The Second Time Around? Repeat Entrepreneurs from MIT
By:
Charles E. Eesley and Edward B. Roberts
October 25, 2006
Abstract
In this paper we explore the factors that condition the likelihood that an entrepreneur
starts a second firm. We use data from survey responses of 1,789 entrepreneurs to examine firm
founding behavior. Results indicate that multiple entrepreneurs differ from single-firm
entrepreneurs in certain demographic and educational characteristics prior to starting a first firm.
The phenomenon of graduates embarking on careers of multiple entrepreneurship appears to be
growing over time. The results also show that the first firms of eventual multiple entrepreneurs
differ from the first firms of single-firm only entrepreneurs. The paper indicates that those
entrepreneurs with the highest probability of starting a second firm have greater time and access
to financial resources to undertake a new venture. Starting a first firm sooner after graduation,
being divorced, the first firm being acquired, and raising initial capital for the first firm from
angel investors all increase the probability that the entrepreneur will start a second firm.

Authors are listed in alphabetical order. MIT Sloan School of Management, 50 Memorial
Drive, Cambridge, MA 02142. eesley@mit.edu; eroberts@mit.edu
1
I. Introduction
A growing literature in strategy and economics has noted that an important source of new
entrants is incumbent firms in the same industry (Klepper 2001; Gompers, Lerner, and
Scharfstein, 2005). However, these studies have developed and emphasized theories regarding
employees leaving incumbent firms to start new ventures. Various lenses, such as agency
theory, organizational capability theories, employee learning theories, and evolutionary theories
have been used to evaluate why employees decide to leave their firms to spin-off new firms.
Most of these theories emphasize some sort of conflict between the spin-off and the incumbent’s
top management. On the other hand, in the finance literature, a growing group of scholars have
examined the determinants of CEO turnover. However, these authors typically focus on forced
CEO turnover in relation to firm and industry performance. CEOs who voluntarily leave and
what they do after leaving are seldom studied (for important exceptions see Wasserman 2003;
Bertrand and Schoar 2003). At the intersection is a relatively important gap in our
understanding-- the founder or CEO who is pushed out or voluntarily leaves a firm and decides
to start a new firm. The existing theories of spin-off activity (with the possible exception of
evolutionary-based theories) do not explain or fit this phenomenon well, and the existing work
on CEO turnover with an emphasis on corporate governance of large firms does not shed much
light on the phenomenon either. As a result, it has proven difficult to determine how prior
founding experience should be interpreted (i.e. as a measure of risk aversion, learning, or
reduced asymmetric information on quality for investors).
The importance of the entrepreneur who starts multiple firms over the course of a lifetime
has been recognized in the academic literature as a potentially widespread aspect of new firm
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creation for at least 35 years (Cooper, 1970). However, in relation to its likely significance for
economic growth and for the field of entrepreneurship the phenomenon has been understudied
(see Table 1). With some recent exceptions (Sarasvathy & Monon, 2005), almost no theoretical
work has been done (Westhead, 1998). As a result, the few empirical studies use different
definitions, populations, and control variables leaving little consensus on foundational questions
about how important or how widespread is this phenomenon in the U.S. or internationally. This
deficiency is especially true in regard to companies formed to exploit new technology.
The
purpose of this paper is to advance our understanding of what factors condition the likelihood
that a founder will leave a firm and start a new firm. Is it differences in underlying individual
demographic characteristics or differences in characteristics of the first firm experience or
performance that motivate novice founders to initiate a new firm founding. To answer these
questions, we use a founder-firm matched panel data set, where we track over time founders
across different firms that they start or choose to remain with. Our cross-industry data also allow
us to examine how industry characteristics may affect how likely a founder will be to try
founding a second new firm.
II. Background Literature / Theory
Especially compared to the early years when entrepreneurs were seen as a homogeneous
group, a number of different groupings of entrepreneurs has emerged in recent years. As Hsu
(2006b) notes, an important recent theme in the literature is heterogeneity among entrants prior
to the development of the venture, especially in terms of the experience of the founder or
founding team. Compared to the literature on differences between entrepreneurs and nonentrepreneurs, scholarly attention to the heterogeneity among entrepreneurs has been very
limited until recently.
3
Prior literature has examined the determinants of self-employment. Carroll and
Mosakowski (1987) show that the transition to self-employment depends on factors including
family background and prior experiences with self-employment. Our research differs in its focus
on high technology entrepreneurs and their firms. As pointed out earlier (Shane & Khurana,
2003), self-employment does not necessarily involve the founding of a new firm, such as when
an individual does independent consulting or contract work. Further, firms started to
commercialize new technologies tend to be larger and have a strong impact on economic
productivity. Therefore repeat entrepreneurs who focus on technology represent a particularly
important and relatively unexplored area of study.
Wasserman (2003) examines Founder-CEO succession in 202 venture-backed Internet
start-ups. His study does not look explicitly at what Founder-CEOs do once replaced or after
leaving the firm. He finds that many Founder-CEOs remain in the firm, even when they are
replaced as CEO. Of importance for the current study, he finds that the likelihood of a FounderCEO being replaced increases with the achievement of critical milestones in building the
company.
Unlike the previous studies looking at the effects of prior career experiences or prior firm
founding on the likelihood of subsequent firm founding, the present study examines individual
and firm-level factors related to the transition from an active founding role in one firm to starting
a second firm. In addition, our research takes advantage of data on the timing of the entire
founding history and on the entrepreneur's personal success.
III. THEORY AND HYPOTHESES
In this paper we develop and test a theory of entrepreneurial exit and then re-entry into
entrepreneurship. Through entrepreneurial experience the founder builds up social and financial
4
capital in addition to personal experience and human capital. As these three forms of capital are
raised by the entrepreneur, he or she may choose to invest them in continuing and advancing the
entrepreneurial career. If the entrepreneur chooses to “reinvest” these forms of capital produced
by the first firm, one possibility is that the capital is invested in such a way as to maximize the
number of high quality business opportunities available to the entrepreneur to pursue at minimal
cost to the entrepreneur in terms of time, effort and/or money. This process of reinvesting the
fruits of entrepreneurship can be conceptualized as a continuum from the initial startup to repeat
entrepreneurship. The entrepreneur may be able to become involved in new firms because the
older “new” firm has progressed to a more stable state where he/she does not need to worry
about it as much and others, perhaps more experienced managers have taken over the day to day
operations. While we portray the progress as a continuum, obviously steps could be skipped,
perhaps indicating a more rapid personal development as a successful multiple entrepreneur.
