staging venture capital: empirical evidence on the differential...
TRANSCRIPT
Staging Venture Capital: Empirical Evidence On The
Differential Roles Of Early Versus Late Rounds
Antonio Davila Graduate School of Business
Stanford University Stanford, CA 94305-5015
Tel: (650) 724 50 60 Fax: (650) 725 04 68
Email: [email protected]
George Foster Graduate School of Business
Stanford University Stanford, CA 94305-5015
Tel: (650) 723 28 21 Fax: (650) 725 04 68
Email: [email protected]
Mahendra Gupta John M. Olin School of Business
Washington University Campus Box 1133
One Brookings Drive St. Louis, MI 63130-4899
Tel: (314) 935 45 65 Fax: (314) 935 63 59
Email: [email protected]
February, 2003
The assistance of Trinet and VentureOne for this research is gratefully appreciated. We are grateful for the comments of participants in the Stanford University research workshop. Financial support is from The Center for Entrepreneurial Studies at the Graduate School of Business, Stanford University and Morgridge Fellowship. Research assistance was provided by Nicole Ang, Jiangyun Liu, Barbara Lubben and Jakub Wilsz.
Staging Venture Capital: Empirical Evidence On The Differential Roles Of Early Versus Late Rounds
Abstract
A characteristic feature of venture capital funding is its staged structure. Startup firms do not
receive all the funding they need to achieve profitability in their first round of venture
funding. Rather venture capitalists invest in stages and their investment today does not
commit them to future funding. The theoretical literature has examined this feature of venture
capital investments beyond its signaling value. An important theoretical prediction is that the
first round of funding has a different role compared with follow-up rounds. The objective of
the first round is to provide capital to a cash-constrained entrepreneur. After this first round,
an agency relationship is established between the entrepreneur and the investor or between
inside and outside investors. Follow-up rounds are intended to mitigate the agency costs
associated with this relationship. While these agency costs may arise for different reasons, in
all models these follow-up rounds happen before the startup firm hits its cash constraint.
Objectives other than removing a cash constraint take precedence in follow up rounds. The
results in this paper provide evidence consistent with this differential empirical prediction.
Finally, the paper also examines whether funding rounds have different signaling value within
the firm.
Staging Venture Capital: Empirical Evidence On The Differential Roles Of Early
Versus Late Rounds
The fundamental need for funding in startup firms comes from the entrepreneur’s wealth
constraints. The entrepreneur needs funds to finance the firm from inception until it
becomes cash flow positive. Venture capitalists provide the funds required to overcome
cash limitations during the initial stages of a firm’s life, before the uncertainty of the venture
is reduced and alternative sources of funding become available. The relevance of venture
capital funding events is reflected in their signaling value. While wealth constraints explain
the initial funding of a start up firm, they do not explain the need for staged funding.
Staged funding is a salient characteristic of venture capital investing. Rather than
committing their funding upfront, stage funding allows venture capitalists to periodically
update their information about the firm, monitor its progress, review its prospects, and
evaluate whether to provide additional funding or abandon the project. Staged financing
provides venture capital with a real option. This option can be exercised or abandoned over
time as the uncertainty about the startup firm is reduced. Staged financing is also advocated
as a control mechanism—theoretical models explain this financing structure as a governance
mechanism to reduce the agency costs implicit in venture-backed startups. These theoretical
models are important to understand the unique nature of venture financing. However, the
empirical literature on entrepreneurial finance has limited evidence on the validity of these
arguments.
This paper provides empirical evidence informative to these theoretical predictions. It
examines two questions related to staged funding in venture-backed startups: (1) Do cash
constraints more severely limit growth in early funding rounds than in later rounds? (2) Is
1
the signaling value of these funding events larger in early rounds compared with later
rounds?
The first question addresses the issue of access to capital, which is central to our
understanding of entrepreneurship (Evans and Jovanovic 1989). The answer to this question
is informative for several reasons. First, recent theoretical work (Bergemann and Hege
1998; Neher 1999; Wang and Zhou 2002) predicts that cash constraints not only have a role
in the growth of firms but also within the staged funding structure that underlies the
governance of venture-backed firms. Second, an uninformed examination of funding
rounds may suggest that all funding rounds serve the same purpose—to provide cash to a
cash-constrained firm. However, theoretical models of staged financing based on rational
expectations predict differences in the role of cash constraints over funding stages. Early
rounds provide the funding that startup firms need to begin to grow. They remove cash
constraints that hold back the development of the firm. Follow-up rounds are intended to
govern the agency relationship established after the first funding round. Thus the cash that
flows to the startup firm in these later rounds provides the leverage required to exercise
control over the firm. In particular, the threat of hitting cash constraints disciplines the
agency relationship, even if in equilibrium these constraints are not binding. Third, models
of staged venture capital predict the need for successive rounds of funding, but they do not
speak to the role of the amount of funding. Our empirical results indicate that the amount of
funding is associated with the growth of startup firms for late funding events (but not for
early funding events).
Our second question empirically investigates whether the signaling value of rounds of
finance differs between early and late stages of the startups’ lifecycles. The asymmetric
2
information associated with startup firms enhances the value of credible signals about the
quality of the firm. Venture capital provides one such powerful signal. However, a real
option framework suggests that uncertainty is resolved over time and the value of these
signals may decrease as uncertainty and the associated information asymmetry decreases.
This paper utilizes a unique dataset that combines employee-level information on the
hiring date, firing date, and monthly cash salary with firm-level information including the
date of venture funding and the amount of funding received. A sample of 170 startup firms
and 268 funding events provides a detailed picture of the evolution of these firms around
these events. The results confirm the predictions of agency-based staged funding models
beyond the signaling value associated with these events. We find that early and late rounds
fulfill different roles in the venture capital firm-startup relationship. Removing cash
constraints that impede the growth of startup firms is a fundamental role of early rounds.
Growth in early rounds is delayed until funds are available whereas in later rounds growth
occurs before and after the financing round date. Average salaries significantly increase
after the firm receives new funds in early rounds but not later rounds. One empirical finding
that current theory does not address involves the amount of funding. The amount of funding
significantly explains growth patterns around later but not early funding rounds. Consistent
with decreasing information asymmetry as uncertainty is reduced, the signaling value of a
venture capital round to explain employee turnover is more significant in early rounds than
in later rounds.
