Inside Rounds and VC Returns
Michael Ewens, Matthew Rhodes-Kropf and Ilya Strebulaev⇤
First version: June 2014
This version: May 4, 2015
Preliminary, please do not cite.
Abstract
We study sequential investment decisions in the venture capital (VC) industry.
VC-backed companies typically need to raise several rounds of funding from VC funds.
The decision whether to provide further funding to the company and the terms of the
new funding determine the returns of VC funds and their ability to back successful
companies. We show that investment outcomes in the VC industry can be predicted
by whether the existing VC investors can attract new outside investors to participate
in the next round. Inside rounds, in which only existing investors participate, lead
to a higher likelihood of failure, lower probability of IPOs, and lower cash on cash
multiples than outside rounds. We explore a number of possible explanations for this
result including escalation of commitment, mismeasurement of returns and too little
capital. The strong relationship between inside rounds and outcomes remains.
⇤
Ewens: California Institute of Technology; Rhodes-Kropf: Harvard University and NBER; Strebulaev:
Stanford University and NBER.
1
1
Introduction
Innovation in technology and business practices is widely believed to drive long-term economic growth. In the Unites States, a sizeable fraction of innovation activity is done by
young high-growth companies financed by the venture capital industry. For example, recent
evidence shows that the companies that had been backed by venture capital generate revenue of about $3 trillion per annum or 20% of the U.S. GDP and employ about 12 million
people in the United States. Understanding the economics of the venture capital industry is
therefore an important pursuit.
Recently, significant progress has been made in exploring this industry. A number of
stylized facts about venture capital (VC) has emerged that suggests that the way the VC
industry operates is di↵erent from many other financial sectors. First, VC funds expect
to lose on many, if not most, of their investments, because predicting the success of highgrowth technologically innovative companies is challenging. Second, VC funds expect to
make money (and cover their losses) from a small number of successful and very profitable
investments. Third, because success is so difficult to predict, VC funds exercise real options
by investing in stages. For example, a typical $100 million early stage VC fund may invest a
relatively small amount of money in up to 15–20 companies. While it will see its investment
evaporate on most of these companies, it will then invest further in remaining companies.
For successful VC-backed companies the number of investment rounds can easily reach 4 or
5. An important determinant of the VC industry returns as well as its ability to channel
capital to successful innovation is therefore the sequential allocation of capital (e.g. Gompers
(1995)). To be successful, VC funds need to allocate sufficiently small amount of capital to
a relatively large number of start-ups, observe the evolution of these start-ups closely, make
efficient and timely calls to liquidate those start-ups that don’t perform well and invest an
increasing amount of capital in those start-ups that show great potential.
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In this paper we address an important gap about our current knowledge of the VC
industry by studying to what extent these sequential decisions are made optimally and how
it a↵ects outcomes and returns of VC investments. While the success of individual funds as
well as the whole industry hinges on the ability to separate lemons from peaches as early
as possible, there are a number of factors that may prevent venture capitalists from making
these decisions optimally. Consider for example, a situation when a VC fund invested in
a start-up that does not perform well. It may be optimal to liquidate this start-up by
freeing VC fund’s capital and its partners’ time to devote to better start-ups in its portfolio.
However, unlike investors in public equity markets, venture capitalists participate actively
in the corporate governance of their portfolio companies. Specifically, they sit on start-up
boards and interact frequently with the founders and executives. This interaction improves
their knowledge of start-up prospects. They may also develop a personal attachment to the
start-up and its employees. A large psychological literature shows that these interactions
frequently lead to the so-called escalation of commitment. E↵ectively, in making future
decisions an agent cannot ignore the past decisions that were made and the costs that were
sunk. In dynamic situations, escalation of commitment can lead to throwing good money
after bad, in industry’s parlance.
Escalation of commitment is an important problem recognized by the VC industry. As
a solution, in each subsequent rounds of funding, the existing VC investors actively seek to
attract those VC investors that have not invested in the same start-up in the past (here,
“outsiders”). By investing, as well as negotiating the terms of the investment round, new
investors signal to the market the fair value of the company as well as unbiased opinion about
its potential. In fact, many limited partners will ignore any marking up of an entrepreneurial
firm that does not include at least one outside investor. The perceived wisdom in the
industry is that sequential transactions that involve outsiders leads to the resolution of the
escalation of commitment problem, while the deals that involve only those investors that
3
already invested in the start-up previously may lead to lower returns and are thus actively
avoided.
In this paper, we explore whether the perceived wisdom has any grounds in the data. We
start by showing that for many VC investments only those VC funds that invested in the
start-up before take part in the next round. We call these round of investments inside rounds.
In our sample that covers the period of 1992–2014 and has a total of 21,596 investment
rounds in 8,990 entrepreneurial firms (excluding the initial investment rounds), there are
6,459 inside rounds. These rounds are such that all investors in that round already invested
in that start-up before.
Such a high fraction of inside rounds is surprising given anecdotal evidence on how
the industry works. But does it matter? We show that it does. Inside rounds are more
likely to lead to failures, are less likely to lead to IPOs, and generate lower cash on cash
multiples than outside rounds. These findings persist when we control for a number of
company-specific and time-series variables. We also show suggestive evidence that suggests
that those VC funds that participate in inside rounds may have lower returns on those inside
investments than those VC funds that participate in outside rounds. We also find that several
features of financings, investors, and firm history are good predictors of the outside investor
participation. Smaller rounds of financing and those that take place later in a firm’s life are
more likely to be inside rounds. Moreover, if the current syndicate lacks a successful exit in
their portfolio in the recent past, we find an 18% higher probability of an inside round.
The analysis considers several possible explanations for the relationship between inside
rounds and investment outcomes. VC firm fixed e↵ects analysis shows that the results are
not driven by the quality of participating investors and the quality of their investments. It
is possible that the underperformance of inside rounds stems from a few insiders failing to
rationally walk away. We find there is no di↵erence in returns and outcomes for inside rounds
with full or little participation by existing investors. Inside rounds have many observable
4
di↵erences with outside rounds, of which total capital invested is primary. Perhaps inside
rounds are VCs’ attempts to increase bargaining power by holding back capital. We find that
inside rounds with dramatically smaller financing amounts relative to firm capital stock have
the same return performance. Finally, it is possible that inside investors do not consider the
current investment in isolation, but rather attempt to maximize the returns on their total
equity position. Our preliminary attempts at measuring the “total” return earned by insiders
– the sum of returns across all investments – still show strong negative correlations. Overall,
the relationship between inside rounds and investment returns is quite robust to a host of
rational and behavioral explanations.
Our results are important because they shed light on the determinants of VC returns
and show that there is a certain degree of cross-sectional predictability in this industry.
While the perceived wisdom would suggest that the escalation of commitment is the leading
mechanism at play, a host of other mechanisms can explain some of our findings. Inside
deals suggest that there is no outside interest and the objective prospects of the company
are poor. The insider investors, however, face agency problems vis-a-vie investors in their
funds (limited partners or LPs). For example, in the absence of another successful deal in
their current fund, they may have incentives to gamble for resurrection. If they are raising
another fund at the same time, they may want to prevent their existing companies to go
bust as this may scare potential LPs. The VC environment arguably constitutes one of the
best grounds to test various facets of agency problems. While we don’t have access to full
contracts signed between VCs and their portfolio companies (or VCs and their LPs), some
of our tests suggest that agency problems can be at play.
Furthermore, an inside round presents a unique view of two opposing hold-up problems
that are the center of several theoretical analyses of staged financing (e.g. Admati and
Pfleiderer (1994) and Fluck, Garrison, and Myers (2006)). The entrepreneur can walk away
from the startup and likely lead to failure of the investment. The existing investors have the
5
capital unavailable to the entrepreneur and can threaten to abandon. As most startups are
cash flow negative and rarely have large capital stocks, this threat is real. It is not clear from
a theoretical standpoint which hold-up problem will dominate in inside rounds. For example,
a VC investor may have incentives stemming from their total portfolio performance that lead
to bargaining power shifted to the entrepreneur. The VC may have the bargaining power if
the inability to raise outside capital stems from a negative shock to the entrepreneurial firm.
An analysis of pricing and returns can shed light on typical hold-up scenario.
While the escalation of commitment and agency costs explanations lead to VCs taking
negative NPV decisions, continuing to invest in the presence of negative information can
also be optimal for a number of reasons. Most importantly, the VC capital allocation is a
multi-stage investment process and negative information at each stage may be insufficiently
negative to abandon a project. In other words, even though NPV is negative, the adjusted
NPV (taking into account embedded real optionality) can still be positive. A classical real
option model of McDonald and Siegel (1986) can be adjusted to demonstrate this result. In
itself, this reasoning is not sufficient to preclude raising funds from the outside investors, to
the extent that outside investors also understand the option nature of the decision making
process. One potential mechanism is the existence of soft information available to insiders
that is difficult for outsiders to evaluate. (Obviously, it also has to be taken into account
that the interactions between VC investors is dynamic, with the inside-outside roles reverse
many times over the course of life of the VC career, and reputation e↵ects are very strong.)
Likewise, down rounds can be consistent with the real option model as well, if the nature
of information (i.e., the shock that agents receive) is within a certain range. Importantly
though, the true NPV of the project should still be positive to the VC insiders. If our proxy
for VC returns captures the ex-post realization of these VC expectations well, then it is less
likely that the real options rationale can explain our stylized facts. Note that VC funds can
get higher returns also through better contracting terms with start-up founders.
6
Our paper builds on both theoretical and empirical research in finance and psychology. Escalation of commitment is defined in psychological and organizational literature as
a greater tendency to continue an endeavor once an investment in money, e↵ort, or time
has been made, even if circumstances should dictate otherwise. It is closely related to the
sunk cost fallacy and the endowment e↵ect. See Arkes and Blumer (1985) for experimental
evidence and Staw and Hoang (1995) for empirical evidence of sunk costs (in the context of
NBA games). There are also two interesting studies of organizational escalation that use the
case studies of very large non-profit projects: Expo86 World Fair and the Shoreham Nuclear
Power Plant (Ross and Staw (1986), Ross and Staw (1993)). In that literature, escalation
e↵ects have been typically explained by prospect theory, self-justification concerns, inside
view, the desire to avoid wasting resources, etc. From the economic viewpoint, escalation
e↵ects are irrational if they lead people to take negative NPV projects.
Escalation of commitment can reveal itself in many ways. For example, investors may
prefer to modify project goals or standards instead of abandoning projects, in an e↵ort to
create a more favorable outcome. In the VC context, a lack of clearly defined milestones
naturally exacerbates the problem. It also could be due to “rational overcommitment”
(Adner (2007)), the tendency of individual managers to continue projects with the hope of
improving the outcomes, especially when their personal interests are at stake.
