Assessing the Startup Bandwagon Effect - The … the Startup Bandwagon Effect: The Role of Past Funding in Venture Capital Investment Enoch Chan and Yuan Fei1 1 Enoch Chan and Yuan

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.

UChicago Undergraduate Business Journal

Spring 2015 1

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

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

Venture Capitalist

Investors Startup


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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

2 Team number variable is also eliminated when we run fixed-effects because it remains constant for each firm during the period we are studying.


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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

3 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))


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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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highest-level-decade-according-moneytree-report/.

Barry, Christopher B. “New Directions in Research on Venture Capital Finance” Financial Management, 1994,

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Chen, Henry, Paul Gompers, Anna Kovner, and Josh Lerner. “Buy Local? The Geography of Successful and

Unsuccessful Venture Capital Expansion” Journal of Urban Economics, 2010, 67: 90-110.

Fried, Vance H., and Robert D. Hisrich. “Toward a Model of Venture Capital Investment Decision Making.”

Financial Management, 1994, 23(3): 28-37.

Gompers, Paul A., Anna Kovner, Josh Lerner, and David Scharfstein. “Venture Capital Investment Cycles: The

Impact of Public Markets” Journal of Financial Economics, 2008, 87: 1-23.

Gompers, Paul A., and Josh Lerner. “What Drives Venture Capital Fundraising?” Brookings Papers on Economic

Activity: Macroeconomics, 1999, 149-192.

Macmillan, Ian C., Lauriann Zemann, and P. N. Subba Narasimha. “Criteria Distinguishing Successful From

Unsuccessful Ventures in the Venture Screening Process.” Journal of Business Venturing, 1987, 2(2): 123-137.

Macmillan, Ian C., Robin Siegel, and P. N. Subba Narasimha. “Criteria used by Venture Capitalists to Evaluate

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Rosenbaum, Eric. “In start-up bubble, $4 billion is the new $1 billion.” CNBC, 2013. Accessed June 8, 2014.

http://www.cnbc.com/id/101141705

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Documents

Assessing the Startup Bandwagon Effect - The … the Startup Bandwagon Effect: The Role of Past Funding in Venture Capital Investment Enoch Chan and Yuan Fei1 1 Enoch Chan and Yuan