The next section of this paper presents the literature on the staged structure of venture
capital financing and the hypotheses. Section 2 describes the research design. Section 3
presents the results and Section 4 concludes.
3
1. THE STAGED STRUCTURE OF VENTURE CAPITAL FINANCING
1.1 Venture Capital
Venture capital firms are financial intermediaries focused on providing capital to small,
fast-growth startup companies that are typically high risk and not amenable to more
traditional financing alternatives. These venture capital firms have some unique
characteristics that separate them from traditional sources of funds. First, their investments
(startup firms) involve higher levels of uncertainty, asymmetric information, and typically
higher intangible assets and growth opportunities (Gompers 1995). Second, venture
capitalists take an equity position in the company and play an active role in the governance
of the firm (Sapienza and Gupta 1994). They typically sit in the board of directors and
regularly monitor performance (Sahlman 1990; Lerner 1995; Robie, Wright et al. 1997).
This monitoring goes beyond what a traditional financing institution would do and includes
spending time at the companies, frequent meetings with managers, and being involved in the
definition of the companies’ strategies, hiring decisions (Hellmann and Puri 2001), and top
management compensation (Kaplan and Stromberg 2000). In addition, venture capitalists
bring their experience in evaluating the prospects of startups through their screening of
potential investments (Hall and Hofer 1993), their collaboration with other startups, their
understanding of the solutions to the problems that these firms may face, and when startups
are best positioned to raise money (Gompers and Lerner 1999). They also assist with their
reputation in the capital (Meggison and Weiss 1991) and product markets. Finally, they
provide access to a strategic network including potential clients or suppliers, management
talent (Bygrave and Timmons 1992), additional funding (Gorman and Shalman 1989),
4
strategic partners (Baum, Calabrese et al. 2000), and infrastructure providers like accounting
firms, law firms, and public relations firms.
1.2 Rounds of Financing
A salient characteristic of venture capital investing is its staging structure through
sequential financing rounds. At each round of financing, a venture capital firm supplies new
financial resources to the startup in exchange for a percentage of the equity of the company.
These rounds of financing are discrete events staged over the life of the company as a
private entity. Rounds of funding are critical in the relationship between venture capitalists
and the startups that they invest in. They are not a mere transfer of financial resources; they
also involve the redefinition of the governance structure of the firm and provide a signal
about its prospects. This new ownership structure affects the control structure of the
company as well as the payoffs of a future liquidity event (whether it is a public offering or
a trade sale).
Several theory papers explore the optimality of staged funding. A common starting
assumption is a wealth-constrained entrepreneur who wishes to retain higher rather than
lower equity in the firm for a given growth path. The first round of funding releases the cash
constraint binding the growth of the firm. Previous empirical research has documented the
significant role of cash constraints in the decision to engage in entrepreneurial work as well
as the size of the investment in the startup endeavor (Holtz-Eakin, Joulfaian et al. 1994a).
This empirical observation is a starting assumption for the agency-based models of staged
venture funding where the first stage of the venture capital investment process removes this
cash constraint. The question that emerges is: why does the investor stage additional
funding rather than providing all the capital upfront?
5
One explanation consistent with staged venture funding relies on real options. Each
funding round provides enough capital to reach the next milestone where investors, in light
of new information, decide whether to fund the next stage or to exercise their abandonment
option. The staged structure gives the investor the option of not further investing in a
project with a negative outcome. Such an option does not exist when all funding happens
upfront. (Sahlman 1990). Bergemann and Hege (1998) provide a more elaborate model of
the real option argument where they include a moral hazard problem. In their model, a
wealth-constrained entrepreneur receives funding from an external investor. The
entrepreneur has private information about how he allocates his effort between generating
information about the project and private consumption. The investor updates his beliefs
about the success of the project at the end of each period and decides whether to fund the
next round. This staging of investment reduces the induced moral hazard problem.
Neher (1999) studies the superiority of staged funding over up-front funding in a perfect
certainty, full information setting (thus without a real option). A wealth-constrained
entrepreneur establishes an agency relationship with an investor who provides the funds
required to start the business. The investment that the entrepreneur makes is partly sunk,
thus the relationship is open to the hold-up problem associated with the entrepreneur
renegotiating the contract after the investment has been made. Staging the investment can
reduce this hold-up problem if the value of the venture’s assets without the entrepreneur
increases over time as the entrepreneur’s specific knowledge is embedded in the assets of
the firm. The cash constraints associated with follow-up rounds, while never binding in
equilibrium, provide the bargaining power that the investor needs to reduce the costs
associated with the hold-up problem.
6
Wang and Zhou (2002) derive the superiority of staged funding over upfront financing
in a setting where there is no information asymmetry between the investor and the
entrepreneur, the entrepreneur provides effort, new information becomes available over time
and only the final output is contractible. In the staged funding solution, the investor keeps
the option of abandoning the project if the new information is not attractive. Staged funding
provides two benefits. First, it reduces the cost associated with the risk of bad information
becoming available. And second, it decreases the costs of the moral hazard problem that
emerges from the agency relationship established when the first investment occurs. The
initial funding provides the resources that the wealth constrained entrepreneur needs. In
contrast, follow up funding relies on the threat that the entrepreneur runs out of resources to
curve down moral hazard, even if in equilibrium this threat is never carried out.
Kockensen and Ozerturk (2002) adopt an incomplete contracting framework to
endogenously derive the optimality of staged venture capital funding. Their model assumes
a wealth-constrained entrepreneur and an initial investor who provides a first round of
funding, but does not commit to further funding. Before the second round funding decision,
new information becomes available. This information is available to both the entrepreneur
and the inside investor but not to potential outside investors. Consequently, the inside
investor has an informational advantage that allows him to offer better terms if the project
remains attractive as well as capture a surplus associated with the private information.
