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Investors as Capabilities: Intra-Investor
Complementarities and Startup Performance
Shai Harel
ABSTRACT
The contribution of investors to the performance of their portfolio companies within the
VC industry has been researched thoroughly. A novel aspect of this paper is that it
examines the impact of a syndicated investment using two different measures: (a) the
total number of investors; and (b) the total number of different investor types. Using a
novel dataset and while controlling for the endogeneity, this paper finds that having more
investors of the same type has a positive curvy-linear impact on the portfolio company's
performance. However, a larger number of investor types has only a positive linear
impact on the company's performance. It is suggested that a tradeoff between having
more investor complementarities and increased coordination costs is the driving
mechanism of these results.
Key Words: Venture Capital Industry, Investor Types, Heterogeneity vs. Homogeneity,
Syndication, Startup Performance
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Investors as Capabilities: Intra-Investor
Complementarities and Startup Performance
INTRODUCTION
The venture capital industry comprises many types of investors including: venture
capital funds (VC), corporate venture capital (CVC), angel investors, incubators,
industrial companies, financial institutions and more. Each of these investor types
has different attributes, investment preferences, constraints or hands-on approach
toward their portfolio companies. On one hand, it is possible that having more
investors means greater support and contribution to the portfolio company. On the
other, having "too many" investors may mean more conflicts, conflicting interests
and additional coordination costs. The aim of this paper is to examine the impact of
having more investors on the performance of portfolio companies. It does so by
deconstructing the concept of having “more investors” into two sub-questions: (a)
what is the impact of having a larger number of investors on the performance of the
portfolio company? and (b) what is the impact of having a larger number of investor
types on the performance of the portfolio company?
The main finding of this paper is that having more investors of the same type
has a curvilinear impact on the portfolio company's performance. However, a larger
number of investor types has only a linear impact on the company's performance.
That is, the paper shows that there is an inverted U-shaped relationship between
the number of investors and the exit prospects of a startup (either by an M&A or an
IPO). Similarly, the paper shows a U-shaped relationship between the number of
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investors and the write-off prospects of the startup. However, when the
contribution of more investor types to the performance of the firm is examined, the
paper shows a linear relationship to exit probability and a negative linear
relationship to write-off. When examining the impact on the time to exit, a U-shaped
relationship is found between the number of investors and the time it takes a
company to exit, and a negative linear relation between the 'number of investor
types' to the time it takes a company to exit.
The explanation for these findings relies on the understanding that a startup
in its early stages relies on external complementarities to succeed (Prahalad &
Hamel, 1990; Teece, 1986). The investors of the startup in most cases supply these
complementarities in the form of hands-on support or active involvement (for
example: Ber & Yafeh, 2007; Davila, Foster, & Gupta, 2003; Engel & Keilbach, 2007;
Hellmann & Puri, 2000, 2002; Hochberg, Ljungqvist, & Lu, 2007; Kortum & Lerner,
2000; Manigart & Van Hyfte, 1999). However, having more investors increases the
coordination costs and conflicts of interest. Thus, beyond a certain number of
investors, the costs exceed the benefits. However, when the 'number of investor
types' increases, the variety of complementarities supplied by more investor types
compensates for the coordination costs and the conflicts of interest.
Investments in the venture capital industry are far from random. Startups
select their investors from among several offers, and similarly, the various investor
types carefully examine potential investments in startups. Several methods were
used to address these selection concerns: First, the empirical analysis control for
endogeneity using an instrumental variable approach. Second, in parts in which
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survival analysis was used, an attempt was made to address endogeneity. Third, a
set of robustness tests were performed, both for the instrumental variable and for
the confounding effect that might occur between the two main dependent variables.
To examine these questions, a unique database was assembled. This database
comprises 1003 angels, 60 CVCs, 601 VCs, 35 incubators, 982 industry-related
companies, 879 finance-related companies, and 55 other investors, participating in
9675 financing rounds investing in 2409 companies during 15 years of activity
(1990-2005). The database contains information on investments made in the Israeli
market, which is one of the world’s most vivid venture capital investment markets.
The rest of the paper is organized as follows: Section 2 sets the theoretical
background and presents the hypotheses. Section 3 describes the database,
empirical approach and presents descriptive statistics. Section 4 contains the
empirical results and Section 5 concludes and sets directions for future research.
LITERATURE REVIEW
The aim of this paper is to assess the impact of the number of investors on
performance of their portfolio companies. However, clarification of what it means to
have "more investors" is required. The first option is that it means the company has
a larger number of investors (i.e. 10 investors rather than two). The second option is
that the company has more investor types (i.e. one angel, one VC and one CVC vs.
just VCs). The analysis in this paper addresses both options. Thus, based on the
importance of the investors' hands-on approach to the performance of the startup,
the literature review begins with an elaboration of the concept of complementarities
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between firms; next is a review of the literature concerning the impact of the
number of syndicate members on the performance of a portfolio company. This is
followed by a review of various investor types, their hands-on approach and an
examination of the impact of number of investor types on the portfolio company's
performance.
INVESTORS AND COMPLEMENTARITIES
Starting any new business is, in most cases, a complex and risky action, all the more
so in the case of startups. Startups usually face great technological challenges,
require longer periods of research & development (that sometimes can even take
years) and need considerable resources. Even when a potentially successful
product/service is developed, the difficulties and complexity associated with
launching it or with other post-sales activities are usually greater than for a
"standard" good or service. Moreover, in many cases of technology-based startups,
the entrepreneurs who initiate them are not experienced business people. For
example, the entrepreneurs can be scientists or engineers with a great and
promising innovative idea; these entrepreneurs might even have the basic skills to
launch their business, but it can be safely assumed that their "personal competitive
advantage" rests in the technological sphere and that their inexperience or limited
managerial skills endanger the process of transforming innovation into a
marketable product/service (regardless of its quality). The company's investors are
aware of these risks and thus take a hands-on approach as they often possess the
complementarities the startup needs.
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But what are these complementarities exactly? The concept of
complementarities evolved from Penrose's (1959) work on the growth of the firm.
According to Penrose, the company’s competitive advantage lay in its ability to
create and sustain a set of unique capabilities, and with these aptitudes, the
company may gain an advantage over its competitors and create a sustainable
competitive advantage (Barney, 1991; Prahalad & Hamel, 1990). Later work by
Teece (1986) and Prahalad and Hamel (1990) suggest that a company should focus
on its unique knowledge and capabilities while obtaining complementary assets and
capabilities from outside the firm (Prahalad & Hamel, 1990; Teece, 1986).
As stated above, within knowledge-intensive companies, the issues
associated with such complementarities are more complex. Due to limited resources
and time constraints, the startup is unable to develop them on its own (Aghion &
Tirole, 1994); thus, the startup must rely on outside support for its development
and commercialization process (Gans, Hsu, & Stern, 2002; Teece, 1986; Tripsas,
1997). The support is needed in almost every aspect of the firm: R&D, production,
marketing, sales, pre- and post-sale service, human resources and financing. Indeed,
most investors in the venture capital industry view themselves as providers of
"Smart Money,” that is, beyond the money they invest in the company, they also
provide additional added value that, they hope, improves the success prospects of
the portfolio company. The second part of this paper reviews this literature, while
the first part reviews the contribution of the syndicate to its portfolio companies.
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THE BENEFITS OF HIGHER NUMBER OF SYNDICATE INVESTORS
The paper by Brander, Amit and Antweiler (2002) aims to understand the
motivations for VCs to syndicate. More specifically, they test two alternate reasons,
(a) an additional VC brings about a useful second opinion; and (b) additional VCs
contribute additional capabilities and added value. Their empirical analysis
supports the added-value explanation and shows that syndicated investments
perform better than standalone investments. Additionally, they find that a larger
number of VCs investing in the same company is associated linearly with increased
returns; they also test for non-linear association (between the number of investors
and the returns) but do not find such an association.
Similarly, Tian (2011) compares the performance of portfolio companies
receiving an investment from a syndication of VCs vs. companies financed by a
single VC investor. His main finding is that a syndication of VCs invest more in young
firms in early financing rounds; this also leads to a better product market value for
portfolio firms. Additionally, VC syndicate-backed firms are more likely to have a
successful exit, receive a higher IPO market valuation, incur less IPO underpricing
and perform better after their IPO.
Das, Jo and Kim (2011) examine an interesting viewpoint concerning the
performance of companies financed by VC syndicates vs. those who receive
financing from non-syndicated VCs. More specifically, they try to understand
whether performance is driven by value added or solely by better selection.
Controlling for endogeneity, they find that companies financed by VC syndicates
have higher exit probabilities and faster time-to-exit. Additionally, their results
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show that the magnitude of exit multiples is a result of the selection of portfolio
companies, whereas the probability and speed to exit are the result of the value-
added they gain.
This paper differs from the above three in several respects. First, it
incorporates a new measure for the syndicate size and deals with a broader range of
investor types. Second, it looks at the impact of varying (continuous) syndicate size
as opposed to the former two (Tian's and Das, Jo and Kim), which examine a single
VC vs. a VC syndicate. Third, while their papers only examine the impact of
syndication on positive results (i.e. exit) this paper also examines the impact on
negative results (i.e. if the company ceases to operate) and on the time to exit.
Fourth, the findings in this paper differ from theirs as the analysis shows a non-
linear relationship between the number of investors to: (a) the exit prospect of the
startup; (b) the failure prospect of the startup; and (c) the startup's time to exit.