Two related papers, Guler (2007a) and Guler (2007b), show that VCs, as a group, tend
to make sequential investments in deals even if objective criteria suggest the deals need to
be abandoned. Guler (2007b) lists the following reasons why escalation of commitment in
face of negative information may be prevalent in the VC setting, based on her reading of
the organizational and psychological literatures: (1) inability to update prior beliefs with
new information; (2) failure to treat prior investments as sunk costs; (3) framing subsequent investments as opportunities to recover prior losses; (4) avoiding cognitive dissonance
and saving face by further committing to earlier decisions. In addition, the organizational
7
structure of the VC funds may be important for how the escalation of commitment works.
Typically, a deal has a pioneer in a VC partnership and the identity of that partner (seniority, previous success) as well as the structure of the company (size, hierarchical nature) may
play a role.
In experimental paper, Tan and Yates (2002) study how financial budgets a↵ect termination decisions. They find that escalation of commitment declines as financial budget gets
binding. In relation to the VC industry, the extent of the escalation of commitment can depend on dry powder, other investments in the same fund, their current perception of success
(future success can also be used as a proxy, assuming that the VC knows better about the
future).
There is also now a broad recognition of emotional biases and the importance of a↵ect in
decision-making (see Loewenstein and Lerner (2003) and Lucey and Dowling (2006)). This
is related to the endowment e↵ect and can lead to escalation. A related e↵ect is gambling
in the presence of losses if there is a chance to break even (Thaler and Johnson (1990)).
The paper proceeds as follows. Section 2 describes the data and variables. Section 3
presents some preliminary empirical tests. Section 4 follows with a set of robustness tests.
Section 5 studies the decision to participate in an inside round and Section 6 concludes.
2
Data and variables
This section describes our data and introduces main variables of interest.
2.1
Data Sources and Definitions of Variables
We use the venture capital database VentureSource from Dow Jones. The data includes
information about financing rounds, entrepreneurial firms, and investors over the period
from 1990 to 2014. VentureSource is augmented and improved with data from Correlation
8
Ventures, a quantitative venture capital fund.1 The additional data includes financing-level
and exit valuations, which are important for returns calculation. The analysis in the paper
considers a subset of all available equity financings.
We start constructing the main dataset by considering only non-first round financings
where we can begin to track the reinvestment decisions of insiders. An entrepreneurial firm is
in the sample if it raised its first round of financing between 1990 and 2008. The upper bound
allows ample time for an exit event for the investment. We exclude all firms that were founded
prior to 1978. Next, each financing event must follow a previous equity financing where at
least one of the investors is a traditional venture capital investor. Such an investor raises the
fixed-life fund (10-12 years) from institutional investors such as endowments, pension funds,
and trusts. In VentureSource, this includes institutional venture capital, diversified private
equity, and SBIC (government grant-backed) funds. Hedge funds, mutual funds, investment
banks, and corporate VC arms are excluded from this definition. Finally, we drop financings
with small investment amounts to avoid potential hidden bridge financings or incorrectly
split equity tranches. Small is defined by industry classification: financings in the lower
5% of investment size over the sample period are dropped. The main specification includes
21,596 financing rounds in 8,890 entrepreneurial firms.
A critical variable of interest is the degree of round’s “insidedness.” The notion of an
inside round concerns the dynamic investment of investors and the staging of financings.
Our sample includes only financing rounds that are preceded by at least one VC financing,
where we can observe the set of participating investors. In each financing round t, t
2,
investors that contributed capital in at least one prior round, are called “Inside” investors or
simply insiders. If an investor in round t did not participate in any of the prior financings of
this company, that investor is called an “Outside” investor at round t. All equity financing
rounds can be classified as either “inside” or “outside” rounds. A dummy variable “Inside
1
Ewens and Rhodes-Kropf are advisors to and investors in the fund.
9
VC Round” equals one for financing round t if all VC investors contributing capital in this
round are insiders. If there is at least one outside VC investor, then the round is “Outside
VC Round.” We can also use more stringent definitions: “Inside Round” equals one if all
(VC and non-VC alike) investors in this round are insiders. if at least one investor is new,
it is then called an ‘Outside Round.” Clearly, all rounds, for which “Inside Round” equals
one, will also have “Inside VC Round” equal one, but the reverse is not necessarily true.
We also consider two continuous counterparts to this dummy variables. First, we specify
the fraction of dollars provided by inside investors in financing round t of entrepreneurial
firm i, regardless of the characteristics of new investors, KIit , which is:
KIit =
where Iit
1
P
j2Iit
1
Kijt
Kit
(1)
is the set of inside investors (i.e., all investors that contributed in at least one
round prior to round t), Kit is the total capital raised in the financing t and Kijt is the
capital contributed by investor j (which can be zero).
Second, we also specify a similar fraction based on the number of investors. Let Invit be
the set of all investors in financing round t. Then,
FracInsideit =
P
j2Iit
1
1[j 2 Invit ]
#Invit
(2)
is the fraction of those investors that are insiders. The indicator 1[j 2 Invit ] is one if investor
j is an investor in round t.
To estimate the value of companies, we use the so-called “Pre-money” and “Post-money”
valuation of entrepreneurial firms. The post-money valuation, VitP ost , values company i in
financing round t taking into account the capital Kit contributed in that round by investors
on an as-converted-common equity basis. In other words, the valuation assumes that all
the securities issued to investors will be converted to common equity upon exit. Although
10
it is a standard valuation technique in the VC industry, it is important to note that the
valuation on an as-converted basis assumes that the eventual exit will be successful and VC
investors that typically receive convertible preferred securities have an incentives to convert
their claims into common equity. This measure gives a good indication of the change in
valuation between rounds. “Pre-money” valuation, VitP re , is the valuation of the company in
round t before the capital injection is taken into account. In other words,
VitP ost = VitP re + Kit .
(3)
We define the financing round t to be an “up” (“down”, “flat”) round, if the pre-money
valuation in round t increases (decreases, is not changed) relative to the post-money valuation
in round t
P re
P re
1 (that is, if VitP re > VitP ost
< VitP ost
= VitP ost
1 , Vit
1 , Vit
1 , respectively). In
empirical analysis, we often will combine the “down” and “flat” rounds.
To calculate the return on a deal for non-failed investments, we require the final exit
valuation (e.g., IPO valuation, acquisition price, etc.) and the dilution of equity due to
subsequent financing events. We focus on the “gross multiple” variable Mit for firm i in
round t, which is defined as:
T
Vi Y
Mit =
Dis ,
Vit s=t+1
where Vi is the exit valuation, T is the total number of equity financing rounds, and the
sequence Dis are the dilutive factors. Any time an entrepreneurial firm raises outside equity,
the positions of previous investors are diluted by one minus the fraction of equity sold. That
is, Dis is calculated as 1 Iis /Vis , where Iis is the total capital raised in financing s. The gross
multiple considers a hypothetical dollar invested in round t, so these new financings directly
impact the equity stake of the investor. For example, suppose a second round financing is
followed one year later by a new equity round that raises capital in return for 30% of the
11
equity. The equity stakes of the investors in the second round financing will all fall by 30%,
so that Dit+1 is .7. Korteweg and Sorensen (2010) provide a detailed description of returns
calculation and the discussion of sample selection issues.2
Another important variable is the eventual outcome of the entrepreneurial firm. In the
VC industry jargon, those are called exits. The three main exits for VC-backed companies
are an initial public o↵ering, an acquisition or a shutdown/failure. For a subset of nonfailures, we explicitly observe the exit valuation Vi . For IPOs, the coverage is nearly 100%,
for acquisitions, it is 54%. As in other research using investment-level returns, these reported
valuations are positively selected. A company without a valuation implies we cannot calculate any financing returns. There is an over-representation of quality exits – IPOs – along
with low coverage of historically lower valued acquisition exits. A counterbalancing measure
is that if the firm fails outright then returns are low and can assumed to be zero. Because
of the selection issues, we consider both the log of exit valuation and dummy variables for
exit types.
2.2
Descriptive statistics
Table 2 provides descriptive statistics on some basic characteristics of the sample. The
general variables, such as financing year, industry distribution, and total capital raised are
consistent with other research. In terms of final outcomes, 10% of start-ups eventually
become publicly listed companies, 44% are acquired, 21% are listed as outright failures, and
26% are still private (as of 2014). The latter category consists mostly of failures, too, given
that they must have received their first equity financing in 2008 or earlier. The distribution
of life spans confirms the failure hypothesis for many of these private firms. Unreported
2
For entrepreneurial firms that were acquired with a known exit valuation, we assume investors receive
their capital back by order of investment stage and when we are unable to calculate returns. That is, if the
total exit valuation is first split so that the last investors are made whole, followed by the previous investors.
This approach assumes that each investor has senior 1X liquidation preference. We gain approximately
1,900 returns from this, however, the results are similar if we continue to treat them as missing.
12
in the table, out of 2,382 firms that are still private, 1,119 have not exited for more than
10 years and 823 for more than 12 years. In addition, as well known in the VC industry,
acquisitions span a wide range in terms of valuations, from outright failures (penny sales) to
huge successes. We have the exit valuation for 2,360 acquired firms, that constitutes about
60% of the total acquired sample. Of those, 1,414 have an exit valuation that exceeds capital
invested.
The average firm is founded in 1998, with the funding year and the company age widely
distributed. More than 40% of the firms in the sample are from California, consistent
with Silicon Valley being the world-wide hub of venture capital activity. The average firm
in the sample raises 4.2 rounds of funding, between the minimum required 2 (by sample
construction) and the maximum of 20. 80.8% of the sample feature syndicated investment,
in which at least two investors provide capital in the round. On average, each round of
financing features a participation of almost 4 investors and a total capital injection of $13.4.
While the median round size is $8.5 million, the amounts vary from insignificant rounds
to more than $1 billion. It takes on average 1.3 years for the firm to raise its next round
of funding, consistent with the prevailing industry notion that most firms raise subsequent
funding in about 12 to 18 months.
Valuations are known only for about 55% of the sample and gross multiple – for 44%.
For the subsample with valuations, the average capital raised is $15.2 million, similar to
the $13.4 million value reported for the total sample. The average (median) post-money
valuation of $87.7 ($38.2) million suggests that this subsample is biased, as expected, towards
more successful companies. However, while the average gross multiple is a healthy 2.72, the
median gross multiple is 0.22, indicating that in at least 50% of deals, for which this variable
is reported, investors lose most of their money. The positive selection bias in this subsample
thus ensures that investors are likely to lose on an overwhelming fraction of their investments.
13
Only the presence of very successful outliers enables some VC funds to make up for losses
and deliver positive returns on their portfolios.