Staged funding provides the inside investor with a surplus that otherwise he would not
capture. As in previous models, the first round removes the initial cash constraint and
allows the entrepreneur to grow its venture. The second round gives the venture capitalist
bargaining power to extract additional rents. The cash constraint implicit in the second
7
round is the threat that strengthens the venture capitalist bargaining position even if rational
expectations ensure that it is never carried out.
These models separate an initial stage where the cash-constrained entrepreneur receives
funding and where an agency relationship is established from follow-up rounds. These
follow-up rounds rely on the threat to the entrepreneur of hitting his cash constraint to
reduce agency costs.
1.3 Signaling
Our second research question examines the differential signaling value of funding
events. A large literature has studied the role of signaling in multiple settings and industries
as a mechanism to reduce uncertainty (Akerlof 1970; Spence 1974; Kreps 1990; Heil and
Robertson 1991). Venture capital firms’ funding decisions act as such a signaling
mechanism. Before each round of financing, venture capitalists perform a thorough analysis
of the company they are intending to fund (Hall and Hofer 1993). They access information
internal to the company and match this information with their experience and knowledge of
the industry to evaluate its prospects.1
Internally a round of financing complements existing employees’ priors about the
quality of the company and its attractiveness as a workplace. The financing event indicates
that outside experts, having access to different and probably richer information than
employees have, find the company attractive to invest. The value of this signal may
decrease over the life of the venture as uncertainty decreases. The shaper prior beliefs that
1 Admati and Pfleiderer (1994) exogenously impose stage financing and examine in a setting with asymmetric information between inside and outside investors a robust financial contracting over the funding stages. Their solution, where the insider maintains a fixed fraction of the equity of the firm, relies on the signaling value that the insider’s decision has on outside investors.
8
employees have in later rounds of funding suggest that the signal associated with later
funding events will have lower value.
1.4 Hypothesis Tested
The first hypothesis is that cash constrains growth of startups more severely in early
rounds than in later rounds. We use both headcount data and average salary data to probe
this hypothesis.2 Our second hypothesis is that the signaling value of funding rounds is
higher in early rounds compared to later rounds. We test this hypothesis using employee
turnover as a measure of the value of the signal within the organization. Employees are one
of the best-informed players. If the uncertainty about the future of the firm is large, the
updating of employees’ beliefs associated with the early funding rounds may be significant
enough to affect their decisions to remain in the firm. As uncertainty decreases, the
relevance of the signal to employees’ decision to remain in the firm will decrease and the
impact upon turnover less significant.
2. RESEARCH METHOD
2.1 Sample selection
To examine the hypothesized relationships, we contacted a Professional Employer
Organization (PEO). The company specializes in providing outsourced human resource
services to small firms mainly in the San Francisco Bay Area but also throughout the United
States. Over time, it has developed a strong relationship with venture capital firms and is
perceived as a cost effective full-service solution for the human resources needs of small
companies. Companies using its services outsource all their human resources needs.
2 Davila, Foster and Gupta (2003) documents a positive association between headcount growth of startups and change in pre-money valuation in sequential rounds of venture-capital financing.
9
Outsourcing non-core activities like payroll is perceived as a way to focus scarce
management attention on more high-value added activities.
The database provides both the number of employees in each of its clients per month as
well as their monthly cash compensation. Thus, the database provides a unique source of
startup firms’ information. The database includes information about 606 venture backed
and non-venture backed entities that were in the system at some point between January 1994
and December 1999. For those entities, we collected payroll information from January 1994
through May 2000. The database grows over time as the PEO, itself also a startup, grew
over this time period. To identify those firms in the sample that received venture funding,
we matched the names of the firms against two proprietary databases that follow the venture
capital industry: VentureOne and Venture Economics. We found 194 firms that were both
in the PEO database and at least in one of the venture capital databases. From Venture One
and Venture Economics we gathered data on the dates of rounds of financing (or IPO), the
amount of funding, as well as information regarding the age of the company. Because firms
disclose the information in these databases on a voluntary basis not all information is
available. In particular, the date of founding is available for 170 firms. In total, the sample
includes 4,155 firm-months observations with 268 of these firm-months having a financing
event.
Several caveats regarding the sample are relevant. The sample is not a random sample
of venture-backed startups. Only companies that choose to outsource their human resources
needs are included. The sample includes mostly technology companies. Given the location
of the PEO firm, it is biased towards Silicon Valley based companies. Finally, the time
period examined may be unique in that venture capital investments were particularly large.
10
Startups leave the database for different reasons. A total of 85 venture-backed firms left
the database during the observation period. One reason is that the company goes out of
business. Another reason is that the PEO’s value proposition is no longer viewed as cost
effective—either because they changed their human resource strategy or because they
outgrew the services provided by the PEO. An analysis of companies exiting indicates that
these companies are relatively smaller or had stayed relatively longer in the database. This
exit pattern apparently reflects two types of startups. One type is smaller companies. The
other is older companies that is consistent with the probability of bringing human resource
management inside increasing with age (Baron, Burton et al. 1996). The loss of smaller
companies may bias our sample towards more successful startups, while the loss of older
ones may introduce a bias towards companies in the early stages of their lives. However,
neither of these two effects is expected to affect the behavior around the rounds of funding.
2.2 Dependent variables
We use two different proxies for cash constraints—employee growth and cash salary.
Startup firms that are cash constrained are unable to grow their headcount until funds
become available. Even if the likelihood of a funding round is credible enough for new
employees to be willing to join the company, the lack of funds precludes growth until funds
are received. This scenario is consistent with our hypothesis of delayed hiring for early
rounds of venture funding. In contrast, startups that do not face cash constraints can fund
their growth as soon as the signal associated with the funding event becomes credible. This
scenario is consistent with later rounds of funding. At this stage in the life of the startup,
cash constraints are used in the bargaining process to reduce agency costs but under rational
expectations are not binding.
11
Employee growth is constructed as follows. For each month we count the number of
employees in each company in the sample. We use this count to estimate the change in the
number of employees. A limitation of this measure is that it does not include outside
consultants and contractors. We do not have hard data to evaluate the potential impact of
this limitation. However, the PEO management believes that the startups in their database
do not use these outside contractors significantly.
