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Journal of Business Venturing 25 (2010) 155–172 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 humancapital and venture capital fund performance. 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. 1 See, for example, Cochrane (2005) , Kaplan and Schoar (2005) , Phalippou and Gottschalg (2006) and Hwang, Quigley and Woodward (2005) 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. Because I collect a different set of manager biographicalvariables, 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. 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 industry3. Theory and hypotheses While the deﬁnitions of these human capital types are unique to the particularempirical setting of this paper, the hypotheses I test are related to existing theories on the role of types of human capital within the Gibbons and Waldman (1999, 2004)put forth a model in which task-speciﬁc human capital is developed and ﬁrm. 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 thecompanies that their ﬁrms ﬁnance by sitting on the companies’ boards of directors or in helping their companies identify good managers or advisors. 3.3. Secondary hypotheses I measure the average number of syndicatepartners 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. 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. 4.1. Sample selection I focus only on venture capital funds, rather then both venture capital and buyout funds, since the typesof 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. I deﬁne a ﬁrst-time fund if it isthe 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. 4.2. Venture capitalist biographical information If they have, I classify these individuals as fund managers, since serving as a board member andmonitoring and advising portfolio companies is the role of a fund manager; the venture capitalist or fund manager who is the “lead,” or responsible decision maker, for a deal often takes a board seat on the portfolio company. To ensure that the main results in the paper are not driven by survivorship bias, I repeat the regression analysis detailed below on the sub-sample of ﬁrst-time funds raised in the 1990s, when there are fewer missing ﬁrst-time funds and ﬁnd that the main ﬁndings are robust to this analysis. 4.3. Summary statistics Table 2 , weWhen we focus on the fund-level averages across ﬁrst-time fund management teams in the second column of see that the fractions of fund management teams that have a particular characteristic roughly line up with the fractions ofindividuals venture capitalists with a particular characteristic. Table 3 presents correlation matrices for the individual venture capitalist human capital variables in Panel A and for thevariables measuring the fraction of venture capital fund managers with a particular human capital characteristic in Panel B. 9 The average number of partners is smaller than in several previous studies because I focus on ﬁrst-time funds, which typically have between 1 and 4 founding I regress the fraction of portfolio companies in which afund invests that exit, my proxy for fund returns, on fund-level top management team human capital measures detailed in Section 3.2 and other fund-level and market-level controls detailed in Section 3.3 as in Eq. The variables Xk,i are fund-level controls – the number of fund managers, the natural logarithm of the size of the fund (in constant year 2000 millions of dollars), the Herﬁndahl–Hirschman index of a fund’s portfoliocompanies by industry, the fraction of a fund’s portfolio companies that are early stage investments, and the fraction of a fund’s portfolio companies that are in each of the six VentureXpert industries. 5.1. Fund performance regressions Thus, the evidence on the impact of more general humancapital on venture capital fund performance is mixed, with only support for the hypothesis that venture capital fund management teams with more general human capital in the form of science and engineering education manage better performing funds (i.e.,Hypothesis 4(a)). In the following subsection, I further explore Hypothesis 3, that measures of industry-speciﬁc human capital may matter more for certain types of venture capital fund investments by interacting the fraction of fund managers with industry-speciﬁc humancapital in non-venture ﬁnance and science and engineering with the fraction of a fund’s portfolio companies that are later stage and high tech, respectively. 5.2. Interacting industry-speciﬁc human capital with fund portfolio company composition I also interact the variable SciEngDegree with the variable FracHighTech to test whether having a degree inscience and engineering has a stronger positive effect on the fraction of portfolio company exits when more of the portfolio companies are in high-tech industries. This indicates that when fund managers have both more general andindustry-speciﬁc human capital in science and engineering, their positive inﬂuence on fund company exits is more important when more of those investment are in high-tech ﬁelds in which such expertise is likely more valuable. 5.3. Fund manager human capital and subsequent fundraising Discussion and future research The preceding analysis has documented that task-speciﬁc human capital, deﬁned as human capital speciﬁc to the tasks of ventureinvesting and of managing a start-up company, strongly predict better venture capital fund performance in the form of greater portfolio company exits and the ability to raise a follow-on fund. The statistically signiﬁcant positive coefﬁcients on the fraction of fund managers who worked previously as venture investors, entrepreneurial managers and as management consultants suggest that the supply of such individuals into the venturecapital industry is restricted or limited in some way or that the ability of individuals entering the venture capital industry to obtain these types of human capital is limited.