Table 3 provides descriptive statistics on our main insidedness variables for the main
sample (Panel A) and for the subsample, in which the amount of financing at the previous
round is known (Panel B). In Panel A, all the investors are equally-weighted, while in Panel
B investors are value-weighted by their capital contributions. Inside investors constitute
64% of all the investors and contribute 61% of the capital in the next round of funding in
an average deal, while the outside VCs constitute on average around 21% of the financing
round. In around 30% of cases, however, there are no new investors in the round and in
almost half of the cases there are no single new outside VC investor (to remind, there could
be non-VC new investors, such as corporations, angel investors, individuals or investment
banks, leading to the discrepancy in the values of these two variables). These findings suggest
that the rounds in which only insiders participate are more common in the VC industry than
some previous anecdotal evidence may have led researchers to conclude.
As a stronger alternative to the presence of outside VCs, we also consider the identity
of lead investors in the financing round. Lead investors are those that contribute the most
capital to the round and are in reality the drivers of creating the funding syndicate. We
identify leads in two ways. First, VentureSource may flag an investor or multiple investors
as leads. Second, if such a flag is not available, then we identify the lead as the investor
who provides the most capital. The division of capital within a round and across investors
is often missing, so we split capital equally to those with unknown contributions. A round
may be considered an outside round even though new investors may have contributed a
small fraction to the financing. The presence of an inside lead VC investor is thus a strong
indicator of insidedness. With this lead investor flag, a round is considered having a new
lead investor if there is at least one new investor (i.e. outsider) who is also identified as a
14
lead in the round. We find that in almost 30% of cases, the round does not feature a new
lead investor.
To explore further the prevalence of insideness, Table 4 reports the insidedness and financing variables by the funding round. Panel A considers the traditional second financing
round, while Panels B and C report the descriptive statistics of 3rd and 4th rounds, respectively. (The financing rounds are known in the industry by letters, e.g. the 2nd round would
typically be called a Series B round.) All the measures of insidedness show a secular increase
over financing rounds, indicating lower outside participation over the lifespan of the firm.
For example, while 40% of 2nd rounds feature no outside VCs, 47% and 51% do not have
outside VCs in the 3rd and 4th rounds, respectively. There are also more 4th rounds, at
36%, that are full inside rounds. Even though the latter panels are subject to significant
survivorship bias, because many deals do not reach another round either because they failed
or because they exited, the results are quantitatively similar for the sample that requires
firms to survive at least N rounds, N being 3 or 4 (see Internet Appendix).
2.3
Inside vs non-inside rounds
Are inside rounds truly di↵erent? In this section, we provide some preliminary comparisons
between inside and outside rounds. Tables 5 and 6 and Figures 1–3 consider di↵erences in
valuation, capital raised, returns, and other financing-related variables.
Figure 1 compares the change in valuation between two subsequent rounds by showing
the kernel densities of the ratio of pre-money valuation in the round to the post-money
valuation in the previous round, VitP re /VitP ost
1 , for inside and outside rounds. This ratio
captures the relative gain in value for existing investors and is a good indicator of whether
the entrepreneurial firm is moving in the right direction. A value of 1 is known as the “flat”
round (also singled out by the vertical line), less than one is “down”, and greater than 1
15
is “up.” E↵ectively, for firms experiencing down or flat rounds the news is negative, either
because of the idiosyncratic shocks or changes in the macroeconomic situation.
The figure shows that inside and outside rounds experience di↵erent valuation dynamics.
Inside rounds are much more likely to be down rounds and also have a larger mass at around
1. Moreover, the inside density attenuates much faster, implying lower likelihod of a round
with a large increase in valuation. Taken together, the figure clearly shows that inside
rounds are relatively worse in terms of valuation dynamics than outside rounds. Note that
the sample of financings with a known valuation is selected positively in that firms fully
reveal valuations when they go public and tend to reveal them if they are growing quickly.
Figure 1, and the analysis below, however, suggests that our data capture a large chunk
of negative valuation outcomes. Moreover, there is no ground to believe there is a strong
selection mechanism that works di↵erently for outside and inside rounds (especially the one
that would lead to inside rounds be more negatively selected). To check that the di↵erence
between inside and outside rounds persevere, Figure 2 presents kernel densities by the log of
capital raised, the financing variable that is available for most of the rounds in our dataset.
Capital injection plays an important role in the VC industry, because corporate governance
considerations make many VC investors demand a certain fraction of ownership in the startup firm, leading to a relationship between valuation and capital injection. The vertical line
shows the mean capital invested, which is approximately $7.3m. The figure shows that the
two round types di↵er dramatically. Inside rounds raise significantly smaller amounts of
money than outside rounds. Small amounts of capital invested in inside rounds could imply
that these rounds are quickly completed and may in fact be hidden bridge rounds. For
example, such rounds could be used by insiders to quickly provide capital to startups when
facing both internal and external shocks. Unreported results show, however, that when we
repeat the analysis that excludes what appear to be bridge rounds (tenth of all financings
16
that raise the least capital and a quarter of all financings that are the earliest to be followed
by the next financing), the results remain unchanged.3
Successful VC backed start-ups tend to raise more capital at each subsequent round of
financing. A slower or flat ramp up may indicate a firm struggling to raise capital or reach
milestones. Figure 3 shows the di↵erences between inside and outside rounds in terms of
relative capital raised across rounds. The leftmost red line in the figure shows a ratio of 1,
while the rightmost line shows the mean across the whole sample. The average financing is
twice as large as the previous. The inside rounds, however, are significantly more likely to
raise less capital than the previous financing.
Table 5 compares inside rounds to outside rounds and to the full sample along a number
of other dimensions. Several results that stand out provide further di↵erentiation between
inside and outside rounds. Inside rounds raise on average more rounds of funding, backed by
VCs for a longer period of time, and are older. However, the span of time it takes for startups to raise the next round of funding is the same for inside and outside rounds, suggesting
that while firms with inside rounds survive longer they do not raise funding at di↵erent
time intervals. While the round syndicate size is significantly smaller for inside rounds
(as expected, because inside rounds by definition are constrained to have a smaller set of
investors), the total number of investors (that includes all previous investors) is virtually
the same for inside and outside rounds. Inside rounds are much more likely to be a “down”
or a “flat” round. For the subsample, for which we have the valuation data, these valuedecreasing rounds comprise 38% of inside rounds but only 20% of outside rounds. Finally,
inside rounds feature significantly lower returns than outside rounds. The mean (median)
gross multiple for inside rounds is 2.2 (0) versus 2.9 (0.3) for outside rounds. Given that it
takes longer for inside rounds to exit, the actual di↵erence in returns is even larger.
3
See the Internet Appendix for the figure and summary statistics.
17
Conventional wisdom often mentioned by venture capitalists and those familiar with
the VC industry is that entrepreneurial firms should do everything possible to avoid down
rounds. Table 6 compares inside and outside rounds depending on whether the round was
an “up” round or a “down” round (“flat” rounds are incorporated in “down” rounds). The
table shows that for the subsample of “down” rounds, inside rounds underperform across
most dimensions. They are more likely to lead to zero returns as well as to fail, less likely
to become public, and show lower gross multiples. In contrast, when valuation increases,
inside rounds have weakly higher returns and better exit rates. These results suggest that
the change in valuation is an important control variable. They also provide a first glimpse
into the economics of inside rounds, suggesting that di↵erent economic mechanisms can be
at play in “down” and “up” rounds, the issue that the next sections will explore in detail.
3
Basic empirical tests
In this section we will explore the impact of insidedness on the economically important
variables, including entrepreneurial firm outcomes, valuation, and investment returns. The
upshot of our findings is that insidedness is an important variable explaining and predicting
all of these variables that are of great interest in understanding better the economics of VC
backed firms.
3.1
Inside rounds and entrepreneurial firm outcomes
The exits of entrepreneurial firms are readily classified and success in the VC industry comes
either as an IPO, widely considered to be the major objective in the VC industry, or as
a combination of IPO and a highly valued M&A. The first question we tackle therefore is
whether insidedness matter for explaining the eventual exit outcome. If inside rounds are
a selection of lower quality investments, then we should expect to observe worse outcomes.
18
Alternatively, if the average inside round represents the insiders keeping the best deals to
themselves, then we should find the opposite. Finally, if the observable features at the time
of financing have little bearing on investment success, we should observe no relationship
between inside rounds and eventual outcomes.
Table 7 reports the results of this analysis, where we regress the eventual outcome of the
entrepreneurial firm on insidedness and many control variables. We consider three outcome
variables. First, in line with the VC conventional wisdom, we define an IPO dummy as one
proxy for success. While the IPO dummy is very popular among researchers and is the most
frequent measure of success used in the VC literature, an important problem with this dummy
variable is that many M&A exits, in which the start-up is sold, are also great successes in
terms of the valuation. Indeed, arguably, there are more successful M&A outcomes than
IPO outcomes in the VC industry, especially recently. We therefore define another dummy,
“Good exit,” which equals 1 if the outcome is either an IPO or an acquisition, in which a
valuation is known and at least two times all the capital invested. While the exact threshold
used in the definition of M&A “success” is necessarily ad hoc, our results are robust to
using a higher threshold. Finally, we also use the eventual exit valuation as an indicator of
success. We have the value of this variable only in cases of IPO or M&A, i.e. the results are
conditioned on non-failure. We also use the log of exit valuation to control for the presence
of many outliers. Specifically, “Log exit valuation” is the log of the reported valuation at sale
or IPO. For the latter, the valuation if the market capitalization of the firm at the o↵ering
price.
The first three columns of Table 7 show our benchmark results. The first two columns
show a strong, negative relationship between inside rounds and quality exit outcomes. The
estimates imply a 14% lower probability of an IPO and a 20% lower probability of a “Good
exit” if the firm had at least one insidedness round. The results for the log value are similar,
but economically larger: firms with at least one inside round have a 34% lower exit valuation
19
conditional on not failing. From Table 5 we know that the average firm with an inside round
fails more often, so the total di↵erence in exit valuation should in fact be larger than 34%.
The final three columns introduce the interaction of the dummy “Had down round”
and the indicator for an inside round. This interaction asks whether the worse outcomes
observed in inside rounds can be explained by the higher propensity for down rounds in
inside rounds. The inclusion of the down round control is motivated by the finding that
inside down rounds seem to be economically di↵erent from inside up rounds. This inclusion
reveals two interesting results. First, the correlation between a down round in the firm’s
history and outcomes is strong and negative. Second, the relationship between past inside
rounds and outcomes remains after the inclusion of this interaction. Taken together, these
results lead to a conclusion that the predictive power of inside rounds for outcomes is strong
and is not accounted for by by past valuation changes.
