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Why Generalists Are Successful but Short-Term Entrepreneurs:
Evidence from the Global Hedge Fund Industry#
Kylie Jiwon Hwang*, Damon J. Phillips, and Evan Rawley
Last Revised January 31st, 2018
Abstract: 165 words
Main Body: 11,092 words (41 pages)
Appendix: 1,594 words (5 pages)
ABSTRACT
Generalists are more likely to become well-performing entrepreneurs. Yet, generalists are also
highly mobile in the labor market, transitioning in and out of employment states. Juxtaposing these
two facts suggests that generalists who are successful entrepreneurial managers may also be less
likely to stay committed to entrepreneurship. This paper explores this paradox by synthesizing two
theoretical literatures on generalists as entrepreneurs and generalists as labor market participants.
Our central insight is that generalists treat entrepreneurship as a temporary state in a career with
abundant options, rather than a final destination. Thus, we should expect to see generalists achieve
higher entrepreneurial performance, but also experience higher entrepreneurial exit, conditional
on performance. Using detailed longitudinal data on asset managers from 1995 to 2009, we find
that generalists, asset managers with MBA degrees in our context, are more likely to have higher
entrepreneurial performance yet also higher entrepreneurial exit rates. We address alternative
explanations through numerous robustness checks and in-depth interviews with asset (hedge fund)
managers and entrepreneurs.
# ACKNOWLEDGEMENTS: We thank Ronnie Chatterji, Tiantian Yang, Dan J. Wang, Michael Mauskapf,
Sungyong Chang, participants at the 2017 SMS annual meeting, and the participants at seminars at Columbia
Business School Management Division and the Eugene Lang Entrepreneurship Center for their helpful comments.
We thank Riako Granzier-Nakajima for her outstanding research assistance. This paper benefitted from thirteen
interviewees working in the asset management industry. * Kylie Jiwon Hwang (Corresponding author, [email protected])
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INTRODUCTION
There is an interesting tension when considering the fate of generalists as entrepreneurs. Prior
entrepreneurship research suggests that generalists – individuals with the mastery of a broad range
of skills – are more likely to be successful as entrepreneurs compared to specialists (Lazear, 2004;
Kacperczyk and Younkin, 2017). Yet other research has shown that generalists experience higher
labor market mobility (Weiss, 1971; Becker, 1975; Merluzzi and Phillips, 2016). Juxtaposing these
two streams of research highlights an apparent paradox: generalists are better at being
entrepreneurs, yet, despite their success also transition in and out of entrepreneurship at a higher
rate. To resolve this paradox, we study whether and why generalists are less likely to stay
committed to entrepreneurship despite their advantages in entrepreneurship.
Research in entrepreneurship suggests that because entrepreneurs manage diverse tasks
and people, generalists are better fit for entrepreneurship (Lazear, 2004). Scholars have
empirically supported this claim by showing that generalists are more likely to become
successful entrepreneurs, due to their ability to broker between diverse domains of skills and
their greater role diversity in the labor market (Åstebro, Chen, and Thompson, 2011; Sørensen
and Phillips, 2011). Based on the premise that well-performing individuals commit (Groysberg,
Nanda, and Prats, 2007) and successful organizations survive (Alchian, 1950; Williamson,
1991), scholars have anticipated that generalists, with their documented entrepreneurial
advantage, will be more likely to persist in their role as entrepreneurs (Lafontaine and Shaw,
2016).
At the same time, labor market scholars have proposed that the diversity and breadth of
generalists’ skills offer a wide range of occupational alternatives (Weiss, 1971; Becker, 1975).
Generalists are more likely to have higher labor market mobility, as their breadth of skills creates
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high-value and diverse outside options for job hopping (Frydman and Saks, 2010; Merluzzi and
Phillips, 2016). Specialists, on the other hand, tend to be more restricted to certain industries and
employers where their specific skills are highly valued (Vardi and Hammer, 1977; Marx,
Strumsky, and Fleming, 2009; Sørensen and Sharkey, 2014).
Given the contrasting findings of these literatures, it follows that breadth of skills may
affect entrepreneurial outcomes in what may be opposite ways. On one hand, a broad range of
skills allows generalists to have advantages in entrepreneurship, with higher entrepreneurial
performance and longevity (e.g. Åstebro, Chen, and Thompson, 2011). On the other hand, the
same broad range of skills is associated with higher opportunity costs of remaining in
entrepreneurship, increasing one’s likelihood of exiting entrepreneurship (e.g. Becker, 1975).
Thus, the breadth of skills that allows generalists to be successful entrepreneurs may also
diminish their likelihood of being committed to their entrepreneurial ventures.
Resolving this paradox is key to understanding the relationship between entrepreneurial
performance and mobility. In particular, while many entrepreneurship scholars have assumed
entrepreneurial performance and entrepreneurial survival (commitment) to be interchangeable
(e.g. Romanelli, 1989; Lafontaine and Shaw, 2016), we argue that for a given level of
performance, some entrepreneurs are more likely to persist than others (Gimeno et al., 1997;
Sørensen and Phillips, 2011). We suggest that in order to understand the relationship between
entrepreneurial performance and commitment, one should more directly take into account
differences that people have with respect to labor market opportunities (Sørensen and Sharkey,
2014). Similar to the choices that general labor market participants face when transitioning
between employment states, entrepreneurs choose to remain in or exit out of their state as an
entrepreneur by considering the relative value of remaining in their own venture to alternative
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labor market opportunities. Significant for the puzzle at hand, we suggest that examining exit
rates and alternative labor market opportunities along with entrepreneurial performance will lead
to a greater understanding of generalist mobility and entrepreneurship.
We synthesize the entrepreneurship literature and the labor mobility literature, in order to
draw a more complete picture of generalists in entrepreneurship. Building on entrepreneurship
research, we first propose that generalists enjoy higher entrepreneurial performance due to their
broad range of skills (Åstebro, Chen, and Thompson, 2011). To understand generalists’
entrepreneurial exit, we integrate the labor market scholarship, which proposes that broad skills
provide generalists with a more extensive range of alternative market opportunities (Becker,
1975). We hypothesize that as generalists have higher opportunity costs of remaining in
entrepreneurship due to the availability of more options, they are more likely to exit out of
entrepreneurship given the same performance level. In other words, whereas the
entrepreneurship literature has assumed that successful performance is associated with high
commitment, we suggest that with generalists there is a paradox where they are both more
successful and more likely to exit compared to others, given the same level of performance.
Our central insight is that generalists, with both entrepreneurial advantages and greater
labor market mobility, are more likely to regard entrepreneurship as one of potentially many
career states that they will transition in and out of, rather than a final destination to which they
will commit. As a result, generalists become successful entrepreneurs with high performance, but
also less committed entrepreneurs by transitioning out of entrepreneurship at higher rates given
the same performance.
Our study suggests a new theoretical framework by synthesizing the theories of
generalists as entrepreneurs and of generalists as mobile labor market participants, and thus,
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offers contributions to both literatures. First, we contribute to the entrepreneurship literature by
distinguishing entrepreneurial performance from entrepreneurial exit, arguing that models that
equate entrepreneurial performance and entrepreneurial commitment should be revised. While
prior research on exits have shown that firm performance is distinct from exit (Mitchell, 1994;
Gimeno et al., 1997), there has been little theoretical guidance suggesting why (Parker, Storey,
and Van Witteloostuijn, 2010). Our study offers a theoretical explanation of why entrepreneurial
performance is often associated with, but does not represent entrepreneurial commitment.
Second, we contribute to a recent stream of literature that emphasizes the importance of
understanding and analyzing entrepreneurship in the general context of the labor market (Aldrich
1999; Sørensen and Sharkey, 2014; Burton, Sørensen, and Dobrev, 2016). Researchers in this
stream have suggested a limitation in prior entrepreneurship research that separates entrepreneurs
from the rest of the participants in the labor market, or separates the spells of one’s
entrepreneurship experience from the spells of employment experience. Just as movement from
one job to another in paid employment is driven by the distribution of available opportunities,
individuals decide to remain in or exit out of entrepreneurship based on the relative attractiveness
of the set of available mobility opportunities (Sørensen and Sharkey, 2014). We contribute to this
stream of entrepreneurship literature by emphasizing that separately analyzing entrepreneurial
performance and exit is particularly important for generalists in entrepreneurship, as they face
opposing pressures from entrepreneurship and the labor market.
We test our theory using data from the global hedge fund industry, which has several
advantages. First, the hedge fund industry is a professional service industry which relies
extensively on human capital (Phillips, 2002; Greenwood and Suddaby, 2006), suggesting that
individual characteristics will have an important impact on entrepreneurial outcomes. Relatedly,
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as part of the asset management industry, it is an industry in which entrepreneurial decisions are
largely due to individual volition (Kacperczyk, 2012). These features of the industry provide a
setting in which we can examine how an entrepreneur’s individual characteristics determine
entrepreneurship outcomes, with less concern for other confounding factors such as institutional
barriers. Secondly, not only can one empirically examine entrepreneurial entry and exit, but it is
also relatively straightforward to measure entrepreneurial performance, an unusual feature for
most data on entrepreneurial ventures. Third, the industry has had high rates of entrepreneurship
over the last three decades and provides a wealth of new ventures to examine (De Figueiredo and
Rawley, 2011; De Figueiredo, Meyer-Doyle, and Rawley, 2013).
Specifically, we examine generalists and entrepreneurship outcomes using data from the
global hedge fund industry during the period of 1995 to 2009. In addition, we conducted 13 in-
depth interviews with current and past asset managers and entrepreneurs in order to gain further
insight for our study. After replicating the findings in the literature that generalists are more
likely to enter entrepreneurship (e.g. Lazear, 2004; Kacperczyk and Younkin, 2017), our
empirical analyses support our main argument that generalist hedge fund managers experience
higher entrepreneurial performance, yet are also twice as likely to exit from entrepreneurship
given the same performance level as non-generalist hedge fund managers. We find additional
support for the underlying mechanism of why generalists are more likely to be successful yet less
committed entrepreneurs, by showing that their alternative labor market options are both broad
and high quality. Finally, we address potential alternative explanations in an appendix using
numerous robustness checks and interviews.
THEORY AND HYPOTHESES
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Generalists in Entrepreneurship
Entrepreneurship scholars have noted advantages generalists possess with respect to
entrepreneurship. The jack-of-all-trades theory links broad skill sets, or experiences, to a higher
likelihood of success in entrepreneurship (Lazear, 2004; Åstebro, Chen, and Thompson, 2011).
The intuition behind this proposition is that entrepreneurs must be able to broker across various
areas of expertise needed for survival and success in a business (Wagner, 2003). For example, a
restaurateur not only needs to understand food and cooking, but also finance, marketing, and
operations in order to effectively make all of the decisions associated with founding and running
a new restaurant. Although entrepreneurs can hire specialists to perform technical activities, they
“must be sufficiently well versed in a variety of fields to judge the quality of applicants” (Lazear,
2004).
Scholars have sought to empirically affirm the core theoretical predictions that generalists
are more likely to become successful entrepreneurs. A stream of studies has verified that
generalists are more likely to become entrepreneurs in the first place (e.g. Wagner, 2003;
Baumol, 2005). For example, Lazear (2004) finds that Stanford alumni who became
entrepreneurs had a greater variety of roles in the labor market prior to becoming an entrepreneur
and had studied a more diversified MBA curriculum. Other studies have directly examined
entrepreneurial success, and have found evidence that generalists are not only more likely to
become entrepreneurs, but are also more successful entrepreneurs (e.g. Stuetzer, Goethner, and
Cantner, 2012). Sørensen and Phillips (2011) find that individuals who are able to integrate a
wide range of skills have higher entrepreneurial income, and Åstebro, Chen, and Thompson
(2011) demonstrate that generalists have higher earnings as entrepreneurs.
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Entrepreneurship scholars have further verified generalists as successful entrepreneurs by
showing that generalists are more likely to remain in entrepreneurship for a longer time span
(e.g. Lafontaine and Shaw, 2016). Many scholars use entrepreneurial survival or commitment as
an alternative measure or proxy for performance, regarding entrepreneurial exit as an
interchangeable measure of low performance (Singh, Tucker, and House, 1986; Romanelli,
1989). For example, Lafontaine and Shaw (2016) show that generalists, as successful
entrepreneurs, are less likely to exit their entrepreneurial ventures and have higher
entrepreneurial survival. However, despite this past work, scholars have not yielded a great deal
of insight on entrepreneurial exit conditional on performance or the destinations of exiting
entrepreneurs.
Generalists and Labor Market Mobility
Individuals are known to experience different labor market mobility patterns based on the
diversity of their inherited characteristics, preferences, skills, and opportunity sets. While there
are also costs associated with being a generalist in the labor market (e.g. Ferguson and Hasan,
2013; Leung and Sharkey, 2013; Kacperczyk and Younkin, 2017), many studies have found a
positive association between an individual’s breadth of skills and experience and her/his labor
market mobility (e.g. Merluzzi and Phillips, 2016). Indeed, since Becker (1975), it has been
commonplace to argue that as an individual’s skills and experience broaden, lateral job
movements become more readily available.
