1. finance
2. investing
3. venture capital

Assessing the Startup Bandwagon Effect:
The Role of Past Funding in Venture Capital Investment
Enoch Chan and Yuan Fei1
1
Enoch Chan and Yuan Fei are members of the class of 2015 at the University of Chicago.
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Abstract
Media reports on the startup industry, and on tech startups in particular, point to a prominent
bandwagon phenomenon. This implies that capital is not being efficiently allocated in the venture capitalist (VC)
and startup industry, which may lead to bubble formation. In our study we attempt to quantitatively analyze the
pure effect of the funding received by a startup in past rounds on the amount they receive in a current round to
test the possibility of inflationary startup valuation due to this bandwagon effect in the industry. Our analysis
incorporates factors highlighted in past qualitative studies in explaining venture capital investment decisions as
controls to isolate the effect of past funding from startup characteristics such as management experience, etc.
With our panel data we use an Ordinary Least Squares (OLS) regression model and a fixed-effects model for
estimation. Our basic OLS regression model shows that past funding has a significant, positive effect on funding
in the current round. Furthermore, we found that past funding plays an increasingly important role in
determining venture investment decisions. However, using a fixed-effects model to remove possible omitted
variable bias caused by time-invariant, unobservable variables, we found that previous funding no longer
positively affects amounts raised. Surprisingly, we found significant, negative effects of past funding, which
provide evidence against venture capitalists being significantly subject to the “hype” surrounding a startup.
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Introduction
The venture capital (VC) industry constitutes an important part of the U.S. economy, providing the
market with a constant source of innovation. Many companies that are now household names were founded
through VC fundraising, including Apple, Amazon and Facebook. According to the National Venture Capital
Association (NVCA), in 2010 there were 11.9 million venture-backed jobs accounting for 11 percent of the U.S
(Venture Impact). private sector employment. Annual VC investment accounts for less than 0.2 percent U.S.
GDP, yet the NVCA estimates that companies that have, at one point, relied on VC funding generate annual
revenue equal to 21 percent of the U.S. GDP. In 2014, there was $48.3 billion in total venture capital investment
in 4,356 deals in the U.S. (NVCA), representing a 61% increase in dollar terms over the previous year.
VC firms support entrepreneurial talent by financing new ideas that often could not be financed through
traditional banking products due to the amount of risk associated with these investments as well as the delayed
potential for returns. Furthermore, VCs act as important intermediaries between providers of capital (hedge
funds, pension funds, etc.) and users of capital (startups and entrepreneurs), by gathering information and
allocating capital in making investment decisions on behalf of providers of capital. Once a startup decides to
pursue venture funding and submits a proposal to a VC firm, a decision must be made on whether to fund the
startup and the amount to be funded.
Figure 1: Flow of Capital in the Venture Investment Process
Investors
Venture
Capitalist
Startup
News media has, in recent years, extensively reported on what is perceived as a “tech startup bubble,”
which serves as the primary inspiration of our research. In our investigation, we want to identify whether VCs
take into account a startups funding history when they make their own decisions on whether to fund a startup. In
addition, we will explore the degree to which funding history factors into their decision after controlling for
other variables. In essence, we want to isolate the pure effect of past funding in order to find evidence for or
against a possible bandwagon effect in the VC-startup industry. If such an effect is found to be significantly
positive, there will be evidence of behavior associated with bubble formation, as high valuations may not be an
accurate reflection of asset value, but rather rampant demand. Our null hypothesis supposes that VCs perform
their function efficiently. This means that the amount of previous funding will play a significant role in
investment decision-making to the extent that it is a good signal of a startup’s overall quality. Once we control
for startup characteristics, however, we postulate that previous funding will no longer play a significant role in
decision making since previous funding captures firm characteristics. More concretely, our null hypothesis
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postulates that given two firms that are exactly identical except for amount of previous funding received, both
firms will receive the same amount of funding.
This paper will begin with a summary of relevant literature review, both qualitative and quantitative in
nature, from which we draw our basic model and control variables. We will subsequently present the panel
dataset used in our analysis followed by a discussion of the estimation results obtained through our models.