We expect the pattern of employee growth to be different for early versus late funding
rounds. For early rounds, due to binding cash constraints, we do not expect any increase in
employee headcount before the funding round and expect any increase only after the
funding round. In contrast, for later rounds growth cash constraints are non-binding in
equilibrium, and hence there should an increase in employee headcount around the funding
event (both before and after the funding event) as soon as the likelihood of a funding
becomes credible.
Our second measure of cash constraint is average cash-equivalent pay. Cash constraints
may translate into lower average cash salaries. Firms in early stages have limited access to
funds and their chance of success depends on minimizing cash outflows. Only when the
cash constraints are removed, can the company increase cash salaries.
We measure cash salary as follows. The database reports up to ten different payments
per employee. Net pay (the actual cash payments to the employee) can vary across
employees because of differences in their pay status, their health plan or retirement plan
choices. We choose gross pay to proxy for cash-equivalent pay. Compared to the net pay,
gross pay does not reflect variance that may be due to differences in the personal tax status
of employees. It also avoids problems with differences in health plan or retirement pension
12
choices. Gross pay has its own limitations because companies offer different packages in
terms of contributions to employees’ retirement or health plan. However, differences in such
benefit packages across startups are not as pronounced as in larger companies. Companies
working with this particular PEO do have choices in terms of benefits other than gross pay,
but the needs and offers of these companies tend to be similar. For our cross-sectional
analyses, we calculate the average cash-equivalent pay as the total payroll in a month
divided by the headcount that month. We expect the average employee salary levels to be
below the sample average due to cash constraints before the early round of funding.
A startup’s ability to secure venture funds sends a strong signal about the prospects of
the startup to both the external and internal constituents in the organization. The signal can
compensate for the uncertainty and risk of the job in the minds of the employees of the
startup and provides a rational for them to stay with the firm. We use employee turnover as
a measure of the signaling value of venture funding
Turnover is measured as follows. The database has information at the employee level
and allows us to track when a particular person left a company. To estimate turnover in a
specific month the number of people that left the company during that month is divided by
the headcount at the end of the previous month. This is computed for each month on a
company-by-company basis.
We expect the value of this signal to decrease from early to late rounds as uncertainty
about the prospects of the firm is reduced and thus to have a lower impact on the
employees’ decisions to stay or leave the firm.
2.3 Additional variables
13
The timing of each funding round provides the event date to anchor the evolution of our
dependent variables. For each company, the date of each financing round (including IPO
and merger and acquisition events) and in most cases the amount of financing is available.
We classify the various rounds of financing as early or late rounds of financing. Early
rounds are seed and first rounds. Second, third, and fourth rounds are classified as late
rounds. This classification is similar to VentureOne’s classification. A small number of
companies go through a fifth and sixth round (12 observations). We separate these rounds
because they may be used for different purposes compared to early and late rounds. In
particular, they may be used as a mezzanine round before the initial public offering.
Alternatively, they may be an interim stage due to the IPO environment not being viewed as
“friendly.” We do not include these rounds in our hypotheses or in our reported results.3
We also include the following control variables: size of the company measured using
the same headcount number used to estimate employee growth and turnover; age of the
company calculated as the time elapsed since its founding (in months); and industry.4
3. ANALYSIS AND RESULTS
3.1 Summary information and descriptive statistics
Table 1 presents descriptive statistics for the sample of firms used in the study. Panel A
presents the industry composition of the sample. The sample is heavily biased towards high
technology industries. This is consistent with the focus of venture capital investing that
3 The final database includes company-month observations with information on headcount, salary, and turnover as well as funding. If a company joined the PEO system after several rounds of funding, we do not have headcount information for the months when these early rounds happened. However, we identify whether a funding event happen right before the company joined PEO and we can identify the first months of headcount, salary, and turnover information as being post-funding event months. Similarly, we do not have information for companies that had funding rounds after they left the headcount database; but if a funding event happened just after the company dropped out of the database, we can flag the last months in the database as pre-funding event months. 4 Industry dummies are included in the tests but not reported.
14
concentrates on technology-intensive firms with significant uncertainty and where
specialized knowledge can be leveraged.
Insert Table 1 around here
Panel B summarizes the average number of rounds for the venture-backed companies
founded in different time periods. The median number of rounds is two. Companies founded
in later periods have fewer rounds in part due to being in existence for a shorter period.
Panel C presents round of financing statistics. First rounds and second rounds are the
most frequent. The amount of funds raised increases from $1.40 (median $1.23) for seed
rounds to $24.68 (median $23.50) for fifth rounds. Early rounds’ funding amounts is
comparable to Gompers (1995), while the amount of funding for later rounds is larger in the
current sample than in Gompers’ sample. Changes in technology that require larger
investments in later stages or particularities of the time period of our study may account for
this difference. The post-money valuation increases from $3.34 (median $3.00) for seed
rounds to $347.84 (median $347.84) for sixth rounds. The companies in the sample had 468
financing events, 222 early round events, 226 late rounds and 20 fifth and sixth rounds.
Panel D provides timelines on the database.
We have headcount growth information for 268 venture funding events (for the rest of
events companies had not signed up for the human resources outsourcing service). Panel A
in Table 2 describes the distribution of these events; 79 correspond to early rounds (seed and
first round), 165 are late rounds (second, third, and fourth) and 12 are fifth and sixth rounds.
An additional 12 events correspond to companies in the sample that went public but
remained in the sample. We have an additional 3,887 firm-month observations with no
funding event.
15
Panel B presents descriptive statistics on the variables used in the study.5 Venture-
backed companies have 31 employees on average with an average monthly growth of 1.80
employees.
Insert Table 2 around here
Panel C presents the correlation among the variables in the research. Growth is
positively correlated with lower turnover suggesting that growth may proxy for success and
thus employee retention. Growth is also correlated with size but negatively with age. The
average salary is also negatively correlated with turnover indicating progression in the
salary levels with tenure of employees in startups. Turnover as well as size increases with
the age of the company.