THE DETRIMENTS OF HIGHER NUMBER OF SYNDICATE INVESTORS
The literature presented above showed the benefits of having more investors. A
company with more investors has more complementarities and capabilities at its
service. However, not all that glitters is gold; having more investors may bring about
difficulties as well. In a closely related working paper, Du (2009) examines the
benefits and costs VCs should consider when selecting syndication partners. More
specifically, Du looks into the impact of syndicating with homogeneous vs.
heterogeneous partners on the VC itself and on the performance of the portfolio
companies. Du claims that potential syndicate members should be viewed through
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the lens of two transaction-cost types: coordination costs and agency costs. For
example, VCs with heterogeneous venture experience may have more
disagreements regarding their advice to portfolio companies, and thus face more
difficulties coordinating actions. Moreover, following research on teamwork, Du
claims that VCs' actions alignment will take longer and require more effort in
heterogeneous syndications. Concerning agency costs, Du claims that when less
experienced VCs syndicate with more experienced VCs, the VC with more
experience may face agency costs as the VC with less experience will exert less effort
in monitoring and advising portfolio companies. On the other hand, Du also
discusses the benefits of heterogeneity and claims that it may also be helpful for the
VCs to syndicate, as one of the properties of the VC industry is that the various
investors face many complex and non-routine problems. The decisions the VC
industry deals with (such as: opportunity assessment, recruiting policies and
preferences, exit or write-off decisions) are subjective and thus, co-investing with
diverse partners can lead to better decision-making (this is similar to the
motivations suggested by Brander, Amit and Antweiler (2002)). The major
difference between this paper and Du's is that she examines whether VCs prefer
similar or different syndicate partners and what the prospects are for the formation
of various syndicate mixtures. Additionally, she examines what the impact of this
choice is on the portfolio company and on the VC itself. This paper, on the other
hand, does not deal with prospects for formation of various syndicate mixtures, nor
does it deal with their impact on the VC itself. Similarly to her work this paper
focuses on the syndicate's implications on its portfolio companies but differs
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empirically in several aspects: (a) in her empirical analysis, Du considers only the
first round of investment while this paper considers all rounds; (b) this paper uses
different measures to examine the impact of the size of the syndicate ('number of
investors') and of diversity in investor types (i.e. VCs, Angels) on the performance of
the portfolio company; (c) this paper has more outcome measures such as write-off
dummy and time to exit; and (d) this paper finds a non-linear relationship between
number of investors to the three dependent variables mentioned above.
Indeed, a few papers find evidence for a negative impact of the number of
investors on the startup's performances. For example, in a working paper, Guler &
McGahan (2007) claim that, in countries with weak legal and institutional systems, a
member of a VC syndicate may behave opportunistically and harm the performance
of portfolio companies. Other evidence for a negative impact was found in a paper
closely related to this one by Agarwal (2011). Similarly to this paper, Agarwal claims
that as the number of investors increases, a negative impact may occur as problems
such as coordination or conflicts of interest arise. Moreover, Agarwal suggests that
adding more investors in later rounds may cause the startup to receive less
monitoring and less added value. Using both a game theoretical model and an
empirical analysis, Agarwal finds that the marginal contribution of each additional
investor diminishes and creates an inverted U-shaped relation between having
more investors and the performance of the portfolio company. Several differences
distinguish his paper and this one. First, this paper tests two facets of additional
investors (such as: greater number of investors; a larger number of investor types)
while his tests only for one. Second, this paper’s analysis examines a broader scope
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of performance indicators (exit dummy, closure/write-off dummy and time to exit).
Third, Agarwal examines this question through the lens of the marginal effort
invested by an additional investor relative to its share in the startup; thus, Agarwal
tests the impact of an additional investor between rounds. This paper examines the
cumulative impact of an additional investor; that is, it tests the impact of all
investors in all rounds together. The reasoning behind this approach is that, when
assessing the impact of an additional investor between rounds, the measures for
failure or success (the dependent variable) should be of the same nature and not the
final outcome of the startup (exit or write-off). For example, a round-based analysis
may address questions such as: by how much has the value of the firm increased or
decreased in that round? How much money was raised in the following round/s?
Though these are intriguing and important questions, the database does not contain
enough information to address them.
NUMBER OF INVESTOR TYPES
Most (if not all) the literature that deals with the impact of a syndicate on the
performance of its portfolio company examined it by counting the number of
investors. However, past research showed that not all investor types support the
same set of activities. This is only logical, as not every investor has the same
capabilities (and even if they did have the same capabilities they probably vary in
quality); thus, different investors may contribute to different aspects and in varying
intensity. To clarify this, the next part briefly elaborates the main finding of the
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hands-on approach of three major types of investors: business angels, VC funds and
CVCs.
The literature on VC funds has shown that VC funds are closely involved in
the operations of portfolio firms. This includes recruiting policies (Davila, Foster, &
Gupta, 2003; Hellmann & Puri, 2000, 2002), innovation effectiveness (Hellmann &
Puri, 2000; Kortum & Lerner, 2000), CEO turnover, and the probability of hiring a
marketing VP or of introducing an employee stock-option scheme (Hellmann & Puri,
2002). VC funding has also been associated with faster growth rates (Davila, Foster,
& Gupta, 2003; Engel & Keilbach, 2007), increased survival probability (Ber & Yafeh,
2007; Hochberg, Ljungqvist, & Lu, 2007; Manigart & Van Hyfte, 1999) and higher
profit volatility (Manigart & Van Hyfte, 1999).
Business angels are also actively involved in their portfolio companies. They
are shown to be involved in the startups in day-to-day operations (Amatucci & Sohl,
2004; Benjamin & Margulis, 1999; Brettel, 2003; Freear, Sohl, & Wetzel, 1995;
Madill, Haines, & Riding, 2005; Stevenson & Coveney, 1996), human resource
operations (Ardichvili, Richard, Tune, & Reinach, 2002; Brettel, 2003; Harrison &
Mason, 1992b), mentoring and business advice (Ardichvili, Richard, Tune, &
Reinach, 2002; Brettel, 2003; Ehrlich, De Noble, Moore, & Weaver, 1994; Lumme,
Mason, & Suomi, 1998; Mason & Harrison, 1996; Paul, Whittam, & Johnston, 2003;
SæTre, 2003; Stevenson & Coveney, 1996; Tashiro, 1999), networking activities
(Amatucci & Sohl, 2004; Ardichvili, Richard, Tune, & Reinach, 2002; Brettel, 2003;
Lumme, Mason, & Suomi, 1998; Mason & Harrison, 1996; Paul, Whittam, & Johnston,
2003; SæTre, 2003; Sørheim, 2005), strategic planning (Amatucci & Sohl, 2004;
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Ardichvili, Richard, Tune, & Reinach, 2002; Brettel, 2003; Ehrlich, De Noble, Moore,
& Weaver, 1994; Harrison & Mason, 1992a; Lumme, Mason, & Suomi, 1998; Mason
& Harrison, 1996) and supervision (Ehrlich, De Noble, Moore, & Weaver, 1994;
SæTre, 2003).
In the case of CVCs, the issue of active involvement and added value is more
complex. On one hand, CVC have been found to support various types of added value
activities and provide complementarities such as supplying infrastructure for
product development, supportive manufacturing resources and marketing, sales
and post-sales activities (Dushnitsky & Lenox, 2005; Katila, Rosenberger, &
Eisenhardt, 2008). On the other, the motivations behind CVC investments are
somewhat different from those of other investors; while other investors mainly seek
financial gains, CVCs view the strategic nature of their investment as more
important than financial considerations; thus, conflicts may arise between the CVC
and the invested company. For example, it was found that the investing CVC may
produce competing products or expropriate the intellectual property of the invested
company (Hellmann, 2002, Dushnitsky and Shaver, 2009). Moreover, the CVC's
oversight teams are less experienced in supporting such investments (compared to
independent VCs) and less financially incentivized, and therefore the quality of their
added value is generally lower than that of independent VCs (Dushnitsky & Shapira,
2010; Ivanov & Xie, 2010). Another conflict potentially arises when other
corporations are reluctant to cooperate with the invested company, as it is at least
partly owned by a competitor (Park & Steensma, 2012). Having said all that, and
despite the drawbacks that accompany CVC, past research has shown that CVC-
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backed companies perform somewhat better than companies backed by
independent VCs (Gompers & Lerner, 2000a; Maula & Murray, 2001).
As this short review shows, only some of the complementarities supplied by
the various investor types overlap. Thus, it may well be that it is not the number of
investors that influences the success prospects of a company, but the number of
investor types a company has that matters (i.e. it is better for a company to have
several investor types such as an angel, a VC and a CVC, rather than having only
VCs). The underlying assumption here is that when several types of investors are
jointly involved in the same company, then the scope of potential complementarities
standing at the service of a startup is greater than when only a single type of
investor is involved.
However, heterogeneity also comes with a price. When more investor types
are involved, the scope of considerations and constraints each type has is much
larger. For example, VCs and angels do not have the same investment strategy,
available capital or investment time horizon. A VC may find it easier to coordinate or
share goals with other VCs but it is not obvious that it can do this as easily with
angels or CVCs. These differences may increase the coordination costs and may even
harm the portfolio company.