Literature Review
Previous research on the VC-startup industries has mostly been qualitative in nature, using survey data
from partners at VC firms. Tyebjee and Bruno (1984), MacMillan et al. (1985), and MacMillan et al. (1987) all
used structured mail questionnaires to induce VCs to rank the importance of various criteria on their investment
decisions. Fried and Hisrich (1994) built on this work by conducting personal interviews with VCs about
different stages of the entire investment process. They identified fifteen criteria common to all types of VC
investment decisions. These were categorized into three main buckets: concept (namely quality of business
model and product), management and return potential. The goal of our analysis is to extend upon these
qualitative results by analyzing the effects of previous funds raised on VC investment decisions, while controlling
for the factors identified in these past studies.
Gompers and Lerner (1999) focused on investigating the drivers of fundraising by VC firms from
investors and was the main source of inspiration for our quantitative methodology. They studied the investor-VC
fundraising part of the process instead of the VC-startup part of the process. As such, our study utilizes and
extends some of the quantitative techniques used by Gompers and Lerner (1999) to analyze the downstream
investment decisions and test our hypothesis.
Data
Data Sources
The data we use to conduct our analysis comes from three sources. The primary source is from
CrunchBase, a dataset devoted exclusively to tracking startup activity. CrunchBase began developing its database
in 2007 and uses a community of contributors to add to and maintain its database of over 500,000 profiles of
companies and people, as well as information on VCs, funding deals and investments. The database markets
itself as reliable for research, as they specifically allow academic researchers greater access to their data. We used
CrunchBase’s API to extract data on:
•
Startup characteristics including date founded, location, industry/market classification, amount of funds
raised, and number of notable team members.
•
Funding round data including date of funding round, amount raised, type of capital (e.g. debt, angel,
venture, etc.), and series and stage of funding (e.g. first, second, third, etc.).
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In order to capture market conditions, we supplemented this data with yearly IPO information as a
measure of cash-out potential that we thought might impact VCs’ decisions. This data was collected from Jay
Ritter, a professor at the Warrington College of Business Administration, University of Florida, who makes this
data freely available online. We also included macroeconomic data from Federal Reserve Economic Data
(FRED) provided by the Federal Reserve Bank of St. Louis such as real GDP.
Data Specification
For this project, since observations of the same firm vary over time we took an unbalanced panel data of
firms that have had at least one instance of VC fundraising from 2010–2014 resulting in 6,949 observations. One
problem that needed to be resolved in our analysis was data imputation. The nature of CrunchBase, being a
relatively new data source that is crowd-sourced, leads to the problem of incomplete entries and observations in
our dataset. Specifically, there are some observations that do not include the amount raised during a fundraising
round. We imputed the missing values with the median amount of funds raised during all fundraising rounds. In
order to conduct robustness checks regarding our decision, we created another variable for funds raised where
we left the missing values as NAs. In addition, we conduct a log transformation of values like the amount raised
and lagged GDP as is standard practice in order to linearize the data and make it suitable for regression analysis.
Data Summary
We compile a table of summary statistics of our dataset with imputed funds raised variables can be seen
in Table 1 below. Logarithms were taken due to the size of funding in each observation. We also include
summary statistics of our dataset when we leave the missing funds raised variables as NAs, in Table 2 below as
well as histograms of the data in Figure 2.
Table 1: Summary Statistics with Imputed Values
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Table 2: Summary Statistics without Imputed Values
Figure 2: Histograms of Log Funding Raised
We see that for each funding round, the amount raised roughly follows a similar distribution. The mean
of this distribution increases as the funding round increases. Notice Round 6 does not resemble much of the
same distribution, likely due to the small sample size. Similarly, we do not include the last two funding rounds
due to the lack of observations.
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Analysis
Basic OLS Model
In order to analyze how previous funding raised affects current funding in isolation from firm
characteristics, we could use an OLS model to regress previous funding on current funding and look at
correlations between the two. However, as discussed above, this correlation captures startup attributes. For
example, if we see a positive correlation between the two, that might be explained by the fact that firms with
more experienced management teams are more trusted by investors and thus happen to gain more current
funding. In that case, the simple correlation between previous funding and current funding would mask many of
the other omitted factors that would cloud our interpretation of what the pure effect of past funding on current
funding is.