The availability of a wide range of attractive alternative options has been suggested as the
main reason for increased mobility by generalists. Scholars have theorized that the diversity of
skills that generalists possess allow them to consider an extensive range of labor market options
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(Marx, Strumsky, and Fleming, 2009; Rider et al., 2013). For example, individuals with skills
and experience in both marketing and financial engineering, are likely to be of use to a wider
range of firms, possibly spanning several industries. In contrast, individuals with highly
specialized skills, such as expertise in managing emerging market hedge funds, may have a more
limited set of labor market options. As such, generalists may have more (realizable) outside
employment opportunities and will be more likely to leave their current employment state.
Studies have empirically verified this in multiple contexts. Marx, Strumsky, and Fleming (2009)
show that inventors with specialized skills are more likely to be immobile compared to those
with generally-applicable skills, as generalists are more likely to find outside options to transition
into. Zuckerman, Kim, Ukanwa, and Rittman (2003) find that highly-tenured generalists have
greater opportunities spanning several segments in the film industry, due to increased access to a
wider array of roles.
Recent studies argue that employers often value generalist candidates compared to
specialists, as they offer differentiated profiles beyond a specialized skill set, particularly in
contexts such as professional labor markets (e.g. Zuckerman et al., 2003) While specialization is
a primary indicator of ability in the absence of other information about a job candidate (Leung,
2014), simply demonstrating specialization is less advantageous in contexts such as professional
labor markets, where there is often less uncertainty about a candidate’s ability (Merluzzi and
Phillips, 2016). Merluzzi and Phillips (2016) show that generalists in investment banking are not
only more likely to receive multiple job offers, but also more compensation compared to similar
candidates with specialized skill sets. Custodio, Ferreira, and Matos (2013) find that CEOs with
generalist work experience profiles earned 19 percent more than CEOs with specialist work
histories.
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While many scholars have focused on how general individual skills shape the disparate
mobility patterns within and between existing firms, increasingly, transitions between established
firms and entrepreneurship have become a large portion of labor market mobility (Sørensen and
Sharkey, 2014). Müller and Arum (2004) found that 40 percent of US men in their early 50s had
experienced self-employment. We, therefore, extend the discussion of generalists in labor market
transitions to transitions between entrepreneurship and established firms.
Resolving the Paradox
The entrepreneurship and labor market literatures suggest a paradox where generalists exit out of
their entrepreneurial ventures at higher rates despite their advantages as entrepreneurs. We
resolve this paradox by addressing the relationship between entrepreneurial performance and
entrepreneurial exit. Entrepreneurship scholars have frequently assumed entrepreneurial
performance and entrepreneurial exit to be opposing concepts. Under the premise that
individuals with higher performance commit to their employment state (Groysberg, Nanda, and
Prats, 2007) and that well-performing organizations survive while poorly performing businesses
disappear (Alchian, 1950; Friedman, 1953; Williamson, 1991), the entrepreneurship literature
has suggested that high entrepreneurial performance leads to less entrepreneurial exits and higher
entrepreneurial survival (commitment). As concepts both encompassing entrepreneurial success,
the two constructs of entrepreneurial performance and entrepreneurial survival have been used as
alternative measures of the same construct (Romanelli, 1989; Lafontaine and Shaw, 2016).
However, we argue that this assumption on the relationship between entrepreneurial performance
and survival is underdeveloped as it does not account for the paradox of generalists as
entrepreneurs.
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We resolve this paradox by building on Burton, Sørensen, and Dobrev (2016) and
Sørensen and Sharkey’s (2014) argument that entrepreneurial choices are similar to labor market
choices. Here, entrepreneurs choose to remain in or exit out of their employment state as an
entrepreneur by considering the relative value of remaining in one’s own business to alternative
labor market opportunities. Entrepreneurs will make their exit decisions not only based on their
entrepreneurial performance but also their alternative opportunities in the labor market. They
may prefer to exit from their entrepreneurial ventures when other labor market alternatives
become relatively more appealing or lucrative (Gimeno et al., 1997; Sørensen and Phillips,
2011). Thus, in order to understand how generalists differ in entrepreneurial exits, it becomes
important to consider the labor market opportunity costs that generalist entrepreneurs face,
alongside their entrepreneurial performance.
To draw a full picture of generalists in entrepreneurship, we first confirm past findings
which show that generalists are more likely to enter entrepreneurship. While there is substantial
evidence on entrepreneurial entry, empirical evidence on entrepreneurial performance is less
common due to the difficulty of obtaining performance data of entrepreneurs. Prior studies have
found indirect evidence for entrepreneurial performance by using measures such as external
funding (Burton, Sørensen, and Beckman, 2002; Beckman, Burton, and O’Reilly, 2007), firm
survival or longevity (Cooper, Gimeno-Gascon, and Woo, 1994), initial public offering (IPO)
(Stuart, Hoang, and Hybels, 1999; Beckman and Burton, 2008), or entrepreneur’s wage
(Sørensen & Phillips, 2011). We thus propose that generalists enjoy higher entrepreneurial
performance, using direct measures of performance.
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Hypothesis 1 (H1): Entrepreneurial performance increases with the extent to which an
individual has a generalist skill set.
To consider both the value of generalists’ entrepreneurial ventures and their alternatives
outside of entrepreneurship, we integrate the labor market scholarship on generalists to argue that
generalist entrepreneurs have a wider and more attractive pool of outside options during their
entrepreneurship spell compared to non-generalist entrepreneurs. These higher-valued set of
alternative labor market opportunities increase a generalist’s opportunity costs of committing to
their entrepreneurial ventures.
We see generalist entrepreneurs as particularly important to scholars in that the value of
both their entrepreneurial ventures and alternative options are likely to be higher than non-
generalists. Generalists, with their broad range of skills, will have the advantage of both higher
entrepreneurial returns but also higher expected returns from alternative labor market options.
While higher entrepreneurial performance of generalists renders them more likely to stay in
entrepreneurship, their outside options generate a higher performance threshold to match in order
to commit to entrepreneurship.
Thus, we argue that generalists are more likely to regard entrepreneurship as if it were
one of the many possible employment states to transition in and out of, rather than a final
destination to which they will commit. As a result, generalists will be less committed
entrepreneurs by transitioning out of entrepreneurship at higher rates given the same
performance, compared to non-generalists.
Hypothesis 2 (H2): Conditional on performance, the likelihood of entrepreneurial exit
increases with the extent to which an individual has a generalist skill set.
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In order to find empirical support for our theory on generalists in entrepreneurship, we
take the following steps using data on the global hedge fund industry. First, we match generalists
to similar non-generalists, in order to compare entrepreneurial behavior between generalists and
non-generalists with similar characteristics other than their breadth of skills. Before going into
the main hypotheses, we confirm findings from past research on generalists in entrepreneurial
entry. We then test our main hypotheses on generalists in entrepreneurial performance and exit.
Finally, we conduct multiple robustness checks to examine the underlying mechanism of
generalists’ choice to exit based on alternative opportunities in the labor market, and to address
potential alternative explanations.
EMPIRICAL CONTEXT & DATA
Empirical Context: Global Hedge Fund Industry
The rapidly growing hedge fund industry provides an excellent setting in which to probe
the relationship between generalists and entrepreneurial outcomes for several reasons. First, the
hedge fund industry is a setting in which new ventures are largely formed at the individual level
(Kacperczyk, 2012). Unlike industries where new venture formation is driven by large project
teams and venture performance is driven by complementary assets or broader operational
systems, the hedge fund industry offers an industry fit for examining how the founder’s human
capital affects entrepreneurship outcomes.
Second, relatively weak institutional barriers, such as the lack of formal intellectual
property rights, make the hedge fund industry an excellent setting to examine labor mobility and
entrepreneurship. Institutional barriers, which commonly affect the formation of new ventures
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and the lateral movements of employees, hinder individuals from founding new firms and
making employment transitions based on their human capital due to institutional pressures
(Stuart and Sorenson, 2003). The weak institutional barriers of the hedge fund industry, thus,
allow us to better capture the choices of entrepreneurship and employment transitions.
Third, the hedge fund industry allows us to overcome one of the greatest challenges of
entrepreneurship research, which has been the lack of detailed data on new venture performance.
Most studies have largely used proxy measures such as employee income, entrepreneurial
survival, IPO events, or acquisitions to substitute for entrepreneurial performance. Hedge funds,
however, report substantial information on their performance as an indirect marketing tool, as
they are prohibited from marketing directly to the general public. Hedge funds, thus, disclose
monthly returns along with other detailed fund and firm information such as assets under
management, trading strategies, and portfolio managers, and we are able to exploit this to
conduct more fine-grained analyses on entrepreneurship outcomes.
Finally, the hedge fund sector has witnessed significant entrepreneurial activity over the
past decades. The number of hedge funds grew from 610 to over 9,925 between the years 1990
and 2016. By 2016, the global hedge fund industry had $3 trillion worth of assets under
management by more than 5,000 hedge fund firms (HFR Global Hedge Fund Industry Report,
2017). This allows for not only a large sample size for our study, but also makes our setting an
important and meaningful context in which to study entrepreneurship.
The global hedge fund industry is also representative of many other professional service
sectors in the economy, making any findings generalizable beyond the hedge fund industry.
Twelve of our thirteen (92 percent) interviewees, including those with entrepreneurship
experience in several industries, regarded entrepreneurship in the hedge fund industry as very
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similar to that in other industries when asked: “Do you think entrepreneurship in the hedge fund
industry is similar to entrepreneurship in other industries?”
“I think they [entrepreneurship in the hedge fund industry and entrepreneurship in
other industries] are similar. (...) I think it’s kind of the same. There’s the whole of
technology, building a new product, creating a trading strategy, and then there’s all
the other stuff of being the CEO. And I think the skills to be able to juggle these
things would be similar for all industries.”
“It’s similar. I think there are similarities in entrepreneurship between other
industries. So the great coders that start businesses are similar to the hedge fund
managers that start businesses.”
Data
To test our theoretical arguments, we combined data from the Hedge Fund Research
(HFR) Database on hedge fund performance with data on managers’ biographies from LinkedIn.
The HFR Database includes monthly size, performance, inception date, portfolio manager, and
firm data for hedge funds from 1978 to 2009. Though the data is self-reported, it is widely
believed to be one of the most representative datasets of the global hedge fund industry (Liang,
2000). As our analyses rely on individual characteristics of managers that influence
entrepreneurship outcomes, we exclude funds that do not list individual portfolio managers and
funds that do not report any single portfolio manager responsible for the fund for at least three
months.
Using the portfolio manager names obtained from HFR, we hand-collected biographical
information on each hedge fund manager from LinkedIn, including education history,
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employment history, and demographic characteristics. Although some of the portfolio managers
from the HFR database were not found in the LinkedIn Database, our concerns were minimized
since the hedge fund managers found in the LinkedIn database were statistically similar to those
in HFR without a LinkedIn account. We merged the LinkedIn data with the HFR dataset,
resulting in 1,770 unique portfolio manager-firm dyads and 12,272 unique portfolio manager-
firm-month dyads from 1995 to 2009, with complete information on funds, firms, and portfolio
managers.
We use the manager-firm dyads as the unit of analysis for examining entrepreneurial
entry, and the manager-firm-month as the unit of analysis for looking into entrepreneurial
performance and exit. To that end, we aggregated observations across funds for any manager
who supervised more than one fund in a hedge fund firm. For robustness, we conducted analyses
on non-aggregated data and obtained similar results.
Dependent Variables
The dependent variable for our first main hypothesis is entrepreneurial performance, which is
measured by hedge fund excess returns. We selected this performance measure based on our
interviewee responses, which corroborated that hedge fund performance measured by fund
excess returns is the most important performance measure compared to other measures (i.e.
assets under management, number of funds). In addition, the measure follows the emerging
standard for assessing hedge fund performance used by financial economists (Sadka, 2010).
Accounting for the general agreement that investors price financial assets controlling for
systematic risk exposure, we use risk-adjusted excess returns based on Fung and Hsieh’s (2001)
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seven-factor asset pricing model.1 The pricing model is specifically designed for pricing risk in
hedge funds by controlling for exposures to linear and nonlinear equity, bond, commodity, and
option-based risk factors. We estimated excess returns as the difference between fund i’s actual
return at time t and the fund’s expected return, using:
𝑅𝑖𝑡 = 𝑎𝑖 + 𝑅𝑓𝑡 + 𝑋𝑡𝛽𝑖 + 𝑒𝑖𝑡 ,
where i and t are funds and time (in months), respectively; 𝑅𝑖𝑡 is a fund’s raw monthly return
from HFR Database; 𝑅𝑓𝑡 is the monthly Treasury bill rate; and the vector 𝑋𝑡 contains the seven
risk factors from Hsieh’s data library. The term 𝑎𝑖 is the time-invariant component of a fund’s
excess performance and 𝑒𝑖𝑡 is a mean-zero residual. We compute 𝑎𝑖, the coefficients of 𝑋𝑡 and 𝑒𝑖𝑡
by running fund-level longitudinal regressions. Risk-adjusted excess returns 𝑌𝑖𝑡 for firm i in any
period t is defined as 𝑌𝑖𝑡 = 𝑎𝑖 + 𝑒𝑖𝑡 where excess returns capture the combination of a fund’s skill
and luck relative to a market benchmark. This resulting measure was used as our baseline
performance, the “seven-factor excess returns.” The excess returns were winsorized at the 1%
and 99% level to control for extreme values, though doing so did not change our results.
