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Journal of Business Venturing 25 (2010) 155–172
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Journal of Business Venturing
The role of top management team human capital in venture capital markets:
☆ Evidence from ﬁrst-time funds Rebecca Zarutskie
Duke University, Fuqua School of Business 1, Towerview Drive, Durham, NC 27708, United States a r t i c l e i n f o a b s t r a c t Article history:
This paper examines whether the human capital of ﬁrst-time venture capital fund management
Received 1 May 2007
teams can predict fund performance and ﬁnds that it can. I ﬁnd that fund management teams
Received in revised form 15 May 2008
with more task-speciﬁc human capital, as measured by more managers having past experience
Accepted 19 May 2008
as venture capitalists and by more managers having past experience as executives at start-up companies, manage funds with greater fractions of portfolio company exits. I also ﬁnd that fund Keywords: management teams with more industry-speciﬁc human capital in strategy and management
consulting and, to a lesser extent, engineering and non-venture ﬁnance manage funds with
greater fractions of portfolio company exits. Perhaps counter-intuitively, I ﬁnd that fund
management teams that have more general human capital in business administration, as
measured by more managers having MBAs, manage funds with lower fractions of portfolio company exits. Overall, measures of task- and industry-speciﬁc human capital are stronger predictors of fund performance than are measures of general human capital.
© 2008 Elsevier Inc. All rights reserved.
1. Executive summary The rapid growth of venture capital as a source of ﬁnancing for start-up companies and as a fraction of institutional investors’
portfolios has made gaining an understanding of the determinants of venture capital investment performance an important
objective. Prior studies have made progress in documenting some general features of venture capital investment performance.
However, there remain many questions about the underlying mechanisms behind the documented features of venture capital
investment performance. In particular, how venture capitalists’ human capital affects the performance of the investments they
make and how differences in human capital of venture capital fund management teams may give some funds a performanceadvantage are two important outstanding questions.
In this paper, I collect data on the educational and work histories of venture capitalists who start ﬁrst-time venture capital funds
and use this information to test several hypotheses about the relation between venture capital fund management team human
capital and venture capital fund performance. If venture capitalists’ skills in managing a venture capital fund’s investments matter
we should see statistically signiﬁcant correlations between measures of venture capitalist human capital and the performance of
their funds. I test several hypotheses about the impact of task-speciﬁc, industry-speciﬁc and general human capital on venturecapital fund performance.
☆ A previous version of this paper was circulated under the title “Do venture capitalists affect investment performance? Evidence from ﬁrst-time funds”. I thank seminar participants at Carnegie Mellon, Duke, the ﬁrst RICAFE2 conference at the London School of Economics, the 2007 American Finance Association Meetings and the 2007 Western Finance Association Meetings for their comments and Sridhar Arcot, Rudiger Fahlenbrach, Simon Gervais, David Hsu, Laura Lindsey, Manju Puri, David Robinson, Catherine Schrand. Dean Shepherd (the associate editor) and two anonymous referees for their suggestions. I thank Joseba Celaya, Adrian Cighi and Ling Luo for research assistance. All errors are mine.
⁎ Tel.: +1 919 660 7981; fax: +1 919 660 8038.
E-mail address: email@example.com .
1 See, for example, Cochrane (2005) , Kaplan and Schoar (2005) , Phalippou and Gottschalg (2006) and Hwang, Quigley and Woodward (2005) .
0883-9026/$ – see front matter © 2008 Elsevier Inc. All rights reserved.