Thus, in order to isolate the effect of previous funding on the amount of funds raised, we add additional
explanatory variables to our OLS model. We turn to the literature for guidance as to what other variables factor
into VCs’ investment decisions. Drawing from Fried and Hisrich (1994), we aim to control for the three main
drivers outlined in the qualitative literature, namely concept, management and return potential through proxy
variables.
In our model, we attempt to capture management quality and experience through the number of notable
upper management that have been with the firm in the last five years according to CrunchBase, as well as the age
of the firm in years at the fundraising date. We also attempt to capture return potential for the VC by
incorporating macroeconomic variables and market conditions in the economy through including lagged GDP
and IPOs as predictor variables. The reason we use lagged figures is drawn from Gompers et al. (2008), who
used lagged figures of market variables such as IPOs to study the effect of public markets on VC investment
activities. Intuitively, this is because VCs are more likely to gauge market conditions by observing past trends
rather than the most recent data. The response variable we are interested in is the logarithm of the funds raised
in a year if a startup chooses to raise capital. Lastly, we control for funding rounds and number of investors,
because later funding rounds may result in higher amounts of funding while a higher number of investors may
result in more funds raised. These control variables will allow us to better isolate the pure effect of previous
funds raised.
Our regression model specification is as follows:
(1)
Where:
• log(Amount.Raisedit) = log-transformation of funds raised in a venture deal
• log(previt) = log-transformation of previous funds raised
• Ageit = age of startup at time of fundraising
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•
•
•
•
•
TeamNumberit = number of upper management team
IPOst = number of venture-backed IPOs lagged one year
GDP.Laggedt = log-transformation of GDP lagged one quarter
Number.Investorsit = number of investors involved in deal
θit = funding round control dummy variables
Table 3 shows our results from our robust linear regression with White standard errors. Column 1 uses
the data with our imputed values, while column 2 uses data leaving out the missing values as a robustness check.
Our initial, pooled regression of the panel data showed surprisingly good results in significance. For our control
variables, in terms of the management aspect, we see that startup age and number of notable upper management
both have significant positive coefficients. Similarly, the number of IPOs shows significant positive coefficients
as well, suggesting that better market conditions do lead to more capital raised. Controlling for the round we are
looking at, we see a result mirroring the trend exhibited in our summary statistics of gradually increasing funds
raised as firms go into later rounds and as more investors participate. Furthermore, we see that our results are
robust when we replace missing funds raised values with the median values from the data.
Focusing on our variable of interest, results indicate that the log-transformed previous amount raised has
a significant positive effect on the amount raised even after controlling for factors that the qualitative literature
deemed to be important for VC’s decision-making. This seems to contradict our initial null hypothesis that the
isolated effect of previous funds raised will have no significant impact on funds raised by the firm. However, we
realize that our pooled OLS regression may not accurately isolate the effect of previous funds raised. The effect
of previous funds raised may vary depending on what round of funding a startup is applying for. For example,
VCs in later funding rounds may put more or less emphasis on previous funds raised compared to in earlier
funding rounds. Startups at the first round of funding will not have previous funding and thus VCs will not have
this information to affect their decision. We are not able to parse out these important differences through our
pooled OLS regression above as they are probably absorbed by our funding-round dummy variables. We thus
impose additional structure on our initial regression model to help us identify these differences and isolate the
effect of previous funds raised.
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Table 3: Basic OLS Regression Results
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Interacted OLS Model
As an extension to our basic OLS model, we are interested in looking at the differences in how
important previous funding is in different rounds to see the results on a more granular level. This will ideally
capture the relative importance of previous funding at different rounds. We interact the funding rounds with
previous funds raised and run a robust linear regression with heteroskedastic-robust standard errors. The
interaction between the funding rounds and previous funds raised allows us to capture whether investors view
startups at different stages of funding differently as mentioned above. We obtain the following results presented
in Table 4.