As a case study for heterogeneity between investors, the next section briefly
reviews the literature that examines the relationship between two types of
investors: angels and VCs. Chemmanur and Chen (2006) formulate a model to
explain the startup's shift from angels to VC financing as the company reaches later
stages. They claim that VCs are able to add value to portfolio companies while angels
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cannot. By contrast, Schwienbacher (2012) claims that the difference between these
types lies elsewhere. While both can add value to the company, VCs possess greater
financial resources, crucial to firm development as it matures. Angels, on the other
hand, are aware of their disadvantage and compensate for it by investing more
effort, believing it will help attract additional investors in later stages. For example,
87% of angels in the US were found to possess experience in operations (Freear,
Sohl, & Wetzel, 1991) while VCs have very little or no experience in operations at all
(Van Osnabrugge & Robinson, 2000). Moreover, Benjamin and Margulis (1999)
argue that some angels work on a regular basis in the startup, making their
investment much more "personal.” A great example of this is the difference in time
perspective. Angels are said to do less intensive and less time-consuming due
diligence (Van Osnabrugge, 1998), but have a longer investment time horizon
(Freear, Sohl, & Wetzel, 1994; Wetzel, 1983). VCs, on the other hand, may not
always take decisions in the best interest of their portfolio companies. VCs were
found to perform “grandstanding,” meaning selling their shares at an IPO at a lower
price to raise further funds (Gompers, 1996; Johnson & Sohl, 2011; Lee & Wahal,
2004). Angels, on the other hand, usually do not face pressure to quickly signal their
high performance to the market, and therefore have a longer time horizon. This also
means their decisions will be in the portfolio company’s best interest (Johnson &
Sohl, 2011). However, this also has some negative effects; Goldfarb, Hoberg, Kirsch
and Triantis (2009) find that companies having only angel investors are more likely
to become “living dead” than when a VC is also involved. They suggest that either
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these firms need more patience or that angels are less prone to force unsuccessful
startups to liquidate.
HYPOTHESES
Summing up the possible impact of the syndication's size and structure on the
performance of the portfolio company, it seems there are two contradictory forces
working simultaneously. On one hand, having more investors/investor types
increases the availability of complementarities and capabilities, but on the other,
having more investors/ investor types increases the likelihood of conflict and raises
coordination costs. Thus, the first set of hypotheses that concern the 'number of
investors,’ the 'number of investor types' and the exit probabilities are:
Hypothesis 1a: There is an inverted U-shaped relationship between the
'number of investors' a company has and its exit probabilities
Hypothesis 2a: There is an inverted U-shaped relationship between the
'number of investor types' a company has and its exit probabilities
However, when the measure for performance is the probability of a write-off,
the expectation is to get the opposite relations. On one hand, as the number of
investors/investor types increases, the bundle of resources the startup has at its
disposal increases; thus, prospects of the company’s failure diminish. On the other
hand, as the number of investors/investor types grows further, the relative share of
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each investor decreases and the company may face a moral hazard problem as each
investor counts on the others to support the startup. Thus, the second set of
hypotheses concerning the 'number of investors,’ the 'number of investor types' and
the write-off probabilities are:
Hypothesis 1b: There is a U-shaped relationship between the 'number of
investors' a company has and its write-off probabilities.
Hypothesis 2b: There is a U-shaped relationship between the 'number of
investor types' a company has and its write-off probabilities.
Last, drawing upon the same reasoning mentioned above, the third set of
hypotheses that concern the 'number of investors,’ the 'number of investor types'
and the 'time to exit' are:
Hypothesis 1c: There is a U-shaped relationship between the 'number of
investors' a company has and the time to exit.
Hypothesis 2c: There is a U-shaped relationship between the 'number of
investor types' a company has and the time to exit.
The next section defines the dependent variables by which these hypotheses
will be tested.
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VARIABLES AND METHOD
DEPENDENT VARIABLES
This paper’s main question concerns the contribution of investors to the
performance of their portfolio companies. In the case of the VC industry, financial
measures such as IRR are not available as the information required to compute it is
usually not disclosed. Thus, following former research (for example: Brander, Amit,
& Antweiler, 2002; Gompers & Lerner, 2000b; Sørensen, 2007) the paper uses exit
as a measure of successful investments. Thus, the first dependent variable is 'Exit
Dummy,' which equals one if the company performed an exit (either through an IPO
or an M&A) and zero otherwise. As the database also contains information on cases
when companies were written off, the second performance variable is 'Write-off
Dummy' (which again equals one if the company ceased operations and zero
otherwise). Last, as this paper aims to assess the complexities involved with
syndications, following Das, Jo and Kim (2011) the third dependent variable is 'Time
to Exit' (measured in years). This is defined as the time passed since the company
was established until it reported an M&A or an IPO. Notably, using and 'Exit' or
'Write-off' may be a noisy measure of performance as not all exits are necessarily
good for the company (for example, consider a case where the company was sold for
a smaller amount than was invested in it).
MAIN INDEPENDENT VARIABLES
The first main independent variable is the total 'number of investors' that invested
in the company in all rounds regardless of their types (if the same investor
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performed a follow-up investment in the same company, it was counted only once).
The logic behind this measure is that more investors result in more
complementarities. The second main independent variable assumes that it is not the
total 'number of investors' that matters but rather the 'number of investor types'
that brings a larger scope of complementarities. Thus, this variable counts the total
number of types that invested in the company in all rounds. Investors are classified
into seven types. To test the non-linear relationships, the quadratic terms of both
variables (the number of investors/ investor types squared) are used.
Another important independent variable used in all regressions is the total
number of rounds each company has. It is reasonable to expect that a company that
undergoes more rounds will have more investors. Similarly, longer years of activity
may increase the chance that a company will have more investors or that it is more
likely to exit; thus, time variables were used by using a dummy variable of the year
the company was established. Additional control variables used are: industry
classification; the geographic location of the company's headquarters; and whether
this company was part of a governmental program.
SELECTION AND ENDOGENEITY TREATMENT
The matching between investors and portfolio companies is not exogenous. On one
hand, this matching may be correlated with firm characteristics that are not
observable to us (for example, a promising company may attract more investors).
On the other hand, an investor may have unobservable characteristics that lead the
portfolio company to prefer it over another (for example, former research showed
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that investees may prefer to have a reputable VC as an investor and are willing to
incur a valuation discount of 10 to 14 percent for that (Hsu, 2004)). Thus, the
performance of the portfolio company may be the result of a selection bias and not a
result of added value activities that investors contribute to the company.
To control for unobserved characteristics and the matching process between
investor-investee, a two-stage model based on an instrumental variable is
employed. Following Giannetti and Yafeh's (2012) instrumental variable (in their
case for a bank-firm match) and implementing Du's (2009) rule of thumb for
potential investors, the paper postulates that investor-investee probability of
matching (independent of the investor's or the investee's characteristics) depends
on the distribution of the 'number of investors' of the same investor type who were
active (performed an investment) in the same industry at the same quarter. Thus,
the instrumental variable is equal to the summation of the number of all potential
investors who were active in the same quarter in the same industry. For example,
suppose that on February 15th 2004, company A, which is active in the 'Internet
industry,’ received an investment from one VC and two angels. The instrumental
variable examines how many VCs and how many angels invested in the 'Internet
industry' during the first quarter of 2004. Suppose that 10 angels and seven VCs
invested in the first quarter of 2004 in the 'Internet industry,' then the instrumental
variable is 17. This instrumental variable was used in the regressions as an
instrument for the linear independent variable. As an instrumental variable to test
the non-linear relationship, the quadratic term of this instrument (the potential
number of investors squared) was used.
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It is important to understand that the instrument has two functions.
Following Bottazzi, Da Rin and Hellmann (2008), Giannetti and Yafeh (2012), and
Sørensen (2007), the identifying assumption is that the investors' characteristics do
not affect the investee's performance directly but affect the performance through
their added-value activity. The decision to invest in a specific company is correlated
with the investor's characteristics and thus any change in the performance of the
firm is a result of the investors' added value.
The analysis involves two main statistical methods: probit regression and
hazard models. To control for endogeneity, a two-stage probit regression was
performed. In the first-stage regression, the instrumental variable was used as a
predictor for the number of investors/investor types and then the fitted results of
this probit regression were entered into the main regression that measured the
impact of the number of investors/investor types on firm performance. To further
validate these regressions, the standard 2SLS model for linear regression was used,
with the same variables used in White's correction (1980) for heteroscedasticity.
Correcting for heteroscedasticity enables the use of a linear regression even though
the dependent variable is a dummy variable (for an elaboration on the validity of
such a treatment see Angrist, 2001).
In the case of hazard models, there is no "formal" treatment for endogeneity.
Thus, to further validate the results and control for endogeneity, the paper mimics
the process of the 2SLS. As a first stage regression, as in the probit models, a simple
regression was performed with the number of potential investors as a predictor for
the actual number of investors/investor types. Then, the fitted results were used in
Page | 22
the second stage as the hazard model regressions. This process is similar to the
process performed by Das, Jo and Kim (2011) when they used a hazard model to
predict the same dependent variable except that, in their case, the first-order
regression was a probit as their dependent variable was a dummy variable.
Last, due to the high correlation between the two main independent
variables, the 'number of investors' and the 'number of investor types,’ the
regressions are performed separately for each of these variables. However,
regressing them separately may result in a confounding effect, meaning it might be
that the results in the regressions when controlling for the 'number of investors'
variable is actually driven by the variable 'number of investor types,’ and vice versa.
To rule out this bias, several robustness tests for the results were performed and
will be discussed in the analysis section.