3.2
Inside rounds and interim valuation changes
One distinctive feature of the VC financing is that, because entrepreneurial firms raise many
rounds of funding, they are valued repeatedly over its life cycle prior to the exit. The change
in valuation across financing rounds reveals the interim success of the firm and the “return”
earned by insiders from the previous round. While in most cases this “return” is paper
money return in that inside investors’ holdings are illiquid and they cannot cash out, to the
extent that prices reveal all available information about the eventual prospect of the firm,
interim returns are useful at gauging the progress of the firm. In this section, we explore to
what extent insidedness can explain changes in interim valuation.
To compute the change in valuation between two subsequent financing events, we first
compute the ratio of the current price over the past price paid. The current price is the
“pre-money” valuation and represents a “market” capitalization prior to the new infusion
of capital. The past price is the “post-money” valuation in the previous round of financing.
20
The log of this ratio thus represents a return to a hypothetical investment of one dollar
in the previous round that was realized at the next round’s price.4 Again, although the
venture capital market typically does not have such a secondary market, these returns are
not directly earned by investors, they represent an implied return to investment.
This dependent variable is especially informative in inside rounds because it represents
the di↵erences in prices paid by the same investors over two rounds of financing. A relatively
larger change in price for inside rounds indicates that either the insiders pay relatively too
high a price at the previous round or they invest at relatively lower prices at the current
round. The latter could stem from either negative shocks to the firm or from increased
bargaining power for the insiders at the current round. In Section 4 we show that, at least
for the sample we have collected, inside rounds do not exhibit di↵erent contractual provisions
both over time series and in cross-section.
Table 8 shows that inside rounds have a significant impact on the changes in valuations.
To understand better the economic intuition of the result and its quantitative significance,
note that the unit of observation in the table is an entrepreneurial firm financing, where one
can observe the current round and previous round firm valuations. The estimates imply that
inside rounds have a 15%-25% lower changes in valuation (or round-to-round return) than
outside rounds. This relationship holds after controlling for financing observables such as
year, industry, round sequence number, and investor count. Column (6) also shows that the
relationship retains its significance, if we used “no new lead” dummy as a stronger proxy for
insidedness. As emphasized above, this relationship is consistent with either the bargaining
power or negative shock story. An analysis of the ultimate returns earned in these financings
is necessary to help distinguish these explanations.
4
This variable is similar to the round-to-round return in Korteweg and Sorensen (2010) and Cochrane
(2005)
21
3.3
Inside round and investment returns
So far, we have shown that inside rounds are characterized by lower success rates, exit
valuations, small capital raises and higher probabilities of down rounds. In this section, we
explore whether the final returns to investment depends on insidedness. This is by far not a
foregone conclusion. For example, the higher failure rate and lower IPO probabilities could
be reflected in lower prices at the time of the inside round. Similarly, the down round events
could be the “right” price that results in lower success, but later exhibiting identical returns
than comparable outside round financings.
Table 9 reports the results on financing-level returns. The dependent variable is the
log of the gross multiple which measures the return of a hypothetical dollar invested in the
financing round accounting for dilution. While this measure ignores the time value of money,
it is a good first step to study returns. We again use two measures of insidedness, the dummy
indicating whether the round is a full inside round and a dummy whether the round did not
have a new outside lead investor. The first four columns presents the main results. In the
first two regressions, we include the full sample of observed gross multiples. Failure outcomes
are set to the log of 25% of capital invested. Controls include capital raised, time since last
financings, and fixed e↵ects for financing year, industry, and the state of entrepreneurial firm
headquarters. The insidedness coefficients imply a 12.8%-15.1% lower gross multiple return
in inside rounds.
Because our assumption about the recovery rate of investors in the failure cases is inevitably ad hoc, we also consider, in Columns (3) and (4), the subsample that includes only
those firms that exited. For the inside round dummy, the coefficient is still negative, but
is about half the size and loses significance; it is still strongly negative and economically
significant for no new lead investor. The final four columns in Table 9 extends the analysis
by including VC firm fixed e↵ects. Introducing VC firm fixed e↵ects allows us to address
two major and related concerns about the previous results. First, inside rounds may simply
22
be conducted by relatively worse investors who have more behavioral biases or su↵er from
worse incentive frictions. Second, VC firm quality is highly correlated with investment quality (e.g. Sørensen (2007) and the importance of deal flow). Introducing a VC firm fixed
e↵ect will control for any time-invariant investment quality at the VC-level that is driving
the results. Simply, we now compare inside and outside rounds within the VC firm portfolio.
The unit of observation is a VC firm in a specific financing. All the patterns found in the
first set of results remain. Thus, VC firm time-invariant quality and, in turn, some unobserved entrepreneurial firm qualities cannot explain the results. More importantly, the fixed
e↵ects estimates imply that within the average VC firm portfolio, inside rounds are relative
underperformers in terms of returns.
4
Robustness and Discussion
Insidedness plays an important role in the outcomes of VC backed companies. In this section
we explore a number of potential explanations for the observed di↵erences, as well as discuss
various robustness tests that deal with measurement issues with investment returns and
sample selection.
4.1
Inside participation
The dummy variable “Inside round” simply requires that there be no new outside investors.
There are no conditions on the number or composition of the insiders who do invest in the
round. One possible explanation for the results above is that a small subset of insiders keep
the entrepreneurial firm alive, while the majority walk away and write o↵ the investment. If
this is the case, then we are potentially underestimating the real returns earned by investors
because so few participate in the financing. Columns (1) and (2) in Table 10 explore this
explanation. The first has the dummy variable “Full participation,” which is equal to one
23
if all past investors invest in the current round. The second column uses a similar variable
that is one if the fraction of insiders that participate is greater than the 75th percentile of
participation across all rounds. If the underperforming inside round is confined to the sample
with low inside participation, than the two interaction terms in columns (1) and (2) should
explain the main results. The coefficients on “Inside rounds” are e↵ectively unchanged with
the inclusion of these controls and the controls themselves are insignificant. The results
indicate that the composition of insiders is not important for the insidedness mechanism.
4.2
Too little capital?
The amount of capital invested is a strong predictor of outcomes. As Figure 2 shows, inside
rounds are on average significantly smaller in terms of capital raised than outside rounds.
To better understand if the size of the inside round is driving the lower returns, we construct
a dummy variable “Capital ramp down.” The variable tracks the sequence of capital raised
between two sequential financings of an entrepreneurial firm and equals to one if the change
in capital raised is in the bottom quartile of changes across the sample. A small or negative
change in capital raised across financings could signal worse prospects or strong financing
constraints for the firm. If within-firm capital ramp downs drive poor results, the interaction
of this dummy with inside round should be negative. As Column (3) of Table 10 shows, while
the level of the variable loads negatively, as expected, there is no di↵erence within the sample
of inside rounds. Thus, within-firm capital restrictions for inside rounds is unlikely to drive
the returns di↵erences.
4.3
Return to total equity position
The returns results indicate that a dollar invested in inside rounds returns less than dollar
invested in similar outside rounds. For existing insiders, however, this may not be the
appropriate comparison. An insider who faces the decision to continue investing, should
24
consider the return to the total equity position she has accumulated in the company after
the financing closes. For example, consider a VC who invested in round A. In Round B,
there are no other investors who are willing to invest in the company. The insider might be
losing on any additional investment, but the total return would include a recovery rate on
her original investment as well. Therefore, it is possible that the insider is willing to invest
at relatively higher prices than an outsider. In other words, while from the perspective
of the single investment, the insider may appear to be investing irrationally, the portfolio
consideration provides a rational justification.
To explore this possibility, we construct the “Total return” variable. The total return
to an investment in a given financing is defined as the dollar-weighted return to the current return and all previous equity financings. This construction accounts for all previous
investments made by the investor in this company. If insiders earn a lower return on the
current financing with the goal of supporting their total equity position, the returns to the
previous financings should in expectation to o↵set the relative losses in the current round.
If this is the main mechanism, once controlled for, the inside round should be statistically
indistinguishable from outside rounds.
The last four columns of Table 10 test this conjecture. the results suggest that the
average insider is not earning a total return that o↵sets the relative losses in the current
financing. The coefficients on each specification are negative. For the full sample, the total
return is 21–23% lower for inside rounds. For the smaller sample of non-failed companies the
results are less significant. The lack of power here stems from the lack of variation of total
return and return to the current financing for failed investments. Simply, the dollar-weighted
mean of zero returns is zero. Overall, there is little evidence that the predictive power of
insidedness for returns is driven by insider’s total equity position.
25
4.4
Can preferred stock explain the results?
The gross multiple used above assumes that the investor purchases a share of common equity
or, equivalently, the investor’s security is converted into common equity upon exit. As is
well known, most VCs purchase preferred shares which include participation and liquidation
rights. As inside rounds are more likely to be acquired and have lower exit valuations, it
is possible that these contract features come into play when an inside round occurs. If
this is the case, then we could be underestimating the true returns earned by investors in
inside rounds. The next two tables address whether inside rounds are more likely to have
non-common contract features.
Table 11 exploits the database of VC contracts provided by VC Experts. This data
set includes a sample of VC-backed companies primarily financed after 2005 and provides
information on the contractual provisions of securities held by VCs. The data set comes
from extraction of terms from articles of incorporation filed in the state of Delaware. For
our analysis, we are concerned that the common equity assumption underestimates the
true returns earned by the VCs. Several contract features would improve the VC returns.
Liquidation preference provides downside protection for a preferred shareholder than can
guarantee at least one times capital invested. These preferences can reach two or three times
capital invested. Next, preferred stock seniority provides an investor additional downside
coverage and priority in lower quality liquidation events. Third, the insider can purchase
“Participating preferred” stock that provides both the liquidation preference and the ability
to participate in the upside without conversion. Compared to a common equity position – or
preferred stock after conversion – participating preferred can increase returns significantly.
Finally, a preferred shareholder can have redemption rights which act as a put on their equity
investment after some period of time. This right can provide bargaining power in liquidation
events.
26
Each column of Table 11 asks whether there is a correlation between observed contract
features and inside rounds. If insiders are shifting to the contracts that are more friendly
to VCs, then the common equity assumption is an underestimate of average returns earned.
The merge of VC Experts with VentureSource results in over 1,400 financing events with
known contracts. Columns (1) - (4) show that insidedness is not statistically related to these
contract features. For example, in Column (2) a dependent variable is a dummy that equals
to one if the liquidation preference of the financing is greater than “one times” (known in the
industry as “1x”). There is a weak positive relationship between insidedness and liquidation
preference, which is not significant. The table also reports the mean of each dependent
variable unconditionally and conditional on inside rounds. Again, the inside rounds tend to
have slightly friendlier terms than outside rounds, but the di↵erence is small.
The final column combines each contract feature with a dependent variable equal to
one if any of these first four column variables equals to one. In this case, the relationship
is significant, although given that over 80% of the contracts contain at least one of these
rights, the evidence is weak. Given a small sample of contractual obligations, it is possible
that there is a weak suggestive evidence that common equity may be an underestimate of
returns for inside rounds.