In addition to this baseline performance measure, we utilized an alternative performance
measure to account for the non-systematic risk exposure not priced by standard market
benchmarks. We computed a dynamic information ratio (IR) by dividing the fund’s seven-factor
excess returns in any time period by the standard deviation of the fund’s excess returns (De
Figueiredo, Meyer-Doyle, and Rawley, 2013). We used an autoregressive lag one (AR1)
correction to control for biases due to self-reporting such as serial correlation in the time series of
returns, controlled for backfill bias by dropping the first reported monthly return, and winsorized
the information ratio at the 1% and 99%. The results are consistent for each of the performance
1 The Fung and Hsieh factors are available at https://faculty.fuqua.duke.edu/~dah7/HFData.htm. The trend-
following risk factors are available at http://faculty.fuqua.duke.edu/~dah7/DataLibrary/TF-FAC.xls.
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measures. However, to preserve space we only present results with the standard performance
(excess returns) measure.
The dependent variable for our second main hypothesis is entrepreneurial exit, where
hedge fund entrepreneurs exit from their new ventures. We identify exit at the manager-month
level, by coding exit as 1 if a hedge fund entrepreneur exits from the firm at time t+1, and 0
otherwise. The LinkedIn database provides full employment histories of hedge fund managers,
providing information on whether a hedge fund manager exited the founded firm and the specific
time of exit.
Explanatory Variables
Our main explanatory variable is whether a hedge fund manager is characterized as a generalist
or a non-generalist. In any given industry, the definition of who is a generalist should be based
upon how general a focal individual’s skills are compared to others in the same industry. In our
study, we define hedge fund managers with an MBA degree to be generalists and those without
an MBA degree to be non-generalists, ceteris paribus. While there are other ways to define
whether a manager is a generalist or a non-generalist in other settings, within the hedge fund
industry an MBA education is a major signal that a manager has general management skills, as
opposed to industry-specific skills (e.g. asset trading). We do not claim that MBA graduates
should be considered generalists in all other settings. However, in the hedge fund industry, after
controlling for all other observable characteristics of a manager, MBA graduates will tend to
possess more general skills than those without an MBA degree, for several reasons.
First, MBA programs typically teach a broad set of skills and experiences through core
curricula that include mandatory courses across a wide range of subjects, such as Marketing,
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Strategy, and Operations Management. Thus, in comparison to individuals who do not obtain an
MBA degree, MBA graduates will possess broader managerial skills outside of the specialized
arena of asset management and trading. Second, eight of our interviewees, regardless of their
personal educational background, reflected our view that individuals with MBA degrees have
broader skills than those without, in the context of the hedge fund industry. In our interviews we
ask for a rating of different educational backgrounds to gauge their suitability for being a hedge
fund portfolio manager as well as a hedge fund entrepreneur. The educational backgrounds
included: an MBA education, Master’s education (in Finance), PhD education (in Finance), and
financial certificate. Eleven of our interviewees rated MBA education as helpful for hedge fund
entrepreneurs because it offers a broad range of education and skills. For example:
“I think you need a broad set of skills [to be a hedge fund entrepreneur]. And I
associate that more with an MBA education.”
“MBA students learn more about fundamental strategies, a broader education in
economics, strategy, industry organization…”
“In that [hedge fund entrepreneur] case, the MBA is more valuable as it is (of) a
broader scope, and the CAIA is more specific [in entrepreneurship]. (…) I think that
an MBA is very compelling because it addresses the general expertise you expect of a
manager or an entrepreneur.”
Other interviewees also referred to “MBA-type of skill sets” as broad skill sets incorporating a
diverse range of expertise needed for running a business. Interviewees agreed that these diverse
20
skill sets were obtained through the diverse experiences incorporated in the overall MBA
program as well as through the MBA coursework. Past research also corroborates our measure,
as scholars have used management education as a proxy for generalist education or generalist
training (i.e. Cumming, Walz, and Werth, 2016).
Third, the hedge fund industry is a specialized field, with the majority of its participants
specialized in the domain of finance and trading. Most hedge fund managers have career tracks
and educational backgrounds closely related to trading and asset management. In our sample, 80
percent of the hedge fund managers graduated with an asset management-related bachelor’s
degree and 80 percent of the hedge fund managers worked only within the financial services
industry. Furthermore, 75 percent of non-MBA hedge fund managers do not have other post-
bachelor's educational degrees. Of the remaining 25 percent of managers who have achieved
other post-bachelor’s educational degrees, most are finance-related master’s degrees or Ph.D.
degrees. Thus, within our particular empirical context, individuals that do not obtain an MBA are
more specialized than those who obtain an MBA degree.
While we use the MBA degree as a proxy for measuring generalist management skills in
the hedge fund industry, we are sensitive to alternative explanations. For example, in examining
the literature, researchers have used the MBA degree as a proxy for human capital, leadership
skills, network, status, family wealth, and risk behavior (e.g. Cai, Gantchev, Sevilir, 2016).
Furthermore, our dataset reveals that the MBA degree is correlated with variables such as
bachelor’s degree prestige, prior employment in a prestigious firm, and average family wealth.
We address these potential alternative characteristics that may be confounded in our measure of
an MBA degree, by including a wide range of control variables, using the propensity score
matching method, and conducting additional robustness checks. We argue that after controlling
21
for these potential alternative characteristics and explanations of an MBA degree, the key
difference between an MBA graduate and a non-MBA graduate in the hedge fund industry is
their level of general skills.
The data on hedge fund managers’ education history was reported in the LinkedIn
Database. We measure generalist as a dummy variable that takes the value of 1 if the hedge fund
manager is an MBA graduate (generalist) and 0 otherwise. Although the extent to which an
individual has general skills is not a dichotomous characteristic, we measure it with a binary
variable because being a generalist is a non-linear jump in this particular context and we can
capture it without measurement error. As one of the most coveted industries for MBA graduates,
the hedge fund industry consists of large numbers of MBA degree holders – our sample contains
522 (37 percent) individuals with an MBA degree. A positive coefficient for the explanatory
variable in the test of our hypotheses on entrepreneurial performance and exit would be evidence
for our argument.
Control Variables
We include three types of control variables in our analyses: individual manager level controls,
hedge fund level controls, and firm level controls.
Individual Attributes. Individual-specific characteristics explain part of the variation in both
one’s likelihood of having high entrepreneurial performance and one’s propensity to exit from
entrepreneurship. Our models account for demographics including gender, age, and family
wealth. Many studies have found that women are more likely to have lower entrepreneurial
performance, compared to their male counterparts (Renzulli, Aldrich, and Moody, 2000;
22
Thebaud and Sharkey, 2016; Guzman and Kacperczyk, 2016; Yang and del Carmen Triana,
2017). Individual age effect on entrepreneurship has also been examined, where scholars have
argued that there is an inverted–U shaped relationship between age and entrepreneurship
formation (Parker, 2004). We thus include gender and age to control for such potential biases. As
both LinkedIn and HFR do not disclose the gender or age of portfolio managers, we created
measures to proxy for both variables. Hedge fund manager’s gender was determined using the
online database genderize.io. This database includes more than 200,000 unique names and
assigns a probability on whether each name is male or female given the distribution of genders
for these names in the database. When the name of the hedge fund manager was not listed in
genderize.io or had a probability lower than 70 percent of being either male or female, we used
an internet search to determine the manager’s gender (through the hedge fund manager’s
LinkedIn profile picture, Bloomberg database, hedge fund firm websites, and individual web
pages). We determined hedge fund manager age using the bachelor’s degree start date, under the
assumption that individuals start their bachelor’s degree at the age of 18. For robustness, we
took a natural logarithm of age, which does not change our results.
Since family wealth can drive entrepreneurial outcomes (Evans and Jovanovic, 1989;
Evans and Leighton, 1989), we created a proxy variable by merging our data with public data on
the average family wealth of students at each US college. Specifically, we used data on the
average parent’s wealth of students of 2200 US Colleges for students in the 1980, 1981 and 1982
birth cohorts (Chetty et al., 2017).2 We use a variable that measures the fraction of the college’s
student whose parents are within the top income quintile.
2 This dataset is available at http://www.equality-of-opportunity.org/data/.
23
Third, past research has shown mixed results for the effect of educational attainment on
entrepreneurship outcomes, particularly entrepreneurial performance (e.g. Robinson and Sexton,
1994; Dutta, Li, and Merenda, 2011). We thus consider individual differences in educational
attainment by including bachelor’s degree prestige variables using both the 2016 QS World
University Ranking and the 2016 US NEWS Liberal College Ranking. The prestige variable
decreases in value as it indicates a bachelor’s degree from a more prestigious institution. To note,
bachelor’s degree prestige is negatively correlated with average family wealth (pairwise
correlation: -0.388), suggesting that individuals from wealthier families are more likely to
graduate from prestigious bachelor’s institutions. We used the natural logarithm of bachelor’s
prestige such that the most prestigious university has a value of zero.
We also coded educational dummies for master’s (excluding MBA), JD, and PhD
degrees, which takes a value of 1 when the focal individual received each of the degrees, and 0
otherwise. We generated a liberal arts college dummy, which takes a value of 1 when the focal
individual received a bachelor’s degree from a liberal arts college and 0 otherwise. We coded
bachelor’s degree and master’s degree majors by generating a variable of related bachelor’s
major and related master’s major which takes a value of 1 if the major is related to finance or
asset management, and 0 otherwise. We also controlled for financial certificates, by coding
individuals with CFA, CAIA, CFP, CPA, ACA, CIIA, ACA, and CIMA as 1, and 0 otherwise.
Lastly, we included controls for individual employment history. Scholars have shown
that past employment experiences such as years of work experience and job titles shape
entrepreneurship outcomes (Rider et al., 2013). Thus we controlled for past employment
variables including the number of prior firms one has worked for, total years of work experience,
years of work experience before MBA education, average job tenure, other industry experience,
24
international job experience, prior entrepreneurship experience, and past experience as a chief
executive or portfolio manager. We obtained these measures through the LinkedIn Database. The
number of prior firms one has worked for, total years of work experience, years of work
experience before MBA education and average job tenure were obtained from each individual’s
employment history. We measured work experience in years and used the natural logarithm for
robustness. Because both measures were highly skewed, we winsorized them at the 5-percent
level to reduce the effect of outliers (Dixon, 1960).
Other industry experience takes the value of 1 if the focal individual worked in at least
one other industry before the focal firm, and 0 otherwise. International job experience takes the
value of 1 if the focal individual worked in more than two countries prior to the focal firm, and 0
otherwise. For prior entrepreneurship experience, we counted how many times the focal
individual was a founder or a founding team member throughout his past employment history.
Past experience as a chief executive officer takes the value of 1 if the focal individual had
experience of being a chief executive officer in any of his/her past occupations, and 0 otherwise.
Similarly, past portfolio manager experience also takes the value of 1 if the focal individual was
a portfolio manager in any of the past occupations, and 0 otherwise.
Fund Attributes. We considered fund attributes that influence entrepreneurial performance and
exit. For example, we controlled for the idiosyncratic risk of funds by calculating the standard
deviation of excess returns, which is the residual risk a manager takes over and above the
systematic market exposure a manager in an index fund would face. In order to measure the
time-varying risk exposure, we used a 12-month moving average standard deviation of excess
25
returns, though the results are not sensitive to the number of months included in the rolling
average. We also included assets under management (AUM) for the fund-month dyads.
We controlled for hedge fund trading strategies (or investment styles) reported in the
HFR database. Trading strategies allow us to take into consideration how strategies of ventures
may influence their performance and growth (Romanelli, 1989; Eisenhardt and Schoonhoven,
1990). Hedge funds may be classified into five broad investment styles, which define the type of
assets the funds invest in and the trading strategies the funds follow. The five investment styles
include, ‘macro funds’, which invest in financial securities based on global macroeconomic
trends; ‘equity long/short funds’, which invest in equities similar to a mutual fund but also
engage in short selling for firms that are viewed as overvalued; ‘event-driven funds’, which
invest in financial securities based on corporate events; ‘relative value funds’, which exploit the
mispricing of securities; and ‘fund-of-funds’, which invest in other hedge funds. Although the
importance of the fund’s stated investment style is often diminished in practice, as there exist
several (often overlapping) trading strategies within each style, we controlled for the trading
strategies to confirm that entrepreneurial outcomes are not driven by different trading strategies.