R. Zarutskie / Journal of Business Venturing 25 (2010) 155–172
I ﬁnd that the strongest predictors of ﬁrst-time venture capital fund performance are what I term task-speciﬁc and industry-speciﬁc
measures of human capital rather than general measures of human capital. In particular, ﬁrst-time funds whose management teams
possess more prior venture capital investing experience and more prior experience managing start-up companies exhibit greater
fractions of portfolio company exits. I also ﬁnd that funds whose management teams possess more prior work experience in
management and strategy consulting, non-venture ﬁnance and professional science and engineering experience greater fractions of
portfolio company exits. I ﬁnd that having more general human capital within a venture capital fund management team, as measured
by managers’ education in particular areas such as business, law and science and engineering, does not robustly predict better
performance. However, I do ﬁnd, perhaps counter-intuitively, that management teams with more general human capital in business
obtained through MBAs perform on average worse than other fund management teams. A possible explanation for this result is that
there is an oversupply of individuals of possessing MBAs relative to those with other educational backgrounds who are typically
candidates to enter the venture capital industry. Finally, I show that the human capital measures that predict the performance ofventure capital funds also predict whether the management team is able to raise a follow-on fund.
The ﬁndings support the idea that differences in venture capitalist human capital partially explain the documented heterogeneity and persistence in venture capital fund performance (e.g., ). The ﬁndings also suggest
which measures of human capital matter and point us towards future research that can shed further light on how venturecapitalists with different types of human capital affect the outcomes of the investments they make.
2. Introduction A central question in the ﬁnancial economics literature is whether there are differences in the abilities of investment managers
exhibit superior investment performance (e.g., ). A central question in the management and strategy literature is how
Fundamentally top management teams affect a ﬁrm’s decisions and subsequent performance (e.g., related to both of these literatures is the labor and organizational economics literature on human capital (e.g., ).
While there is a large empirical management literature that investigates the role of human capital on organizational outcomes
there is comparatively little research in ﬁnancial economics on the role that human capital plays in explaining differences in
investment performance, despite a fairly large empirical literature which looks for predictability in investment performance more
generally (e.g. This is surprising since making and managing
investments is a research- and information-based activity which requires a large amount of human effort. A natural place to look for
evidence that differences in investor human capital, or skill, predict investment performance is professionally managed investment
vehicles, such as mutual funds, hedge funds and venture capital and private equity funds. The researcher can often observe theidentities of the managers of these investment vehicles as well as measures of investment performance of these variables.
This paper investigates the role the human capital of venture capital fund management teams plays in predicting the
performance of venture capital funds. The paper’s contributions are fourfold. First, I contribute to the ﬁnancial economics literature
on investment performance predictability. Additionally, because I focus on fund-level performance I am able to examine whether
human capital measures which predict the performance of ﬁrst-time funds also predict whether a follow-on fund is raised,
enabling one test of whether human capital differences can explain persistence in venture capital fund performance documentedin previous work.
Second, I am able to test several new hypotheses about how the human capital of top management teams affects ﬁrm
performance. In particular, I test new hypotheses about how two types of speciﬁc human capital, which I call task-speciﬁc and
industry-speciﬁc human capital, are related to ﬁrst-time venture capital fund performance. The hypotheses I test are related tothose tested in a recent study by which examines the inﬂuence of general and speciﬁc, broadly
deﬁned, human capital on the performance of venture capital ﬁrms. Because I collect a different set of manager biographical
variables, I test a different, more nuanced, set of hypotheses about the roles of speciﬁc and general human capital types, such astask- and industry-speciﬁc human capital, in venture capital fund performance.
Third, my larger data sample allows me to control for a variety of other factors that may inﬂuence venture capital fund
performance in order to more accurately assess the inﬂuence of management team human capital on fund performance. In so
doing, I am able to test a number of previously tested secondary hypotheses about the impact of non-human capital measures onventure capital fund performance in a more recent sample of venture capital funds.
Finally, by focusing my analysis on ﬁrst-time venture capital funds, I generate some novel statistics on the education and
employment histories of venture capitalists who form ﬁrst- time funds and how these venture capitalists join together in teams.
Understanding who forms ﬁrst-time funds and what determines which funds succeed is important not only for investors seeking
to invest in such funds but also for understanding which types of venture capitalists may be needed to create a successful venture
capital market in regions where such markets are nascent or nonexistent. The rest of the paper is structured as follows. Section 3
discusses the hypotheses to be tested and the theoretical literature motivating them. Section 4 introduces the data and describes
the characteristics of venture capitalists raising ﬁrst-time funds. Section 5 presents the empirical analysis and the main ﬁndings.Section 6 discusses the main ﬁndings and suggests future directions for research. Section 7 concludes.