A nascent entrepreneur begins with relatively little human, social, and financial capital
and access to many ideas, but probably only a few high quality entrepreneurial opportunities. If
the entrepreneur succeeds in the first start-up and decides to start a second firm, the various
forms of capital raised through the first startup will presumably tend to make this second effort
somewhat easier.
The buildup of human, social and financial capital with the entrepreneur is likely to occur
with a substantial lag after the founding of the initial firm. The first firm of a nascent
entrepreneur requires an incredible amount of effort, time and energy. As the firm gains its
operational experience, the entrepreneur has increasing opportunity to reflect and begins to
accumulate skills related to the entrepreneurial process along with important social contacts and,
slowly, financial capital as well. As a result of the startup and operation of the first firm, the
5
entrepreneur may see various business opportunities or shadow options (McGrath, 1999) that he
or she may not have been able or willing to pursue at the time, but which can now serve as the
basis for a subsequent firm. An emerging literature has been developing to apply real options
theory to strategy as a way of increasing flexibility (Bowman & Hurry, 1993; McGrath, 1996).
As McGrath (1996, p. 103) points out, among several factors that may relate to the likelihood of
recognizing viable new opportunities is previous entrepreneurial experience.
While McGrath and MacMillan (2000) suggest that entrepreneurs with previous founding
experience have developed a “mindset” or the ability and competence to pursue only the best
opportunities, this may not be the entire story. It may be that while novice entrepreneurs only
pursue what they see as the very best opportunities, repeat entrepreneurs may take a longer view.
Repeat entrepreneurs may pursue a wider range of opportunities in round 1, allowing them to see
many shadow options and choose only the best opportunities to invest in as real options for
greater performance in round 2.
Having reached the level of repeat entrepreneur, the individual will have built up higher
levels of all types of capital and, also through the operation of the previous businesses, customer
contacts or social contacts will have perceived a number of other business opportunities. At any
point in this progression, as the entrepreneur builds sufficient financial capital, he or she may
invest a portion of assets in early-stage ventures. In the investment community jargon,
investments made by wealthy individuals have become known as angel investing and such
individuals have been coined as angel investors or informal investors to distinguish them from
venture capital institutions (Freear, Sohl, & Wetzel, 1994; Wetzel, 1983). These deals may be
undertaken serially or in parallel and offer the entrepreneur multiple opportunities for entering
into the venture as an active entrepreneur as well. In each stage of the continuum (from the
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bottom left of Figure 2 to the top right), a greater number of business opportunities will arise (or
perhaps be sought out by the entrepreneur) and will be available for pursuit or active
involvement as a founder. Further, these opportunities become available to the entrepreneur at
smaller and smaller cost in terms of relative time and effort necessary to encounter them and
commercialize them. Indeed, other scholars have asserted that prior startup experience reduces
the costs of entrepreneurial activity and increases the probability of acting on opportunities
(Carroll & Mosakowski, 1987).
Depending on personal preferences and style, an entrepreneur may not choose to reinvest
her or his capital in further entrepreneurship. Especially if the first firm is still in operation and
he is happy with his role there, the founder may choose to stay with the first firm and not
continue down the path of multiple entrepreneurship. Our point is simply that this option
becomes open to him or her and is easier to achieve after a first founding. One may also think of
the progression described above as shifting into a role of opportunity recognition and generation,
of “rainmaker” so to speak in the entrepreneurial process. The entrepreneur may then be in less
of a day to day operational role in any one firm, but is engaged more fully in a business idea
generation and evaluation role. These rare individuals may be playing a very valuable role in the
entrepreneurial processes within one or more industries.
Why Start a Second Firm?
When asking who starts multiple firms and what conditions this choice, we are essentially
asking about what motivates or enables an entrepreneur to start a second firm. At the individual
level and at the first firm level, a number of factors may lead to the choice to start another firm.
If starting a second firm is motivated by expectations of the returns to reinvesting financial,
human and social capital back into further entrepreneurial activities, then factors that increase the
7
expected return should result in a higher likelihood for starting a new firm. Similarly, factors
which decrease the expected return should decrease the probability the entrepreneur will decide
to undertake a second firm. The “expected returns” to investing in entrepreneurial activities may
be financial or non-financial. Nonetheless, the first firm experience is likely to condition the
level of non-financial rewards anticipated as well as the relative importance of these compared to
financial returns. During the first firm experience, the entrepreneur may discover, in fact,
whether the non-pecuniary benefits to entrepreneurship make it more attractive than returning to
regular employment (Barton 2002). First, simply having the time and energy to start another
firm may be an important factor. If the entrepreneur is quite old, then he or she may expect that
the fruits of a second firm creation will not accrue with enough lifetime left to enjoy them.
Hypothesis 1: Entrepreneurs who are younger at the time of their first firm founding or
otherwise have more time to spend on entrepreneurship will be more likely to start a
second firm.
Another possibility is that greater confidence leads to a higher likelihood of starting a
subsequent firm. This would lead one to hypothesize that the better the outcome of the first firm
or the better its performance relative to the entrepreneur’s expectations, the more likely the
entrepreneur will undertake another start-up. Of course it is difficult to disentangle the effect
that better first firm performance would have on confidence and the expectations of future
success from the effect success has on the level of financial and social capital. The arguments
outlined previously as to human, social and financial capital provide strong support that a
successful first venture should increase the likelihood of starting a subsequent firm. Albeit, a
very successful first venture could last so long as to diminish the time available for a second
firm, or generate so much wealth or personal satisfaction as to diminish the motivation to do it
again.
8
Higher financial performance of a first firm may also mean that the firm has progressed
to a more steady state where the founder does not need to worry about its survival and has time
to think about a new start-up. Either way would suggest that higher success would generally
lead to more second firms. This is especially true since performance below a reasonable return
to the founder’s human capital or attractive outside options for regular employment should lead
to an exit from entrepreneurship (Gimeno, Folta, Cooper, & Woo, 1997). However, very high
levels of success, especially financial success could result in the entrepreneur simply retiring and
thus lower the chances of a second firm founding. Moderate levels of success should lead to
higher probabilities of a second founding, since if the first firm was not particularly successful
then the entrepreneur would have had a taste of success and may desire even more in a
subsequent firm.