3.2 The differential role of cash constraints over funding rounds
The analysis in this section uses an event study design centered on the months with a
round of funding. We examine the path of employee growth and average salary in the
months prior and subsequent to the funding event, using the following pooled regression
specification:
Dependent_variablej,t = α + Σi=-3,3 γi Early_Roundj,t * Month_Dummyi,j,t + Σi=-3,3 ηi
Late_Roundj,t * Month_Dummyi,j,t + Σ βi Control_Variablesj,t + ε
For each of the 268 venture-capital financing events in our sample we identify the month
in which the event happened (termed month 0). We restrict our study to month 0 and the six
months surrounding this event month (prior three months and subsequent three months)
5 The descriptive statistics in Panel B are based on the sample after removing the top and bottom 0.5% observations. The observations removed include negative monthly salaries and monthly salaries that reached in one extreme case $2.3 million. These observations are probably due to unusual events or coding mistakes.
16
when we expect the impact of funding to be most significant to the startup. The three
months prior to the financing event are designated months -3, -2, -1, and the three months
after the event as months +1, +2, +3. Monthly dummies take a value of one for company j if
month t is i months away from the funding month (where i takes seven values from –3 to 3),
zero otherwise. We also use a different dummy variable to distinguish the two types of
rounds (and control for other financing events). Early round is a dummy variable that takes
a value of one if the financing round is a seed or a first round and zero otherwise. Late
round is defined in a similar way for rounds two, three and four. We also use a different
dummy for fifth and sixth rounds as well as IPOs and include them as controls.
The dependent variable is growth in number of employees and average salary for
company j in month t. When using change in headcount, we use absolute rather than
relative growth because of the small size of the companies involved mostly for early rounds;
we include a size variable in the regressions to control for the potential effect of size. From
our previous discussion, firms that receive early rounds of funding should reveal a pattern
consistent with the release of the cash constraint. The coefficients for the three months prior
to the funding round should be negative or/and the coefficients for the three months
subsequent to the funding round should be positive. Negative coefficients for the months
prior to the funding round would indicate that cash constraints limit growth and/or average
salary. Positive coefficients for the months subsequent to the funding round would indicate
that cash constraints have been released to allow growth to happen and or average salaries to
increase. The reference point against which the coefficients in the regression model are
gauged is growth (average salary) for months outside the [-3, +3] time window around the
funding events.
17
We also include age, size, and industry (not reported) as additional controls. Larger
firms may experience larger absolute growth or offer larger salaries. Holding size constant,
older firms may be less successful and grow at a lower rate. Finally, growth and salaries
may vary across industries.
Insert Table 3 around here
Table 3 presents the results. Model 1 has employee growth as its independent variable,
while Model 2 has average salary. The results are consistent with theory-based predictions
indicating that early rounds release cash constraints that limit the growth of startup firms,
while later rounds serve as monitoring or real option mechanisms where cash constraints are
not binding in equilibrium.
Growth around early round events only happens after funds are received but not before.
The coefficients for the months prior to the event are not significant, but the coefficients for
the months subsequent to the event are positive and significant. On average, compared to
the growth in non-funding months, companies in our sample grew by 0.60, 1.21, 1.15 and
0.83 people per month in the zero, first, second, and third month after an early round
funding event. Also consistent with predictions, the average cash salary before an early
round is significantly smaller than the average salary in non-funding months. The
coefficients vary between -$925 and -$630 for the three months before an early round that
approximately represent over 10% of the average monthly salary of $7,310 (Panel B, Table
2). This difference ceases to be significant the month after the funding event.
In late rounds we observe a strikingly different pattern. Reinforcing the idea that cash
constraints play less of a role in these later rounds, employee growth is significant before as
well as after the funding event for each month during the event window [-3.3]. Moreover,
18
the growth rate in later rounds is higher than the growth rate in earlier rounds. Differences
in patterns between early and late rounds are also present for average salaries. None of the
average salary coefficients of the “late round” dummies is significant suggesting that the
later rounds funding event itself does not impact average salary. There is no significant
shortfall in salary before the later funding rounds and no significant increase in salary after
the funding rounds. This pattern is consistent with non-binding cash constraint in late rounds
enabling the startup to be at the average salary levels.6
3.3 The differential signaling value of funding events
Table 4 uses the same event study research approach to examine turnover centered on
the month when a funding event happens (month 0). The dependent variable is employee
turnover. The signaling value of funding events is reflected in the increased attractiveness
of remaining in the firm as the signal becomes credible while outside options remain the
same. As uncertainty about prospects of the startup decreases over time, the value of the
signal is expected to decrease. Table 4 presents the results.
Insert Table 4 around here
Turnover is generally lower around the early funding rounds. Five out of the seven
months bracketing an early funding event are significant. This observation is consistent
with the signaling value of a funding event being more valuable early in the life of the firm.
If the uncertainty of a startup firm decreases over its life, as assumed in real option models
of venture funding and as argued in the field-based research literature, the value of an
external signal is more valuable early on. Initially, employees may assign a significant
6 We also examined whether this salary increase holds for all employees in the company (consistent with an agency problem) or rather it is associated with new employees only (indicating that the increase in average salary is associated with hiring more expensive human capital). A means comparison indicates no significant difference in the average salaries between new and existing employees.
19
value to the decisions of external investors; at this stage, uncertainty is large and any piece
of information from players with access to different information has a significant impact on
the employees’ updating of the firm prospects. However, the value of an additional signal is
less relevant in updating employees’ beliefs when the uncertainty is lower.
We performed several robustness checks on Tables 3 and 4. (1) We controlled for the
possibility of a momentum where past growth fuels future growth as argued in the
practitioners’ literature (Cox and Camp 1999) running an alternative model including
employee growth in the previous month. We use an instrumental variable approach to
proxy for the lagged dependent variable (Kennedy 1997), we also control for autocorrelation
in the residuals using an AR(1) model and use a Prais-Winsten estimator (Greene 2000). (2)
We also used percentage growth rather than absolute growth. In all cases results were
comparable.
We also controlled for the potential effect that the amount of funding may have. Larger
amounts of funding may be associated with higher growth for several reasons. First, venture
capitalists may affect the growth of the firm through the amount of funds that they make
available to the firm. Larger amounts of funding permit growth strategies not available if
the funding amount is smaller. Second, firms that receive larger amount of funds may use
the free cash flow to grow faster or increase salaries. Finally, previous research has found
that entrepreneurs that receive higher cash flows—through inheritances—also achieve
higher top line growth (Holtz-Eakin, Joulfaian et al. 1994b). We control for the funding
amount interacting the month dummy variables around the funding event with the amount of
funds received. Table 5 presents the results.