2 Two notable exceptions are empirical studies on the role investor human capital plays in predicting investment performance are Chevalier and Ellison (1999) and Golec (1996) in the setting of the mutual fund industry.
R. Zarutskie / Journal of Business Venturing 25 (2010) 155–172
3. Theory and hypotheses
3.1. Human capital theory, upper echelon theory and venture capital markets Any hypothesis that investment manager human capital should predict investment performance implicitly invokes upper echelon theory (e.g.,
) and the resource-based view of the ﬁrm A fundamental premise of upper echelon theory is that top management teams matter for ﬁrm
performance. In the case of a venture capital fund characteristics of the fund management team should be able to predict the
performance of the fund. According to the resource-based view of the ﬁrm, there must be some limit to the ability of some fund
managers to obtain the human capital known to improve investment performance in order for certain fund management teams toperform better than others.
Motivated by work in upper echelon theory, an empirical literature has developed to assess the role top management teams
play in ﬁrm outcomes. This literature is closely related to the labor economics literature on human capital. According to upper
echelon theory team processes that drive decision-making matter for ﬁrm performance. Since the empiricist typically cannot
observe these team-level processes directly, he must rely on observable team characteristics that proxy for them. Many of the
management team characteristics studied in the context of upper echelons are measures of educational level, educational specialty
and work background, classical measurements of human capital. Human capital theory argues that, to the extent these variables
proxy for scarce skill or skills that are costly to acquire, these measures should be correlated with better ﬁrm performance.
Measures of the collective human capital of a management team are arguably correlated with the kinds of processes and dynamics
the team experiences and should also, therefore, be correlated with ﬁrm performance. For example,
also examine the role of management team tenure on ﬁrm performance while
examine management team tenure heterogeneity along with management team age and education level on ﬁrm innovation. In
addition, management team composition and heterogeneity along demographic variables as well as networking or social capital of
management teams have been examined in the context of ﬁrm performance and other outcomes (e.g.,
). Another line of inquiry related to both the human capital and upper echelon literatures is an
empirical literature which explores the relation between measures of entrepreneurs’ backgrounds on the performance of the ﬁrms
they start. For example, each
examine the relation between top management team characteristics in new ventures and the subsequent performance of thosenew ventures.
The present study adds to the empirical literatures on top management teams and human capital by testing several hypotheses
about the relation between types of speciﬁc and general human capital of fund management teams on venture capital fundperformance. The distinction between general and speciﬁc human capital has been made in several studies, beginning with
While deﬁnitions have varied from one setting to another, typically general human capital is deﬁned as skills that can be
generally applied across most ﬁrms and settings and speciﬁc human capital is speciﬁc to a particular time or setting, such as human
capital speciﬁc to a ﬁrm, industry or task. In this paper, I make the distinction amongst task-speciﬁc, industry- speciﬁc and general
human capital of venture capital fund managers. While the deﬁnitions of these human capital types are unique to the particular
empirical setting of this paper, the hypotheses I test are related to existing theories on the role of types of human capital within the
put forth a model in which task-speciﬁc human capital is developed and ﬁrm. For example,
determines the wage and promotion structure inside the ﬁrm. develops a model of job-speciﬁc human capital,
which can be used also be used to motivate the measures of task-speciﬁc human capital I employ. Further, the idea of industry-
. speciﬁc human capital is discussed in In the next subsection, I formalize the hypotheses I test and relate them to existing theory.
3.2. Primary hypotheses In this paper I assess the impact of three different types of manager human capital, task-speciﬁc, industry-speciﬁc and general
human capital, on the performance of venture capital funds. In particular, I deﬁne task-speciﬁc human capital as human capital
speciﬁc to two primary tasks of participants in the venture capital markets — managing a venture capital fund and running a start-
up company. Using motivating theory from , I argue that venture capital
fund manager teams with more experience in these two tasks should be more likely to manage better performing funds. Fund
managers with experience managing funds will likely have learned skills necessary for running a fund through trial and error — a
learning process less likely to be obtained through other work or educational avenues. Fund managers who have managed funds
previously should be more experienced and be better able to run a ﬁrst-time fund due to a combination of a better understanding
of which companies to fund, better access to these good companies as well as knowing how to actively manage those investments.