Organizational Attributes. An important determinant of entrepreneurial performance and exit is
the variance in the organizational attributes of the newly found venture. We controlled for firm
size using the natural logarithm of the total assets under management of the firm, observed
monthly. While the total assets under management represents a standard measure of firm size in
hedge funds, we also used the count of funds inside an organization as another alternative
measure for robustness. The results were robust to both measures. We further control for firm
age, which is measured as the number of years since the firm’s inception. We used a natural
26
logarithm of firm age for robustness. Moreover, we controlled for the extent to which the new
venture is diversified. To measure the extent of firm diversification, we generated a dummy
variable which takes a value of 1 if there exist funds with different investment objectives within
the firm, and 0 otherwise. We included region dummies for the hedge fund firms (Chang, Kogut,
and Yang, 2016). Region dummies include USA, UK, EUROPE, and ASIA.
We controlled for average firm performance when testing our hypothesis on
entrepreneurial exit. We captured average performance by using a standard measure of average
excess returns, average cumulative abnormal returns (CAR). At the fund level 𝐶𝐴𝑅 = ∑ 𝑌𝑖𝑛/𝑛,
the sum of n lagged excess returns divided by the number of months the fund was in operation at
time t, up to a maximum of 24 months. An equal weighted average of fund returns gives a firm-
level CAR.
EMPIRICAL APPROACH
The ideal experiment would feature randomly assigned identical individuals to a treatment of
generalist and a control for non-generalist. We would then observe the causal implication of
generalists on subsequent entrepreneurial performance and exit. In practice, we do not have
random assignment and must utilize self-selected populations. For example, the naive
(endogenous) correlation between generalists and entrepreneurial outcomes is measured through
OLS regressions as:
𝑌𝑖𝑡 = 𝑎 + 𝐺𝑒𝑛𝑒𝑟𝑎𝑙𝑖𝑠𝑡𝑖 + 𝑋𝑐𝛽𝑐 + 𝜀𝑖𝑡,
where i and t index managers and years, respectively; 𝑌𝑖𝑡 is entrepreneurial performance or
entrepreneurial exit; 𝐺𝑒𝑛𝑒𝑟𝑎𝑙𝑖𝑠𝑡𝑖 is a dummy with a value of 1 if a manager is a generalist; and
𝑋𝑐 contains the vector of control variables. Because we rely on data generated by a non-
27
experimental process, the estimates are potentially affected by endogeneity issues. Since we are
particularly concerned about managers selecting into becoming a generalist, we address this
issue through our empirical strategy.
Empirical Strategy: Propensity Score Matching
As generalists are self-selected, generalist selection issues are important to consider carefully in
our context. Specifically, individual characteristics that are correlated with the treatment (i.e.,
generalist) and the outcome of interest (i.e., entrepreneurial performance and entrepreneurial
exit) may bias our findings. For example, individuals with higher academic ability may be more
likely to be generalists, and subsequently more likely to perform better in entrepreneurship. Such
issues would bias the coefficient of generalist in entrepreneurial performance upwards, and bias
the causal effect of generalists on entrepreneurial performance. Similarly, other individual
characteristics such as gender and family wealth may be correlated with both the choice of
becoming a generalist, and simultaneously entrepreneurial performance and exit.
Our empirical strategy aims to mitigate such endogeneity concerns. Firstly, to eliminate
non-comparable treatment and control group observations due to selection, we use the propensity
score matching method. The propensity score matching method mitigates endogeneity by
creating a matched sample of treatment and control observations that are similar in ex-ante
observable characteristics (Rosenbaum and Rubin, 1983). Specifically, we used the variables of
bachelor’s prestige deciles, bachelor’s prestige squared, average family wealth, liberal college
dummy, financial certificate, related bachelor’s major, gender, missing data dummies for each
variable (dummies takes the value of 1 when data was imputed because of missing data and 0
otherwise), and interaction terms as the observable ex-ante characteristics that may plausibly
28
affect individual’s decision to obtain an MBA education. Through propensity matching we
created a matched treatment and control group that are similar in these ex-ante characteristics,
resolving the problem of non-comparable treatment and control group observations.
We estimated the propensity score by a probit of the individual manager’s decision to
become a generalist, which in our context is an individual manager’s decision to obtain an MBA
degree, on observable ex-ante characteristics. We use fitted values from the probit model as
estimates of the propensity score:
𝑃𝑟(𝐺𝑒𝑛𝑒𝑟𝑎𝑙𝑖𝑠𝑡𝑖𝑡 = 1|𝑋𝑖𝑡−1),
where 𝑋𝑖𝑡−1 includes all the ex-ante observable characteristics of individuals that plausibly have
an effect on the treatment of being a generalist, or in our context the decision to obtain an MBA
degree. Following De Figueiredo and Rawley (2011) and De Figueiredo, Meyer-Doyle, and
Rawley (2013), we obtained our matched sample by matching each treatment observation to a
single control group observation without replacement, with a maximum of one match per
manager.3 We then trimmed extreme values and observations off the common support of the
propensity score distribution to obtain our matched sample.
In addition to propensity score matching, we included manager-level, fund-level, and
firm-level controls to further reduce potential biases. We included controls that are included in
the propensity score matching estimation in our analyses for robustness (De Figueiredo and
Rawley, 2011). After propensity score matching and including detailed controls, we are able to
examine the effect of generalists on entrepreneurship outcomes by comparing treatment group
3 The advantage of one-to-one matching is that we do not have to reweight our sample by the inverse probability of
selection when specifying our model of ex post behavior. The disadvantage of one-to-one matching is that the
matched sample generated by propensity score matching is not identical across runs, since the program randomly
selects which extra control group observations to drop. We verify that our results are not sensitive to randomly
dropping different control group observations from the final match.
29
managers (i.e., hedge fund managers with MBA degrees) with matched counterfactual control
group managers (i.e., hedge fund managers without MBA degrees) that have similar observable
characteristics.
While detailed controls and matching address the most pressing endogeneity concerns,
the cross-sectional structure of the data does not allow us to control directly for a manager’s
unobservable characteristics. In the absence of omitted variable bias from unobservable
differences between individuals with and without an MBA degree, we can interpret the matched
sample correlation between generalists and entrepreneurship outcomes as a causal relationship.
However, if unobservable individual characteristics are uncorrelated with observable individual
characteristics but are correlated with the manager’s decision to select into becoming a
generalist, our results will be biased. We must, therefore, interpret the results cautiously. We
address these concerns in more detail in the alternative explanations and appendix section of the
paper.
RESULTS
We first present descriptive statistics and correlations for the main variables in Table 1 and Table
2.
[Insert Table 1 Here]
[Insert Table 2 Here]
Next, before reporting our results for the main hypotheses, we show the propensity score
matching results in Table 3 and Figure 1, and verify generalists’ positive effect in entrepreneurial
entry in the hedge fund industry in Table 4. Finally, in Table 5 and 6, we show our results for the
main hypotheses: generalist effect in entrepreneurial performance and entrepreneurial exit.
30
Propensity Score Matching
To implement the propensity score matching method, we estimate a probit of the individual
hedge fund manager to obtain MBA education and use the fitted values as the estimated
propensity score. Table 3 column 2 shows the probit regression model. Figure 1 depicts why our
propensity score matching method helps resolve endogeneity issues. Panel 1a shows that the
distributions of the propensity scores are different between the treatment and control groups
before matching. This is statistically shown in Table 3 column 1. Before matching, many of
covariates are statistically different between the treatment and control groups (e.g., bachelor’s
wealth), suggesting that the treatment and control groups are dissimilar in terms of the ex-ante
observed characteristics. Furthermore, the F-test for the joint difference in means between the
treatment and control groups before matching (Table 3 column 1) is statistically significant at the
one percent level.
[Insert Figure 1 Here]
[Insert Table 3 Here]
After matching, however, the difference between the treatment and control groups
diminishes (Table 3 column 3). Panel 1b of Figure 1 shows that the distributions of the
propensity scores have a tighter fit between the two groups after matching. Table 3 column 3
shows that the difference between the treatment group and control group decreases significantly,
where none of the variables have a statistically significant difference between the treatment and
control groups. The F-test results also show that the joint difference between the two groups
decreases significantly from 2.73 to 1.33, suggesting that the propensity score matching
approach generates a treatment and matched control group of hedge fund managers that are
similar in ex ante observable characteristics. We used this matched sample for our analyses.
31
Generalist Effect on Entrepreneurial Entry
Before showing our results for the two main hypotheses, we first confirm past research on how
generalists are more likely to enter into entrepreneurship in Table 4. Columns 1 through 3 show
results from a linear probability model (an OLS regression when the outcome is binary) on the
matched sample. Column 4 shows probit regression results on the matched sample and column 5
shows linear probability model results on the unmatched sample as a robustness check. Column
3 shows that the coefficient estimate for generalists is 0.063, which is significant at the five
percent level. Given the base entrepreneurial entry rate of 23.63 percent, being a generalist
increases the rate of entering into entrepreneurship to 29.98 percent, which is a 27 percent
increase controlling for all other variables. One advantage of using linear probability model
(LPM) estimates is that the coefficients are unbiased and directly interpretable. However,
because LPMs may generate predictions outside the zero to one interval, we ensured that our
results were robust by re-running the analysis using the probit (see Hernandez and Shaver 2017
for the use of LPMs and probit regressions for comparison). Column 4 reports the marginal
effects from the probit, and the results are similar to that of the LPM: a significant positive effect
of generalists on entrepreneurial entry. Finally, column 5 reports the LPM regression on the
unmatched sample. Comparing the matched sample and the unmatched sample results shows that
selection effect bias the unmatched sample results downward. Taken together, the results confirm
past research that the generalist effect on entrepreneurial entry is positive and both economically
and statistically significant.
[Insert Table 4 Here]
32
Generalist Effect on Entrepreneurial Performance
To test our first main hypothesis of a generalist effect on entrepreneurial performance, we merge
the matched sample of manager-level data with monthly hedge fund returns data. Table 5 shows
our main LPM regression results: regression of entrepreneurial performance (measured by excess
returns) on generalists (MBA degree). In column 1, we included all control variables, year
dummies, firm region dummies, fund strategy dummies, and missing data dummies, excluding
the education related variables. In column 2, we included all controls including the education
related variables except for the main explanatory variable, generalist (MBA education). PhD
education as well as prior employment in one of the big 5 firms, past portfolio manager, and firm
age is shown to have a significantly negative effect on entrepreneurial performance. Risk,
international experience, age, and firm age are shown to have a positive relationship with
entrepreneurial performance.
[Insert Table 5 Here]
We finally include the main explanatory variable in column 3, which shows that the
coefficient estimate for generalist is 0.426, which is significant at the one percent level. Thus,
generalists have 42.6 basis points per month higher excess return controlling for risk. With an
excess return of 26 basis points per month for non-generalists and an overall industry average of
33 basis points per month (Sadka, 2010), being a generalist more than doubles performance.
Taken together, the results suggest that the generalist effect on entrepreneurial performance is
positive, and both economically and statistically significant.
Here, we point out two additional interesting findings. First, individuals who have a PhD
degree have a significant negative effect on their entrepreneurial performance. Our interviews
corroborated that PhD graduates would generally be those with the most specialized skill sets,
33
due to their focus on a certain domain. The fact that PhD graduates, as specialists, had negative
entrepreneurial performance is in line with our theory. Second, the dummy variables for fund
trading strategies were not statistically significant, suggesting that performance was not driven
by fund strategy. This result addresses some of the concerns raised by interviewees that
entrepreneurship outcomes will vary by the trading strategy of the funds.
Generalist Effect on Entrepreneurial Exit
Table 6 shows our second set of main regression results: regression of entrepreneurial exit on
generalists (MBA degree). Column 1 and column 2 show LPM results on control variables and
the full set of dummies. We add our explanatory variable in column 3, which shows that the
LPM coefficient estimate for generalist is 0.010, which is significant at the one percent level.
The interpretation is that controlling for all other variables including, generalists have 1 percent
higher likelihood of exiting out of their entrepreneurial ventures compared to non-generalists.
Compared to the non-generalist base exit rate of 0.92 percent, the generalist exit rate of 1.92
percent represents a twofold increase in the probability of exiting out of a new venture. As a
sensitivity analysis, we ran a probit regression for the binary dependent variable of
entrepreneurial exit and show marginal effects in column 4. The results from the probit
regression support our LPM findings that generalists have a higher probability of exiting out of
their entrepreneurial ventures.
[Insert Table 6 Here]
Interestingly, Table 6 also shows that better performance is negatively correlated with
entrepreneurial exit, which supports prior entrepreneurship literature that well-performing
entrepreneurs are less likely to exit out of their ventures. Indeed, our theory is consistent with a
34
negative association between entrepreneurial performance and entrepreneurial exit. However we
suggest that generalist skills influence entrepreneurial exit independent of entrepreneurial
performance. Taken together, the results suggest that the generalist effect on entrepreneurial exit
is positive, and both economically and statistically significant.