DATA AND DESCRIPTIVE STATISTICS
Insert Table I about here
The data in this study was taken from the Israel Venture Capital (IVC) database1, the
most comprehensive database available on the Israeli VC industry2. The entire
database was documented during October 2008. The original data included
information on 192 angels, seven CVCs, 481 foreign investors, 165 investment
companies, 215 VCs and 3281 unclassified investor types. Since such a large number
1 http://www.ivc-online.com/ 2 As such, formal publications of the Israeli Central Bureau of Statistics concerning the VC industry in Israel are also based on data from this website.
Page | 23
of unclassified investors may bias the results substantially, the unclassified
investors were recoded into seven types: (1) VC; (2) angel investors; (3) CVC; (4)
industry-related companies; (5) finance-related companies; (6) incubators; and (7)
others. The recoding yields the following distribution: 1003 angels (this includes
any individual reported to invest in a company3); 60 CVCs; 601 VCs (405 foreign VCs
and 196 local VCs); 35 incubators; 982 industry- related companies (this includes all
investors who invested in startups and who do not have a CVC arm; this can be, for
example, a consulting company, a communications company, etc.); 879 finance-
related companies (this includes any investor whose primary occupation is in the
finance sector: investment banks, holding companies, hedge funds, etc.); and 55
other investor types (this includes investors who do not follow any of the above
definitions; this could be, for example, a governmental fund, a charity fund, a
hospital, university technology transfer company, etc.). Table I presents descriptive
statistics by investor types. Interestingly, all types of investors have a fairly similar
percent of write-offs, varying between 10 and 20 percent. Similarly, their success
rates are somewhat alike and vary between 30 and 40 percent except for
incubators, probably because incubators are, by definition, biased toward early-
stage investments. The average amount invested is fairly different and varies
between USD10M and USD37M. Another interesting observation is that the
involvement of foreign investors is relatively high and that all investor types include
3 Following Harrison and Mason (2000) and Johnson and Sohl (2011), which exclude family finance as angel's investment, any individual who had the same last name as the founder/s was not considered as an angel investor.
Page | 24
foreign investors. It can also be seen that CVCs and VCs invest on average in more
companies and have more follow-up investments.
Insert Table II about here
The data also include information on 7131 startup companies. For 2910 of
these, the data include detailed information on 16,245 investment rounds (of these,
10,958 rounds are first investments in a company and the rest are follow-up
investments). However, years preceding 1990 were not used in this research as the
Israeli VC industry in those years was fairly small and the data may be partial or
inaccurate. Additionally, the analysis involves performance measures based on the
exit or the write-off of companies. Exit or write-off of companies does not take place
instantly after the company's inception; thus, companies established during the
years 2006-2008 were also not used in this study, and hence the final sample
includes 2409 companies participating in 9675 investment rounds.
Table II presents descriptive statistics of the companies in the sample. Most
of them (57%) are located in the center of Israel, either in Tel Aviv or the central
area, and most belong to the Information and Communications Technology (ICT)
industry in its various forms (Internet, IT & enterprise software, communications)
or the life-science industry, which is also fairly developed, accounting for 24% of
startups. Regarding their performance, 22% of the companies performed an exit,
which on average, took 5.75 years since inception with an average exit amount of
USD152M (at 2008 values). Thirty percent of the companies were closed and, on
Page | 25
average, 4.15 years passed until they were closed. Within the context of this
research it is interesting to acknowledge that the average total 'number of investors'
in a single company is 4.02 with 2.11 investor types participating in an average of
2.24 rounds.
Insert Table III about here
ANALYSIS AND RESULTS
Table III presents the correlation matrix between the main independent variables.
As this table shows, all variables are significantly and highly correlated. To avoid
multicollinearity, each of the two main independent variables was used separately
and not together in the same regression. Though running these variables separately
does not allow “horse racing” between the two variables and understanding which
has the greatest impact, running them together would result in highly multicollinear
results that would not be valid anyway. However, this issue is addressed extensively
in the robustness tests that follow the main statistical analysis.
EXIT AND WRITE-OFF PROSPECTS
Table IV presents the analysis for exit and write-off prospects using a probit model
where the dependent variables are 'Exit Dummy' or 'Write-off Dummy.’ In Models 1
– 4 the main independent variable is the 'number of investors,' while in Models 5 – 8,
the main independent variable is the 'number of investor types.’ Model 1 indicates a
significant and positive relation between the 'number of investors' and 'Exit
Dummy.’ Model 2 adds the 'number of investors squared'; this variable is also
Page | 26
significant, suggesting a non-linear relation between the number of investors and
the exit prospects of the company. Similarly, Model 3 shows a negative significant
relationship between the 'number of investors' and write-off prospects. However,
once the 'number of investors squared' is added in Model 4, a positive and
significant relation is visible. This means there is a U-shaped relationship between
the number of investors and the write-off prospects of the startup.
The right side of Table IV analyses the relationship between the 'number of
investor types' and the performance of the startup. Model 5 shows a significant and
positive relation between the 'number of investor types' and 'Exit Dummy.’ Yet,
Model 6 shows that once the 'number of investor types squared' is added, there is a
negative relation, though not statistically significant. This means the relationship
between the exit prospects and the number of investor types is linear and positive.
Models 7 and 8 show a similar result—a linear and negative significant relationship
between the number of investor types and the 'Write-Off Dummy,' but not a U-
shaped relationship.
Insert Table IV about here
TIME TO EXIT
Another aspect of performance that was suggested concerns the impact of the
'number of investors' and of the 'number of investor types' on the 'Time to Exit.’
These relations between the variables were tested using a Cox proportional hazard
model. Table V reports the coefficients of the hazard model. Model 1 examines the
relationship between the 'number of investors' and the time to exit and shows that
Page | 27
there is a significant and negative relation. Model 2 shows that when the 'number of
investors squared' is added, its coefficient is positive and significant; hence, there is
a U-shaped relationship between the 'number of investors' and the time to exit.
Model 3 presents the impact of the 'number of investor types' on the 'Time to Exit'
and shows a linear and negative relationship between the 'number of investor types'
and the 'time to exit.’ However, Model 4 shows that when the 'number of investor
types squared' is added, its coefficient is positive but not significant; thus, contrary
to expectation, this relationship is not U-shaped. This suggests that the relation
between the 'number of investor types' and 'time to exit' is negative and linear; thus,
more investor types lead to a shorter time to exit.
Insert Table V about here
To conclude this section, the main finding is that of an inverted U-shaped
relationship between the 'number of investors' and the exit prospects of the firm.
Additionally, findings indicate that the 'number of investors' has a U-shaped
relationship with the 'write-off prospect's of the firm and with the 'time to exit.’ The
'number of investor types' is linearly and positively related with the startup's exit
prospects and linearly and negatively related with the 'write-off prospect's of the
firm and with the 'time to exit.' The next section validates these findings using
several robustness tests.
Page | 28
Robustness Tests
To further validate the results, several robustness tests were performed. The
robustness tests aim to address two central issues that may bias the findings. The
first bias may occur as a result of endogeneity; hence, the first part of the robustness
tests discusses the measures taken to address this issue. The second issue that may
bias the results is that, as the correlation between the 'number of investors' and the
'number of investor types' is high, the regressions performed regressed these two
variables separately. However, it may be that interpreting the results as derived
from the 'number of investors' is flawed, and the results are actually driven by
having a larger 'number of investor types' (and vice versa). Thus, the second part of
the robustness tests addresses this issue and validates the results both for the
'number of investors' and for the 'number of investor types.’
ENDOGENEITY
Insert Table VI about here
Models 1 – 4 in Table VI describe several robustness tests for endogeneity. Models 1
and 2 use the instrumental variable approach and a probit model to address the
issue of endogeneity. As can be seen, the results are the same as before. However,
one of the requirements of the probit model when using a instrumental variable
approach is that the exogenous variables will be continuous variables. The
instrument used is discrete; to verify the robustness of the results in Models 3 and 4,
the standard 2SLS model was used. As mentioned above, the standard 2SLS process
Page | 29
may be used with a binary dependent variable while performing White's (1980)
correction for heteroscedasticity. Both models indicate the results to be the same;
thus, there is an inverted U-shaped relation between the number of investors and
the success prospects of a startup. Similarly, there is a U-shaped relation between
the write-off prospects of the startup and the number of investors.
Models 1 – 4 in Table VII control for endogeneity for the results of the
'number of investor types' variable. Models 1 and 2 show the results when an
instrumental variable with a probit model was used. Model 1 indeed validates
former findings but in Model 2, though the coefficient is negative, it is not
statistically significant. Again, to further validate the results, Models 3 and 4 use
2SLS regressions while controlling for heteroscedasticity; this time the results are
the same as in Models 5 and 7 in Table IV. Hence, this validates the results and the
conclusion is that there is a linear and positive connection between the 'number of
investor types' and the prospects for exits of the firm. Additionally, there is a linear
and negative connection between the 'number of investor types' and the prospect
for write-off.
Insert Table VII about here
The next section addresses the issue of endogeneity in the hazard models. As
mentioned above, there is no direct way to control for endogeneity in hazard
models. Hence, following Das, Jo and Kim (2011) a process was performed that
mimics the logic of instrumental variables in regressions. Models 1 and 2 in Table
Page | 30
VIII show the results using this technique, which uses the residual of the main
independent variables4 in the hazard model. Both models validate former findings;
Model 1 shows a U-shaped relation between the 'number of investors' and the time
it takes the company to exit. Model 2 shows a linear and negative relation between
the 'number of investor types' and the 'time to exit.’