Insert Table 5 around here
20
The pattern for early rounds is similar to Table 3. In early rounds, employee growth and
average salary are mostly independent of the amount of funding received. The effect of the
amount of funds received is only significant for the funding month (month 0 with a
coefficient of 0.057, t=1.97). The pattern for the relevance of the funding amount in later
rounds contrasts sharply with early rounds. The significance of the event itself disappears
and we observe a significant association between the amount of funding and both growth
and average salary. The growth compared to non-funding months ranges between 0.063 and
0.238 people per additional million dollars in funding.7 This pattern is consistent with
various explanations such as free cash flow problems associated with larger funds or an
expanded growth strategy set that allows more aggressive growth. The results for turnover
are also consistent with earlier rounds having a stronger signaling value. In terms of the
relevance of the funding amount, only the coefficient for the month of a late funding round
is negative and significant. Thus, it provides some evidence that the amount of funding
rather than the funding event itself may have signaling value in later rounds.
4. CONCLUSIONS
This paper addresses the role of cash constraints over the staged funding structure of
venture capital investments. Cash constraints are an important topic in entrepreneurship
finance because of their role in inhibiting new ventures’ growth. While this effect is
important in early rounds of venture-backed companies, staged funding theory assigns a
different role to these constraints in later rounds of funding. Consistent with theory
predictions, we find very different patterns between early rounds and late rounds of venture
funding. The behavior of employee growth and average salary in early rounds is consistent
7 An increase in the month following the funding event could be driven by a bonus linked to it. However, we observe this pattern throughout a six-month period.
21
with the role of external funding as releasing cash constraints. The event itself significantly
impacts growth and average salary but the amount of funding does not. The pattern in later
rounds indicates a different role. Here, cash constraints are part of the bargaining between
the various players—entrepreneur, existing investors and external investors—but in
equilibrium they are not binding. We find that the amount of funding is significantly
associated growth and average salaries in these later rounds. Several explanations are
consistent with this pattern including agency costs associated with free cash flows.
Alternatively, the larger variance in the amount of funding that exists in these later rounds
may allow an expanded set of growth strategies that does not exist in early rounds. We also
document that the signaling value of a funding event is significant in early rounds of
funding when uncertainty about the prospects of the firm is larger. In later rounds, we find
some evidence that the amount of funding has signaling value but not the event itself.
The focus of this paper is the funding rounds of companies that did one or more venture
rounds. Here there is an observable event—a funding round with a date and an amount. A
related study would be to examine the impact of non-funding “events” (i.e., unsuccessful
funding rounds) on variables such as headcount, average salary and turnover. A key
challenge here is that there rarely is an observable event. Startups do not announce that they
have been unsuccessful in raising money. Typically they approach multiple parties and
continue to seek money at ever more unfavorable terms as their cash balance approaches
zero. Some of these companies eventually shut down with the resultant observable effect on
headcount, average salary and turnover. Some continue to struggle along with a dramatically
reduced scale of operations. Others are acquired, sometimes as a distress sale.
22
The sample studied in this paper is not a random sample. It includes companies that
were successful in obtaining one of more rounds of venture funding. This is an important
sample in and of itself because it matches the on-going concern assumption underlying the
arguments put forward in the theoretical models. Thus, the sample provides an ideal setting
to test some of their predictions. However, our results need not be generalizable to the
broader set of firms seeking venture funding.
The timing of the study is characterized by a favorable investment environment. When
this environment is harsher, the distinct behavior between early and later rounds may
become less pronounced. In the extreme, where the venture community abandons the
notion of a pre-commitment to sub-sequent rounds, the results we report for early rounds
may be descriptive for later rounds. An extension of our research would be to examine the
robustness of our results to periods where venture funding for later rounds is extremely
difficult.
23
BIBLIOGRAPHY
Admati, A. R. and P. Pfleiderer (1994). “Robust financial contracting and the role of venture capitalists.” The Journal of Finance 49: 371-402. Akerlof, G. A. (1970). “The market for "lemons":Quality uncertainty and the market mechanisms.” Quarterly Journal of Economics 84: 488-500. Baron, J. N., D. M. Burton, and M. T. Hannan. (1996). “The road taken: origins and evolution of employment systems in emerging companies.” Industrial and Corporate Change 5: 239-275. Baum, J. A. C., T. Calabrese, and B. S. Siverman (2000). “Don't go it alone: Alliance network composition and startups' performance in Canadian biotechnology.” Strategic Management Journal 21: 267-294. Bergemann, D. and U. Hege (1998). “Venture capital, moral hazard, and learning.” Journal of Banking and Finance 22: 703-735. Bygrave, W. and J. Timmons (1992). Venture capital at the crossroads. Boston, MA, Harvard Business School Press. Cox, L. W. and S. M. Camp (1999). Survey of innovative practices. Kansas City, Kauffman Center for Entrepreneurial Leadership. Davila, A., G. Foster, and M. Gupta. (2003 forthcoming). “Venture capital financing and the growth of startup firms.” Journal of Business Venturing. Evans, D. S. and B. Jovanovic (1989). “An estimated model of entrepreneurial choice under liquidity constraints.” Journal of Political Economy 97(4): 808-827. Gompers, P. A. (1995). “Optimal investment, monitoring, and the staging of venture capital.” The Journal of Finance 50: 1461-1489. Gompers, P. A. and J. Lerner (1999). The venture capital cycle. Cambridge, Ma, MIT Press. Gorman, M. and W. A. Shalman (1989). “What do venture capitalists do?” Journal of Business Venturing 4: 231-248. Greene, W. H. (2000). Econometric analysis. Upper Saddle River, NJ, Prentice Hall. Hall, J. and C. W. Hofer (1993). “Venture capitalists' decision criteria in new venture evaluation.” Journal of Business Venturing 8: 25-43.