In sum, our empirical results support our two main hypotheses on generalists’
entrepreneurial performance and exit, as well as confirm past research on generalists’
entrepreneurial entry rates. Generalists are more likely to have higher entrepreneurial entry and
performance, and conditional on performance, are twice as likely to exit out of their
entrepreneurial ventures. Our findings are summarized in Figure 2, where each panel shows how
the entrepreneurial outcomes differ for individuals with generalist skills.
One of the control variables, past chief executive experience, has a noteworthy influence
on entrepreneurial outcomes. Our results show that hedge fund managers with past chief
executive experience have higher entrepreneurial entry but lower entrepreneurial exit. We
suggest that past chief executives have higher entrepreneurial entry due to their leadership and
managerial experience, which make them fit for entrepreneurship. However, as chief executives
are not inherently generalists (Custodio, Ferreira, and Matos, 2013), they do not show the same
mobility pattern as generalists. Past chief executive experience allows individuals to gain
leadership skills that help run a business but also a long-term perspective that may increase their
time commitment to an entrepreneurial venture. Thus, in the context of the hedge fund industry,
past chief executives show a different pattern from generalists in entrepreneurship. Or put
another way, the effect of being a generalist that we capture is net of the skills associated with
being a CEO.
35
ROBUSTNESS CHECKS AND ALTERNATIVE EXPLANATIONS
MBA as a Measure of Being a Generalist (see Appendix)
While our study uses MBA education to measure generalists in our context, there lies the
concern that other characteristics of MBA graduates may generate entrepreneurial outcomes. In
this case, we need to exercise caution in interpreting our results as a generalist effect. We
identify, through our interviews with hedge fund managers and past studies (e.g. Cai, Gantchev,
Sevilir, 2016), several potential alternative mechanisms by which MBA education can influence
entrepreneurship outcomes: (1) social capital, (2) signaling, (3) inherent ability, (4) family
wealth, (5) past employment, and (6) risk-taking. We address each of these alternative
mechanisms in the appendix using a set of robustness checks and interview analysis.
The Mechanism of Entrepreneurial Exit
We show that generalists have higher entrepreneurial performance and yet higher
entrepreneurial exit. In this section, we conduct further analyses in order to validate the
underlying mechanism and to address alternative explanations. Specifically, we provide evidence
on the mechanism as to why generalists exit despite entrepreneurial advantages, by examining
exit destinations of entrepreneurs and the subsample of low performance exits.
We theorize that the underlying mechanism as to why generalists exit despite
entrepreneurial advantages is that generalists have higher-value and more diverse outside options
in the labor market compared to non-generalists. In order to confirm this mechanism, we look
into whether generalist entrepreneurs differ from non-generalists entrepreneurs in their
destinations after they exit out of their ventures.
Although it is difficult to find objective measures for higher-value jobs or diverse
employment options, we utilize four different measures in order to gage the differences in post-
36
exit destinations of generalist and non-generalist entrepreneurs: (1) exit to subsequent
entrepreneurship state by starting another fund and becoming serial entrepreneurs, (2) exit to top-
ten prestigious asset management firms, (3) exit to chief executive positions, and (4) exit to other
industries.
First, we examine whether generalists exit to a subsequent entrepreneurship state to
become serial entrepreneurs or return to paid employment upon their entrepreneurial exit.
Column 1 of Table 7 shows that generalists and non-generalists show no significant difference in
their exit destinations, in terms of entrepreneurship or paid-employment. This shows that
generalists are not more or less likely to become a serial entrepreneur or return to paid
employment, upon their exit from entrepreneurship. This argues against the possible alternative
underlying mechanism that generalists are more likely to exit from entrepreneurship because
they are more likely to be serial entrepreneurs.
Next, we proxy high-value post-exit destinations of entrepreneurs based on employer
prestige and job title. Upon cross-sectional examination, we find that 20 percent of generalist
entrepreneurs who exited out of entrepreneurship transitioned to the top ten largest firms in the
asset management industry. In contrast, only 9.9 percent of non-generalist entrepreneurs returned
to the same top ten firms after exiting their ventures. Column 2 of Table 7 shows empirical
support that generalists are more likely to exit out to the ten most prestigious firms in the
industry compared to non-generalists. Furthermore, on examining the post-exit job titles of
entrepreneurs (Table 7 column 3), we find that generalists were more likely to exit out of
entrepreneurship to chief executive positions, compared to non-generalists. Lastly, we proxy
more diverse post-exit destinations of entrepreneurs based on the industry of the job. Column 4
of Table 7 shows that generalist entrepreneurs were more likely to exit out to positions in
37
industries other than the asset management industry. These results support the rationale that
generalists are more likely to exit out of their entrepreneurial ventures, conditional on
performance, because of higher-valued and more diverse outside opportunities. Figure 3 shows
that summary of predicted post-exit destination differences between generalists and non-
generalists.
[Insert Table 7 Here]
[Insert Figure 3 Here]
In addition to examining exit destinations, we consider the possibility that the high
entrepreneurial exit of generalists is driven by generalists achieving more successful exits, such
as the sales of a venture. In such cases, higher entrepreneurial exit of generalists would not be
driven by generalists’ mobility but by their ability to successfully exit their ventures, which may
not be directly explained by performance (though typically will have a high correlation with
performance) (Freeman, Carroll, and Hannan, 1983). Thus, we run a supplementary analysis for
a subsample of ventures with lower than median performance. If generalists had high
entrepreneurial exit due to their ability to achieve successful exits, we would expect to find null
results for generalists’ exit from lower performing ventures.
As one can see in Table 8, we find that generalists are also more likely to exit out of their
ventures, independent of performance in the sub-analysis of firms with lower than median
performance. The results buttress the idea that high rates of entrepreneurial exit by generalists
are not only being driven by successful exits. Our interviewees verified this when asked why
hedge fund entrepreneurs exit out of their ventures, as most of the interviewees answered that the
majority of entrepreneurial exits were due to the entrepreneur deciding the venture was “not as
profitable as expected” or “worth doing”.
38
[Insert Table 8 Here]
DISCUSSION
Entrepreneurship research has long suggested that entrepreneurs benefit from a breadth of skills
(e.g., Lazear, 2004). At the same time, a line of labor market research has verified increased
labor market mobility for generalists (e.g., Becker, 1975). Hence past studies imply a paradox for
generalists in entrepreneurship: generalists are more likely to become successful entrepreneurs,
but they are less likely to stay committed to their successful entrepreneurial venture.
We theorized and found evidence that the generalist paradox can be reconciled by
synthesizing and extending the two theories of generalists as entrepreneurs and labor market
participants. Building on research on generalists as entrepreneurs, we verified that generalists
indeed become more successful entrepreneurs. We integrated the labor market literature to
theorize that entrepreneurs make their entrepreneurial commitment or exit decisions not only
based on entrepreneurial performance but also their alternative opportunities in the labor market.
Thus, we found that generalist entrepreneurs are less likely to stay committed to their
entrepreneurial ventures conditional on performance, due to their abundant alternative labor
market options. Taken together, our results suggest that generalists’ advantages in
entrepreneurship and their extensive alternative labor market opportunities make it more likely
that generalists will treat entrepreneurship as one of many employment states rather than a final
destination.
This study makes a number of contributions, first, by extending research on generalists in
entrepreneurship, based on the well-established notion that generalists are more likely to become
successful entrepreneurs (e.g., Lazear, 2004). Prior research has shown that generalists are more
39
likely to succeed as entrepreneurs by examining success using both entrepreneurial performance
(Åstebro, Chen, and Thompson, 2011) and entrepreneurial commitment or survival (Lafontaine
and Shaw, 2016). We argue that while entrepreneurial performance and entrepreneurial
commitment are positively associated, they are not interchangeable. By highlighting that
entrepreneurial commitment is determined by entrepreneurial performance and the relative value
of alternative labor market opportunities, our study offers an explanation of why generalists are
likely to be successful yet less committed as entrepreneurs.
We also contribute to the labor market literature by integrating the concept of generalists
in entrepreneurship, and verifying the underlying mechanism of why generalists show certain
mobility patterns in entrepreneurship. The labor market literature has devoted relatively little
attention to labor market transitions between entrepreneurship and paid-employment and the
implications of labor market mobility on entrepreneurial outcomes. Our study joins a rising tide
of scholars in claiming that unless labor market dynamics are considered in entrepreneurial
transitions, it is difficult to fully understand different patterns of entrepreneurial outcomes. We
extend the focus by explaining how the labor market mobility of generalists can affect their
entrepreneurial decisions. We explain why generalists are more likely to regard entrepreneurship
as a temporary state (versus a destination) by verifying the underlying mechanism that
generalists have higher valued and more extensive alternative labor market options compared to
non-generalists.
More generally, we extend the emerging stream of research on the importance of
understanding entrepreneurship in the overall context of the labor market (Burton, Sørensen, and
Dobrev, 2016). An increasing number of scholars have suggested a limitation in prior
entrepreneurship research that separates entrepreneurs from the rest of the participants in the
40
labor market, and separates the spells of one’s entrepreneurship experience from the spells of
employment experience. Just as movement from one job to another in paid employment is driven
by the distribution of available opportunities, individuals decide to remain in or exit out of
entrepreneurship based on the relative attractiveness of the set of available mobility opportunities
(e.g., Sørensen and Sharkey, 2014). We contribute to this stream of entrepreneurship literature by
emphasizing that separately analyzing entrepreneurial performance and exit is particularly
important for generalists in entrepreneurship, as they face opportunities from entrepreneurship
and the labor market.
We also empirically contribute to the entrepreneurship literature by providing evidence
on all three stages of entrepreneurship (entry, performance, and exit). While past studies have
each verified part of the three stages of entrepreneurship, we separately test each of the stages
using the rich data from the hedge fund industry. By examining the entirety of the three stages of
entrepreneurship, this paper provides a more comprehensive understanding of entrepreneurial
behavior.
Finally, our theory suggests a need to redefine what it means to be a successful
entrepreneur, which is a label that has been reserved for entrepreneurs who are both well-
performing and committed to entrepreneurship. This makes us question: can generalists - with
higher entrepreneurial performance but also higher entrepreneurial exit - be considered
successful entrepreneurs? If so, we face a task of redefining success for an entrepreneur to
include those that are successful in entrepreneurship as a state rather than a final destination. We
hope this work sparks a discussion on what defines a successful entrepreneur in a way that more
holistically recognizes the role of generalist entrepreneurs.
41
Although our study provides insight into how generalists are more likely to be successful
yet less committed entrepreneurs, future research should unpack the underlying mechanisms with
greater precision. A potential avenue of inquiry could examine whether the value of
entrepreneurial experience in the labor market differs for those who are generalists. If generalists
are more likely to gain labor market capital through entrepreneurship, this could further explain
the entrepreneurial transitions of generalists.
Our findings are based on a proxy variable of having an MBA degree to measure the
extent to which a hedge fund manager is a generalist. Although measuring generalist skills with
an MBA degree has some limitations, in the context of the hedge fund industry this was the most
appropriate measure that could be obtained. Particularly, with our empirical strategy of
propensity matching and extensive controls, we are capturing characteristics of hedge fund
managers that exclude general human capital (bachelor’s degree prestige), leadership experience
(past CEO experience), trading experience (past portfolio manager experience), etc. Thus, ceteris
paribus, we are capturing the diversity of skills, or diversity of business skills, with our measure
of the MBA degree. We have confidence in this variable as our robustness checks and interviews
further verified the appropriateness of our measure (Appendix). That said, in future studies a
more direct measure of capturing the variety of skills between business functions such as
management, operations, finance, and so forth would be useful, particularly in contexts other
than the hedge fund industry.
42
APPENDIX: MBA as a Measure of Being a Generalist
In this appendix we address potential alternative explanations of how an MBA education may
influence entrepreneurial outcomes. We identify six major alternative explanations by which an
MBA education may affect entrepreneurship: (1) social capital, (2) signaling, (3) inherent ability,
(4) family wealth, (5) prior employment, and (6) risk-taking. We address each of these
alternative mechanisms with additional empirical analyses. Lastly, we analyze our interviews
with hedge fund managers and entrepreneurs to validate our measure of MBA graduates as
generalists in the hedge fund industry.
Firstly, MBA education, similar to other higher education institutions, offers valuable
social capital and positive signaling effects (Rider, 2014; Cai, Gantchev, and Sevilir, 2016). Both
social capital and positive signaling facilitate potential hedge fund entrepreneurs to find
investors, to recruit higher quality employees, and to secure better employers as exit options,
leading to the same results of our paper (Aldrich and Zimmer, 1986; Shane and Stuart, 2002;
Adler and Kwon, 2002). In order to separate out these possible mechanisms, we utilize the fact
that the prestige of the institution one receives an MBA degree is positively correlated with the
social capital and positive signaling one gains, but not correlated with the level of generalist
skills.