Lastly, a robustness test was performed for the instrumental variable used. A
constructed variable was created as an instrument to control for endogeneity. This
variable was based on the number of potential investors of the same type investing
at the same quarter in a company in the same industry as the controlled company.
To further validate this inclusion criterion, another instrument was constructed, this
time with the criterion that the same investor type invested in a company in the
same industry within a six-month time frame. The results stayed the same (these
regressions are not reported but the results are available upon request).
Validating the Results of the Number of Investors
As explained above, there may be a bias in the analysis as a result of a confounding
effect between the 'number of investors' and the 'number of investor types.’ To
further validate the finding regarding the 'number of investors,' several robustness
tests were performed. Models 5 and 6 in Table VI examine a subset of the database in
which the 'number of investor types' is held constant and is equal to one. The
reasoning behind this is that in this case, if the results are the same as before, then it
is certainly driven solely by the 'number of investors' as the 'number of investor
types' is fixed. Indeed, Model 5 shows that while holding the 'number of investor
4 'Number of Investors,’ 'Number of Investors2' and 'Number of Investor Types'
Page | 31
types' fixed, there is still an inverted U-shaped relation between the 'number of
investors' and the exit prospects. However, in Model 6, which examines the relation
between the write-off prospects and the number of investors, there is only a
significant negative relation but the positive relation of the squared term is not
significant. As this can be the result of the smaller set of observations, in Model 7 the
constraint of having robust standard errors was relaxed while the same probit
regression was performed as in Model 6. This time, the former results are validated
and the model shows a U-shaped relation between the write-off prospects and the
'number of investors.’
Insert Table VIII about here
Next, using the same method (holding the 'number of investor types' equal to
one) a test was performed for a confounding effect within the hazard models. Model
3 in Table VIII tests for this regarding the 'time to exit' and finds a U-shaped relation
between the two, thus validating the former findings.
Validating the Results of the Number of Investor Types
The next section validates the finding regarding the 'number of investor types' using
two validation techniques. First, a sub-sample of firms was used where the 'number
of investor' equals six. Second, an alternative measure is introduced and tested for
the variable 'number of investor types.’
Page | 32
Limiting the 'number of investors' to no more than six (nearly the maximum
number of investor types a company may have), Models 5 and 6 in Table VII confirm
the results from Models 5 and 7 in Table IV. An attempt to perform the same process
for the hazard model presented in Table V did not yield significant results (the
results are not reported). Hence, to confirm the finding, several regressions were
performed using an alternative measure of investor types.
The alternative measure is based on a Herfindahl index value of the
proportion of each group of investor type. The Herfindahl index is a measure usually
used to evaluate the level of concentration in ownership of an industry. The index
examines the share of each company (in percents) in the industry out of the total
number of companies. When computed to measure industry concentration, the
Herfindahl index is computed by the formula5:
The product of this computation is that the higher the value is, the more
concentrated the industry is. Hence, it is expected that this industry will be less
competitive. Within the context of this paper, the reason this measure is used as a
robustness test for the 'number of investor types' is that it is driven by both the
'number of investors' and the 'number of investor types.’ The underlying
assumption is that, for each type to actively contribute and be involved in the
5 Where si is the market share (in percents) of firms in the industry.
Page | 33
decision-making, the relative size of its group (as measured by the seven types
above) should be considerable. To better understand this point, let us consider, for
example, two hypothetical companies: (a) Company A has nine angels and one VC
investor; and (b) Company B has five angels and five VC investors. In this example,
Company A's Herfindahl index would be: 0.92+0.12=0.82 while Company B's would
be: 0.52+0.52=0.5. According to this measure, the composition of Company B’s
investors is less concentrated than Company A’s. Assuming that each type has
similar complementarities, Company A may be biased towards the capabilities of
angels, while Company B enjoys a more balanced set of complementarities.
Moreover, the investors are in most cases part of the company's board of directors
(hereby after BoD) and are intensely involved in its decisions. Thus, it may be
speculated that if the company has one dominant type of investor, it will be similar
to the case of having fewer investor types and this may reduce the contribution of
having several types6. It is important to note that, due to data availability, the entire
set of measures used in this research does not take into consideration the relative
share of each investor in the startup's ownership. Incorporating this information
into the measures would improve their accuracy.
6 The logic behind this claim derives from a different strand of literature: the literature on women's representation in BoDs and their impact on the performance of the firms. Kanter (1977), who introduced the critical mass theory of women’s representation in BoDs, suggests that the impact of women on the BoD's decisions and on the performance of companies requires a critical mass of about 30%-35% (Kanter, 1977; Rosener, 1995; Schwartz-Ziv, 2012; Shrader, Blackburn, & Iles, 1997). Thus, following this logic, what matters is the relative size of the group of each type of investor. This means that comparing a company with a diverse group of investor types but with unbalanced proportions of the various types to a company with a more balanced proportion of investor types (and the same diversity), will not benefit from the bundle of capabilities that stands at its disposal and thus will be less successful.
Page | 34
Model 7 in Table VII shows that a more concentrated group is negatively and
significantly related to the 'Exit Dummy.’ This means that a diverse group of
investor types with balanced proportions of the various types performs better than
an equally diverse group with less balanced proportions of investor types. This
validates the findings in Model 5 in Table IV. Similarly, Model 6 shows that a more
concentrated group of investors in positively and significantly correlated with the
write-off prospects, thus validating the findings in Model 7 in Table IV. Models 9-12
are the same regressions controlling for endogeneity7. Models 9 and 10 present IV
probit regression and Models 11 and 12 are regressions using 2SLS. As can be seen,
these regressions yield the same results as before. The first stage's results are also
reported at the bottom of the table, where all the relevant variables can be seen to
be statistically significant. Using the Herfindahl measure, the same logic is used in
Table VIII to further validate the hazard models. Model 4 is a hazard model using the
Herfindahl measure itself, while Model 5 is the residual value of the Herfindahl
measure, thus controlling for endogeneity. Both models yield similar results and
validate the above findings.
DISCUSSION AND CONCLUSIONS
This paper has several contributions. First, it contributes to the literature on
venture capital as it elucidates and sharpens the concept of what it means when a
company has more investors. It does so by distinguishing between two possible
7 As an instrumental variable for the Herfindahl index, the Herfindahl index for potential investors was computed. In the example used above for computing potential investors (in which ten angels and seven VCs invested in the first quarter of 2004 in the “Internet industry”) the instrumental variable
for the Herfindahl index would be:
Page | 35
options: (a) having a larger number of investors regardless of their type; and (b)
having a larger number of investor types. Second, this paper is one of the first
attempts to address the impact of a large variety of investor types on firm
performance. Moreover, in the VC industry, many investments are in the form of a
syndicate and involve several types of investors. Thus, past research that focused
mostly on one or two types of investors at most might not have captured the entire
picture. As this paper shows, the number of investor types influences the startup
performance; thus, analyzing one type of investor in companies with more than one
type may bias the results.
The most prominent finding of this paper is that having a larger number of
investors does not necessarily mean that the company's performance is better.
However, having more types of investors has a linear and positive impact. These
findings are consistent for all three performance measures: exit prospects, failure
prospects and the time it takes the company to exit. These findings are very
interesting within the context of the benefits and detriments of heterogeneity
among investors. It seems that in accordance with the concept of complementarities,
the benefits to the portfolio company from a greater scope of complementarities are
greater than the difficulties it may face as a result of more coordination efforts.
However, this is not the case when more investors of the same type are involved. In
this case, having more investors may harm the company's performance. This finding
emphasizes the tradeoff the entrepreneur faces and suggests that they should
carefully consider numbers and types of investors in seeking further financing. This
Page | 36
also bears a considerable importance for the financial intermediaries themselves,
stressing the importance of carefully choosing whether and with whom to syndicate.
LIMITATIONS AND FUTURE RESEARCH
This research has several limitations: First, though extensive effort was made to
address the issue of endogeneity, it may be that not all unknown variations were
addressed. For example, the instrument is based on the assumption that good
investments are available for everyone; it may be that once an investor invests in
what they conceive as a very promising company, they would prefer to avoid
additional investors. A second limitation may be that the analysis performed
referred to all rounds together and not per round. The main obstacle that should be
addressed is the formalization of a success/failure measure on a per round basis.
The most reasonable measure is company valuation; however, the database used in
this research contains only partial information on firm valuation between rounds.
This stream of research contains many interesting questions that may
provide a basis for future research. For example, it would be interesting to compare
different syndicates and see whether certain syndicate profiles outperform others
(i.e. maybe a syndicate of VCs and angels is better than a syndicate of CVCs,
incubators and VCs, or vice versa). Another option is to examine what types of
added value different syndicate profiles provide. It may also be interesting to
examine the pattern of syndicate formation along the life cycle of the startup. For
example, angels and incubators are usually early-stage investors, while VCs and
CVCs invest in later rounds; it might be interesting to examine whether syndicate
Page | 37
formation patterns exist, and which investor type enters at which stage and what
future investors it draws. Moreover, within the scope of "smart money" and
investor-added value it may be very interesting to see if one investor type is a
substitute for another. For example, it might be that angel investors are a substitute
for incubators or that VCs are a substitute for CVCs (i.e. each investor type can
provide the same added value at the same stages as the other type). Another avenue
for future research may be to examine the impact of foreign investors (of any type)
and compare their investment patterns within various local syndicates.