Likewise, since venture capitalists must screen for good managers and start-ups when choosing in which companies to invest and
may also assist companies in selecting managers after investing in those companies, having experience in the task of managing a
start-up may aid the performance of venture capital fund managers in these tasks. Once again the trial and error learning that a
fund manager may achieve by starting and running his or her own ﬁrm may allow him or her to acquire skills not easily obtainedelsewhere.
While fund returns have become recently available to researchers, they are usually done so under the condition of fund
R. Zarutskie / Journal of Business Venturing 25 (2010) 155–172
managers. I must, therefore, rely on alternative measures of fund performance that serve as proxies for fund returns. The metric of
fund performance I use is the fraction of the fund’s portfolio companies that have been exited, either through an IPO or an
acquisition. Since venture capital funds can only return cash to their investors when a company is exited, the fraction of companies
that exit should be positively correlated with the fund’s return. Previous studies have shown that the correlation coefﬁcient
between the fraction of companies exited and the funds liquidation IRR is around 0.6 (e.g., ). In
the analysis, I ﬁnd the main empirical results are robust to the use of alternative proxies for fund returns, such as the ability of a
3 fund management team to raise a follow-on fund.
The ﬁrst hypothesis that task-speciﬁc human capital will be positively related to venture capital fund performance is:
Hypothesis 1. Venture capital fund management teams with more task-speciﬁc human capital, in the form of a greater fraction of
fund managers with past experience (a) being a venture capital fund manager and (b) being a manager in a start-up company willhave a greater fraction of portfolio companies that exit.
The second type of speciﬁc human capital, which I call industry-speciﬁc human capital, is also motivated by theories such as
which posit that experience
in a particular job or industry should enhance a worker’s productivity in that job, regardless of the ﬁrm for which he is employed.
In the case of industry-speciﬁc human capital, the impact on venture capital fund performance will come through experience in
tasks and skills learned in the prior industries in which fund managers worked, rather than skills learned directly from the tasks
of venture investing and managing start-ups. I choose the three most common industries in which ﬁrst-time venture capital fund
mangers have previously worked – strategy and management consulting, non-venture ﬁnance and professional science and
engineering – to measure industry-speciﬁc human capital. While these three industries are the three most common, it is not clear
that each should matter in the same way for fund performance. In particular, experience in strategy and management consulting will
likely be correlated with fund managers have more skills in business management and business strategic decisions based on the
problems they faced as consultants. However, fund managers with past experience in non-venture ﬁnance may be better at helping
their portfolio companies obtain alternative sources of ﬁnancing. Finally, fund managers with prior experience in science and
engineering should have an advantage in selecting and advising high-tech companies or companies developing new products. Thesecond primary hypothesis, that industry-speciﬁc human capital should be positively related to fund performance, follows:
Hypothesis 2. Venture capital fund management teams with a greater fraction of fund managers having worked (a) as strategy and
management consultants, (b) in non-venture ﬁnance and (c) as industrial engineers or professional scientists will have a greaterfraction of portfolio companies that exit.
The venture capital markets are particularly well-suited for an investigation of the roles of industry-speciﬁc human capital of
managers since venture capitalists are active investors who often become involved in the governance and strategic decisions of the
companies that their ﬁrms ﬁnance by sitting on the companies’ boards of directors or in helping their companies identify good
managers or advisors. Indeed, past empirical work has documented some of these value-added activities of venture capitalists.
). As argued for hypothesis 2, since most venture capitalists work in non-venture industries prior to
becoming venture capitalists, it is likely that experience in these industries is also relevant for venture capital fund management.