Our new interacted model specification is as follows:
(2)
In the results we see that, firstly, the control variables retain the same sign and significance under the
interacted model. The un-interacted, previous amount of funding received loses significance in the results, which
corresponds to the round 1 effect, which is an intuitive result since firms going through round 1 funding have no
previous venture funding. Turning to the interacted terms, we see that for rounds 2 through 5, the effect of
previous funding seems to increase as a startup approaches the later stages of funding. However, the coefficient
loses significance once the startup starts round 6 and 7 of VC funding. Our results are largely robust when we
replace missing funds raised values with the median values from the data.
There are certain rounds that have caused previous funding to lose significance in our results, which may
be due to insufficient observations in the specific round of funding. However, the result of these models still
exhibit significant positive coefficients for certain rounds, which contradicts our initial hypothesis that previous
funding has no effects. There are two possible situations: 1) Our initial null hypothesis is incorrect, or 2) There
are still biases that we have not accounted for in our interacted OLS model. We maintain that 2) is the likely
situation due to the significance of unobservable variables and thus continue our investigation of the data.
In spite of the increased granularity of our results, as a further point of consideration we have to take
into account the potential bias that may occur due to the time-invariant, unobservable qualities of a startup.
These qualities include the competitiveness of its product and business model and are unlikely to change in the
course of five years. We hypothesize that endogeneity arising from these omitted variables are likely to cause
upwards bias in the previous funds raised coefficient due to the intuitive positive correlation between these
unobservable qualities and the success of past funding rounds (i.e. a more “competitive” product or business
model will correlate positively with previous funding).
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Fixed-Effects Model
As mentioned in previous sections, one important consideration in analyzing the data lies in the
unobservable quality of “concept” factor to a startup’s business model. It may be difficult to gauge, from an
academic perspective, how well a startup’s product and business model is perceived by a VC, however, it has
been noted that VCs do indeed incorporate these assessments into their investment decisions. As such, the
startup’s product and business model quality may bias our results upwards as the previous funding coefficient
estimates may be picking up these unobserved effects. Since we may face difficulty in finding a suitable
instrument for previous funding, in order to correct for this potential bias, we implement a fixed-effects model.
The identifying assumption for this model is that these unobserved factors are time-invariant. We believe this to
be a reasonable assumption because our sample has been restricted to a five-year period, so large fluctuations in
product quality or business model would be less likely to occur compare to a longer period. Furthermore, this
unobservable quality is likely to be correlated with predictors such as the amount of funding previously received.
Thus, we can implement a fixed-effects model to obtain the estimator that incorporates the previous interaction
effects.
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Table 4: Interacted OLS Regression Results
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For our fixed-effects model we postulate the following data-generating process:
(3)
Where vi captures the firm-specific, time-invariant “concept” factor that captures the firm's product
quality and business model. We can then run a fixed-effects regression to eliminate vi as follows2:
This fixed-effects model, as can be seen in the equation above, allows us to eliminate the effects of a
firm’s product quality and business model on current funds raised. This allows us to better isolate the pure effect
of past funding on current funding. Table 5 contains the results of our fixed-effects estimation, where column 1
uses the data with our imputed values, while column 2 uses data leaving out the missing values as a robustness
check. The results indicate that some of our control predictors lose significance, namely, age and IPO. The
baseline coefficient for previous funds raised remains insignificant. However, more interestingly, we see that
some of our interacted terms have changed signs in the fixed-effects output while the interacted term for the
fourth round has lost significance. One implication of this result is the evidence supporting the highly correlated
nature of previous funds raised with time-invariant, unobservable factors, causing upwards bias to the extent that
that, once we correct for it, the coefficients on some covariates become significantly negative.
The result of most interest is the sign changes of the interacted coefficients when compared to the
previous interacted OLS model. There are several possible explanations for this, including the fact that, taking
into account the quality of a startup, seeking more capital through venture funding may be a troubling signal for
VCs. For example, consider two startups, A and B, that both ask for further funding: startup A previously raised
only $100,000, and startup B raised $10,000,000. Controlling for the qualities of the startup, macroeconomic
environment, and funding round at the time when both startups ask for additional funding, startup A may be
perceived to be a company that utilizes capital more efficiently in its growth process.