Hypothesis 2: We anticipate an inverse U-shaped curve relating success of a prior new
venture and the likelihood that a subsequent new venture will be undertaken.
If it is true that the financial capital of the entrepreneur increases with multiple firm
foundings, then we should see the net worth of the founders increasing with the number of prior
firms. This statement may seem obvious. However, if multiple entrepreneurs are typically
starting a series of small firms that are relatively unsuccessful, then the result would not
necessarily hold.
Hypothesis 3: The performance of the entrepreneur in terms of net worth (financial
capital) will be higher with a greater number of prior firms.
Alsos (1998) has investigated differences in firm formation processes between multiple
entrepreneurs and first-time entrepreneurs. Few significant differences were found between
novices and repeat entrepreneurs. Differences in motivation were found with repeat
entrepreneurs more likely to indicate motivations related to instrumentality of wealth. Novice
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entrepreneurs were more likely to stress a need for independence. This study could not discern
whether the repeat entrepreneurs’ motivations changed between the first firm and later firms or
whether these results may indicate that the multiple entrepreneur was destined to start multiple
firms even prior to starting his/her first firm.
Hypothesis 4a: Repeat entrepreneurs differ from single-firm entrepreneurs even prior to
starting the first firm in terms of demographic variables.
One motivation for starting a second firm may be that the entrepreneur is highly
innovative and does not have the freedom to innovate as widely as he would like within the
confines of the prior firm. This idea is supported by Klepper and Thompson’s (2006) model of
spinoff activity resulting from disagreements within the existing firm. In this case, it is likely
that those whose backgrounds suggest they may be highly innovative would be more likely to
initiate a career of multiple foundings.
Hypothesis 4b: Entrepreneurs with a background in the sciences or engineering will be
more likely to start a second firm.
At the first firm level, the tendency to be highly innovative should manifest in founding
first firms that hold patents as opposed to first firms which do not hold patents.
Hypothesis 4c: Entrepreneurs with patents in their first firm will be more likely to found
a second firm.
Aspects of the first firm may make it more likely for the founder to start a second firm.
Alternatively, it is difficult to dismiss the possibility that the causation may be reversed, thus we
propose the joint hypotheses 5a and 5b.
Hypothesis 5a: Certain aspects of the first firm founding will condition the likelihood that
a second entrepreneurial venture is started.
Hypothesis 5b: Repeat entrepreneurs differ from single-firm entrepreneurs in the
characteristics of the first firms they found.
10
Many prior studies have argued that access to financial capital is a limitation to entry,
even if the level of personal assets are not predictive of who will enter self-employment (see Acs
& Audretsch, 1989; Dunn & Holtz-Eakin, 2000 and others). Entrepreneurs entering industries
with requirements for high amounts of initial capital should face a higher hurdle to start a second
firm as well, resulting in decreased rates of second firm foundings. Alternatively, those founders
in industries with low requirements for initial capital will be more likely to be able to fund a new
venture themselves from the profits of the first firm.
Hypothesis 5c: Entrepreneurs starting first firms in industries requiring high amounts of
initial capital will be less likely to start multiple firms.
In addition, if financial capital is a constraint for entrepreneurs, then those who have been
successful in fundraising and in building links to sources of initial capital should have an
advantage in raising money in the future. Indeed, Hsu (2006a) finds that experienced
entrepreneurs have quicker access to venture capital than inexperienced founders.
Hypothesis 5d: Entrepreneurs successful at raising initial capital from venture capital
firms or angel investors will be more likely to start a second firm.
Simply having access to fellow entrepreneurs who are willing and qualified to undertake
the risks of a new start-up is likely to be a constraint for some entrepreneurs interested in starting
a second firm. Therefore, we suggest that entrepreneurs who built larger teams for their first
start-up should have more options for cofounders for a second start-up. This may be true in part
because entrepreneurs with larger founder teams have been found to be more successful than
those who are sole founders or have small teams (Roberts, 1991). Consequently, we hypothesize
that holding the success of the first firm constant (controlling for first firm performance), those
starting the first firm with a higher number of cofounders will be more likely to start a second
firm.
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Hypothesis 5e: All else equal, entrepreneurs who started a first firm with a higher
number of cofounders will be more likely to start a second firm.
IV. Methods
Data and Sample
The data come from a survey administered in 2003 to all living MIT alumni who had
previously self-identified as founding at least one venture. The dataset of MIT alumni contains
over 40,000 records including basic information on date of birth, country of citizenship, gender,
major at MIT, highest attained degree, and new venture founding history.
Out of 7,798 alumni indicating that they had founded a company, 2,111 founder surveys
were completed, representing a response rate of 27.1%. Out of 3,156 alumni indicating that they
had started multiple companies, 1,004 completed the survey for a multi-founder response rate of
31.8%. A total of 1,107 single-firm founders responded to the survey giving a 21.8% response
rate out of the 5,086 single-firm alumni founders. Some of these 1,107 single-firm founders may
later go on to become multiple entrepreneurs, however we can isolate 728 single-firm founders
over the age of 59 (95% of firms are started by entrepreneurs under the age of 60) and be
reasonably confident in having eliminated such right-side censoring problems. One of the key
features of this dataset is its long time horizon in the cross section (1930s-2001) which allows us
to analyze entire entrepreneurial careers of a large number of founders. In addition, this
represents what we believe is one of the first datasets which includes both orderings and the
timings of the firms of multiple entrepreneurs, but also individual level performance data in
addition to firm level performance measures.
To address the distinction between self employment and new ventures, we eliminate any
12
firms identified by the respondents as architecture firms1, consulting firms, and
law/miscellaneous firms from the database.2 Eliminating these firms brings the sample size to
1,789 firms.
Measures
Dependent Variables. Second firm founded indicates the year that the entrepreneur’s
second firm was founded and is our primary dependent variable.