Experience in solving business and management problems generally is likely a skill learned in strategy and management
consulting that may also be valuable as a venture capital fund manager. Likewise, experience in non-venture ﬁnance should be
particularly important in later stage companies when understanding and enabling a company’s access to alternative sources of
non-venture ﬁnance become more important. As a company grows and develops positive cash ﬂows, it is often useful for it to have
alternative sources of ﬁnancing to venture capital. In addition, as a company gets older, exit via acquisition or IPO becomes more
likely. Fund managers with non-venture ﬁnance industry experience should be more able to help their portfolio companies ﬁnd
alternative sources of ﬁnancing as well as line up exit partners, such as investment banks underwriters or potential acquirers.
Finally, having experience as a professional scientist or engineer may be particularly valuable for fund managers that focus on high-
tech investments. Having experience as a professional scientist or engineer should be useful in allowing a fund manager to identifyand advise companies that are developing products in areas in which the fund managers have technical expertise.
Because experience in non-venture ﬁnance and professional science and engineering may be more important for certain kinds
of investments (e.g., later stage investments for non-venture ﬁnance and high-tech investments for professional scientists and
engineers) relative to experience in management and strategy consulting, I posit the third and ﬁnal hypothesis related to speciﬁchuman capital in this study:
Hypothesis 3. (a) Venture capital fund management teams with a greater fraction of fund managers having worked in non-
venture ﬁnance will have a greater fraction of portfolio companies that exit when a greater fraction of the companies are later-
stage investments. (b) Venture capital fund management teams with a greater fraction of fund managers having worked as
professional scientists and engineers will have a greater fraction of portfolio companies that exit when a greater fraction of thecompanies are high-tech companies.
3 In unreported results, I also ﬁnd that my results are robust when I use the fraction of ﬁrms that go public as my measure of fund performance. However, the results are stronger when I use the fraction of total exits, both IPOs and acquisitions, consistent with this measure being the more accurate proxy for fund returns.
4 Recent work by
shows that venture capitalists with prior non-venture ﬁnance experience, indeed, are less likely to make early-stage investments.
R. Zarutskie / Journal of Business Venturing 25 (2010) 155–172Having deﬁned the two types of speciﬁc human capital I measure and described how they are predicted to be related to
venture capital fund performance, I turn to predictions about the relation between general human capital and venture capital
fund performance. I measure general human capital as education in several ﬁelds of study at university – science and engineering,
business administration and law. These are the three most common educational backgrounds of venture capitalists in my data
sets. Having more general knowledge in science and engineering may aid fund managers in selecting and advising their portfolio
companies, especially those in high-tech industries, for which having an understanding of the underlying product and technology
is important. Having general knowledge in law may aid fund managers in understanding and crafting contractual agreements
with their portfolio companies since these venture capitalists will have more knowledge of such contractual arrangements as well
as perhaps better legal contacts. Finally, having more general human capital in business may aid fund managers in both selecting
and advising portfolio companies. I depart from previous studies which include some ﬁelds of study as measures of speciﬁchuman capital (e.g.,
While focusing study on an area is speciﬁc in one sense compared to studying a
broader range of subjects, relative to the industry- and task-speciﬁc human capital measures discussed above, having studied an
area such as engineering or business or law is more general than having speciﬁc work experience in those ﬁelds. Thus, I use these
educational measures as measures of human capital that is general relative to the measures of work- experience-based speciﬁchuman capital.
I also measure the quality, or reputation, of the general human capital possessed by the venture capital fund managers. Since all
venture capitalists in my sample attended university, I measure the reputation of the university they attended. In particular, I
measure whether a venture capitalist attended an ivy league university, regarded by many as having a “higher reputation” student
body and faculty. Measuring the reputation of educational human capital is another form of testing for the effect of more general
human capital on venture capital fund performance. It has been argued (e.g., ) that having studied at a
reputable institution should be correlated with greater general human capital to the extent that better education is received at such
I choose to measure reputation of a university as whether or not the university is in the set of “ivy league”
universities, traditionally the most competitive to which to gain admission, as a proxy for quality of general human capital qualitysince my data sample allows me to generate this variable.