24
Heil, O. and T. S. Robertson (1991). “Toward a theory of competitive market signaling: A research.” Strategic Management Journal 12: 403-419. Hellmann, T. and M. Puri (2001). “Venture capital and the professionalization of start-up firms: empirical evidence.” Journal of Finance forthcoming. Holtz-Eakin, D., D. Joulfaian, and H. S. Rosen (1994a). “Entrepreneurial decisions and liquidity constraints.” RAND Journal of Economics 25(2): 334-347. Holtz-Eakin, D., D. Joulfaian, and H. S. Rosen (1994b). “Sticking it out: Entrepreneurial survival and liquidity constraints.” Journal of Political Economy 102(1): 53-75. Kennedy, P. (1997). A guide to econometrics. Cambridge, Ma, MIT Press. Kockesen, L. and S. Ozerturk (2002). “Staged financing and endogenous lock-in: A model of start-up finance.” Working paper, Columbia University. Kreps, D. M. (1990). A course in microeconomic theory. Princeton, N.J., Princeton University Press. Lerner, J. (1995). “Venture capitalists and the oversight of private firms.” The Journal of Finance 50: 301-319. Meggison, W. L. and K. A. Weiss (1991). “Venture capital certification in initial public offerings.” Jounral of Finance 46. Neher, D. V. (1999). “Staged financing: An agency perspective.” Review of Economic Studies 66: 255-274. Robie, K. M., M. Wright, et al. (1997). “The monitoring of venture capital firms.” Entrepreneurship Theory and Practice: 9-27. Sahlman, W. A. (1990). “The structure and governance of venture-capital organizations.” Business Economics 29: 35-37. Sapienza, H. J. and A. K. Gupta (1994). “Impact of agency risks and task uncertainty on venture capitalists-CEO interaction.” Academy of Management Journal(37): 1618-1632. Spence, M. A. (1974). Market signaling: Information transfer in hiring and related screening processes. Cambridge, Ma, Harvard University Press. Wang, S. and H. Zhou (2002). “Staged financing in venture capital: Moral hazard and risks.” Journal of Corporate Finance forthcoming.
25
TABLE 1 Descriptive statistics for firms in the sample
Panel A: Industry statistics Venture-backed firms
Communications and networking 28 Electronics & Computer Hardware 8 Semiconductors 8 Software 51 Information Services 35 Healthcare & Biotechnology 19 Business & Consumer Services and Products 21 Total 170 Panel B: Number of financing rounds for companies in the sample
Founded Mean Median Min. Max. St. Dev. # of Companies
Before 1994 3.10 3 1 6 1.97 20 1994-95 3.41 3.5 1 6 1.28 34 1996-97 3.00 3 1 6 1.49 62 1998-99 1.93 2 1 5 0.93 54
Total 2.75 2 1 6 1.47 170 Panel C: Valuation amounts for companies in the sample Rounds of financing
Amount raised (in millions of dollars)
Post-money valuation (in millions of dollars)
Number of Rounds
Mean Median St. Dev. Mean Median St. Dev. Early rounds Seed $1.40 $1.23 $0.99 $3.34 $3.00 $1.70 60 1 5.65 4.00 7.48 13.70 10.50 13.86 162 Late rounds 2 9.80 7.50 8.73 47.07 30.00 50.78 122 3 14.48 9.28 16.82 57.00 49.00 44.90 67 4 16.43 11.00 19.31 112.40 66.00 149.16 37 Other rounds 5 24.68 23.50 19.02 147.70 135.37 70.06 16 6 18.61 12.13 22.66 347.84 347.84 172.76 4
Overall $9.08 $5.00 $12.35 $46.31 $20.00 $74.40 468 Panel D: Evolution of the database over time
Year 1994 1995 1996 1997 1998 1999 2000 Total
Venture-backed firms year end 19 29 64 47 98 109 101 194
Number of employees 389 832 1,832 3,755 5,426 9,371 6,477 27,193
Bi-monthly data points 5,400 11,446 23,264 51,798 74,980 116,120 49,270 585,497
26
27
TABLE 2 Descriptive statistics for research variables
Panel A: Events in the sample
Number of months 4,155
Number of months with financing event 268
Number of early round events (rounds zero and one) 79
Number of late round events (rounds two, three, and four) 165
Number of other rounds (round five and six) 12
Number of IPO 12
Panel B: Growth, turnover and average salary for venture-backed startups
Mean Standard
deviation Minimum Maximum Median
Employee growth (per month) 1.80 3.75 -14 27 1
Number of employees 31.19 33.89 1 336 21
Turnover 0.032 0.063 0 0.739 0
Average salary (in $) 7,310 2,732 1,672 30,394 6,935
Time in the sample (in months) 18.5 15.0 1 77 14
Panel C: Correlation table
Average salary
Turnover Ln (size) Ln (age)
Growth 0.05*** -0.29*** 0.27*** -0.12***
Average salary -0.04*** -0.02 -0.02
Turnover 0.05*** 0.16***
Ln (size) 0.39***
Correlations are all significant at 1% level except for the correlation between average salary and Ln (size) and Ln (age) that are not significant.
28
TABLE 3 Rounds of funding and changes in startup firms
Model 1
Employee Growthj,t
Model 2
Average salaryj,t
Coefficient t-statistic Coefficient t-statistic
Constant 1.61** 5.46 6,983** 27.52 Ln (Size)j,t-1 1.17** 13.05 -80 -1.12 Ln (Age)j,t -0.99** -12.27 -113 -1.68
Early Round—monthj,-3 0.09 0.34 -925** -2.92 Early Round—monthj,-2 0.26 0.90 -776** -2.63 Early Round—monthj,-1 0.45 1.38 -630* -2.16 Early Round—monthj,0 0.60* 2.52 -372 -1.31 Early Round—monthj,+1 1.21** 3.35 -84 -0.26 Early Round—monthj,+2 1.15** 3.79 -80 -0.30 Early Round—monthj,+3 0.83** 2.85 -70 -0.32
Late Round—monthj,-3 0.65* 2.26 -279 -1.61 Late Round—monthj,-2 0.84** 2.74 -22 -0.13 Late Round—monthj,-1 0.49 1.72 -185 1.06 Late Round—monthj,0 1.23** 3.64 102 0.49 Late Round—monthj,+1 2.06** 5.91 221 1.08 Late Round—monthj,+2 1.59** 4.23 50 0.27 Late Round—monthj,+3 1.52** 3.94 237 1.50
Adjusted R-squared 0.17 0.08
# of observations 4,132 4,131
Early Round—monthj, i, and Late Round—monthj, i are dummy variables that take value of one if month t is i months before/after a month when the company received external funding (where i takes values of –3 to 3). Control dummies for the months around fifth and sixth rounds as well as for industry are included but not reported. The reference industry is business and consumer services and products. To avoid the influence of outliers, we delete the top and bottom 0.5% observations for the dependent variables. Standard errors are White-adjusted. *, or ** indicate that the coefficient is significant at the 5%, 1% level (2-tailed).