Independent Variables. The independent variables pertain to: 1) the individual level, and
2) characteristics of the start-up process and outcomes of the first firm. Academic major is
categorized according to degrees (Bachelor’s, Master’s, or Doctorate) in engineering,
management, social science and the humanities, architecture, and natural science. We also have
data on the highest degree attained by the founders. Country of citizenship is categorized into
Latin America, Asia, Europe, the Middle East, Africa, and North America.3 Graduation year
indicates the year of the founder’s highest degree from MIT. First start-up founded (or second,
and third) is the year of the founding of the first (or later) firm. The entrepreneurs indicated on
the survey their current marital status. To measure net worth, the founders were asked to
indicate their net worth within five categories ranging from $0 to $100,000, $100,000 to
$500,000, $500,000 to $1 million, $1 million to $10 million and over $10 million. The mean in
each range was used and although $10 million is likely a low upper range, this was used for the
fifth and highest category. Note that the net worth data are not ex ante, but rather the cumulative
There is a very high rate of “serial” entrepreneurship for architects, but this is likely due to the flexible partnership
nature of the business and low technology nature of these firms.
2
The analyses were run with a cutoff of 10 employees or more, 5 employees or more, and all firms included. Since
the results and conclusions were largely similar for all three, only results for ‘all firms’ are reported. The only
variable that changes significance is management degree which becomes significant when firms below 5 employees
are included. The most likely interpretation is that this is capturing some services/consulting firms that were selfcategorized under a different industry and thus not eliminated.
3
We use responses for country of citizenship since only 182 of the founders provided information on country of
origin, compared to 366 with information on country of citizenship. In only 14 cases does the information on
country of origin differ from the corresponding country of citizenship data.
1
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net worth evaluated as of the time of the survey (2003). We also use data on the total lifetime of
firms founded by the entrepreneur.
Respondents indicated a coarse industry segment within which the firm was founded.4
The industry categories represent check boxes on the survey in which the entrepreneurs were
asked to classify their firms. While these might be thought of as control variables, there may be
relevant differences within these areas that influence how easy it is to start multiple ventures
within that industry grouping. Lag to first firm measures the lag in years from graduation to the
founding of the entrepreneur’s first firm. Held patents is coded as a 0 if the firm never patented
and as a 1 if the firm was granted patents by the USPTO. # Cofounders is a count of the number
of cofounders listed for the first firm. Team from Social Activities and Team from Work indicate
the source of the co-founding team and Team from Research is the excluded category in the
regression models. Idea from Research, Idea from Work, and Idea from Networking are 0/1
variables capturing the original source of the idea, and Idea from Extracurricular Activities is the
excluded category. Respondents indicated all sources of initial capital as well as the total
amount (initial capital) for the firm including VC funded and Angel funded. Acquired and Public
capture firm outcomes (as of 2003). Employees and Revenues are the number of employees at
the firm and revenues (for a specific year of operation). A first founding during an economic
recession might force the entrepreneur to be careful about costs and be more disciplined but the
struggle might make him/her less likely to start a second firm. A first firm founding during an
economic expansion might bolster the founder’s confidence and make subsequent startups easier.
Recession Year is a dummy variable obtained from the National Bureau of Economic Research
(NBER) chronology of the U.S. business cycle. Finally, operating years is a count of the
4
These include aerospace, architecture, biomedical, chemicals, consumer products, consulting, electronics, energy,
finance, law/miscellaneous, machine tools, publishing, software, telecommunications as well as other services, and
other manufacturing.
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number of years the firm has been in operation or the number of years from founding to closing.
The variable log revenues is the natural log of the revenue for the firm as reported by the
entrepreneur. The respondents were asked to report revenues for the most recent fiscal year
available; therefore these firms are not necessarily still in operation. Since revenues show an
upward trend over the age of the startup, we normalize the revenue data by dividing by the age of
the firm at the year for which revenues are reported. We adjust for inflation (2001 $) and
because of the skewed distribution take the natural log of this measure for our dependent variable
in the multivariate analysis. We also look at acquisitions.
Founders were asked to indicate what role they played at the time of the founding of the
company and what role they play now.5 If they were not currently active with the firm, founders
were asked to indicate whether they were: consulting, involved with another startup, employed
elsewhere, retired, working in another company that they had founded, or other.
V. Results
We first present descriptive statistics followed by the regression results. Figure 1 shows
a histogram of the number of 1st firms, 2nd firms, 3rd firms, 4th firms, and 5th firms and greater
(subsequently referred to as graphed by firm ranking) by their founding year. First firms are the
most prevalent and the number of firm foundings increases over the years. Separate from any
trends, we expect this increase due to our panel data set and the fact that each year adds another
year of graduates at risk for entering entrepreneurship. Figure 3 plots the percentage of founders
starting a second firm by deciles of first firm revenues (divided by the operating year of the
firm). The line appears to be concave and begins decreasing again for the top decile of revenues.
Figure 4 shows the percentage of novice founders and repeat founders with Bachelor’s, Master’s,
5
Possible roles include President/CEO, Chairman (if not also CEO), Chief Operating Officer, Chief Scientist/Chief
Technology Officer/Chief of R&D, Chief Finance Officer, Chief Information Officer, Vice President, Outside
Director, Consultant or Advisor, Other employee.
15
and Doctorate degrees. Proportionately more of the repeat founders are non-U.S. citizens and a
slightly higher proportion of the repeat entrepreneurs hold Master’s degrees. Figure 5
demonstrates that novice entrepreneurs differ significantly from the other categories of founders
(p<0.001) in being older and having a longer lag from graduation to the founding of their first
firms. Repeat entrepreneurs enter entrepreneurship much sooner, which likely gives them time
for subsequent firms.
Next, we move from the individual level to examining characteristics of the first firm
experience. Figure 6 shows that the types of entrepreneurs show differences in their first startup
experiences in terms of the amount of capital raised and the level of revenues achieved. Repeat
founders raise significantly higher amounts of initial capital than novice-only entrepreneurs
(p<0.05). Figure 7 graphs that more of the repeat founders had first firms which were acquired
or went out of business. In Figure 8 we see that higher levels of net worth are associated with
repeat entrepreneurs. Figure 9 shows the roles the entrepreneurs played in their firms at
founding. Figure 10 shows the roles that the founders currently play in their firms. Over 60% of
the founders were President/CEO of the firm at founding, however the most likely roles currently
are COO or not active.
Table 1 contains the estimated economic impact of the novice and repeat entrepreneurs in
terms of firm foundings, revenues and employees. Only the revenues and employees are
estimated since we do not currently have these figures for the prior firms of the multiple
entrepreneurs. Therefore we extrapolate by multiplying the number of these prior firms by the
mean revenues and mean employees for those firms founded by that category of entrepreneur in
our sample. These estimates are likely to be quite conservative relative to the (real data) for
novice entrepreneurs since we included novice entrepreneurs of all ages and some of the repeat
16
entrepreneurs may start still more firms.6 Thus, the percentage of novice founders is surely an
overestimate since many of these novice entrepreneurs will go on to found multiple firms over
their entrepreneurial careers. From these estimates we see that repeat entrepreneurs have a
substantial economic impact relative to their percentage of total entrepreneurs.