Page | 38
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Page | 43
Table I
Descriptive Statistics By Investor Type
Variables Angels CVCs
Finance Related
Companies
Incubators
Industry Related
Companies Other VCs
Average Amount Invested (M. USD)
mean 10.4 26.3 14.7 10.0 9.34 10.3 37.5 N 347 36 352 21 331 27 360
Foreign Dummy mean 0.22 0.85 0.69 0.029 0.44 0.60 0.67 N 967 60 859 35 775 43 601
Percent of Companies Written-off
mean 0.15 0.19 0.17 0.11 0.22 0.19 0.15 N 1,003 60 879 35 982 55 601
Percent of Companies Exits
mean 0.30 0.44 0.41 0.074 0.30 0.34 0.35 N 1,003 60 879 35 982 55 601
Total Activity Times in years
mean 1.53 4.24 1.80 3.16 1.36 2.93 3.95 N 1,003 60 879 35 982 55 601
Total Number Of Companies Invested
mean 1.80 5.98 2.69 5.17 1.76 4.67 5.83 N 1,003 60 879 35 982 55 601
Total Number Of Follow up Investment
mean 0.57 3.67 0.99 0.60 0.54 0.33 4.69 N 1,003 60 879 35 982 55 601
Total Number Of Rounds Participated
mean 2.37 9.65 3.67 5.77 2.30 5 10.5 N 1,003 60 879 35 982 55 601
The data in this table is based on the sample of 1003 angels ;60 CVCs; 601 VCs (405 Foreign VCs and 196 Local VCs); 35 incubators; 982 industry-related companies ; 879 finance-related companies; and 55 other investor types. 'Average Amount Invested' is the average amount in USD in 2008 values. 'Foreign Dummy' is a dummy variable that equals 1 if the investor is of foreign origin. 'Percent of Companies Written-off' is the number of companies closed out of total number of companies invested. 'Percent of Companies Exits' off' is the number of companies exited (IPO or M&A) out of total number of companies invested. 'Total Activity Times in years' is the time passed from first to last investment. 'Total Number Of Companies Invested' is the number of companies invested. 'Total Number Of Follow up Investment' is the total number of companies receiving additional investment from the same investor. 'Total Number Of Rounds Participated' is the total number of rounds a specific investor participated in.
Page | 44
Table II
Descriptive Statistics of Companies Variables N Mean SD Min Max
Company Stage
Seed 1,950 0.064 0.24 0 1
R&D 1,950 0.34 0.47 0 1
Initial Revenues 1,950 0.45 0.5 0 1
Revenue Growth 1,950 0.15 0.35 0 1
Industry
Miscellaneous Technologies 2,377 0.093 0.29 0 1
IT & Enterprise Software 2,377 0.24 0.43 0 1
Communications 2,377 0.2 0.4 0 1
Life Sciences 2,377 0.24 0.43 0 1
Semiconductors 2,377 0.059 0.24 0 1
Clean-tech 2,377 0.05 0.22 0 1
Internet 2,377 0.12 0.32 0 1
Geographic Location Of Main Offices
Tel Aviv 2,305 0.12 0.33 0 1
Center 2,305 0.45 0.5 0 1
Jerusalem 2,305 0.069 0.25 0 1
North 2,305 0.081 0.27 0 1
Haifa 2,305 0.054 0.23 0 1
South 2,305 0.032 0.18 0 1
West Bank 2,305 0.0043 0.066 0 1
Abroad 2,305 0.18 0.39 0 1
Investments
Originated From Incubators Dummy 2,409 0.18 0.38 0 1 Number of Rounds 2,409 2.24 1.69 1 14 Number of Investors 2,409 4.02 3.84 1 33 Number of Investor Types 2,409 2.11 1.17 1 6 Total Number of Potential Investors 2,409 6.12 7.74 1 66
Performance
Exit Dummy 2,409 0.22 0.42 0 1
Years To Exit 535 5.75 2.98 0.18 16
Closure Dummy 2,409 0.3 0.46 0 1
Years To Closure 717 4.15 2.41 0.13 14.4
Amount Of Exit 405 152 592 0.28 11,275
The data in this table is based on the sample of 2409 startups and the unit of analysis is a single startup. Industry is the dummy variable indicating the industry of the startup. 'Geographic Location Of Main Offices ' is the location of the main office of the startup. 'Originated From Incubators Dummy' is a dummy variable indicating whether the company originates in a governmental program. 'Number of Rounds' is the total number of rounds the company had. 'Number of Investors' is the accumulated 'number of investors' that invested in that specific company (if the company has two investments from the same investor, it was counted only once). 'Number of Investor Types' is the total number of different types of investors a company got investment from. 'Entropy of The Total 'number of investors' is a measure calculated based on the entropy calculation presented in the paper. 'Exit Dummy' is a dummy variable indicating if the company exited (IPO or M&A). 'Years To Exit' is the total time in years from the company inception until its exit. 'Write-off Dummy' is a dummy variable indicating if the company was closed. 'Years To Write-off' is the total time in years from the company inception until it was closed. 'Amount Of Exit' is the total amount of exit either trough a IPO or M&A that a company got.
Page | 45
Table III
Correlation Matrix
Number of Investors
Number of Investors2
Number of Investor
Types
Number of Investor Types2
Number of Investors 1 Number of Investors2 0.903*** 1
Number of Investor Types 0.769*** 0.556*** 1
Number of Investor Types2 0.771*** 0.594*** 0.974*** 1
* p<0.05, ** p<0.01, *** p<0.001
The data in this table is based on the sample of 2409 startups and the unit of analysis is a single startup. 'Number of investors' is the accumulated number of investors that invested in that specific company (if the company has two investments from the same investor, it was counted only once). 'Number of Investors2' is the same number squared. 'Number of Investor Types' is the total number of different types of investors a company got investment from. 'Number of Investor Types2' is the same number squared.
Page | 46
Table IV
Exit and Write-off Prospects by the 'Number of Investors' and by the 'Number Of Investor Types' Number of Investors Types of Investors
Treatment Probit Probit
Dependent Variable Exit Dummy Exit Dummy Failure Dummy
Failure Dummy
Exit Dummy
Exit Dummy Failure Dummy
Failure Dummy
Labels Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
Endogeneity Control X X X X X X X X
Number of Investors 0.082*** 0.15*** -0.050*** -0.10***
0.013 0.023 0.017 0.027
Number of Investors2
-0.0033***
0.0033**
0.00095 0.0013
Number of Investor Types
0.16*** 0.24* -0.092** -0.25**
0.037 0.12 0.037 0.13
Number of Investor Types2
-0.015
0.033
0.023 0.025
Industry
Communications 0.51** 0.50** 0.36** 0.36** 0.56*** 0.56*** 0.34** 0.34**
0.2 0.2 0.16 0.16 0.2 0.2 0.16 0.16
IT & Enterprise Software 0.61*** 0.60*** 0.24 0.24 0.64*** 0.64*** 0.23 0.23
0.2 0.2 0.15 0.15 0.2 0.2 0.16 0.15
Internet 0.25 0.25 0.52*** 0.52*** 0.34 0.34 0.49*** 0.50***
0.21 0.22 0.17 0.17 0.22 0.22 0.17 0.17
Life Sciences 0.36* 0.35* 0.044 0.045 0.40** 0.40** 0.033 0.035
0.2 0.2 0.15 0.15 0.2 0.2 0.15 0.15
Miscellaneous Technologies 0.23 0.22 0.057 0.06 0.23 0.23 0.062 0.071
0.21 0.22 0.17 0.17 0.22 0.22 0.17 0.17
Semiconductors 0.46** 0.44* 0.38* 0.38* 0.52** 0.52** 0.36* 0.36*
0.22 0.22 0.2 0.2 0.23 0.23 0.2 0.2
Other Controls
Number of Rounds -0.037 -0.057* -0.24*** -0.22*** 0.03 0.031 -0.28*** -0.28***
0.029 0.029 0.037 0.037 0.025 0.025 0.032 0.032
Originated From Incubators Dummy -0.47*** -0.47*** 0.031 0.031 -0.50*** -0.51*** 0.047 0.055
0.11 0.11 0.091 0.091 0.11 0.11 0.091 0.091
Headquarter Location Dummies √ √ √ √ √ √ √ √
Year Dummies √ √ √ √ √ √ √ √
Constant 0.86** 0.75* -5.29*** -5.18*** 0.65 0.57 -4.94*** -4.79***
0.44 0.44 0.18 0.2 0.44 0.46 0.19 0.22
Observations 2,278 2,278 2,278 2,278 2,278 2,278 2,278 2,278
Page | 47
Table IV
Exit and Write-off Prospects by the 'Number of Investors' and by the 'Number Of Investor Types' Number of Investors Types of Investors
Treatment Probit Probit
Dependent Variable Exit Dummy Exit Dummy Failure Dummy
Failure Dummy
Exit Dummy
Exit Dummy Failure Dummy
Failure Dummy
Labels Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
Endogeneity Control X X X X X X X X
Prob > chi2 0 0 0 0 0 0 0 0 Pseudo R2 0.18 0.19 0.18 0.18 0.17 0.17 0.18 0.18
The regressions in this table are based on the sample of 2409 startups and the unit of analysis is a single startup. 'Number of Investors' is the accumulative 'number of investors' that invested in that specific company (if the company has two investments from the same investor, it was counted only once). 'Number of Investors2' is the same variable squared. 'Number of Investor Types' is the accumulative number of investor types that invested in that specific company (if the company has two investments from the same type of investor, it was counted only once). 'Number of Investor Types2' is the same variable squared. The dependent variable is 'Exit Dummy' or 'Write-off Dummy,' which equal 1 if Exited/Written-off and 0 if it was not. Industry is the dummy variable indicating the industry of the startup. 'Headquarter Location Dummies' is the location of the main office of the startup. 'Originated From Incubators Dummy' is a dummy variable indicating whether the company originates in a governmental program. In industries dummies, the missing dummy is 'Miscellaneous Technologies Dummy.’ The 'office dummies' indicate where the main office of the company is located; the company may have additional offices elsewhere. 'Number of Rounds' is the total number of rounds the company had. The symbols ***, **, and * denotes significance at the 1%, 5%, and 10% level (two-sided), respectively.
