.0049) New technology × Investor protection 0.0064 0.0083 (0.0067) (0.0070) Prior startup experience × investor protection -0.0152* -0.0143* (0.0083) (0.0083) Size 0.3372*** 0.3449*** 0.3553*** 0.3504*** (0.0310) (-0.3457) (0.0314) (0.0317) Log of GNP per capita -0.3218*** 0.0309 -0.3831*** -0.3574*** (0.0940) (0.0928) (0.0968) (0.0983) Stock market cap. /GDP 0.1548 0.1633 0.0899 0.0817 (0.1335) (0.1338) (0.1352) (0.1351) Self-investment -1.1223*** -1.1213*** -1.1211*** -1.1138*** (0.1093) (0.1093) (0.1112) (0.1114) Intercept -0.1236 0.2171 -0.7718 -0.9614 (0.2445) (0.2082) (0.6432) (0.6623) Number of observation 1869 1869 1831 1831 AIC 2053.985 2056.898 2006.041 2008.254 -2 Log L 2037.985 2040.898 1990.041 1984.254 ***1% significant **5% significant *10% significant 28
  30. 30. Table 8 Institutional Financing These are Logit regressions of the presence of institutional financing on informational characteristics (product type, production technology and entrepreneur experience), investor protection and their interactive terms. New product=1 if product is new to all or some consumers, 0 otherwise; New technology =1 if the production technology was not available 1 year ago, zero otherwise; Prior startup experience=1 if entrepreneurs have prior startup experience, 0 otherwise. Investor protection is equal to the sum of protection of private property, quality of contract enforcement and freedom from corruption. The higher the score, the better investors are protected. (Standard error is provided in the parentheses.) 1 2 3 4 New product -0.3318 -0.3899 (0.3789) (0.3911) New technology 0.1933 0.2492 (0.5207) (0.5393) Prior startup experience 1.5123* 1.4269* (0.7753) (0.7749) Investor protection 0.0047 0.0084*** 0.0251*** 0.0198** (0.0029) (0.0024) (0.0087) (0.0089) New product * Investor protection 0.0085* 0.0085* (0.0045) (0.0047) New technology * Investor protection 0.0020 0.0002 (0.0062) (0.0064) Prior startup experience * investor protection -0.0176** -0.0165* (0.0090) (0.0090) Size -0.0506* -0.0590** -0.0561** -0.0518* (0.0264) (0.0262) (0.0264) (0.0268) Log of GNP per capita 0.1432 0.1186 0.0828 0.1053 (0.0926) (0.0912) (0.0935) (0.0953) Stock market cap. /GDP -0.3861*** -0.3785*** -0.3447*** -0.3522** (0.1258) (0.1256) (0.1271) (0.1284) Self-investment -1.0426*** -1.0268*** -1.0280*** -1.0349*** (0.1039) (0.1036) (0.1049) (0.1056) Intercept -0.5119** -0.6910*** -2.0746*** -1.8425** (0.2424) (0.2048) (0.7524) (0.7666) Number of observation 1869 1869 1831 1831 AIC 2368.538 2377.824 2332.807 2323.246 -2 Log L 2352.538 2361.824 2316.807 2299.246 ***1% significant **5% significant *10% significant 29
  31. 31. Appendix Filter Criteria to Enter Sample We only use observations of those who are starting up or have started up in the past 6 months a new business. Sample is cleaned further by applying following filters: (1) If sumoney1 – sumoney 7 are all missing, then drop; (2) If sumoney1 – sumoney 7 are all ‘don’t know’, then drop; (3) If any of sumoney1 – sumoney 7 is equal to "yes", then missing value and ‘don’t know’ are reset equal to ‘No’; (4) if none of sumoney1 – sumoney 7 is "yes" and "don't know", but some are missing values, then drop; (5) if some of sumoney1 – sumoney 7 are "no" and "don't know" while some missing values, then drop; (6) If total investment in USD less than 50, then drop; (7) Observation from Crotia, Uganda and Iceland are dropped due to the lack of country level data After applying all filters, the final sample consists of 1869 startups. Table A1 Survey questions and corresponding variables Only those who are starting up or have started up in the past 6 months a new business are asked following questions regarding startup financing. Dummy variables (sumoney1-sumoney7) are multiple choices. Respondents can indicate any number of them as long as the corresponding source provided part/all starting-up capital. Variable Survey Questions SUMONTUS (USD) Start-up Money: Total Money required -US$ conversion? SUMONOUS (USD) Start-up Money: Personal money invested -US$ conversion? Yes No Don’t know SUMONEY1 1 0 8 Start-up money: from self, savings & income? SUMONEY2 1 0 8 Start-up Money: From close family member sibling? SUMONEY3 1 0 8 Start-up Money: From work colleague? SUMONEY4 1 0 8 Start-up Money: from employer? SUMONEY5 1 0 8 Start-up Money: from friends, neighbors? SUMONEY6 1 0 8 Start-up Money: from Banks, financial institutions? SUMONEY7 1 0 8 Start-up Money: from Government programs? 30