Under the alternative explanation that MBA social capital or positive signaling is driving
the results of higher entrepreneurial performance and exit, we would see entrepreneurial
outcomes to be higher for individuals graduating from prestigious MBA programs. To test this
alternative explanation, we control for the prestige of MBA education on entrepreneurship.
Specifically, we collect data on business school rankings from Forbes 2016, which is one of the
most reputable rankings of business schools in the world. We generate an MBA prestige variable
for each manager with an MBA degree to account for the ranking of the institution she/he
43
obtained her/his degree. Similar to the measure for the bachelor’s degree prestige, we take the
natural log of MBA prestige variable, such that a lower value indicates a degree from a more
prestigious institution. To generate discrete variables, we further generate group variables Top
Ten MBA, Top Twenty MBA, and Top Fifty MBA that takes a value of 1 if the manager
obtained their MBA degree from an institution that falls within the ranking of the group variable
and 0 otherwise. The results are consistent independent of the specification of prestige used.
Column 1 of Table 9 shows that MBA prestige has null effects on entrepreneurial
performance. Column 2 of Table 9 shows that entrepreneurial exit is also unaffected by MBA
prestige. This suggests that social capital and positive signaling effects of MBA education do not
explain the MBA effect on entrepreneurial performance and exit. These findings were supported
by interviewees who stated that the hedge fund industry is an industry in which pedigree or
education signaling has null effects on entrepreneurial performance and exit. Interviewees
emphasized that investors focused more on potential hedge fund manager’s observable trading
ability rather than their pedigree in choosing where to invest their assets. This confirmed our
finding that social capital and positive signaling have no effects on entrepreneurial performance
and entrepreneurial exit.
[Insert Table 9 Here]
Next, we test for the alternative explanation that MBA graduates are inherently different
from those who do not select into MBA education. In particular, we consider two main
characteristics of concern that may be driving the results: inherent ability and family wealth. If
individuals who select into MBA education have higher inherent ability than those who do not,
this inherent ability will drive entrepreneurship outcomes rather than the treatment effect of
generalist skills (MBA education). Similarly, if individuals from wealthy families are more likely
44
to select into MBA education, wealth effects will bias entrepreneurship outcomes. Our empirical
strategy of propensity score matching addresses such selection issue. That said, here we review
the two main characteristics of inherent ability and wealth in further detail.
First, when we examine how inherent ability influences selection into MBA education,
we find that individuals with less prestigious bachelor’s degree are more likely to select into
MBA education. This runs counter to the concern that MBA education may be capturing higher
inherent ability rather than generalist skills. Moreover, our matched sample analysis mitigates
this selection effect, as the treatment group of MBA graduates and control group of non-MBA
graduates is similar in inherent ability (bachelor’s degree prestige) after matching. Second, we
find that wealth has no significant effect in MBA education selection, thus suggesting that
(within the asset management industry) individuals from wealthy families are not more likely to
select into MBA education. This alleviates our concern that the MBA education variable is
capturing family wealth differences instead of generalist skills.
We also consider whether MBA graduates have significant differences from non-MBA
graduates in terms of the firms they are part of before entrepreneurship. Numerous scholars have
examined how past employment shapes entrepreneurship outcomes and have suggested that
individuals who previously worked for large, prominent, or entrepreneurial firms will start more
ventures and perform better than their competitors (Phillips, 2002; Burton, Sørensen, and
Beckman, 2002; Gompers, Lerner, and Scharfstein, 2005; Klepper and Sleeper, 2005; Chatterji,
2008). In the case that MBA graduates are more likely to have worked at larger and more
prominent firms, our results for entrepreneurial entry and performance will be biased upward.
To address this, we compared the previous employer of both MBA graduates and non-
MBA graduates to find any apparent differences. In particular, we generated a dummy variable
45
which took a value of 1 if the hedge fund manager previously worked at one of the top 5 most
prestigious asset management firms, including Goldman Sachs, Morgan Stanley, and J.P.
Morgan. By including this variable, we are able to control for any large (prestigious) parent firm
effects that are correlated with being a generalist (having an MBA degree). Inconsistent with the
alternative explanation that our MBA measure of being a generalist is instead picking up
previous employer size and prestige, the dummy variable is insignificant for entrepreneurial
entry and exit. We find a significant negative effect for prior employees of the big five
prestigious firms in the industry on entrepreneurial performance. Past work has suggested that
employees of larger organizations tend to be specialists due to narrowly defined jobs, while at
smaller firms, employees are more likely to be generalists due to more flexible and widely
defined jobs (Baron, Davis-Blake, and Bielby, 1986). In line with this, Sørensen and Phillips
(2011) have shown that employees from larger organizations will have a disadvantage in
entrepreneurial income, as they are less likely to have developed a broader skill set. Thus our
results provide further support for this stream of study and our argument that generalists are
more likely to have higher entrepreneurial performance.
Lastly, we address the concern that different risk-taking behavior of MBA graduates may
influence entrepreneurial outcomes. In the case that MBA graduates are more likely to take high
risks, high risk-taking may lead to higher entrepreneurial entry. We run supplementary analyses
to compare the risk taken by MBA graduates (generalists) and non-MBA graduates in
entrepreneurship. We find that MBA graduates are less likely to take risk, measured by the
standard seven factor of hedge funds, in their entrepreneurial ventures. This result rules out the
concern that the MBA degree variable is capturing the risk-taking behavior of hedge fund
managers, rather than their breadth of skills.
46
In addition to these robustness checks, we asked interviewees to rank how helpful an
MBA education would be for employee hedge fund managers or hedge fund entrepreneurs, nine
out of thirteen interviewees stated that an MBA education would be more helpful for hedge fund
entrepreneurs compared to hedge fund managers as employees. The nine interviewees, including
those with MBA degrees, PhD degrees, financial certificates, or none of the above, responded
with consistent answers that MBA education is more important to hedge fund entrepreneurs. For
example:
“I think the hardest part (of being a hedge fund entrepreneur) was that you have to
find investors and also work on your (trading) strategy at the same time. So you have
multiple hats on. (...) I think it’s hard for one person to have all of these skill sets -
you know being good at operations, being good at management, and generating the
alpha.”
“Basically you’re doing kind of two things at once. You’re running a business so you
have to worry about anything an entrepreneur has to worry about. And you’ve got to
keep following markets and finding your alpha in a highly competitive world.”
“As a hedge fund manager, you are very specialized. There’s not that much you need
to worry about (...) At the end of the day you need to decide what are the best
investments and when you have to exit those investments. (...) But when starting a
firm, those are people that are not only good investors, but also good businessmen
and good entrepreneurs. That is what’s important.”
47
The rationale for their answers was also consistent: generalist skills associated with an
MBA meant the entrepreneur was better able to span several roles important to entrepreneurial
success. While a hedge fund manager employed in an established firm has a very focused and
specialized role, a hedge fund entrepreneur is expected to fulfill the roles of diverse expertise and
dimensions. Interviewees thus accounted for the breadth of roles and skills needed to be an
entrepreneur as the main difference between entrepreneurship and paid employment, and stated
this as the reason why an MBA education would be more useful for hedge fund entrepreneurs.
48
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Zuckerman, E. W., T. Kim, K. Ukanwa, and J. von Rittman
2003 “Robust identities or nonentities? Typecasting in the feature-film labor market.” American
Journal of Sociology, 108: 1018–1074.
57
Variables N Mean S.D. Min. Max.
Education Variables
Generalist (MBA Education) 1770 0.374 0.430 0 1
Master's Education 1770 0.236 0.357 0 1
PhD Education 1770 0.057 0.206 0 1
JD Education 1770 0.040 0.180 0 1
Financial Certificate 1770 0.060 0.210 0 1
Bachelor's Prestige (1 is most prestigious) 1770 301 272 1 751
MBA Prestige (1 is most prestigious) 1770 557 316 1 801
Liberal Arts College 1770 0.080 0.272 0 1
Related Bachelor's Major 1770 0.630 0.370 0 1
Related Master's Major 1770 0.640 0.180 0 1
Entrepreneurship Variables
Entrepreneurial Entry 1770 0.247 0.432 0 1
Entrepreneurial Performance (Excess Returns) 12272 0.257 4.085 -13.17 14.42
Entrepreneurial Exit 12272 0.009 0.096 0 1
Employment Variables
Prior Work Experience (years) 1770 12.18 8.00 0.25 89.55
Number of Prior Firms 1770 2.85 2.04 1 22