29
TABLE 4 Rounds of financing and employee turnover
Turnoverj,t
Coefficient t-statistic Constant 0.011 1.96 Ln (Size)j,t-1 -0.002 -1.46 Ln (Age)j,t 0.012** 7.81 Early Round—monthj,-3 -0.012* -1.96 Early Round—monthj,-2 -0.010 -1.43 Early Round—monthj,-1 -0.013* -2.44 Early Round—monthj,0 -0.012* -2.36 Early Round—monthj,+1 -0.005 -0.65 Early Round—monthj,+2 -0.013** -3.59 Early Round—monthj,+3 -0.011** -2.87
Late Round—monthj,-3 -0.003 -0.65 Late Round—monthj,-2 -0.005 -1.12 Late Round—monthj,-1 -0.006 -1.69 Late Round—monthj,0 0.004 0.78 Late Round—monthj,+1 -0.008* -2.28 Late Round—monthj,+2 -0.008** -2.57 Late Round—monthj,+3 -0.005 -1.34 Adjusted R-squared 0.04 # of observations 4,151
Early Round—monthj, i, and Late Round—monthj, i are dummy variables that take value of one if month t is i months before/after a month when the company received external funding (where i takes values of –3 to 3). Control dummies for the months around sixth and seventh rounds as well as for industry are included
but not reported. The reference industry is business and consumer services and products. To avoid the influence of outliers, we delete the top and bottom 0.5% observations for the dependent variables. Standard errors are White-adjusted. *, or ** indicate that the coefficient is significant at the 5%, 1% level (2-tailed).
30
TABLE 5 Rounds of financing and funding amount
Model 1 Model 2 Model 3 Employee Growthj,t Average salaryj,t Turnoverj,t
Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic
Constant 1.88** 6.44 6,996** 27.32 0.010 1.85 Ln (Size)j,t-1 0.98** 11.20 -108 -1.48 -0.002 -1.23 Ln (Age)j,t -0.86** -11.23 -78 -1.14 0.116** 7.49
Early Round—monthj,-3 0.06 0.17 -802** -2.99 -0.015* -1.98 Early Round—monthj,-2 0.14 0.47 -718* -2.08 -0.009 -1.19 Early Round—monthj,-1 0.20 0.65 -583* -2.02 -0.012 -1.92 Early Round—monthj,0 0.25 0.92 -563 -1.76 -0.096 -1.44 Early Round—monthj,+1 0.77* 2.32 -34 -0.09 -0.005 -0.56 Early Round—monthj,+2 1.13** 3.10 -54 -0.18 -0.012** -2.90 Early Round—monthj,+3 0.65* 2.09 -114 -0.46 -0.010* -2.20
Late Round—monthj,-3 0.23 0.5 -746 -3.06 -0.003 -0.40 Late Round—monthj,-2 -0.59 -1.09 -393 -1.72 -0.001 -0.11 Late Round—monthj,-1 -0.10 -0.22 -64 -0.27 -0.004 -0.76 Late Round—monthj,0 -0.64 -1.36 -400 -1.36 0.010 1.51 Late Round—monthj,+1 0.59 1.20 235 0.70 -0.009 -1.72 Late Round—monthj,+2 -0.52 -0.98 -313 -1.17 -0.006 -1.17 Late Round—monthj,+3 -0.84 -1.57 -256 -1.13 -0.000 -0.03
Early Round j,-3 * Amount -0.008 -0.30 -21 -0.99 0.001 0.44 Early Round j,-2 * Amount 0.009 0.50 -9 -0.44 -0.0002 -0.19 Early Round j,-1 * Amount 0.033 0.83 -8 -0.28 -0.0001 -0.18 Early Round j,0 * Amount 0.057* 1.97 38 0.86 -0.0005 -0.97 Early Round j,1 * Amount 0.089 1.23 -9 -0.24 0.0002 0.27 Early Round j,2 * Amount 0.000 0.01 -2 -0.08 -0.0004 -0.85 Early Round j,3 * Amount 0.060 1.14 21 0.64 -0.0004 -0.84
Late Round j,-3 * Amount 0.044 1.09 46** 3.60 0.000 -0.01 Late Round j,-2 * Amount 0.142** 2.82 37** 2.74 -0.0004 -0.15 Late Round j,-1 * Amount 0.063* 1.81 26 1.66 -0.0001 -0.48 Late Round j,0 * Amount 0.186** 4.54 50** 2.71 -0.0007* -2.18 Late Round j,1 * Amount 0.146** 3.22 1 0.07 0.000 0.34 Late Round j,2 * Amount 0.213** 3.73 38 1.92 -0.0002 -0.83 Late Round j,3 * Amount 0.238** 4.08 49** 3.17 -0.0004 -1.69
Adjusted R-squared 0.21 0.10 0.04 # of observations 4,132 4,131 4,151
Early Round—monthj, i, and Late Round—monthj, i are dummy variables that take value of one if month t is i months before/after a month when the company received external funding (where i takes values of –3 to 3). Control dummies for the months around fifth and sixth rounds as well as for industry are included but not reported. The reference industry is business and consumer services and products. To avoid the influence of outliers, we delete the top and bottom 0.5% observations for the dependent variables. Standard errors are White-adjusted. *, or ** indicate that the coefficient is significant at the 5%, 1% level (2-tailed).