Table 2 presents by their decade of graduation the number of entrepreneurs founding first
firms all the way up to 11th firms. The row labeled “% serial” is the percentage of founders from
that decade of graduation who have started more than one firm. Across the decades, from 3050% of the founders have chosen to found multiple start-ups.7 The decrease in the percentage
from the 1980s on is likely due to right-censoring in that the most recent graduates have not yet
had time to start a second firm and may do so in the future. Table 3 examines the industry
distribution across firm rank. Here we include architecture, consulting, and law firms in order to
show the complete data. Elsewhere these firms are deleted from the sample because we are
interested in new firm creation and the flexible nature of partnerships or self-employment in
architecture, law, and consulting is likely to be of a different nature or motivated by entirely
different dynamics. Software and electronics represent the largest proportion of the sample and
we find that across multiple ventures the percentages in each industry grouping remain nearly
constant. In Table 4 we present summary statistics and variable definitions. As expected second
and third firms are on average founded in more recent years than first firms.
We use Cox (1972) hazard regression models for our analyses. First, the model estimates
the probability of founding a second firm in a given year conditional on not having founded a
6
One approach to this problem would be to perform these calculations only for entrepreneurs over age 60 who are
unlikely to found more firms. Since the phenomenon of multiple entrepreneurship is increasing with each
graduation year, we do not take this approach as it would produce a drastic underestimate by eliminating many of
the multiple entrepreneurs and their firms.
7
This statistic uses the total number of firms the entrepreneur claimed to have started. For the remainder of the
analyses, we use the number of firms that that they listed company names and founding dates for in the survey. The
latter is more reliable and conservative but was capped by the survey instrument at five.
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(second) firm up until that time period. Thus, it adjusts for the right-censoring of the data by
explicitly taking the timing of events into account. Second, the model is semi-parametric, which
allows us to estimate the impact of independent variables on the hazard of founding a firm while
leaving the baseline hazard function unspecified. Founders start being “at risk” of founding a
second firm at the time of their first firm founding, and a “failure” event occurs the year the
individual founds a subsequent firm (otherwise, the founding year is considered censored for that
individual as of the year 2003).8 Reported coefficients are hazard ratios and are in comparison to
the omitted group. Values above 1.0 represent increases in the hazard of founding a subsequent
firm and values below 1.0 represent decreases in the hazard. Statistically significant estimates are
indicated through asterisks. The specification of the Cox (1972) model is as follows:
 t | X   o (t ) exp  X 
(1)
where the vector X includes our founder and first firm experience characteristics.  t | X  is the
rate at which founders will start a second firm at any particular date, given that they have not
founded a second firm up until that point in time. Equation (1) specifies the hazard rate as the
product of two components: a function of the spell length (i.e. delay time since founding the first
firm), o (t ) or baseline hazard, and a function of the observable individual and first firm
characteristics, denoted by the vector X. Thus, each of our models predicts the likelihood that an
entrepreneur will found a second firm. The Cox nonparametric estimation allows us to
estimate  without needing to make a distributional assumption about o (t ) .
8
We have also run these analyses with individuals becoming at risk of founding a firm at their year of university
graduation. The results are stronger than those reported here, which is easy to understand since the same number of
firm founding events are being predicted over a shorter time horizon (graduation versus birth year). We choose to
report the more conservative birth year entry estimates because we are agnostic as to when an individual might start
a venture. As well, the graduation event depends on the degree the individual received.
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Table 5 shows the regression results using the year that a second start-up was founded as
the dependent variable and characteristics of the founder as the independent variables. In Model
5-1 we find that more recent graduates, males, and those who are divorced are significantly more
likely to found a second firm. Model 5-2 tests the academic majors and finds that those with
management degrees are more likely to start a second firm. Model 5-3 tests differences in
country of citizenship and finds that citizens from Latin American and Asian countries are more
likely to start a second firm. In the fully specified model (5-4) the graduation year, gender,
divorced, management degree, Latin American, and Asian citizen variables remain significant.
The regressions in Table 6 use the year a second start-up was founded as the dependent
variable and show the effects of first firm characteristics as the independent variables. In Model
6-1 variables capturing the initial conditions at founding are included. We find that a longer lag
from graduation to the start of the first firm decreases the likelihood by 5.2% (for each year) that
a second start-up will be founded. When the first firm was started during a period of economic
recession, the entrepreneur is not less likely to attempt a second firm. Each additional cofounder
involved in the first experience increases the chance of a second firm by 24.2% (p<0.001). If the
source of the first firm idea came from research, then this is associated with a decrease in the
chance that a second start-up will be attempted. The industry average R&D to sales ratio is
significant and indicates that those in higher R&D intensity industries are 32.1% more likely to
do another start-up.
Model 6-2 examines the fundraising and outcomes of the first firm to see if these play a
role in the decision to start a second firm. If the first firm was acquired, this is associated with a
50.1% higher likelihood of a second start-up attempt. While we are not confident the IPO
coefficient is different from zero, we expect that if the IPO valuation was taken into account then
19
we may find significant effects. The number of years in operation and whether the first firm
went out of business are not associated with higher or lower likelihoods that the founder will
attempt a second founding. Model 6-3 estimates a model with the previous characteristics and
examines whether entrepreneurs starting first firms within certain industries have a higher
likelihood of starting a second firm. Entrepreneurs with first firms in biomedical industries or in
telecommunications showed a significantly higher likelihood for a second founding.
In the fully specified model (6-4), we drop the industry dummy variables and include
instead industry average R&D to sales ratio and average industry fixed assets. Shorter lags from
graduation to the first firm continue to increase the chance of a second firm. We are no longer
confident that first firm ideas from research are associated with fewer second firm attempts. We
find that a second firm is 80.6% more likely to be founded if the first firm was funded by angel
(but not VC) investors. In addition, first firms in R&D intensive industries continue to be
associated with greater likelihood of a second founding.
