Determinants of Start-up Firm External Financing WorldwideDocument Transcript
Determinants of Start-up Firm External Financing Worldwide
John R. Nofsinger
Washington State University
College of Business
Pullman, WA 99164-4746
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
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
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
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).
See Avery et al. (1998), Berger and Udell (1998), and Gregory et al. (2005), and Vos et al. (2007).
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.
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
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
Signal 1b: A new product signals risk and more uncertainty, which leads to worse access to external
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
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
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
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
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
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
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.
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
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
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
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
(1) Self saving & income, (2) close family member, (3) work colleague, (4) employer, (5) friends & neighbors, (6) banks &
financial institutions, and (7) government program
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
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
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
index subgroup, simple averages of external financing ratio and external financing diversity are
[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
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.
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.
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.
[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
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]
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.
Size-weighted results are qualitatively similar to the equal-weighted average and are therefore not reported.
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
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
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
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
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.
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.
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
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
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.
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
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
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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
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
(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
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.
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
External financing diversity External financing ratio
yes 1.35 0.41
no 1.15 0.40
yes vs no t-stat 4.65*** 0.32
yes 1.40 0.45
no 1.22 0.40
yes vs. no t-stat 3.06*** 2.32**
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
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.
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
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
External Financing Ratio
1 2 3 4
New product -0.0028 -0.0038
New technology 0.0342** 0.0276*
prior starting-up experience 0.0486** 0.0471**
log GNP per capita 0.017**
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
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
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
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***
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***
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
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**
New technology -0.4195 -0.6083
Prior startup experience 1.1073* 1.0343
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**
New technology × Investor protection 0.0064 0.0083
Prior startup experience × investor protection -0.0152* -0.0143*
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
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
1 2 3 4
New product -0.3318 -0.3899
New technology 0.1933 0.2492
Prior startup experience 1.5123* 1.4269*
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*
New technology * Investor protection 0.0020 0.0002
Prior startup experience * investor protection -0.0176** -0.0165*
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
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
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
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?
Table A2 Definition of Variables
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
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
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
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
Size Log of total investment, Source: Global Entrepreneurship
Monitor (GEM)Adult Population Survey 2003