In the absence of better data on contracts, we proceed with the following exercise to
explore in more detail the impact of better cash flow rights of VCs. Would these contracts
matter if inside rounds were to have these provisions? Table 12 imposes these contracts on
returns to understand if they can explain earlier results. In column (2), all inside rounds that
have a return greater than 0 and less than two time capital invested are given a two times
capital return. Similarly, outside rounds with observed multiples between zero and one are
assigned the latter. This adjustment improves the returns on the average non-failed inside
round. This is an extreme assumption, because the observed fraction of 2X liquidation in
VC Experts is less than 15%, while this adjustment results in over 35% of inside rounds
27
having such a feature. As expected, the coefficient on the inside round dummy decreases;
however, we still find a statistically significant negative relationship with returns. Column
(3) takes an even stronger view. It treats all missing acquisition returns for inside rounds
as a 2X gross multiple if the capital is available (i.e. the exit valuation is at least two times
capital invested). We replace outside round missing returns similarly with 1X returns. After
this adjustment, over 50% of the inside round sample have these features. Only after this
strong assumption do inside rounds have no relationship with log returns. We conclude from
this exercise that it is unlikely that strong preferred contract features would explain away
the main di↵erences in financing returns.
4.5
Sample selection
The analysis above that uses exit valuations, financing valuations and returns requires significant disclosure of information. As discussed and addressed in work by Korteweg and
Sorensen (2010) and Cochrane (2005), this sample selection can dramatically bias estimates
of returns. The bias is positive because the disclosure of valuation information often follows success and quality entrepreneurial firms have more investment events in the data.
This bias should attenuate our findings on the relative underperformance of inside rounds.5
Nonetheless, it is important to address the selection issues to ensure our results cannot be
explained by missing data patterns. Columns (4) and (5) take two approaches to address
the issue. Each are motivated by the techniques in Korteweg and Sorensen (2010). The
selection bias in returns results in an over-sampling of publicly-held VC-backed firms and
an underrepresentation of acquired firms (we observe all failed outcomes). We compute the
fraction of each exit type in the full sample – IPOs, acquisitions and failed. These fractions
are then imposed on the regression analysis through a weighted regression and resampling.
Column (4) conducts a weighted least squares where the weights are the inverse of the true
5
The omitted variable can be entrepreneurial firm quality, which is positively correlated with the probability of being in the data and negatively correlated with inside rounds.
28
exit probabilities. Column (5) creates a random sample of the main sample where the final
proportion of exit types matches the full sample. In both columns, the negative relationship
between inside rounds and returns holds.
5
What drives inside round financings?
The tables discussed above make clear that inside rounds and insider participation correlates with financing- and firm-level observables. We next ask whether we can predict the
prevalence of insiders in a financing. Table 13 considers the dummy for a financing having
all insiders, however, the results are robust with a dependent variable that is the fraction of
inside capital. Each specification includes the interaction of industry and year fixed e↵ects.
Most of the controls have strong predictive power for inside rounds. Less capital raised
in the previous round and fewer investors implies a higher probability of inside round or
fraction of insiders. Larger previous syndicates weakly predicts more insider participation,
perhaps because large snydicates have relatively more uncommitted investors. Perhaps surprisingly, the longer one waits for new round of financing, the higher the probability it has
outsiders. This correlation could simply follow from a higher chance of finding a new capital
provider over time. Although the results do not point to one story, the ability to predict
insideness of financing suggests that the variable captures something meaningful about financings. Columns (2) - (5) of Table 13 ask whether investor characteristics predict inside
participation.
Column (2) of Table 13 introduces the dummy variable “Syndicate fund raising” that
is equal to one if we can identify at least one investor in the process of fund-raising. An
investor is fund-raising if they are within two years of their next fund closing. We discuss
above how LPs discourage inside rounds as means of marking up investments, so we would
predict less inside round events. Column (2) bears out this prediction. Column (3) of Table
29
13 asks where the current investors’ recent success can predict outside investor participation.
The variable “Big exit last 2 years” is equal to one if at least one of the active investors had
an IPO or large acquisition in the previous two years. Recent success could indicate both
a higher quality investment but also a weaker tendency for the investor to put good money
after bad. The coefficient on this variable suggests success lowers the probability of an inside
round.
The next column (4) introduces a measure of the experience of the existing investors of
the entrepreneurial firm at each financing event. The variable “Log syndicate experience”
is the log of the total investments done by all insiders prior to the financing event. The
coefficient on the variable is positive and significant. It appears that more experienced
investors have a higher probability of participating in an inside round. Such a relationship
is not driven by any changes in investors’ stage of investment as we include round number
fixed e↵ects. The final column of Table 13 asks whether the VC fundraising environment
can explain the prevalence of inside rounds. Perhaps we observe more inside rounds when
there are less funds available to all VC-backed firm, which also coincides with weaker market
conditions overall. This variable varies within-year, so attempts to capture time variation
that is unavailable with financing year fixed e↵ects. The results indicate that when there
are more funds raised in the previous two years, there are relatively more inside rounds.
This result and that of column (5) are conflict with some of the results about returns and
outcomes, so warrant further analysis.
6
Conclusion
The results above present a preliminary view on the relationship between outside investor
participation and entrepreneurial firm investment outcomes. We study sequential investment
decisions in the venture capital (VC) industry. VC-backed companies typically need to raise
30
several rounds of funding from VC funds, and the decision whether to provide further funding
to the company as well as the terms of the new funding determine to a large extent the returns
of VC funds and their ability to back successful companies. We show that investment
outcomes in the VC industry can be predicted by whether the existing VC investors can
attract new outside investors to participate in the next round. Inside rounds, in which
only existing investors participate, lead to a higher likelihood of failure, lower probability
of IPO, and lower cash on cash multiples than outside rounds. We explore to mechanisms
that explain these stylized facts: the escalation of commitment that leads VC investors to
irrationally make negative NPV decisions, and agency costs between VC funds their investors
that make VC investors gamble with their investors’ money. Ongoing work will introduce
new measures of agency frictions and investment heterogeneity.
31
7
Figures and Tables
Figure 1: Change in valuation: inside vs. outside rounds
Notes: The figure reports the ratio of the current financing pre-money valuation over the previous financing’s
post-money valuation. We can only calculate this ratio for two financings where valuation is revealed. This
sample is positively selected towards high-quality entrepreneurial firms (e.g. those that go public). The red
vertical line is for the ratio value of one, which is called a “flat” round.
32
Figure 2: Log capital invested: inside vs. outside rounds
Notes: The figure reports the kernel densities of log of capital invested for a financing event in two samples.
“Outside” are those financings with at least one VC investor who is new to the pool of investors. “Inside” are
those financings where all investors were previously invested in the firm.
33
Figure 3: Capital ramp up: inside vs. outside rounds
Notes: The graph compares Raised t / raised t
be less than the 95th percentile.
1 when the variable is avaiable. The variable is winsorized to
34
Table 1: Main variable description
Notes: The table defines the major variables used throughout the analysis.
% insiders (relative last syndicate)
% insider investors
% VC outsiders
(relative
last
syndicate)
% investors that
are outsider VC
% insider dollars (relative last
syndicate)
% inside dollars
% VC outsider
dollars (relative
last syndicate)
Full inside round
Full VC inside (no new
non-VCs)
% insiders participate
No new lead?
Years since last
financing
Round number
Capital raised
Revenues / profitable?
Fraction of investors in financing that are old, where any investor
that was not in the previous syndicate is considered new.
Fraction of investors in financings that are old, where insiders are
defined as any previous investor. A new investor has never invested
in the company.
Fraction of investors that are new VC investors (rather than corporate and other) who did not invest in the previous syndicate.
Fraction of the investors in the financing that are new and VCs,
where new investors must not have invested at any point before.
Fraction of the dollars in a financing provided by the investors in
the previous financing syndicate.
Fraction of the dollars in the financing provided by any existing
investor.
Fraction of dollars in the financing that were provided by new VC
investors where new is defined as any investor not in the previous
financing and is a VC.
Equals one if the financing had all existing investors who were from
the previous financing.
Equals one if the financing had no new VC investor in the round.
The fraction of inside investors that are investing in the current
financing.
A dummy variable equal to one if the financing has only a lead
investor that was a previous investor.
Years from the last financing to the current financing.
The sequence number of the financing event.
The total capital invested at the time of the financing (in millions
of USD).
A dummy variable that is one if the firm reports having revenues
or profits at the time of the financing.
35
Table 2: Summary statistics
Notes: Table reports the summary statistics of the firms and financings in our sample. The main criteria for
inclusion are post-first financing equity rounds for entrepreneurial firms where they have at least one traditional
VC investor and received capital from 1990 to 2008. Panel A summarizes the set of entrepreneurial firms.
“Information Technology” and “Healthcare” are firm industry categories. “Public?” is a dummy equal to one
if the firm had an IPO by the end of the sample. “Acquired?” and “Failed?” are dummies for firms that were
acquired or failed, respectively, by the end of the sample. “Year founded” is the year the entrepreneurial firm
was founded. “First capita raised” is the capital raised in the entrepreneurial firm’s first financing (in millions).
“Total financings” is the total number of financings raised by the entrepreneurial firm prior to exit. Panel B
summarizes the financing event characterstics, valuation measures and returns. “Round number” is the sequence
of the financing event. “Years since last fin.” is the number of years between the current and previous financing.
“Syndicate size” is the count of the number of unique investors in the current financing. “Capital raised” is the
total capital investors in the current financing. “Firm age” is the age in years of the entrepreneurial firm by
the financing event. “Financing year” is the year of the investment. The valuation measures – pre-money and
post-money – are often missing, so the summary considers only a subsample of the data. The returns are also
missing for many financings, so “Gross multiple” summarizes the set of financing returns where we observe the
exit valuation and financing valuations. “Gross multiple” is the multiple of money earned by a hypothetical
dollar invested in the financing accounting for any future dilution (zero for failed firms).
Information Technology
Healthcare
Public?
Acquired?
Failed?
Still private?
Year founded
First capital raised (m)
Total financings
California
New York
Illinois
Texas
Round number
Syndicate size
Years since last fin.
Capital raised (m USD)
Firm age (as of financing)
Revenues/Profitable?