VI. Conclusion
Overall, the data support the idea that both first firm level factors and individual level
characteristics influence the entrepreneur’s decision to start a second firm. In addition aspects of
the business environment at the time of the first start-up (such as recessionary economy) do not
appear to influence repeat entrepreneurship. However, it is difficult to disentangle the direction
of causation from first firm experience characteristics to the decision to start a second firm. It
may be that these types of founders have differences in style from the very beginning, or that
characteristics of the first experience allow the founders to pursue involvement in the founding
of multiple firms versus ceasing new firm founding after the first firm.
20
Hypothesis 1 was that younger first time entrepreneurs and those with more time in
general will have a higher chance of founding a second firm and it is supported. Shorter lags
from graduation to the first firm founding are associated with higher hazard rates of founding a
second firm. Furthermore, those who are divorced presumably have fewer family commitments
and thus more time to devote to founding a second firm. (Perhaps the formerly married are more
inclined to manifest their need for achievement in building companies instead of building
families. We are not aware of any prior work examining this question.) Both of these factors
appear to make an entrepreneur more likely to start a subsequent firm. From a rational
expectations perspective, it is interesting that having a first firm go out of business is not
associated with entrepreneurs who are less likely to found second firms. One explanation may
be that the entrepreneurs who have higher human capital are more likely to close a low
performing business and expect to do better if they try again. The results indicate that age and
time are important factors in the decision to enter a career of multiple entrepreneurship.
Hypothesis 2 was that we would see an inverse-U relationship between first firm
performance and the likelihood of founding another start-up. Looking at revenues, this
hypothesis was not supported. The coefficients on the revenues and revenues squared variables
were not significantly different from zero. If the first firm underwent an IPO, then this has no
significant impact on the likelihood, which may be because the value of the company at IPO or
beyond is unknown and could range from quite low to very high. However, first firms that were
acquired are associated with higher likelihood of a second founding.
Hypothesis 3 was that greater numbers of start-ups would be associated with higher
financial capital for the entrepreneur. The data, especially in Figure 8, support the hypothesis.
21
Hypothesis 4a was that repeat entrepreneurs differ from single-firm entrepreneurs even
prior to starting the first firm. This hypothesis is supported. When repeat entrepreneur is defined
as those starting at least a second firm, we see that males, those who are divorced, and those who
are citizens of Latin American or Asian countries are more likely to be repeat entrepreneurs.
Hypothesis 4b was not supported since entrepreneurs with a background in engineering or the
sciences are no more likely to start a second firm. Also, hypothesis 4c was not supported since
first firms with patents are no more likely to be followed by a second founding. However, these
variables may be poor proxies for the level of innovativeness of the entrepreneur.
Hypotheses 5a and 5b were the joint hypotheses that aspects of the first firm experience
would condition the likelihood of the founder starting a second firm versus multiple
entrepreneurs choosing to start different types of first firms or going through a different startup
process from those destined to only found a single firm. With these data we are not able to
ascertain which direction the causality is flowing, however we can say with certainty that the
firms of single-firm entrepreneurs do differ from the first firms of repeat entrepreneurs. The first
firms of those who will eventually start a second firm are started sooner after graduation, and are
funded by angel investors (Hypothesis 5e). Other factors associated with higher likelihood of a
second startup include if the first firm was acquired (giving the entrepreneur time to do a second
start-up), and being in an R&D intensive industry.
The hypothesis that entrepreneurs starting first firms in high capital industries will be less
likely to start multiple firms was not supported (hypothesis 5c). Industry fixed assets for the first
firm appears to have no impact. First firms in biomedical and telecommunications industries are
associated with an increased likelihood of the founder initiating a career of multiple
entrepreneurship. First firms in software do not result in an increased likelihood of a subsequent
22
firm founding. While prior studies have noted that initial financial capital constraints may not
inhibit many aspiring entrepreneurs from attempting to start a business (Dunn and Holtz-Eakin,
2000), such constraints appear not to cause entrepreneurs to think twice about a second attempt.
Hypothesis 5d is related in that it proposes that entrepreneurs who have raised initial capital from
VCs or angel investors will show an increased hazard for starting a second firm due to easier
access to capital. This hypothesis is partially supported. Future work should explore more
deeply why funding from angels but not from VCs increases the chances for a second start-up.
Previous financing from angels may also increase the probability of a second startup due to the
founder receiving mentoring from these investors and this may foster the ability or inclination to
do another startup.
Robustness and Limitations. This study is not without limitations. Year and sector
dummy variables help alleviate concerns that various sources of unobserved heterogeneity drive
our findings. The entrepreneurs were not randomly assigned to different conditions and thus the
choice to start a new firm is endogenously determined by the entrepreneur. One might be
concerned that the decision to found another firm is not based on the factors we propose, but
rather from idiosyncratic or lucky private decisions by the entrepreneur or by others who
approach the entrepreneur about joining their new start-up. Many factors are likely to have an
impact on the decision to found a firm, however the individual level characteristics analyzed
would not be expected to have any explanatory power if this was entirely true.
The current data also may suffer from a self-report bias since both the dependent and
some of the independent variables were reported by the entrepreneur. Net worth data in
particular are likely to suffer from response bias. In general, while we observe a wide range of
outcomes and firm sizes, it is likely that we do not observe every startup firm attempted by this
23
group of entrepreneurs. It could be that the single-firm entrepreneurs attempted to start other
firms and failed or did not report them. We also cannot ascertain their view for the reasons for
deciding to start a new firm.
VII.
Discussion
The results on net worth indicate that a more subtle question needs to be asked about
whether entrepreneurship pays (Barton, 2000). Entrepreneurship may indeed pay relative to
regular employment for multiple entrepreneurs, though possibly not for single-firm
entrepreneurs.
In this paper we explore the factors that condition the likelihood that an entrepreneur
starts a second firm. We use data from survey responses of 2,111 entrepreneurs to examine firm
founding behavior. Results indicate that multiple entrepreneurs differ from single-firm
entrepreneurs in certain demographic and educational characteristics prior to starting a first firm.
The phenomenon of graduates embarking on careers of multiple entrepreneurship appears to be
growing over time. The results also show that the first firms of eventual multiple entrepreneurs
differ from the first firms of single-firm only entrepreneurs. The paper indicates that the factors
associated with the highest probability that the entrepreneur will start a second firm are that they
have greater time and access to financial resources to undertake a new venture. Starting a first
firm sooner after graduation, in an R&D intensive industry, being divorced, having a first firm
undergo an acquisition, and raising initial capital for the first firm from angel investors all
increase the probability that the entrepreneur will start a second firm. The results all indicate
factors which increase the expected returns (financial or non-financial) and reduce the costs to
reinvesting time and money back into gaining access to new business opportunities via
entrepreneurial activity.