Prior Entrepreneurship (number) 1770 0.122 0.317 0 5
Prior Employment in Big 5 Firms 1770 0.087 0.280 0 1
Prior Employment in Big 10 Firms 1770 0.058 0.230 0 1
Pre-MBA Work Experience (years) 1770 3.625 1.501 0 20
Other Industry Experience 1770 0.360 0.375 0 1
International Experience 1770 0.162 0.216 0 1
Past Chief Executive Officer 1770 0.180 0.320 0 1
Past Portfolio Manager 1770 0.163 0.304 0 1
Average Job Tenure (years) 1770 5.277 3.649 0.25 40.61
Demoraphic Variables
Age 1770 35.49 5.78 16 70
Female 1770 0.084 0.277 0 1
Average Family Wealth 1770 0.637 0.061 0.114 0.813
Fund Variables
Risk (Standard Deviation) 12272 3.714 3.011 0.39 17.04
Ln(Fund Assets Under Management) 12272 16.40 2.59 0 20.97
Firm Variables
Average Performance 12272 0.488 1.491 -13.17 14.42
Diversified Firm 12272 0.691 0.462 0 1
Ln(Firm Assets Under Management) 12272 16.69 4.28 0 21.38
Firm Age 12272 46.85 35.85 0 181
Table 1. Descriptive Statistics
58
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
(19)
(20)
(21)
(22)
(23)
(24)
(25)
(1)
Entr
epre
neu
rial
Per
form
ance
1.0
00
(2)
Entr
epre
neu
rial
Exit
-0.0
25
1.0
00
(3)
Gen
era
list
(M
BA
Educa
tion)
0.0
10
0.0
12
1.0
00
(4)
Mas
ter'
s E
ducat
ion
0.0
07
0.0
15
-0.0
40
1.0
00
(5)
PhD
Educat
ion
-0.0
38
-0.0
01
-0.0
27
0.1
66
1.0
00
(6)
JD E
duca
tion
0.0
18
-0.0
01
0.0
03
-0.0
94
0.2
99
1.0
00
(7)
Ln(B
achel
or'
s P
rest
ige)
-0.0
14
0.0
27
-0.1
44
0.0
31
-0.0
47
-0.0
98
1.0
00
(8)
Rel
ated
Bach
elor'
s M
ajor
-0.0
27
-0.0
22
0.0
31
-0.4
48
-0.2
95
-0.1
83
0.2
09
1.0
00
(9)
Rel
ated
Mas
ter'
s M
ajor
-0.0
55
0.0
08
0.0
55
0.0
00
0.1
59
0.0
27
0.1
59
0.0
84
1.0
00
(10)
Fin
anci
al C
erti
fica
te0.0
01
0.0
06
-0.0
17
-0.0
80
-0.0
48
-0.0
70
0.2
09
0.0
82
0.0
24
1.0
00
(11)
Lib
eral
Coll
ege
Dum
my
-0.0
01
-0.0
12
0.2
42
-0.0
08
-0.0
86
-0.0
78
-0.2
82
0.1
14
0.0
88
-0.0
66
1.0
00
(12)
Ln(P
rior
Work
Experi
ence)
-0.0
06
-0.0
10
-0.2
01
-0.0
93
-0.1
56
-0.0
59
-0.0
41
0.0
74
-0.1
29
0.0
22
-0.4
08
1.0
00
(13)
Pri
or
Em
plo
ym
ent
in B
ig 5
-0.0
13
0.0
11
0.1
11
0.2
95
0.4
71
0.0
58
-0.1
15
-0.2
68
0.2
31
-0.0
44
-0.0
77
-0.2
59
1.0
00
(14)
Pas
t C
hie
f E
xec
uti
ve
Off
icer
0.0
21
-0.0
23
-0.2
20
0.1
36
0.2
63
0.2
73
0.1
46
-0.1
60
0.0
35
0.0
79
-0.1
61
0.1
25
0.0
82
1.0
00
(15)
Pas
t P
ort
foli
o M
anager
-0.0
05
0.0
25
-0.0
51
-0.0
28
-0.0
87
-0.1
23
0.1
24
-0.0
27
0.0
66
-0.0
47
-0.1
17
0.1
08
-0.0
88
-0.1
58
1.0
00
(16)
Num
ber
of
Pri
or
Fir
ms
-0.0
23
0.0
05
-0.0
67
-0.0
68
-0.0
65
0.0
11
-0.0
61
-0.0
09
0.0
52
-0.0
58
-0.1
08
0.4
23
-0.0
35
-0.2
08
0.1
01
1.0
00
(17)
Pri
or
Entr
epre
neurs
hip
-0.0
14
-0.0
21
-0.2
72
-0.1
77
-0.0
86
0.0
06
-0.1
68
0.1
81
0.0
03
-0.0
48
-0.1
74
0.4
19
-0.0
79
-0.0
66
-0.1
21
0.4
91
1.0
00
(18)
Oth
er I
ndust
ry E
xperi
ence
0.0
02
-0.0
18
0.0
44
-0.0
02
-0.1
51
0.1
76
-0.0
92
-0.1
71
-0.0
99
-0.0
57
-0.3
53
0.4
61
-0.1
28
-0.0
28
0.0
21
0.4
45
0.3
96
1.0
00
(19)
Inte
rnat
ional
Experi
ence
0.0
14
0.0
03
-0.0
77
0.0
28
-0.1
13
-0.0
22
0.2
06
0.1
07
-0.0
98
0.0
88
-0.1
45
0.0
24
-0.0
47
-0.0
12
0.1
86
-0.0
22
0.0
55
0.0
35
1.0
00
(20)
Pre
-MB
A W
ork
Experi
ence
0.0
07
0.0
16
-0.2
36
0.1
12
-0.0
96
-0.1
80
0.2
01
-0.0
68
0.0
00
-0.0
01
0.0
26
-0.1
30
-0.1
29
-0.1
70
0.1
01
-0.1
46
0.0
21
-0.0
53
0.0
52
1.0
00
(21)
Ln(A
vera
ge J
ob T
enure
)0.0
11
-0.0
11
-0.1
51
-0.1
16
-0.0
85
-0.0
39
-0.0
60
0.1
81
-0.1
49
0.0
63
-0.1
85
0.6
17
-0.2
38
0.1
77
-0.0
55
-0.2
17
0.0
73
0.0
28
-0.0
25
0.0
26
1.0
00
(22)
Aver
age
Fam
ily W
ealt
h0.0
04
-0.0
02
-0.0
96
0.0
51
0.3
45
0.4
39
-0.3
88
-0.3
75
0.0
64
-0.1
48
-0.0
24
0.0
55
0.2
08
0.1
69
-0.0
62
-0.0
06
-0.0
95
0.0
11
0.0
21
-0.2
02
0.0
36
1.0
00
(23)
Ln(A
ge)
0.0
41
0.0
18
0.2
52
0.0
58
0.0
09
0.2
00
0.1
00
-0.1
41
-0.0
10
0.0
91
0.0
20
0.0
14
0.0
94
0.2
57
0.0
91
-0.1
39
-0.5
55
-0.0
48
-0.0
66
-0.1
65
0.1
82
0.0
80
1.0
00
(24)
Fem
ale
0.0
13
0.0
04
0.0
20
0.2
48
-0.0
47
-0.0
67
0.1
89
-0.0
58
0.0
99
-0.0
59
-0.0
99
-0.2
60
0.1
98
-0.0
12
-0.0
29
-0.1
08
-0.0
95
-0.0
48
-0.0
21
0.1
71
-0.1
89
-0.1
56
0.0
24
1.0
00
(25)
Ln(M
BA
Pre
stig
e)-0
.012
-0.0
04
-0.8
85
0.0
27
-0.0
39
-0.0
40
0.3
12
0.0
32
-0.0
47
0.0
82
-0.3
13
0.2
21
-0.1
81
0.2
20
0.0
62
0.0
13
0.2
23
-0.0
72
0.0
96
0.2
64
0.1
70
-0.0
22
-0.2
74
-0.0
11
1.0
00
Tab
le 2
. C
orrel
ati
on
Tab
le (
N=
12,2
72)
59
T-test BEFORE matching Probit for p-score matching T-test AFTER matching
(1) (2) (3)
Bachelor's Prestige (Deciles) -3.04** 0.052 -1.56
(0.026)
Bachelor's Prestige^2 -0.33 -0.000** -0.90
(0.000)
Bachelor's Prestige * Liberal College -3.35*** -0.024 -1.35
(0.026)
Average Family Wealth (Deciles) 3.96*** -0.010 -0.01
-0.006
Liberal College Dummy 1.94+ -0.135+ -0.61
(0.813)
Female 0.44 0.035 0.68
(0.066)
Financial Certificate -1.77+ -0.063 -1.18
(0.592)
Bachelor's Prestige * Female -0.56 -0.000 -0.85
(0.000)
Related Bachelor's Major -1.45 -0.039 -1.21
(0.0329)
Missing Data Dummies Y* Y* Y
N 1770 1770 992
Pseudo R-sq 0.191
F-test on Joint Difference in Means 2.73** 1.33
Table 3. Propensity Score Matching Results: Matching Generalists (MBA) to Non-Generalists (Non-MBA)
The unit of analysis is manager-firm. T-statistics are reported on the difference in means between the "treated" group (generalist
managers or managers with MBA degrees) and the "control" group before and after matching. We matched 496 treated and control
dyads using propensity score matching through probit regression. Column (2) reports marginal effects of the probit regression. The joint
difference of means between the treatment and control groups becomes small and insignificant after matching.
*** p<0.001; ** p< 0.01; * p< 0.05; + p< 0.1
60
61
(1) LPM (2) LPM (3) LPM (4) Probit (5) Un-Matched LPM
Generalist (MBA Education) 0.063* 0.065* 0.051*
(0.029) (0.028) (0.025)
Master's Education (non-MBA) 0.042 0.059 0.058 0.049
(0.039) (0.040) (0.038) (0.030)
PhD Education -0.057 -0.049 -0.064 0.059
(0.075) (0.075) (0.084) (0.050)
JD Education 0.042 0.054 0.050 0.074
(0.068) (0.068) (0.065) (0.057)
Ln(Bachelor's Prestige) -0.020* -0.019* -0.019* -0.018*
(0.009) (0.009) (0.009) (0.007)
Related Bachelor's Major 0.032 0.035 0.034 -0.007
(0.035) (0.035) (0.034) (0.029)
Related Master's Major 0.021 0.027 0.025 -0.029
(0.073) (0.073) (0.070) (0.056)
Liberal College Dummy 0.019 0.022 0.023 0.017
(0.050) (0.050) (0.047) (0.042)
Financial Certificate 0.135* 0.121+ 0.123+ 0.106+ 0.151**
(0.062) (0.063) (0.063) (0.055) (0.048)
Ln(Prior Work Experience) 0.039 0.042 0.038 0.049 0.028
(0.054) (0.054) (0.054) (0.052) (0.045)
Prior Employment in Big 5 0.080 0.090 0.087 0.087+ 0.057
(0.055) (0.055) (0.056) (0.052) (0.045)
Past Chief Executive Officer 0.085+ 0.083+ 0.084+ 0.073+ 0.130***
(0.045) (0.045) (0.045) (0.041) (0.034)
Past Portfolio Manager 0.089* 0.085* 0.077+ 0.068+ 0.074*
(0.042) (0.042) (0.042) (0.038) (0.034)
Number of Prior Firms 0.011 0.011 0.011 0.008 0.009
(0.016) (0.016) (0.016) (0.014) (0.014)
Prior Entrepreneurship 0.182*** 0.180*** 0.187*** 0.151*** 0.169***
(0.045) (0.045) (0.045) (0.040) (0.033)
Other Industry Experience -0.048 -0.049 -0.052 -0.053 -0.060*
(0.036) (0.036) (0.036) (0.036) (0.029)
International Experience 0.031 0.043 0.053 0.047 0.013
(0.064) (0.065) (0.065) (0.058) (0.049)
Ln(Average Job Tenure) -0.004 -0.007 -0.001 -0.009 0.023
(0.056) (0.056) (0.056) (0.053) (0.047)
Average Family Wealth -0.000 -0.005 -0.006 -0.006 -0.006
(0.006) (0.006) (0.006) (0.006) (0.005)
Ln(Age) -0.033 -0.029 -0.051 -0.057 -0.098
(0.089) (0.090) (0.090) (0.090) (0.072)
Female -0.056 -0.053 -0.053 -0.059 -0.037
(0.049) (0.049) (0.049) (0.053) (0.037)
Pre-MBA Work Experience -0.0053 -0.005 -0.0035
(0.0073) (0.007) (0.0068)
Constant Y Y Y Y Y
Missing Data Dummies Y Y Y Y Y
N 992 992 992 992 1770
Adj. R-sq / Pseudo R-sq 0.048 0.049 0.053 0.072 0.050
*** p<0.001; ** p< 0.01; * p< 0.05; + p< 0.1
Dependent Variable: Entrepreneurial Entry
Table 4. Matched Sample Regression Predicting Entrepreneurial Entry
Marginal effects are reported for probit model.
62
(1) OLS (2) OLS (3) OLS
Generalist (MBA Education) 0.426**
(0.161)
Master's Education (non-MBA) 0.180 0.168
(0.202) (0.194)
PhD Education -1.318*** -1.428***
(0.291) (0.280)
JD Education 0.016 -0.052
(0.239) (0.253)
Ln(Bachelor's Prestige) -0.049 -0.025
(0.052) (0.052)
Related Bachelor's Major -0.073 -0.114
(0.223) (0.213)
Related Master's Major -0.201 -0.265
(0.509) (0.502)
Liberal College Dummy -0.080 -0.214
(0.252) (0.257)
Risk (Standard Deviation) 0.072+ 0.071+ 0.077*
(0.040) (0.037) (0.036)
Financial Certificate -0.016 0.112 0.176
(0.353) (0.335) (0.287)
Ln(Prior Work Experience) -0.114 -0.418 -0.586
(0.495) (0.459) (0.455)
Prior Employment in Big 5 -1.079*** -0.736* -0.855*
(0.250) (0.299) (0.333)
Past Chief Executive Officer -0.142 0.118 0.260
(0.217) (0.202) (0.189)
Past Portfolio Manager -0.340 -0.314 -0.168
(0.232) (0.222) (0.227)
Number of Prior Firms 0.026 0.136 0.190
(0.187) (0.176) (0.172)
Prior Entrepreneurship 0.010 0.005 0.046
(0.093) (0.095) (0.093)
Other Industry Experience -0.030 -0.198 -0.265
(0.196) (0.219) (0.222)
International Experience 0.454+ 0.426 0.344
(0.253) (0.260) (0.253)
Ln(Average Job Tenure) 0.030 0.317 0.448
(0.517) (0.471) (0.456)
Average Family Wealth 0.025 0.036 0.050+
(0.024) (0.026) (0.028)
Ln(Age) 1.742** 1.468* 1.227*
(0.630) (0.660) (0.608)
Female 0.387 0.267 0.185
(0.237) (0.228) (0.217)
Ln(Fund AUM) 0.040+ 0.040+ 0.041+
(0.022) (0.022) (0.022)
Diversified Firm -0.106 -0.075 -0.051
(0.144) (0.150) (0.150)
Ln(Firm AUM) -0.002 -0.008 -0.006
(0.017) (0.016) (0.017)
Firm Age -0.006** -0.006** -0.007***
(0.002) (0.002) (0.002)
Pre-MBA Work Experience -0.008
(0.069)
Constant Y Y Y
Year Dummies Y Y Y
Region Dummies Y Y Y
Fund Strategy Dummies Y Y Y
Missing Data Dummies Y Y Y
N 12272 12272 12272
Adj. R-sq 0.019 0.020 0.021
Table 5. Matched Sample OLS Regression Predicting Entrepreneurial Performance
*** p<0.001; ** p< 0.01; * p< 0.05; + p< 0.1.
Dependent Variable: Entrepreneurial Performance (Excess Returns)
Standard errors are clustered at the individual hedge fund manager level. Results are consistent when we use robust standard errors.