Financing year
Post-money valuation
Pre-money valuation
Gross multiple
Panel A
Firm characteristics
mean
sd
min
p25 p50 p75
max count
0.53
0.50
0
0
1
1
1
8890
0.23
0.42
0
0
0
0
1
8890
0.10
0.30
0
0
0
0
1
8890
0.44
0.50
0
0
0
1
1
8890
0.21
0.40
0
0
0
0
1
8890
0.26
0.44
0
0
0
1
1
8890
1998.6 5.19
1978 1996 1999 2002 2007 8890
5.91
8.33 0.0100 1.70 3.52
7
232.6 8890
4.17
2.07
2
3
4
5
20
8890
0.42
0.49
0
0
0
1
1
8890
0.048 0.21
0
0
0
0
1
8890
0.018 0.13
0
0
0
0
1
8890
0.050 0.22
0
0
0
0
1
8890
Panel B
Financing characteristics
mean
sd
min
p25 p50 p75
max count
3.27
1.49
2
2
3
4
13
21596
3.94
2.69
1
2
3
5
25
21596
1.33
1.00 0.0027 0.71 1.13 1.69 18.1 21596
13.4
21.6 0.030
4
8.25 16.0 1500 21596
5.03
3.73 0.022 2.33 4.07 6.74 35.5 21596
0.060 0.24
0
0
0
0
1
21596
2003.4 5.32
1990 2000 2003 2008 2015 21596
Valuations (when known)
mean
sd
min
p25 p50 p75
max count
88.6 566.3 0.60
19
38.3
80 50000 11592
73.4 548.3 0.070 12.1 26.6 60.9 48500 11592
Investment returns (when known)
mean
sd
min
p25 p50 p75
max count
2.74
14.0
0
0
0.22 1.64 559.8 9468
36
Table 3: Characteristics of the various inside variables
Notes: Table reports the characteristics of the main insideness variables. Panel A includes all financings where
we could determine the insideness. Panel B restricts the sample to those where we have a complete history of
capital invested by investors. It is often unknown how much capital was provided by past investors because
they are general “buckets” (e.g. “Individual Investors”) or lack a VC firm identifier in VentureSource. Variables
are defined in Table 1.
Full inside round
% insider investors
No new outside VC
% investors that are outsider VC
% insiders participate
No new lead
Observations
Full inside round
% inside dollars
No new outside VC
% inside VC dollars
% investors that are outsider VC
% insiders participate
No new lead
Observations
mean
0.30
0.62
0.48
0.22
0.62
0.59
21596
mean
0.35
0.61
0.49
0.73
0.22
0.62
0.56
14439
sd
0.46
0.33
0.50
0.27
0.33
0.49
p90
1
1
1
0.50
1
1
max
1
1
1
1
1
1
Panel B
Financings with known past dollars
sd min p10 p25 p50 p75 p90
0.48
0
0
0
0
1
1
0.36
0
0
0.33 0.60
1
1
0.50
0
0
0
0
1
1
0.32
0
0.25 0.50 0.92
1
1
0.28
0
0
0
0.12 0.33 0.60
0.36
0
0
0.33 0.67
1
1
0.50
0
0
0
1
1
1
max
1
1
1
1
1
1
1
37
min
0
0
0
0
0
0
Panel A
All financings
p10 p25 p50
0
0
0
0
0.40 0.67
0
0
0
0
0
0.14
0.091 0.33 0.67
0
0
1
p75
1
1
1
0.33
1
1
38
sd
min
0.31
0
0.26
0
0.44
0
0.49
0
5.06 1978
0.88 0.0027
13.8 0.080
0.50
0
sd
min
0.31
0
0.25
0
0.45
0
0.50
0
5.07 1978
0.87 0.0027
18.3 0.10
0.49
0
sd
min
0.30
0
0.26
0
0.48
0
0.50
0
5.02 1978
0.89 0.0027
17.5 0.040
0.48
0
mean
0.60
0.25
0.27
0.40
1998.8
1.32
11.8
0.55
7602
mean
0.65
0.19
0.28
0.47
1999.0
1.34
14.7
0.58
4704
mean
0.72
0.18
0.36
0.51
1999.2
1.32
15.5
0.63
2547
% insider investors
% investors that are outsider VC
Full inside round
No new outside VC
Year founded
Years since last fin.
Capital raised (m USD)
No new lead
Observations
% insider investors
% investors that are outsider VC
Full inside round
No new outside VC
Year founded
Years since last fin.
Capital raised (m USD)
No new lead
Observations
% insider investors
% investors that are outsider VC
Full inside round
No new outside VC
Year founded
Years since last fin.
Capital raised (m USD)
No new lead
Observations
p75
1
0.40
1
1
2002
1.66
15
1
p75
1
0.33
1
1
2003
1.73
18.5
1
p75
1
0.25
1
1
2003
1.74
20
1
Panel A: 2nd round
p10 p25 p50
0.20 0.40 0.60
0
0
0.25
0
0
0
0
0
0
1992 1996 1999
0.48 0.75 1.13
1.75
4
8
0
0
1
Panel B: 3rd round
p10 p25 p50
0.20 0.50 0.67
0
0
0.14
0
0
0
0
0
0
1992 1996 1999
0.44 0.75 1.17
2
4.80
10
0
0
1
Panel C: 4th round
p10 p25 p50
0.29 0.57 0.75
0
0
0
0
0
0
0
0
1
1992 1996 2000
0.39 0.72 1.14
2.11
5
10
0
0
1
p90
1
0.50
1
1
2006
2.39
33.5
1
max
1
1
1
1
2007
7.34
300
1
p90 max
1
1
0.50
1
1
1
1
1
2006 2007
2.37 9.18
31 325.2
1
1
p90 max
1
1
0.57
1
1
1
1
1
2005 2007
2.30 14.2
25.2 212.8
1
1
Notes: Table reports the characteristics of the main insideness variables. Panel A includes all financings where we could determine the insideness. Panel
B restricts the sample to those where we have a complete history of capital invested by investors. It is often unknown how much capital was provided by
past investors because they are general “buckets” (e.g. “Individual Investors”) or lack a VC firm identifier in VentureSource.
Table 4: Characteristics of the various inside variables: by round type
Table 5: Inside vs. non-inside financing rounds
Notes: Table compares financings where there the only investors are those with an existing equity stake to
those with at least one outside investor of any kind. The numbers are the mean and median respectively, by
sub-sample and for the full sample (i.e. “Total”). Sample includes all entrepreneurial firms that were founded
prior to 2008 to give ample time for an exit event and who had at least two financing events. The first panel
considers characteristics of financings independent of whether we can calculate a return or observe a valuation.
The remaining panels require non-missing data on each of these dimensions for sample inclusion. “Years VCbacked” is the number of years from the first observed VC financing to the current. “Revenues/Profitable” is
a dummy equal to one if the firm reports some revenues or profit as of the financing. “# VC investors (all)” is
a count of the total number in past investors. “Pre$t / Post$t 1 ” is the change in valuation from previous to
the current financing. “Up round” is a dummy variable equal to one if this value is greater than one (less than
one for “Down round” and 1 for “Flat round”). Other variables are as defined in Table 2.
Full sample
Inside round
7.400
4.600
Outside round
15.99
10.30
Total
13.42
8.250
Round number
3.945
3
3.575
3
3.686
3
Years VC-backed
3.617
2.946
3.123
2.341
3.271
2.511
Firm age (as of financing)
5.560
4.704
4.801
3.819
5.028
4.066
Years since last fin.
1.347
1.172
1.328
1.101
1.334
1.125
Revenues/Profitable?
0.0683
0
0.0562
0
0.0598
0
Syndicate size
2.571
2
4.528
4
3.943
3
Capital raised (m USD)
# VC investors (t
1)
Number Financings
Pre$ t / Post $ t
1
4.729
4.489
4
4
6459
15137
Financing valuations
Inside round Outside round
1.480
2.124
1.108
1.538
Up round
0.614
1
Down or flat round
0.386
0
1976
Number observations
Gross multiple
0.796
1
0.204
0
6033
Financing returns
Inside round Outside round
2.203
2.930
0
0.322
4.561
4
21596
Total
1.965
1.424
0.752
1
0.248
0
8009
Total
2.737
0.220
Gross multiple (95% winsorized)
1.351
0
1.628
0.322
1.555
0.220
Zero multiple
0.523
1
2512
0.439
0
6956
0.461
0
9468
Number observations
39
Table 6: Inside vs. non-inside returns and outcomes: up vs. down financings
Notes: Table compares financings where there the only investors are those with an existing equity stake to those
with at least one outside investor of any kind. Down round is when the valuation falls or is unchanged between
financing events. Sample includes all entrepreneurial firms that were founded prior to 2008 to give ample time
for an exit event. The sample is also constrained by our ability to calculate the return, so comparing Table 5’s
first panel to this one will reveal some sample selection issues. “Gross multiple” is the assumed cash-on-cash
return for a hypothetical common equity purchase in the financing in question that accounts for future dilution
and the final exit value. “Zero return” is a dummy equal to one if the multiple was zero (i.e. return -100%).
Other variables are as defined in Table 2.
Inside down
2.375
Outside down
2.468
Gross multiple (95% winsorized)
2.092
2.075
Zero return
0.417
0.342
Round number
3.849
3.771
Years since last fin.
1.358
1.419
Capital raised (m USD)
6.825
16.53
Public?
0.256
0.304
Failed?
0.342
0.253
Acquired?
0.402
0.444
5.697
6.071
403
Inside up
4.985
728
Outside up
4.771
Gross multiple (95% winsorized)
3.008
2.873
Zero return
0.309
0.291
Round number
3.309
3.131
Years since last fin.
1.154
1.137
Capital raised (m USD)
8.856
17.52
Public?
0.424
0.410
Failed?
0.244
0.223
Acquired?
0.332
0.366
4.757
4.552
779
3266
Gross multiple
# VC investors (t
1)
Number Financings
Gross multiple
# VC investors (t
1)
Number Financings
40
Di↵./s.e.
-0.0930
(0.387)
0.0170
(0.223)
0.0748⇤
(0.0299)
0.0780
(0.108)
-0.0614
(0.0525)
-9.707⇤⇤⇤
(1.123)
-0.0480
(0.0281)
0.0897⇤⇤
(0.0279)
-0.0417
(0.0307)
-0.374
(0.250)
1131
Di↵./s.e.
0.214
(0.811)
0.135
(0.176)
0.0184
(0.0182)
0.178⇤⇤
(0.0557)
0.0163
(0.0278)
-8.659⇤⇤⇤
(0.770)
0.0132
(0.0196)
0.0206
(0.0167)
-0.0338
(0.0191)
0.206
(0.130)
4045
Table 7: Entrepreneurial firm outcomes: inside vs. outside rounds
Notes: Table reports probit and OLS regressions of entrepreneurial firm outcomes on a set of observables.
Column (1) uses the dependent variable “IPO” that is equal to one if the firm went public by the end of the
sample. Column (2) considers “Good exit” which is equal to one if the firm had an IPO or an acquisition with
a reported valuation greater than two times capital invested. “Log exit value” in column (3) is the log of the
valuation (log of 25% of capital raised if a failure). The variable “Had inside round” is a dummy variable equal
to one if the entrepreneurial firm ever had an inside round in its financings. Columns (3) - (6) introduce controls
for whether the firm had a “down” round in its history. Robust standard errors clustered at the financing year
reported in parentheses. ⇤ , ⇤⇤ , ⇤⇤⇤ represent significance at the 10%, 5% and 1% level respectively.