Page | 48
Table V 'Time To Exit' by 'Number of Investors' and by the 'Number Of Investor Types'
Number of Investors Types of Investors
Treatment Hazard Models Hazard Models
Dependent Variable Time to
Exit Time to
Exit Time to
Exit Time to
Exit
Labels Model 1 Model 2 Model 3 Model 4
Endogeneity Control X X X X
Number of Investors -0.028** -0.076**
0.014 0.031
Number of Investors2 0.0021* 0.0012
Number of Investor Types
-0.100* -0.36**
0.052 0.17
Number of Investor Types2
0.048
0.029
Industry
Communications 1.07*** 1.09*** 1.07*** 1.06***
0.39 0.39 0.39 0.39
IT & Enterprise Software 0.90** 0.92** 0.90** 0.89**
0.38 0.38 0.38 0.38
Internet 0.96** 0.96** 0.89** 0.85**
0.42 0.41 0.41 0.41
Life Sciences 0.73* 0.74* 0.73* 0.69*
0.38 0.38 0.38 0.38
Miscellaneous Technologies 0.81* 0.82** 0.81* 0.80*
0.41 0.41 0.41 0.41
Semiconductors 1.08** 1.11*** 1.07** 1.06**
0.42 0.42 0.42 0.42
Other Controls
Number of Rounds -0.16*** -0.14*** -0.17*** -0.17***
0.039 0.04 0.036 0.036
Originated From Incubators Dummy 0.058 0.061 0.069 0.072
0.2 0.2 0.2 0.2
Headquarter Location Dummies √ √ √ √
Observations 457 457 457 457 Prob > chi2 0 0 0 0 LR chi2 82.7 85.7 82.4 84.9
The Hazard models in this table are based on the sample of 2409 startups s and the unit of analysis is a single startup. 'Number of Investors' is the accumulative 'number of investors' that invested in that specific company (if the company has two investments from the same investor, it was counted only once). 'Number of Investors2' is the same variable squared. 'Number of Investor Types' is the accumulative number of investor types that invested in that specific company (if the company has two investments from the same type of investor, it was counted only once). 'Number of Investor Types2' is the same variable squared. The dependent variable is 'Exit Dummy' or 'Write-off Dummy,' which equal 1 if Exited/Written-off and 0 if it was not. Industry is the dummy variable indicating the industry of the startup. 'Headquarter Location Dummies' is the location of the main office of the startup. 'Originated From Incubators Dummy' is a dummy variable indicating whether the company originates in a governmental program. In industries dummies, the missing dummy is 'Miscellaneous Technologies Dummy.’ The 'office dummies' indicate where the main office of the company is located; the company may have additional offices elsewhere. 'Number of Rounds' is the total number of rounds the company had. The symbols ***, **, and * denotes significance at the 1%, 5%, and 10% level (two-sided), respectively.
Page | 49
Table VI
Robustness Tests for Exit and Write-off Prospects by the 'Number of Investors'
Robustness for Number of Investors
Treatment Probit with IV 2SLS Types of Investors = 1
Dependent Variable Exit
Dummy Failure Dummy
Exit Dummy Failure Dummy
Exit Dummy
Failure Dummy
Failure Dummy
Labels Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Endogeneity Control √ √ √ √ X X X
Number of Investors 0.17*** -0.055 0.054*** -0.035*** 0.63*** -0.41** -0.41**
0.03 0.039 0.012 0.007 0.19 0.19 0.18
Number of Investors2 -0.0050*** 0.0018 -0.0016*** 0.0013*** -0.073** 0.05 0.050*
0.0013 0.0021 0.00059 0.00032 0.032 0.032 0.029
Industry
Communications 0.46** 0.35** 0.12*** 0.10** 0.42 0.41* 0.41*
0.22 0.16 0.045 0.044 0.37 0.22 0.23
IT & Enterprise Software 0.50** 0.24 0.13*** 0.068 0.31 0.37* 0.37*
0.21 0.16 0.043 0.043 0.37 0.21 0.22
Internet 0.19 0.51*** 0.044 0.15*** 0.19 0.56** 0.56**
0.24 0.17 0.048 0.048 0.39 0.23 0.24
Life Sciences 0.27 0.035 0.059 0.014 0.31 0.15 0.15
0.21 0.15 0.04 0.042 0.37 0.2 0.21
Miscellaneous Technologies 0.15 0.056 0.019 0.015 0.35 0.0054 0.0054
0.23 0.17 0.046 0.048 0.38 0.24 0.24
Semiconductors 0.41* 0.36* 0.10* 0.098* 0.39 0.34 0.34
0.24 0.19 0.057 0.052 0.44 0.29 0.3
Other Controls
Number of Rounds -0.049 -0.27*** -0.019* -0.049*** 0.11 -0.14* -0.14
0.034 0.043 0.011 0.0075 0.096 0.087 0.095
Originated From Incubators Dummy -0.48*** 0.033 -0.10*** 0.0076 -0.61*** 0.038 0.038
0.13 0.092 0.026 0.025 0.23 0.13 0.14
Headquarter Location Dummies √ √ √ √ √ √ √
Year Dummies √ √ √ √ √ √ √
Constant 0.76* -5.3 0.73*** 0.16** -0.34 -5.19*** -5.19
0.45 144 0.085 0.063 0.73 0.34 142
Observations 1,666 2,278 1,666 2,278 887 926 926 Prob > chi2 0 0 0 0 0 0 0 Pseudo R2
0.17 0.188 0.18 0.16 0.16
First Stage Probit
Dependent Variable Number of Investors
Number of Investors
Number of Investors
Number of Investors
Number of Potential Investors 0.643 0.657 0.643 0.657
0 0 0 0
Page | 50
Table VI
Robustness Tests for Exit and Write-off Prospects by the 'Number of Investors'
Robustness for Number of Investors
Treatment Probit with IV 2SLS Types of Investors = 1
Dependent Variable Exit
Dummy Failure Dummy
Exit Dummy Failure Dummy
Exit Dummy
Failure Dummy
Failure Dummy
Labels Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Endogeneity Control √ √ √ √ X X X
Number of Potential Investors2 -0.004 -0.005 -0.004 -0.005
0 0 0 0
Industry Dummies √ √ √ √
Number of Rounds √ √ √ √
Originated From Incubators Dummy √ √ √ √
Headquarter Location Dummies √ √ √ √
Year Dummies √ √ √ √
Observations 1666 2,278 1666 2278
Prob > F 0 0 0 0
Adj R-squared 0.861 0.863 0.864 0.865
Dependent Variable Number of Investors2
Number of Investors2
Number of Investors2
Number of Investors2
Total Number of Potential Investors 5.135 5.005 5.135 5.006
0 0 0 0
Total Number of Potential Investors2 0.093 0.096 0.093 0.096
0 0 0.001 0
Industry Dummies √ √ √ √
Number of Rounds √ √ √ √
Originated From Incubators Dummy √ √ √ √
Headquarter Location Dummies √ √ √ √
Year Dummies √ √ √ √
Observations 1666 2,278 1666 2278
Prob > F 0 0 0 0
Adj R-squared 0.736 0.742 0.741 0.745
The regressions in this table are based on the sample of 2409 startups s and the unit of analysis is a single startup. 'Number of Investors' is the accumulative 'number of investors' that invested in that specific company (if the company has two investments from the same investor, it was counted only once). 'Number of Investors2' is the same variable squared. The dependent variable is 'Exit Dummy' or 'Write-off Dummy,' which equal 1 if Exited/Written-off and 0 if it was not. Industry is the dummy variable indicating the industry of the startup. 'Headquarter Location Dummies' is the location of the main office of the startup. 'Originated From Incubators Dummy' is a dummy variable indicating whether the company originates in a governmental program. In industries dummies, the missing dummy is 'Miscellaneous Technologies Dummy.’ The 'office dummies' indicate where the main office of the company is located; the company may have additional offices elsewhere. 'Number of Rounds' is the total number of rounds the company had. The symbols ***, **, and * denotes significance at the 1%, 5%, and 10% level (two-sided), respectively.