63
(1) LPM (2) LPM (3) LPM (4) Probit
Generalist (MBA Education) 0.010* 0.018*
(0.005) (0.008)
Master's Education (non-MBA) -0.002 -0.002 0.002
(0.004) (0.004) (0.004)
PhD Education -0.006 -0.008 -0.003
(0.006) (0.007) (0.008)
JD Education 0.006 0.006 0.008
(0.004) (0.004) (0.005)
Ln(Bachelor's Prestige) 0.001 0.001 0.001
(0.001) (0.001) (0.001)
Related Bachelor's Major -0.012** -0.011** -0.007+
(0.004) (0.004) (0.004)
Related Master's Major 0.013* 0.012+ 0.008
(0.007) (0.007) (0.009)
Liberal College Dummy 0.001 0.002 -0.001
(0.004) (0.004) (0.004)
Average Performance -0.002*** -0.002*** -0.002*** -0.003***
(0.001) (0.001) (0.001) (0.001)
Financial Certificate -0.000 -0.002 -0.002 -0.002
(0.005) (0.006) (0.006) (0.006)
Ln(Prior Work Experience) 0.012 0.018* 0.018* 0.014
(0.008) (0.008) (0.008) (0.010)
Prior Employment in Big 5 Firm 0.003 0.003 0.003 0.001
(0.005) (0.008) (0.009) (0.008)
Past Chief Executive Officer -0.003 -0.007+ -0.007+ -0.003
(0.003) (0.004) (0.004) (0.004)
Past Portfolio Manager 0.007+ 0.005 0.004 0.004
(0.004) (0.004) (0.004) (0.004)
Number of Prior Firms -0.005+ -0.007* -0.007* -0.005
(0.003) (0.003) (0.003) (0.004)
Prior Entrepreneurship 0.005** 0.006*** 0.006*** 0.003
(0.001) (0.001) (0.001) (0.002)
Other Industry Experience -0.010** -0.014*** -0.015*** -0.012**
(0.003) (0.004) (0.004) (0.004)
International Experience -0.009+ -0.007 -0.010* -0.007
(0.005) (0.005) (0.005) (0.005)
Ln(Average Job Tenure) -0.013+ -0.017* -0.017* -0.012
(0.008) (0.008) (0.008) (0.011)
Average Family Wealth 0.001 0.000 0.000 0.000
(0.000) (0.000) (0.001) (0.001)
Ln(Age) 0.010 0.010 0.011 0.004
(0.007) (0.008) (0.008) (0.009)
Female 0.008+ 0.007 0.008+ 0.009
(0.005) (0.005) (0.005) (0.006)
Diversified Firm -0.008* -0.009** -0.009** -0.007*
(0.003) (0.003) (0.003) (0.003)
Ln(Firm AUM) -0.001* -0.001* -0.001* -0.000+
(0.000) (0.000) (0.000) (0.000)
Firm Age 0.000** 0.000** 0.000** 0.000***
(0.000) (0.000) (0.000) (0.000)
Pre-MBA Work Experience -0.000 0.000
(0.001) (0.001)
Constant Y Y Y Y
Year Dummies Y Y Y Y
Region Dummies Y Y Y Y
Fund Strategy Dummies Y Y Y Y
Missing Data Dummies Y Y Y Y
N 12272 12272 12272 11,339
Adj. R-sq / Pseudo R-sq 0.022 0.023 0.024 0.197
Dependent Variable: Entrepreneurial Exit
Table 6. Matched Sample Regression Predicting Entrepreneurial Exit
*** p<0.001; ** p< 0.01; * p< 0.05; + p< 0.1.
Standard errors are clustered at the individual hedge fund manager level. Results are consistent when we use robust standard errors. Marginal effects are reported for probit model.
64
Figure2.SummaryofPredictedEntrepreneurialEntry,Performance,andExitforGeneralistsandNon-Generalists
0.26%
0.69%
0.0%
0.2%
0.4%
0.6%
0.8%
1.0%
Non-Generalist Generalist
P anel 2B. Predicted
Ent repreneurial Peformance(Excess R eturns)
0.92%
1.92%
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
Non-Generalist Generalist
P anel 2C. Predicted Li kelihood
of Ent repreneurial Exit
23.63%
29.98%
0%
5%
10%
15%
20%
25%
30%
35%
Non-Generalist Generalist
P anel 2A. Predi ct ed Likelihood
of Ent repreneurial Entry
65
Dependent Variable Exit to Entrepreneurship Exit to Big 10 Firms Exit to Chief Executive Position Exit to Other Industry
(1) LPM (2) LPM (3) LPM (4) LPM
Generalist (MBA Education) -0.594 0.294* 0.693* 0.314*
(0.469) (0.116) (0.310) (0.141)
Master's Education (non-MBA) 0.107 -0.181*** 0.538*** 0.312*
(0.162) (0.046) (0.129) (0.128)
PhD Education 0.127 0.045 -0.638* -0.401
(0.480) (0.127) (0.298) (0.342)
JD Education -0.379 0.055 0.263 -0.168
(0.275) (0.066) (0.212) (0.219)
Ln(Bachelor's Prestige) 0.038 -0.014 0.059+ -0.005
(0.053) (0.011) (0.032) (0.031)
Related Bachelor's Major 0.038 -0.075+ 0.186+ 0.120
(0.164) (0.043) (0.101) (0.118)
Related Master's Major 0.085 -0.139 0.659* 0.099
(0.382) (0.091) (0.305) (0.254)
Liberal College Dummy -0.306 -0.033 -0.387* -0.374+
(0.278) (0.059) (0.154) (0.201)
Average Performance 0.032 -0.004 0.098+ 0.062
(0.065) (0.018) (0.052) (0.051)
Financial Certificate 0.262 0.004 -0.303 0.106
(0.271) (0.066) (0.190) (0.197)
Ln(Prior Work Experience) -0.371 0.127 -0.099 -0.269
(0.395) (0.108) (0.354) (0.179)
Prior Employment in Big 5 Firm 0.209 0.150 -0.101 -0.667*
(0.444) (0.104) (0.212) (0.310)
Past Chief Executive Officer 0.449+ -0.032 0.710*** 0.216
(0.257) (0.062) (0.159) (0.176)
Past Portfolio Manager -0.362* -0.006 -0.268* 0.214+
(0.150) (0.040) (0.106) (0.120)
Number of Prior Firms 0.161 -0.052 0.003 0.090+
(0.153) (0.042) (0.133) (0.048)
Prior Entrepreneurship -0.131 0.048 -0.058 0.252*
(0.162) (0.040) (0.134) (0.119)
Other Industry Experience -0.014 -0.100 0.233+ 0.041
(0.183) (0.061) (0.135) (0.166)
International Experience -0.081 -0.084 -0.068 0.207
(0.226) (0.069) (0.155) (0.208)
Pre-MBA Work Experience 0.001 0.030* -0.046 -0.000
(0.037) (0.012) (0.032) (0.030)
Ln(Average Job Tenure) 0.648 -0.084 0.212 0.035
(0.395) (0.109) (0.358) (0.160)
Average Family Wealth -0.021 -0.006 -0.022 -0.002
(0.026) (0.007) (0.021) (0.022)
Ln(Age) -1.255+ 0.040 0.071 -0.194
(0.658) (0.160) (0.490) (0.470)
Female -0.380 0.019 -0.550* -0.217
(0.345) (0.090) (0.248) (0.256)
Diversified Firm 0.204 0.031 0.099 -0.286*
(0.168) (0.046) (0.127) (0.135)
Ln(Firm AUM) 0.044*** -0.002 -0.004 0.030*
(0.012) (0.004) (0.021) (0.013)
Firm Age 0.003 -0.000 -0.000 0.005*
(0.002) (0.001) (0.002) (0.002)
Constant Y Y Y Y
Year Dummies Y Y Y Y
Region Dummies Y Y Y Y
Fund Strategy Dummies Y Y Y Y
Missing Data Dummies Y Y Y Y
N 113 113 113 113
Adj. R-sq 0.572 0.728 0.653 0.461
*** p<0.001; ** p< 0.01; * p< 0.05; + p< 0.1.
Table 7. Matched Sample LPM Regression Predicting Post-Exit Destinations of Entrepreneurs
66
Figure3.SummaryofPredictedPost-ExitDestinationforGeneralistandNon-GeneralistEntrepreneurs
3.54%
32.94%
0%
5%
10%
15%
20%
25%
30%
35%
Non-Generalist Generalist
P anel 3A. Predi ct ed Likelihood
of Exi t to Prestigious Firm
18.58%
87.88%
0%
20%
40%
60%
80%
100%
Non-Generalist Generalist
P anel 3B. Predicted Likeli hood
of Exi t to Chi ef Executive Offi cer P osi tion
37.45%
68.85%
0%
10%
20%
30%
40%
50%
60%
70%
80%
Non-Generalist Generalist
P anel 3C. Predicted Likeli hood
of Exi t to Other Indus try
67
(1) LPM (2) LPM (3) LPM
Generalist (MBA Education) 0.031**
(0.010)
Masters Education (non-MBA) -0.004 -0.005
(0.007) (0.007)
PhD Education -0.006 -0.011
(0.011) (0.012)
JD Education -0.006 -0.004
(0.012) (0.013)
Ln(Bachelor's Prestige) 0.003* 0.004*
(0.002) (0.002)
Related Bachelor's Major -0.024** -0.023**
(0.008) (0.007)
Related Master's Major 0.005 0.003
(0.012) (0.012)
Liberal College Dummy -0.001 -0.002
(0.007) (0.007)
Average Performance -0.002 -0.002 -0.002
(0.002) (0.002) (0.002)
Financial Certificate 0.010 0.008 0.010
(0.008) (0.009) (0.008)
Ln(Prior Work Experience) 0.020 0.025 0.026+
(0.013) (0.016) (0.015)
Prior Employment in Big 5 Firm -0.003 -0.003 -0.002
(0.006) (0.010) (0.012)
Past Chief Executive Officer -0.007 -0.015* -0.013+
(0.004) (0.007) (0.007)
Past Portfolio Manager 0.004 -0.001 -0.002
(0.007) (0.006) (0.007)
Number of Prior Firms -0.005 -0.007 -0.006
(0.005) (0.005) (0.005)
Prior Entrepreneurship 0.003 0.005* 0.006*
(0.003) (0.002) (0.002)
Other Industry Experience -0.016** -0.022** -0.028***
(0.006) (0.007) (0.007)
International Experience -0.014+ -0.012 -0.017*
(0.008) (0.008) (0.008)
Pre-MBA Work Experience 0.000
(0.002)
Ln(Average Job Tenure) -0.022+ -0.023 -0.022
(0.013) (0.014) (0.014)
Average Family Wealth 0.002* 0.001 0.001
(0.001) (0.001) (0.001)
Ln(Age) 0.025* 0.033* 0.034*
(0.012) (0.014) (0.013)
Female 0.006 0.005 0.009
(0.008) (0.008) (0.007)
Diversified Firm -0.008 -0.008 -0.009+
(0.005) (0.006) (0.005)
Ln(Firm AUM) -0.001* -0.001* -0.001+
(0.001) (0.001) (0.001)
Firm Age 0.000* 0.000** 0.000***
(0.000) (0.000) (0.000)
Constant Y+ Y* Y**
Year Dummies Y Y Y
Region Dummies Y Y Y
Fund Strategy Dummies Y Y Y
Missing Data Dummies Y Y Y
N 6013 6013 6013
Adj. R-sq 0.022 0.024 0.026
Table 8. Matched Sample LPM Regression Predicting Entrepreneurial Exit for Subsample of Entrepreneurs with Lower than
Median PerformanceDependent Variable: Entrepreneurial Exit
*** p<0.001; ** p< 0.01; * p< 0.05; + p< 0.1.
Standard errors are clustered at the individual hedge fund manager level. Results are consistent when we use robust standard errors.
68
Dependent Variable Entrepreneurial Performance Entrepreneurial Exit
Treatment Group MBA MBA
Control Group Non-MBA Non-MBA
(1) OLS (2) LPM
Generalist (MBA Education) 0.392* 0.011*
(0.161) (0.005)
Ln(MBA Prestige) -0.008 -0.000
(0.033) (0.001)
Master's Education (non-MBA) 0.167 -0.002
(0.148) (0.004)
PhD Education -1.440*** -0.011
(0.317) (0.009)
JD Education -0.046 0.003
(0.192) (0.006)
Ln(Bachelor's Prestige) -0.024 0.001
(0.036) (0.001)
Related Bachelor's Major -0.115 -0.010*
(0.137) (0.004)
Related Master's Major -0.264 0.007
(0.284) (0.008)
Liberal College Dummy -0.219 -0.003
(0.170) (0.005)
Financial Certificate 0.179 -0.000
(0.189) (0.005)
Ln(Prior Work Experience) -0.585* -0.000
(0.297) (0.006)
Prior Employment in Big 5 -0.858* 0.004
(0.341) (0.010)
Past Chief Executive Officer 0.261+ -0.004
(0.141) (0.004)
Past Portfolio Manager -0.169 0.003
(0.128) (0.005)
Number of Prior Firms 0.190+ -0.000
(0.106) (0.002)
Prior Entrepreneurship 0.046 0.004*
(0.060) (0.002)
Other Industry Experience -0.272+ -0.015***
(0.144) (0.004)
International Experience 0.342* -0.007
(0.174) (0.006)
Pre-MBA Work Experience -0.007 0.000
(0.039) (0.001)
Ln(Average Job Tenure) 0.449 0.001
(0.280) (0.006)
Average Family Wealth 0.050* 0.000
(0.020) (0.001)
Ln(Age) 1.215*** 0.015
(0.362) (0.011)
Female 0.186 0.005
(0.169) (0.006)
Diversified Firm -0.054 -0.009**
(0.106) (0.003)
Ln(Firm AUM) -0.006 -0.000
(0.016) (0.000)
Firm Age -0.007*** 0.000***
(0.002) (0.000)
Average Performance -0.002***
(0.000)
Risk (Standard Deviation) 0.076**
(0.026)
Ln(Fund AUM) 0.041*
(0.019)
Constant Y* Y
Missing Data Dummies Y Y
Year Dummies Y Y
Region Dummies Y Y
Fund Strategy Dummies Y Y
N 12272 12272
Adj. R-Sq 0.021 0.022
Table 9. Matched Sample Regression Predicting Entrepreneurial Outcomes with MBA Prestige
Notes: *** p<0.001; ** p< 0.01; * p< 0.05; + p< 0.1