Had inside round
IPO
Good exit
(1)
-0.0376⇤⇤⇤
(0.00645)
(2)
-0.0741⇤⇤⇤
(0.00889)
Log exit value
Non-failed
(3)
-0.342⇤⇤⇤
(0.0610)
0.0431⇤⇤⇤
(0.00315)
0.00247⇤
(0.00140)
-0.144⇤⇤⇤
(0.0536)
8632
0.129
Y
Y
Y
0.0447⇤⇤⇤
(0.00418)
-0.00303⇤
(0.00169)
-0.150⇤⇤
(0.0757)
8632
0.101
Y
Y
Y
0.598⇤⇤⇤
(0.0333)
-0.0433⇤⇤⇤
(0.00991)
3.939⇤⇤⇤
(0.495)
3004
0.170
Y
Y
Y
Had down round
Had inside X Had down round
Log total capital
Total unique VCs
Constant
Observations
R2
Founding year FE?
Industry FE?
State FE?
41
IPO
Good exit
(4)
-0.0473⇤⇤⇤
(0.0164)
-0.0447⇤⇤
(0.0195)
-0.00300
(0.0256)
0.0814⇤⇤⇤
(0.00648)
-0.00576⇤⇤⇤
(0.00216)
0.242
(0.264)
3914
0.133
Y
Y
Y
(5)
-0.0764⇤⇤⇤
(0.0192)
-0.0449⇤
(0.0239)
-0.0402
(0.0308)
0.0682⇤⇤⇤
(0.00751)
-0.0101⇤⇤⇤
(0.00239)
0.273
(0.254)
3914
0.115
Y
Y
Y
Log exit value
Non-failed
(6)
-0.256⇤⇤
(0.101)
-0.528⇤⇤⇤
(0.113)
-0.0276
(0.156)
0.646⇤⇤⇤
(0.0458)
-0.0549⇤⇤⇤
(0.0120)
3.962⇤⇤⇤
(0.861)
1848
0.184
Y
Y
Y
Table 8: Change in valuation: inside vs. outside rounds
Notes: Table reports linear regressions of the log change in valuation between an entrepreneurial firm’s two
finaning events on set of observables. The dependent variable Log(Pret /$Post$t 1 ) takes the log of the ratio
of the current pre-money valuation over the previous post-money valuation (if both are reported). The value
is also called a “round-to-round returns” and captures the return of investing at the t 1 round and selling at
the t round. “Inside round” is a dummy variable equal to one if the current financing has no outside investors.
Column (6) introduces the “No new lead” dummy which is one if a lead investor is also not a new outside. “$
raised t / $ raised t 1” is the ratio of capital raised in the current financing over the previous. “Log raised” is
the log of the current capital invested. “Years since last fin.” is the number of years since the firm’s previous
financing event. “# VC investors (t 1)” is the count of the unique number of investors who have invested prior
to this financings. “Log total raised” is the sum of non-first round capital raised by the firm. “YearxIndustry
FE” are fixed e↵ects of interactions for the industry and financing year. “Round # FE” are fixed e↵ects for the
financing sequence number. Standard errors clustered at the entrepreneurial firm are reported in parentheses.
⇤ , ⇤⇤ , ⇤⇤⇤ represent significance at the 10%, 5% and 1% level respectively.
(1)
-0.260⇤⇤⇤
(0.0192)
Inside round
Log(Pre $t / Post $t 1)
(3)
(4)
(5)
-0.150⇤⇤⇤
-0.155⇤⇤⇤
-0.147⇤⇤⇤
(0.0201)
(0.0198)
(0.0197)
(2)
-0.258⇤⇤⇤
(0.0190)
No new lead
$ raised t / $ raised t
1
0.00365⇤⇤⇤
(0.000730)
0.00321⇤⇤⇤
(0.000563)
0.134⇤⇤⇤
(0.00921)
0.00309⇤⇤⇤
(0.000550)
0.133⇤⇤⇤
(0.00916)
-0.131⇤⇤⇤
(0.0135)
1.110⇤⇤⇤
(0.197)
8006
0.294
4041
Y
Y
Y
Y
0.997⇤⇤⇤
(0.193)
8006
0.315
4041
Y
Y
Y
Y
1.009⇤⇤⇤
(0.194)
8006
0.329
4041
Y
Y
Y
Y
Log raised
Years since last fin.
# VCs investors (t
Constant
Observations
R2
Num. firms
Year FE?
Industry FE?
YearxIndustry FE?
Round # FE?
1)
1.117⇤⇤⇤
(0.197)
8006
0.285
4041
Y
Y
Y
Y
42
0.00297⇤⇤⇤
(0.000467)
0.150⇤⇤⇤
(0.00930)
-0.124⇤⇤⇤
(0.0134)
-0.0287⇤⇤⇤
(0.00322)
1.012⇤⇤⇤
(0.197)
8006
0.336
4041
Y
Y
Y
Y
(6)
-0.126⇤⇤⇤
(0.0158)
0.00289⇤⇤⇤
(0.000488)
0.163⇤⇤⇤
(0.00925)
-0.124⇤⇤⇤
(0.0134)
-0.0311⇤⇤⇤
(0.00332)
0.336⇤⇤
(0.171)
8006
0.305
4041
Y
Y
Y
Y
Table 9: Di↵erences in returns: log gross multiple
Notes: Table reports regressions of a the log of the gross multiple when known on a set of observables. The dependendent variable is the financing-level
return (logged) assuming common equity for a dollar invested in the round. If the company exits via an acquisition and the reported price is less than
total capital raised, we assume that the last investors receive all their capital back before earlier investors share in the return. For failures, we assume
that the final investors received 25% of the capital invested back, with all previous financings recieving 15%. The first four columns are OLS regressions
at the financing-level. The last four columns use VC firm fixed e↵ects. “Full inside round ” is a dummy equal to to one if all the investors were insiders.
Columns (1)-(2) and (5)-(6) includes all financing returns. The remaining columns condition on non-failed companies. “Log total capital” is the log of
the sum of total capital raised. “Log raised (m USD)” is the log of capital raised in the current round. All fixed e↵ects are as defined in Table 8. Robust
standard errors clustered at the financing year reported in parentheses. ⇤ , ⇤⇤ , ⇤⇤⇤ represent significance at the 10%, 5% and 1% level respectively.
Full inside round
Log return
All returns
(1)
-0.128⇤⇤⇤
(0.0385)
No new lead
43
Log raised
Years since last fin.
Total VC investors
Log total capital
Constant
Observations
R2
Number firms
Number VCs
VC FE?
Industry FE ?
Year FE ?
Round # FE?
State FE?
0.0598
(0.0422)
-0.0571
(0.0361)
-0.00666
(0.00801)
0.112⇤⇤
(0.0476)
-1.524⇤⇤⇤
(0.471)
8773
0.135
4094
N
Y
Y
Y
Y
Log return Log return
All returns
Non-fail
(2)
(3)
-0.0728
(0.0453)
-0.151⇤⇤⇤
(0.0384)
0.0568
-0.152⇤⇤⇤
(0.0391)
(0.0374)
-0.0565
-0.0821⇤⇤
(0.0357)
(0.0382)
-0.00549
-0.0162⇤⇤
(0.00809)
(0.00779)
0.113⇤⇤
0.127⇤⇤
(0.0479)
(0.0609)
-1.458⇤⇤⇤
1.961⇤⇤⇤
(0.465)
(0.518)
8773
4712
0.136
0.167
4094
2171
N
Y
Y
Y
Y
N
Y
Y
Y
Y
Log return
Non-Fail
(4)
-0.115⇤⇤
(0.0441)
-0.159⇤⇤⇤
(0.0326)
-0.0817⇤⇤
(0.0380)
-0.0148⇤
(0.00789)
0.129⇤⇤
(0.0613)
2.026⇤⇤⇤
(0.508)
4712
0.168
2171
N
Y
Y
Y
Y
Log return
All returns
(5)
-0.0968⇤⇤⇤
(0.0216)
0.0582⇤⇤⇤
(0.0123)
-0.0820⇤⇤⇤
(0.0140)
-0.00666⇤
(0.00398)
0.0209
(0.0128)
-2.741⇤⇤⇤
(0.566)
30618
0.143
4094
2087
Y
Y
Y
Y
Y
Log Return
All returns
(6)
-0.0835⇤⇤⇤
(0.0168)
0.0602⇤⇤⇤
(0.0116)
-0.0809⇤⇤⇤
(0.0140)
-0.00627
(0.00393)
0.0217⇤
(0.0128)
-2.672⇤⇤⇤
(0.565)
30618
0.143
4094
2087
Y
Y
Y
Y
Y
Log return
Non-fail
(7)
-0.0395
(0.0283)
-0.134⇤⇤⇤
(0.0165)
-0.115⇤⇤⇤
(0.0183)
-0.0128⇤⇤
(0.00517)
0.0231
(0.0202)
0.0990
(0.509)
17708
0.190
2171
1629
Y
Y
Y
Y
Y
Log return
Non-Fail
(8)
-0.0434⇤⇤
(0.0216)
-0.135⇤⇤⇤
(0.0155)
-0.114⇤⇤⇤
(0.0183)
-0.0124⇤⇤
(0.00518)
0.0238
(0.0202)
0.135
(0.506)
17708
0.190
2171
1629
Y
Y
Y
Y
Y
Table 10: Di↵erences in gross multiple returns: participation, capital and measurement
Notes: Table reports regressions as describe in Table 9 with additional controls and interactions. The first three
columns use the log gross multiple for all returns as the dependent variable. Columns (1) and (2) introduce
two variables that summarize insider participation. The variable “Full participation” is a dummy equal to one
if all the past investors have invested in the current financing. “High inside participation” is a dummy for
whether the fraction of insiders currently participating is above the sample median. Column (3) introduces the
dummy “Capital ramp down” which is equal to one if the ratio of amount of capital in the current financing
to the previous is below the 25th percentile observed in the sample (zero otherwise). The final three columns
use the “Total return” dependent variable defined as the dollar-weighted observe multiple for the current and
past investments. Robust standard errors clustered at the financing year reported in parentheses. ⇤ , ⇤⇤ , ⇤⇤⇤
represent significance at the 10%, 5% and 1% level respectively.