24
Future work in this area could investigate the dynamics of the relative importance of
financial versus non-pecuniary incentives for engaging in entrepreneurship as the entrepreneurial
career progresses. Our results clearly indicate that financial incentives and expected financial
returns appear to be important in deciding whether to engage in subsequent entrepreneurial
activities. However, more detailed data on the entrepreneur’s perceptions of non-pecuniary
motivations at various levels of prior entrepreneurial experience would provide the basis for an
interesting study for future researchers in this area.
These results have important implications for entrepreneurs and for those who invest in
entrepreneurs. For venture capitalists and investors, our results could be used to identify who
among the first time entrepreneurs they see are likely to become repeat entrepreneurs. Then,
they could give more attention, funding or mentoring to these more promising first time
entrepreneurs and put themselves and their firms in a position to be the ones that get to fund their
subsequent firms as well. VC firms benefit since they can be more likely to pull eventual repeat
entrepreneurs into their network before competing firms have a chance to do so (Gompers et al.,
2006). Managers of large firms could use these results when attempting to hire individuals who
are most likely to lead growth or new initiatives within the firm. For entrepreneurs, our results
could imply greater mentoring for those who are most promising and encouragement to embark
on a career of entrepreneurship sooner after graduation. The results also provide some guidance
on which industries an entrepreneur might choose if becoming a repeat entrepreneur is the goal.
Furthermore, for policymakers, our results could be used to structure programs to increase the
incentives to engage first time entrepreneurs in becoming repeat entrepreneurs. The results also
provide evidence of the important and significant economic and job growth impacts of repeat
entrepreneurs relative to single-firm only entrepreneurs.
25
We see this paper as greatly advancing the literature by making significant strides toward
both theory and empirical evidence on when first-time entrepreneurs will become repeat
entrepreneurs. The results show factors that make an individual more likely to decide to reinvest
their personal benefits from entrepreneurship back into new firm creation activities. Such a
decision is clearly an important and overlooked aspect of entrepreneurship and this paper lays
important groundwork for future researchers in this area.
26
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29
Figure 1
Year of Founding
50
45
40
35
2nd firms
25
3rd firms
4th firms
20
>=5th firms
15
10
5
2002
1999
1996
1993
1990
1987
1984
1981
1978
1975
1972
1969
1966
1963
1960
1957
1954
1951
1948
0
1945
# of firms
1st firms
30
30
Figure 2
Model of the Entrepreneurial Career
31
6
3
-8
-4
-2
8,
44
4
5,
25
4
98
,7
2
79
,3
3
6
24
,5
83
-9
-4
9
79
-1
4,
79
-7
6
6
-1
1,
,8
81
14
4,
,4
42
21
1
-5
5,
,
25
25
1,
1,
24
24
8
8
an
d
ab
ov
e
89
8,
72
47
9,
33
22
4,
58
98
,4
44
45
,2
54
14
,7
99
77
9
0
%
Figure 3
Proportion of Founders Starting a 2nd Firm
0.70
0.65
0.60
0.55
0.50
0.45
0.40
0.35
0.30
Revenues of 1st Firm / Firm Age
32
Figure 4
Education and Citizenship
50
45
40
35
% Bachelor's Degree
%
30
% Master's Degree
25
% Doctorate Degree
20
Non-U.S. citizen %
15
10
5
0
Novice (0 prior, lifetime)
Repeat Entrepreneurs
33
Figure 5
Differences in Age at First Founding
45
40
35
# Years
30
25
Lag to First Firm
Age founded
20
15
10
5
0
1st firm – novice only
1st firm – eventual repeat
entrepreneur
34
Figure 6
First Firms of Different Entrepreneur Types
1000.0
900.0
Thousands of $
800.0
700.0
600.0
Median Revenues /
Operating Year (in 2001 $)
500.0
Median Initial Capital (2001
$)
400.0
300.0
200.0
100.0
0.0
1st firm – novice only
1st firm – eventual
repeat entrepreneur
35
Figure 7
First Firm Outcomes
60
50
40
%
Acquired (%)
30
Public (%)
Out of Business (%)
20
10
0
1st firm – novice only
1st firm – eventual repeat
entrepreneur
36
Figure 8
Net Worth Adjusted for Age
90.0
80.0
net worth / age
70.0
60.0
Net worth / age
50.0
40.0
30.0
20.0
1
2
3
4
5
6 or
more
Lifetime Firms
37
Figure 9
Founding Roles
60
CFO
COO
Chairman not also CEO
40
%
Chief Scientist/CTO/Chief of
R&D
Consultant or Advisor
President/CEO
20
VP
0
1st firms
2nd firms
3rd firms
4th firms or
higher
38
Figure 10
Current Roles
45
40
COO
35
Chairman (if not also CEO)
Scientist/CTO/Chief of R&D
25
Consultant or Advisor
%
30
Not Active
20
Other employee
15
Outside Director
President/CEO
10
VP
5
0
1st firms
2nd firms
3rd firms
4th firms or
higher
39
Table 1
Estimated Economic Impact of Novice and Repeat Entrepreneurs
Category of Entrepreneur
Total Revenues (in '000 $2001)
Total Employment
Total # of Firms Founded
Total Founders in the Sample
Percentage of Entrepreneurs
Percentage of Firms
Percentage of Total Revenues
Percentage of Total Employment
Novice entrepreneurs
(all ages)
$20,736,000
1,353,420
954
954
72.1
22.7
11.6
51.3
Repeat
Entrepreneurs
$157,029,700
1,285,886
3254
878
27.9
77.3
88.2
48.7
40
Table 2