Thus, the general human capital measures I employ, while measuring particular types of general human capital, are all general
relative to the work-related speciﬁc human capital measures discussed in Hypotheses 1 to 3. The fourth hypothesis I test is asfollows:
Hypothesis 4. Venture capital fund management teams with a greater fraction of fund managers having an educational degree (a)
in science and engineering, (b) in law (JD), (c) in business (MBA) and (d) from an ivy league university will have a greater fractionof portfolio companies that exit.
3.3. Secondary hypotheses
An advantage of my data sample is that I can control for factors other than the human capital of fund managers that may affect a
venture capital fund’s performance. In this subsection, I posit ﬁve additional hypotheses about the impact other fund-level and
market-level factors may have on venture capital fund performance that have been tested in other setting for which I also test inmy analysis.
First, venture capital funds that are larger and have more resources should be able to invest in more portfolio companies and
better enable them to reach exit. In particular, funds with more assets under management will have more money to make follow-
on rounds of ﬁnancing in order to get their funds to exit in addition to possibly attracting better portfolio companies. The positive
impact of fund size, however, may decrease after a certain point since if a fund becomes too large fund managers may begin to
make more marginal investments in order to invest the entire fund. Thus, we should expect a positive relationship between the
natural logarithm of a fund’s assets and its performance. We should also expect a positive relationship between the number of fund
managers and fund performance since more fund managers will provide more human capital in general to the fund and providemore labor to monitor and screen investments.
Second, the industrial sectors in which a fund invests may have an impact on the exit rates of its portfolio companies, in
particular, if certain industrial sectors experience hot or cold cycles in the public markets over the sample period. To control for
these industrial sector effects, I control for the fraction of portfolio companies in a particular industrial sector, with no a priori
8 In addition, the degree to which a fund has hypothesis on the directional impact of that sector on the fund’s performance.
5 All of the venture capitalists in my data sample attended university. I measure general human capital by recording the types of ﬁelds studied by venture
capitalists. In particular, I measure general human capital in science and engineering by recording whether a venture capitalist has any degree in science and engineering. I measure general human capital in business by recording whether a venture capitalist has an MBA. I measure general human capital in law by recording whether a venture capitalist has a JD.
6 This measure is related to the measure of general human capital employed by to examine the impact of university reputation on
mutual fund manager performance. In their study, they use the average SAT score of admitted students as a measure of the quality of a university attended by mutual fund mangers.
7 It may also be the case that having attended a high reputation university increases general human capital by providing a fund manager access to a larger network of individuals later on in life rather than by providing more general human capital in the classroom.
8 I use the six major industrial classiﬁcations of VentureXpert, one of the primary data sets used in the analysis, to control for the industrial composition of
R. Zarutskie / Journal of Business Venturing 25 (2010) 155–172
concentrated its investments in a particular industrial sector may also impact the performance of the fund. Both previous
theoretical and empirical studies have documented that organizations and managers which specialize in a particular activity
exhibit better performance (e.g., I, therefore, also
control for the degree to which a fund’s portfolio companies are concentrated in industrial sectors by controlling for Herﬁndahl–Hirschman index of industry shares in the fund.
Third, the number of syndicate partners a venture capital fund has may impact the fund’s performance. In particular, if a venture
capital fund has a greater number of syndicate partners, the better it may perform, since its larger group of syndicate partners may
provide more access to deals that the syndicate partners source and since more syndicate partners can provide second opinions on
deal evaluation and monitoring (e.g., I measure the average number of syndicate
partners a venture capital fund has in the rounds in which it ﬁrst invests in each of its portfolio companies and use this measure as acontrol variable in the empirical analysis.
Fourth, the stage of the companies in which a venture capital fund invests may affect the performance of the fund. In particular,
if a fund invests in more companies that are early stage investments, the probability of those early stage companies reaching an exit
is lower than if the venture capital fund invested in these companies when they had reached a later stage since new ﬁrms are
riskier than ﬁrms with some track record (e.g., I control for the fraction of a fund’s portfolio companies are earlystage investments in the empirical analysis.