Full inside round
Log return
Log return
Log return
Total return
(1)
-0.128⇤⇤⇤
(0.0402)
(2)
-0.146⇤⇤⇤
(0.0495)
(3)
-0.124⇤⇤⇤
(0.0319)
(4)
-0.219⇤⇤⇤
(0.0469)
Total return
Non-failed
(5)
-0.0990
(0.0861)
No new lead
Full participation
Inside X Full participation
Capital ramp down X Inside
0.0591
(0.0420)
-0.0571
(0.0361)
-0.00488
(0.00797)
0.112⇤⇤
(0.0476)
-1.553⇤⇤⇤
(0.473)
8773
0.135
0.0547
(0.0415)
-0.0569
(0.0361)
-0.00430
(0.00800)
0.112⇤⇤
(0.0477)
-1.541⇤⇤⇤
(0.472)
8773
0.135
0.0273
(0.0606)
-0.0963⇤
(0.0531)
0.0471
(0.0459)
-0.0533
(0.0370)
-0.00503
(0.00775)
0.117⇤⇤
(0.0492)
-1.551⇤⇤⇤
(0.468)
8773
0.135
Y
Y
Y
Y
Y
Y
Y
Y
Y
Capital ramp down
Total VC investors
Log total capital
Constant
Observations
R2
Number VCs
Industry FE ?
Round # FE?
State FE?
-0.231⇤⇤⇤
(0.0478)
-0.158⇤⇤
(0.0683)
0.0515
(0.0352)
0.0317
(0.0619)
Inside X high inside part.
Years since last fin.
(6)
Total return
Non-failed
(7)
0.0595
(0.0528)
-0.0190
(0.0789)
High inside part.
Log raised
Total return
44
0.110⇤
(0.0548)
-0.0874⇤
(0.0457)
-0.0147
(0.0126)
0.203⇤⇤⇤
(0.0509)
-3.146⇤⇤⇤
(0.519)
6616
0.181
-0.121⇤⇤
(0.0442)
-0.100⇤⇤
(0.0461)
-0.0394⇤⇤⇤
(0.0113)
0.117
(0.0700)
3.171⇤⇤⇤
(0.467)
3079
0.186
0.111⇤⇤
(0.0535)
-0.0851⇤
(0.0449)
-0.0134
(0.0127)
0.204⇤⇤⇤
(0.0515)
-3.028⇤⇤⇤
(0.496)
6616
0.182
-0.129⇤⇤⇤
(0.0419)
-0.0984⇤⇤
(0.0461)
-0.0375⇤⇤⇤
(0.0115)
0.119
(0.0706)
3.414⇤⇤⇤
(0.469)
3079
0.187
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Table 11: Inside rounds and current contract features
Notes: The table reports the correlations between inside rounds and strong contract features. Contracts data
from VC Experts was merged onto VentureSource financing when possible. Column (1) has a dependent variable
equal to one if the financing had senior equity. Column (2) has a dependent variable equal to one if the contract
has a liquidation preference greater than 1X. Column (3) has a dependent variable equal to 1 if the contract
had participating preferred. Column (4) has a dependent variable equal to one of the contract had redemption
rights. The last column has a dependent variable equal to one if any of these contract types exist. All variables
are as defined in above tables. Robust standard errors clustered at the financing year reported in parentheses.
⇤ , ⇤⇤ , ⇤⇤⇤ represent significance at the 10%, 5% and 1% level respectively.
Full inside round
Log raised
Years since last fin.
Observations
Pseudo-R2
Num. firms
Mean dep. var
Mean dep. var. | Inside
Founding Year
Industry FE ?
Round # FE?
Senior
(1)
> 1X?
(2)
Part. Pref.?
(3)
Redemption?
(4)
Any?
(5)
-0.0460
(0.0840)
0.00215
(0.0418)
0.0316
(0.0412)
1434
0.0735
1012
0.429
0.419
Y
Y
Y
0.160
(0.111)
-0.109⇤
(0.0570)
-0.0848
(0.0554)
1413
0.0980
1003
0.105
0.138
Y
Y
Y
0.0831
(0.0856)
-0.196⇤⇤⇤
(0.0430)
-0.0110
(0.0427)
1434
0.0677
1010
0.585
0.649
Y
Y
Y
0.0857
(0.0838)
-0.154⇤⇤⇤
(0.0415)
-0.00148
(0.0427)
1435
0.0556
1010
0.429
0.483
Y
Y
Y
0.227⇤⇤
(0.102)
-0.170⇤⇤⇤
(0.0497)
-0.0431
(0.0495)
1360
0.0784
953
0.810
0.870
Y
Y
Y
45
Table 12: Di↵erences in returns: gross multiple robustness
Notes: Table reports regressions of a the log of the gross multiple or dollar-weighted multiple when known on
a set of observables. The dependendent variable is the financing-level return (logged) assuming common equity
for a dollar invested in the round. If the company exits via an acquisition and the reported price is less than
total capital raised, we assume that the last investors receive all their capital back before earlier investors share
in the return. For failures, we assume that the final investors received 25% of the capital invested back, with
all previous financings recieving 15%. “Full inside round ” is a dummy equal to to one if all the investors were
insiders. Column (1) repeats the estimate from column (1) of Table 9. Column (2) replaces the gross multiple
of known inside returns that are less than 2X capital with 2. That is we assume that inside rounds have at least
a 2-times liquidation preference. Outside rounds are assumed to have 1X for non-failures. Column (3) replaces
missing inside round returns with 2X and missing outside returns with a 1-times capital invested. Column
(4) estimates a weighted OLS regression that corrects for the over-representation of IPOs. In this regression,
acquisition returns are weighted so that they have importance equal to the full sample distribution of acquisition
rates. The final column resamples the full sample of returns data to create a final sample with the full-sample
exit proportions for IPOs, acquisitions and failures (see Korteweg and Sorensen (2010)). “Log total capital”
is the log of the sum of total capital raised. “Log raised (m USD)” is the log of capital raised in the current
round. “YearXIndustry FE” and the interaction of the firm’s industry and current financing year FE. Robust
standard errors clustered at the financing year reported in parentheses. ⇤ , ⇤⇤ , ⇤⇤⇤ represent significance at the
10%, 5% and 1% level respectively.
All returns
Full inside round
Log raised
Years since last fin.
Total VC investors
Log total capital
Constant
Observations
R2
Founding YearxIndustry FE?
Industry FE ?
Round # FE?
State FE?
(1)
-0.128⇤⇤⇤
(0.0385)
0.0598
(0.0422)
-0.0571
(0.0361)
-0.00666
(0.00801)
0.112⇤⇤
(0.0476)
-1.524⇤⇤⇤
(0.471)
8773
0.135
Y
Y
Y
Y
Inside 2X
Outside 1X
(2)
-0.0718⇤
(0.0390)
0.0506
(0.0434)
-0.0480
(0.0362)
-0.00784
(0.00793)
0.129⇤⇤
(0.0476)
-1.776⇤⇤⇤
(0.475)
8773
0.132
Y
Y
Y
Y
46
Missing is 2X
Outside 1X
(3)
-0.0156
(0.0408)
0.0534
(0.0388)
-0.0410
(0.0313)
-0.00687
(0.00716)
0.0936⇤⇤
(0.0453)
-1.633⇤⇤⇤
(0.395)
9617
0.121
Y
Y
Y
Y
Exit-Weighted
Re-sampled
(4)
-0.173⇤⇤⇤
(0.0488)
0.0447
(0.0467)
-0.0872
(0.0558)
-0.0150
(0.0109)
0.282⇤⇤⇤
(0.0485)
-1.109
(0.716)
8698
0.201
Y
Y
Y
Y
(5)
-0.161⇤⇤
(0.0647)
0.0759
(0.0510)
-0.0754
(0.0555)
-0.0189
(0.0140)
0.294⇤⇤⇤
(0.0404)
-1.691⇤
(0.972)
4077
0.216
Y
Y
Y
Y
Table 13: Prediciting insideness
Notes: Table reports regressions of a financings fraction of inside investors on a set of controls. The dependent
variable is equal to one if all the current investors are insiders or previous investors. The controls are all lagged
to the previous financing event (all financings are at least the second round). “Last log raised” is the log capital
invested in the firm’s previous round. “Years since last financing” is the years since the previous financing
event. “First capital raised” is the total capital raised in the entrepreneurial firm’s first financing event. “Last
# investors” is the count of the total number of active investors in all previous rounds. “Last syndicate size”
is the total number of investors in the previous round. “Syndicate fund raising” is a dummy equal to one if at
least one participating investor is within two years of raising their next fund. “Big exit last 2 years” is a dummy
variable equal to one if at last one syndicate member have a large exit (IPO or big acquisition) in the previous
two years. “Log syndicate exper. (# deals)” is the log of the total investments done by the insiders prior to
this round. “Log new funds last 2 years” is the log of the total new funds raised over the previous two years
(lagged one year prior to the financing). “YearXIndustry FE” and the interaction of the firm’s industry and
current financing year FE. Robust standard errors clustered at the entrepeneurial firm reported in parentheses.
⇤ , ⇤⇤ , ⇤⇤⇤ represent significance at the 10%, 5% and 1% level respectively.
Syndicate fund raising
Inside?
(1)
-0.0795⇤⇤⇤
(0.0134)
Inside?
(2)
Inside?
(3)
-0.0894⇤⇤⇤
(0.0131)
Big exit last 2 years
0.00780⇤⇤⇤
(0.00302)
Log syndicate exper. (# deals)
Log new funds last 2 years
Last log raised
Years since last fin.
Last # investors
First capital raised
Last syndicate size
Constant
Observations
R2
Num. firms
YearxIndustry FE?
Round # FE?
State FE?
Inside ?
(4)
-0.0118
(0.00927)
-0.00223
(0.0106)
-0.00820⇤⇤
(0.00345)
0.00130
(0.00109)
0.000179
(0.00327)
0.775⇤⇤⇤
(0.165)
4304
0.114
2684
Y
Y
Y
47
-0.0300⇤⇤⇤
(0.00486)
-0.00703
(0.00456)
-0.00802⇤⇤⇤
(0.00191)
0.00151⇤⇤⇤
(0.000461)
0.0000869
(0.00203)
1.701⇤⇤⇤
(0.242)
12630
0.0703
5755
Y
Y
Y
-0.0340⇤⇤⇤
(0.00501)
-0.00918⇤⇤
(0.00455)
-0.00801⇤⇤⇤
(0.00189)
0.00150⇤⇤⇤
(0.000449)
-0.00153
(0.00207)
0.948⇤⇤⇤
(0.304)
12500
0.0688
5714
Y
Y
Y
0.0436⇤⇤⇤
(0.0108)
-0.0366⇤⇤⇤
(0.00477)
0.00361
(0.00409)
-0.00481⇤⇤
(0.00190)
0.00173⇤⇤⇤
(0.000473)
-0.00204
(0.00201)
1.461⇤⇤⇤
(0.208)
12742
0.0375
5776
N
Y
Y
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