Page | 51
Table VII
Robustness Tests for Exit and Write-off Prospects by the 'Number Of Investor Types'
Dependent Variable Exit
Dummy Failure Dummy
Exit Dummy
Failure Dummy
Exit Dummy
Failure Dummy
Exit Dummy
Failure Dummy
Exit Dummy
Failure Dummy
Exit Dummy
Failure Dummy
Treatment Probit with IV 2SLS Number of investors
<= 6 Probit Probit with IV 2SLS
Labels Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Model 10 Model 11 Model 12
Endogeneity Control √ √ √ √ X X X X √ √ √ √
Number of Investor Types 0.40*** -0.16 0.15*** -0.050* 0.084* -0.085*
0.12 0.15 0.047 0.028 0.049 0.044
Number of Investor Types2
Herfindahl of Number of Investors
-0.45*** 0.34*** -0.45*** 0.38*** -0.12*** 0.15***
0.14 0.13 0.15 0.13 0.042 0.037
Industry
Communications 0.50** 0.35** 0.13*** 0.097** 0.52** 0.36** 0.57*** 0.34** 0.51** 0.34** 0.13*** 0.095**
0.22 0.16 0.047 0.045 0.22 0.16 0.2 0.16 0.22 0.16 0.045 0.045
IT & Enterprise Software 0.55** 0.23 0.14*** 0.062 0.58*** 0.27* 0.64*** 0.23 0.53** 0.23 0.13*** 0.063
0.21 0.16 0.046 0.043 0.22 0.16 0.2 0.16 0.21 0.16 0.043 0.043
Internet 0.27 0.49*** 0.064 0.15*** 0.14 0.57*** 0.34 0.50*** 0.27 0.49*** 0.059 0.15***
0.24 0.17 0.051 0.048 0.24 0.17 0.22 0.17 0.24 0.17 0.049 0.048
Life Sciences 0.32 0.037 0.068 0.011 0.29 0.055 0.41** 0.032 0.34 0.029 0.070* 0.01
0.21 0.15 0.043 0.042 0.22 0.15 0.2 0.15 0.21 0.15 0.04 0.042
Miscellaneous Technologies 0.18 0.059 0.024 0.012 0.24 0.11 0.22 0.069 0.15 0.064 0.013 0.015
0.23 0.17 0.05 0.048 0.23 0.18 0.22 0.17 0.23 0.17 0.047 0.048
Semiconductors 0.47* 0.36* 0.11* 0.087* 0.34 0.53** 0.53** 0.36* 0.51** 0.36* 0.13** 0.086
0.24 0.19 0.06 0.053 0.26 0.21 0.23 0.2 0.24 0.19 0.058 0.053
Other Controls
Number of Rounds -0.082 -0.25*** -0.035* -0.050*** 0.061 -0.27*** 0.060*** -0.28*** 0.055** -0.28*** 0.018** -0.059***
0.057 0.069 0.021 0.013 0.038 0.043 0.022 0.03 0.023 0.03 0.0073 0.0055
Originated From Incubators Dummy -0.51*** 0.057 -0.11*** 0.017 -0.54*** 0.061 -0.50*** 0.057 -0.53*** 0.059 -0.12*** 0.019
0.13 0.094 0.027 0.025 0.13 0.095 0.11 0.091 0.13 0.092 0.026 0.025
Headquarter Location Dummies √ √ √ √ √ √ √ √ √ √ √ √
Year Dummies √ √ √ √ √ √ √ √ √ √ √ √
Constant 0.42 -4.84 0.61*** 0.17** 0.86* -4.86*** 1.22*** -5.35*** 1.32*** -5.39 0.89*** -0.021
0.47 83.2 0.1 0.074 0.48 0.21 0.45 0.24 0.47 82.9 0.087 0.069
Observations 1,666 2,278 1,666 2,278 1,876 1,876 2,278 2,278 1,666 2,278 1,666 2,278
Page | 52
Table VII
Robustness Tests for Exit and Write-off Prospects by the 'Number Of Investor Types'
Dependent Variable Exit
Dummy Failure Dummy
Exit Dummy
Failure Dummy
Exit Dummy
Failure Dummy
Exit Dummy
Failure Dummy
Exit Dummy
Failure Dummy
Exit Dummy
Failure Dummy
Treatment Probit with IV 2SLS Number of investors
<= 6 Probit Probit with IV 2SLS
Labels Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Model 10 Model 11 Model 12
Endogeneity Control √ √ √ √ X X X X √ √ √ √
Prob > chi2 0 0 0 0 0 0 0 0 0 0 0 0 Pseudo R2
0.11 0.18 0.17 0.16 0.17 0.18
0.15 0.18
First Stage Probit
Dependent Variable Number
of Investors
Number of Investors
Number of Investors
Number of Investors
Number of Investors
Number of Investors
Number of Investors
Number of
Investors
Total Number of Potential Investors 0.056 0.06 0.056 0.06
0 0 0 0
Herfindahl of Total Potential Investors 0.978 0.982 0.978 0.982
0 0 0 0
Industry Dummies √ √ √ √
√ √ √ √
Number of Rounds √ √ √ √
√ √ √ √
Originated From Incubators Dummy √ √ √ √
√ √ √ √
Headquarter Location Dummies √ √ √ √
√ √ √ √
Year Dummies √ √ √ √
√ √ √ √
Observations 1666 2278 1666 2278
1666 2278 1666 1666
Prob > F 0 0 0 0
0 0 0 0
Adj R-squared 0.519 0.509 0.527 0.516
0.978 0.981 0.978 0.981
The regressions in this table are based on the sample of 2409 startups s and the unit of analysis is a single startup. 'Number of Investor Types' is the accumulative number of investor types that invested in that specific company (if the company has two investments from the same type of investor, it was counted only once). 'Number of Investor Types2' is the same variable squared. The dependent variable is 'Exit Dummy' or 'Write-off Dummy,' which equal 1 if Exited/Written-off and 0 if it was not. Industry is the dummy variable indicating the industry of the startup. 'Headquarter Location Dummies' is the location of the main office of the startup. 'Originated From Incubators Dummy' is a dummy variable indicating whether the company originates in a governmental program. In industries dummies, the missing dummy is 'Miscellaneous Technologies Dummy.’ The 'office dummies' indicate where the main office of the company is located; the company may have additional offices elsewhere. 'Number of Rounds' is the total number of rounds the company had. The symbols ***, **, and * denotes significance at the 1%, 5%, and 10% level (two-sided), respectively.
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Table VIII
Robustness Tests for 'Time To Exit' by 'Number of Investors' and by the
'Number Of Investor Types'
Number
of Investors
Types of Investor
Types
Number of
Investors
Types of Investor
Types
Types of Investor
Types
Treatment IV Proxy IV Proxy Types of
Investors = 1
Hazard Models
IV Proxy
Dependent Variable Time to
Exit Time to
Exit Time to
Exit Time to
Exit Time to
Exit
Labels Model 1 Model 2 Model 3 Model 4 Model 5
Endogeneity Control √ √ X X √
Number of Investors -0.54* 0.32
Number of Investors2
0.082*
0.05
Residual Of The Number of Investors -0.11***
0.039
Residual Of The Number of Investors2
0.0032*
0.0018
Residual Of The Number of Investor Types -0.36**
0.14
Herfindahl Of The Number of Investors
0.39*
0.21
Residual Of The Herfindahl Of The Number of Investors
0.39*
0.21
Industry
Communications 1.10*** 1.07*** 1.06 1.05*** 1.06***
0.39 0.39 0.81 0.39 0.39
IT & Enterprise Software 0.93** 0.89** 0.67 0.88** 0.90**
0.38 0.38 0.8 0.38 0.38
Internet 1.00** 0.95** 0.59 0.86** 0.87**
0.41 0.41 0.83 0.41 0.41
Life Sciences 0.75** 0.73* 0.54 0.70* 0.71*
0.38 0.38 0.76 0.38 0.38
Miscellaneous Technologies 0.81* 0.80* 1.1 0.80* 0.82**
0.42 0.42 0.91 0.41 0.41
Semiconductors 1.11*** 1.04** 0.82 1.05** 1.06**
0.42 0.42 0.89 0.42 0.42
Other Controls
Number of Rounds -0.10** -0.031 -0.1 -0.19*** -0.18***
0.047 0.075 0.17 0.032 0.032
Originated From Incubators Dummy 0.068 0.096 0.19 0.061 0.076
0.2 0.2 0.56 0.2 0.2
Headquarter Location Dummies √ √ √ √ √
Observations 457 457 109 457 457 Prob > chi2 0 0 0.6656 0 0 LR chi2 88.3 85.7 13.1 82 82
The Hazard models in this table are based on the sample of 2409 startups s and the unit of analysis is a single startup. 'Number of Investors' is the accumulative 'number of investors' that invested in that specific company (if the company has two investments from the same investor, it was counted only once). 'Number of Investors2' is the same variable squared. 'Number of Investor Types' is the accumulative number of investor types that invested in that specific company (if the company has two investments from the same type of investor, it was counted only once). 'Number of Investor Types2' is the same variable squared. The dependent variable is 'Exit Dummy' or 'Write-off Dummy,' which equal 1 if Exited/Written-off and 0 if it was not. Industry is the dummy variable indicating the industry of the startup. 'Headquarter Location Dummies' is the location of the main office of the startup. 'Originated From Incubators Dummy' is a dummy variable indicating whether the company originates in a governmental program. In industries dummies, the missing dummy is 'Miscellaneous Technologies Dummy.’ The 'office dummies' indicate where the main office of the company is located; the company may have additional offices elsewhere. 'Number of Rounds' is the total number of rounds the company had. The symbols ***, **, and * denotes significance at the 1%, 5%, and 10% level (two-sided), respectively.
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