Overall, we see that after correcting for the unobserved “concept” factor, previous funds raised has a
significant negative impact on the magnitude of funds raised during rounds 2, 3 and 5, and does not have a
significant impact on funds raised in other rounds. These results are robust when we replace missing funds raised
Team number variable is also eliminated when we run fixed-effects because it remains constant for each firm during the
period we are studying.
2
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values with the median values from the data. Indeed, this corresponds to a narrative where once startups raise
funding from previous venture rounds, they are expected to develop self-sufficiency and decrease their reliance
on VCs. The empirical results obtained in this investigation provide evidence in support of this narrative once we
control for the characteristics of a startup.
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Table 5: Fixed-Effects Regression Results
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Discussion of Limitations
Throughout our investigation, starting with a basic OLS model, we aimed to provide more structure to
our initial model in order to limit the possible biases in our results to improve accuracy. However, there are
several limitations to our paper that should prompt more research and investigation.
First, there are potential problems with the completeness and accuracy of our data, representing
measurement error in our dataset. Given the crowd-sourced nature of the data, there were both missing and
questionable parts of the dataset that we identified. For example, some funding deals, perhaps due to
confidentiality agreements, did not contain detailed amounts of funding. If these omissions and errors were
prevalent, our results could be biased in many ways. Indeed, measurement error would be exacerbated in our
fixed-effects estimation. One approach to correcting potential measurement error could be to validate
CrunchBase’s information with other, better-known sources such as ThomsonOne to reconcile missing and
questionable data. Another approach could be to identify valid instrumental variables and rerun our estimations
to compare our results.
Furthermore, there is the possibility of selection bias. Again, this is due to the fact that CrunchBase is
crowd-sourced, so there may be a bias towards firms that are more successful and have received funding. One
possible method to account for the bias is using a two-stage Heckman selection correction through identifying
potential exclusionary variables3 and subsequently running a probit/logit estimation. Doing so would require
more data collection and thus could be an area for future research.
Conclusion
In our study we set out to answer the question of whether there is a positive pure effect of past funding
on the current funding in VC funding rounds. Our initial null hypothesis stipulated that past funding will not
have a significant effect on funding obtained in a current round. This follows a narrative where VCs in general
are not subject to the bandwagon effect. VCs will, in essence, perform their roles properly in the venture
investment process and will value startups purely based on evaluating future viability, in contrast to increasing
the level of funding purely because other VCs have significantly invested in the company.
Starting with a basic funding model built upon the results of past qualitative research, we obtained a
significant positive coefficient for previous funds raised, controlling for three main categories for startups
including business concept, management, and cash-out potential. This result seemed to contradict our initial
hypothesis that previous funding will be insignificant once we account for firm characteristics. Our first step in
refining our estimate was to break down the effect of funding into more granular levels by incorporating
One possible exclusion restriction is geographic location of the startup. Past studies have shown that venture capitalists are
more likely to fund startups in the same geographic location since it will be easier to monitor the startups’ growth. It is
intuitively unlikely to significantly affect funding amount. (cf. Chen et al. (2009))
3
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interaction effects with different rounds. We saw that even though some rounds showed loss of significance for
previous funding, rounds 2 to round 5 maintained significant positive coefficients for the variable. Subsequently,
when a fixed-effects model was run to take into account time-invariant unobservable factors, the interacted
coefficients changed signs and became significantly negative. While this result does not validate our hypothesis of
past funding having no effect, it does provide evidence that past funding does not have a positive effect on
current funding. This also affirms the notion that past funding is highly correlated with the unobservable quality
of a startup. Indeed, controlling for all other factors that may determine the amount of funding a startup
receives, VCs are less inclined to fund startups with significant amounts of past venture funding. We merely
document this phenomenon in this empirical paper and do not seek to provide conclusive explanations for why
this is the case. One possible explanation is that high previous funding, all else equal, is a signal to investors that
the start-up utilizes its capital inefficiently, thus deterring them from providing funding. Verifying this
explanation and testing other possible explanations is an area for future research.
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