Contents lists available at ScienceDirect

  Journal of Business Venturing 25 (2010) 155–172

  

Contents lists available at

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

  Venture capital

  consulting and, to a lesser extent, engineering and non-venture finance manage funds with

  Human capital

  greater fractions of portfolio company exits. Perhaps counter-intuitively, I find that fund

  First-time funds

  management teams that have more general human capital in business administration, as

  Investment management

  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

  1

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 performance

advantage 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 venture

capital 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: rebeccaz@duke.edu .

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) 155172

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 of

venture 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 venture

capitalists 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

(e.g.,

  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

  2

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 the

identities 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 documented

in 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 to

those 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 as

task- 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 on

venture 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) 155172

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 to

perform 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 those

new 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 fund

performance. 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 obtained

elsewhere.

  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) 155172

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 will

have 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. The

second 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 greater

fraction 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.

(e.g.,

). 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

  4

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 identify

and 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 specific

human 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 the

companies 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) 155172

Having 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,

  5

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 specific

human 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 specific

human 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

  6

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

  7 institutions.

  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 quality

since 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 as

follows:

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 fraction

of 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 in

my 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 provide

more 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) 155172

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 a

control 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 early

stage 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 the

likelihood 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 still

active 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 team

human 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 capital

funds.

   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) 155172

  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

  Fund size

  51.9

  1.8

  38.4

  39.3

  32.5

  40.0

  35.6

  28.2

  32.2

  41.5

  17.5

  32.6

  5.9

  46.7

  42.1

  59.1

  66.3

  65.2 85.7 101.8 (millions 2000 $) Companies

  22.0

  44.2

  34.6

  31.3

  26.5

  25.3

  24.6

  17.9

  13.9

  18.1

  14.7

  10.5

  5.0

  12.3

  11.2

  19.3

  21.9

  18.3

  16.6

  12.9 per fund Fraction of

  0.54

  0.65

  0.60

  0.55

  0.62

  0.58

  0.58

  0.62

  0.55

  0.49

  0.60

  0.57

  1.00

  0.55

  0.54

  0.46

  0.51

  0.46

  0.48

  0.42 companies exited per fund Number of funds 318

  12

  25

  23

  24

  31

  17

  20

  19

  8

  10

  4

  1

  3

  6

  7

  20

  26

  35

  27 Panel B- First-time U.S. venture capital funds raised between 1980 and 1998 with collected venture capitalist histories Fund size

  61.9

  83.9

  66.9

  54.6

  40.6

  36.5

  55.3

  28.7

  40.4

  49.5

  17.4

  35.3

  5.9

  61.3

  43.2

  59.1

  69.6

  62.6 92.9 104.5 (millions 2000 $) Companies

  22.9

  78.3

  43.7

  35.5

  32.8

  30.2

  26.7

  20.9

  13.9

  20.0

  15.7

  11.3

  5.0

  15.0

  11.8

  19.3

  22.7

  19.3

  16.6

  12.9 per fund Fraction of

  0.53

  0.68

  0.65

  0.58

  0.62

  0.55

  0.64

  0.60

  0.54

  0.60

  0.58

  0.55

  1.00

  0.68

  0.52

  0.46

  0.52

  0.47

  0.48

  0.40 companies exited per fund Number of funds 222

  3

  8

  11

  14

  19

  7

  16

  13

  6

  7

  3

  1

  2

  5

  7

  19

  25

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