Finally, the competitive environment for venture capital deals may affect the performance of venture capital funds. Past studies
have shown that the more money that ﬂows into the venture capital industry the higher the price paid for deals and the more
marginal are deals that are undertaken (e.g., ). Thus, we would expect an inverse relation between the
amount of competition in the venture capital industry as measured by the amount of venture capital raised by all funds and thelikelihood that a venture capital fund’s companies are exited.
By controlling for these ﬁve additional drivers of venture capital fund performance, I am better able to test the four primary hypotheses on human capital in the analysis below.
4. Data I use two data sources in my analysis. First, I use the Thomson Financial/Venture Economics VentureXpert data set to identify
venture capital funds, the portfolio companies in which they invest and the outcomes of those investments. The basic unit of
observation in VentureXpert is a ﬁnancing deal, or round. VentureXpert records the identities of the participating venture capital
funds in each round as well as the portfolio company receiving the investment. The data set also records the outcomes of the
portfolio companies receiving venture capital, including whether they went public, were acquired, were shut down, or are stillactive investments.
Second, I use a hand-collected data set containing the work and educational histories of the individual venture capitalists
managing the venture capital funds identiﬁed in VentureXpert. I use this database to form the measures of top management teamhuman capital which I use to test the primary hypotheses posited in Section 3.
4.1. Sample selection
I restrict my sample of venture capital funds along the following dimensions: First, I only include funds whose managing ﬁrms
are based in the United States and which are classiﬁed as “Private Equity Firms Investing Own Capital”. The impact of venture
capitalists’ human capital on the performance of ﬁrst-time venture capital funds connected to banks, corporations or governments
may be different than its impact on funds managed by independent investment ﬁrms due to differing incentives and resources of
being connected to a larger organization. Second, I restrict the sample to include only funds that were raised between 1980 and
1998. The typical life span of a venture capital investment is around three to ﬁve years and the typical life span of a venture capital
fund is around ten years. Funds make most of their investments within three years of the fund’s start. Therefore, funds that were
started after 1998 may not have had enough time to exit their investments, making comparisons between the performance of these
younger funds and older funds difﬁcult. Third, I select only funds that are classiﬁed as venture capital funds and exclude those
classiﬁed as buyout funds. I focus only on venture capital funds, rather then both venture capital and buyout funds, since the types
of investments these two types of fund make can be very different and thus skill sets that are likely required for fund success will
vary between venture capital and buyout funds. Fourth, I restrict the sample to include only venture capital funds that invested in
ﬁve or more portfolio companies and which have non-missing size information. Imposing these sample selection criteria leaves a
sample of 1184 venture capital funds. Finally, I restrict my sample to include only ﬁrst-time funds. I deﬁne a ﬁrst-time fund if it is
the ﬁrst fund reported as managed by a venture capital ﬁrm and has a vintage year of no more than two years after the founding
date of the managing venture capital ﬁrm. This ﬁnal sample selection criterion leaves a sample of 318 ﬁrst-time venture capitalfunds.
Panel A presents a longitudinal view of this sample of 318 venture capital funds. It reports average statistics on fund size,
number of portfolio companies per fund, and the fraction of these portfolio companies that exit via IPO or acquisition. The funds
contributing to an average in a particular year are the funds that were closed in that year, or have that year as their “vintage year”.
The sample average fund size is 52 million year 2000 dollars and the average number of portfolio companies in which a fund
invests is 22. The average fraction of funds’ portfolio companies that exit, via IPO or acquisition, is 0.54. The average size of a ﬁrst-
R. Zarutskie / Journal of Business Venturing 25 (2010) 155–172
Table 1 Venture capital fund summary statistics.
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1980– 1998
Panel A — First-time U.S. venture capital funds raised between 1980 and 1998
65.2 85.7 101.8 (millions 2000 $) Companies
12.9 per fund Fraction of
0.42 companies exited per fund Number of funds 318
27 Panel B- First-time U.S. venture capital funds raised between 1980 and 1998 with collected venture capitalist histories Fund size
62.6 92.9 104.5 (millions 2000 $) Companies
12.9 per fund Fraction of
0.40 companies exited per fund Number of funds 222