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, JHwang20@gsb.columbia.edu)

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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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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