  32. 32. Table A2 Definition of Variables Variable Description External Financing Ratio Total investment minus self-investment divided by total investment Source : Global Entrepreneurship Monitor (GEM)Adult Population Survey 2003 External Financing Sum of the number of external financing channels including: Diversity (1) work colleague (2) employer (3) friends & neighbors (4) banks & other financial institutions (5) government program Source: Global Entrepreneurship Monitor (GEM)Adult Population Survey 2003 New product Dummy variable: 1= start-ups producing new product, 0 otherwise. Source : Global Entrepreneurship Monitor (GEM)Adult Population Survey 2003 New technology Dummy variable: 1= start-up using new technology unavailable one year ago, 0 otherwise. Source : Global Entrepreneurship Monitor Prior startup experience Dummy variable: 1= entrepreneur with prior starting-up experience, 0 otherwise. Source : Global Entrepreneurship Monitor (GEM)Adult Population Survey 2003 Property Rights Range: 1-7 where financial assets and wealth are : (1= poorly delineated and not protected by law, 7= clearly delineated and protected by law) Source: The Global Competitive Report 2001-2002 Contract Enforcement Range: 1-7 where 7= Judiciary is completely independent from government and/or parties to disputed; government completely neutral among bidders when deciding upon public contracts ; 1= opposite. Source: The Global Competitive Report 2001-2002 Corruption Freedom Range: 0-100 where 100 indicates completely free from corruption. Source: Index of Economic Freedom 2003 by The Heritage Foundation Investor Protection The sum of property rights, contract enforcement and corruption freedom. Stock market Domestic stock market capitalization/GDP , Source: World capitalization/GDP Bank 2003 Log GNP per capita Log of GNP per capita in 2003, Source: World Development Indicator 2003 Size Log of total investment, Source: Global Entrepreneurship Monitor (GEM)Adult Population Survey 2003 31
  33. 33. Table A3 Country Breakdown of Financing Sources from self, savings & From close family from Banks, financial from Government Country Size income member sibling From work colleague from employer from friends, neighbors institutions programs Yes No Yes No Yes No Yes No Yes No Yes No Yes No Australia 38 10 28 3 35 1 37 5 33 28 10 6 32 5 33 Belgium 30 5 25 2 28 3 27 1 29 22 8 10 20 4 26 Brazil 54 18 36 9 45 5 49 8 46 25 29 20 34 9 45 Canada 42 13 29 7 35 5 37 8 34 31 11 15 27 11 31 Chile 127 46 81 22 105 11 116 8 119 75 52 45 82 14 113 China 136 89 47 47 89 20 116 53 83 59 77 21 115 1 135 Denmark 27 11 16 2 25 2 25 3 24 15 12 3 24 6 21 Finland 17 4 13 2 15 2 15 2 15 14 3 14 3 6 11 France 14 6 8 2 12 0 14 1 13 9 5 7 7 4 10 Germany 154 60 94 19 135 9 145 14 140 98 56 84 70 11 143 Greece 22 8 14 5 17 0 22 3 19 13 9 6 16 0 22 Hong Kong 13 7 6 2 11 2 11 6 7 4 9 3 10 0 13 Ireland 57 21 36 4 53 5 52 7 50 39 18 24 33 8 49 Italy 11 6 5 3 8 0 11 0 11 3 8 5 6 1 10 Japan 6 1 5 0 6 0 6 2 4 2 4 5 1 1 5 Netherland 22 9 13 1 21 3 19 3 19 13 9 7 15 2 20 New Zealand 81 25 56 9 72 9 72 8 3 55 26 16 65 17 64 Norway 34 13 21 4 30 6 28 4 30 24 10 18 16 13 21 Singapore 29 9 20 6 23 5 24 5 24 15 14 10 19 6 23 Slovenia 33 15 18 1 32 3 30 6 27 8 25 9 24 5 28 South Africa 44 22 22 12 32 0 44 4 40 25 19 13 31 2 42 Spain 76 22 54 9 67 5 71 13 63 55 21 21 55 0 76 Sweden 22 5 17 2 20 1 21 0 22 16 6 9 13 3 19 Switzerland 41 29 18 6 41 8 39 8 39 26 21 9 38 10 37 U.K. 239 70 169 24 215 10 229 18 221 157 82 85 154 55 184 U.S. 271 98 173 4 267 18 253 47 224 177 94 102 169 10 261 Venezuela 223 125 98 32 191 11 212 28 195 50 173 67 156 2 221 32

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