MIT Repeat Founders by Decade of Graduation
Decade
Total # firms = 1
% serial
=2
=3
=4
=5
=6
=7
=8
=9
=10
=11
Totals
1930s
6
33.3
0
0
1
2
0
0
0
0
0
0
9
1940s
75
39
14
11
10
3
4
0
2
0
0
4
123
1950s
161
44.5
60
29
19
10
4
0
5
0
0
2
290
1960s
231
46.2
85
47
28
14
7
5
2
5
2
3
429
1970s
238
52.3
115
79
30
16
7
2
3
2
1
6
499
1980s
242
43.3
92
49
23
9
3
3
0
1
1
4
427
1990s
182
38.9
69
27
9
6
0
3
0
0
1
1
298
2000s
19
40.6
9
3
1
0
0
0
0
0
0
0
32
41
Table 3
Industry Distribution of Founders
# total firms
Manufacturing
Aerospace
Drugs & medical
Chemicals &
materials
Consumer Products
Electronics
Machinery
Other Manufacturing
Manufacturing total
Services
Architecture
Energy & utilities
Telecommunications
Finance
Management &
finance consulting
Publishing & schools
Software
Law & accounting
Other services
Services total
1st firm
% of
(include 1108 1st
s novices firms
25
51
23
2nd
firm
2.2 3
4.6 21
2.1 7
% of 345 2nd 3rd
firms
firm
8.7 0
6.1 9
2.0 1
% of 3rd 4th
firms
firm
0.0 1
5.6 5
0.6 0
% of 4th
firms
5th firms
and
higher
% of 5th
firms and
higher
Total #
firms
2.0 1
10.0 1
0.0 3
3.3 30
3.3 87
10.0 34
29
181
14
30
353
2.6
16.3
1.3
2.7
31.9
9
55
3
11
109
2.6
15.9
0.9
3.2
31.6
7
33
0
5
55
4.3
20.5
0.0
3.1
34.2
2
8
2
2
20
4.0
16.0
4.0
4.0
40.0
0
2
1
2
10
0.0
6.7
3.3
6.7
33.3
47
279
20
50
547
53
29
34
41
89
4.8
2.6
3.1
3.7
8.0
13
14
7
19
28
3.8
4.1
2.0
5.5
8.1
6
5
11
6
15
3.7
3.1
6.8
3.7
9.3
2
0
2
2
5
4.0
0.0
4.0
4.0
10.0
1
1
2
1
3
3.3
3.3
6.7
3.3
10.0
75
49
56
69
140
21
196
47
245
755
1.9
17.7
4.2
22.1
68.1
12
78
8
57
236
3.5
22.6
2.3
16.5
68.4
1
36
6
20
106
0.6
22.4
3.7
12.4
65.8
0
9
3
7
30
0.0
18.0
6.0
14.0
60.0
1
3
2
6
20
3.3
10.0
6.7
20.0
66.7
35
322
66
355
1147
42
Table 4
Summary Statistics and Variable Definitions
VARIABLE9
First start-up founded
Second start-up founded
DEFINITION
Year in which first firm was founded
(censored if not observed by 2003)
Year in which second firm was founded
(censored if not observed by 2003)
MEAN
SD
1985.10
12.30
1989.96
10.43
Individual Characteristics
Graduation year
Male
Academic major
Country of citizenship
Year of MIT graduation
1973.25
Dummy = 1 if the individual is male
0.93
Set of dummies for academic major: engineering (53%),
management (14%), social science (5%), architecture (4%), and
natural science (the excluded category)
Set of dummies for country of citizenship: Latin America (2%),
Asia (7%), Europe (6%), Middle East (1%), Africa (1%) or North
America (the excluded category)
15.04
0.26
First Firm Level Characteristics
Age at 1st firm founding
Recession Year
Lag to First Firm
Held Patents
Team from Research
Team from Work
Idea from Research
Idea from Work
Idea from Networking
VC funded
Angel funded
L Initial Capital
Acquired
Public
L Revenues
# Cofounders
Out of Business
Operating Years
Industry
9
Age of the entrepreneur the year the first firm
37.5
10.27
was founded
Dummy = 1 if the firm was founded during a
0.22
0.41
recession year as categorized by the NBER.
Lag (in years) from graduation to the first firm
14.28
9.95
founding
Dummy = 1 if the firm held patents
0.14
0.35
Dummy = 1 if the team met doing research / in a
0.11
0.32
lab
Dummy = 1 if the team met via work
0.21
0.41
Dummy = 1 if the source of the idea was
research
0.14
0.35
Dummy = 1 if the source of the idea came from
work
0.62
0.49
Dummy = 1 if the source of the idea came from
networking
-0.26
0.60
Dummy = 1 if the firm received venture capital
funding
0.14
0.35
Dummy = 1 if the firm received funding from
angel investors
0.09
0.29
Capital “raised to get the company off the
ground”
11.97
2.71
Dummy = 1 if the firm was acquired
0.21
0.41
Dummy = 1 if the firm had an IPO
0.13
0.33
Firm revenues for a specific year
14.24
3.03
Number of cofounders
0.15
0.36
Dummy = 1 if
15.27
12.01
Number of years the firm has been in operation
1.17
0.78
Set of dummies for self-reported industry: aerospace, architecture, biomedical,
chemicals, consumer products, consulting, electronics, energy, finance,
law/miscellaneous, machine tools, publishing, software, telecommunications as
well as other services, and other manufacturing. (see Table 3 for percentage in
each category.)
“L” preceding the variable name in the regression tables denotes natural log.
43
Table 5
Entrepreneurship Cox Hazard Rate Regressions
(Individual level of analysis)
Dependent Variable = Second start-up founded
(subjects start being at risk at year of first firm founding)
Note: reported coefficients are hazard ratios
Independent Variables
(5-1)
(5-2)
(5-3)
(5-4)
Demographic vars. Education vars. Citizenship vars. Full model
Graduation year
1.028***
1.027***
(0.003)
(0.003)
Male
1.593*
1.578*
(0.307)
(0.308)
Single
1.027
1.072
(0.165)
(0.174)
Divorced
1.396+
1.399+
(0.286)
(0.288)
Bachelor’s degree
0.954
1.037
(0.084)
(0.097)
Doctorate degree
0.909
0.967
(0.110)
(0.122)
Engineering major
0.908
1.112
(0.088)
(0.189)
Management major
1.236+
1.373+
(0.156)
(0.257)
Science major
0.861
1.081
(0.112)
(0.211)
Architecture major
0.769
0.875
(0.188)
(0.246)
Latin American citizen
1.877***
1.606**
(0.333)
(0.297)
Asian citizen
1.757***
1.486*
(0.299)
(0.259)
European citizen
1.327+
1.265
(0.200)
(0.198)
Middle Eastern citizen
0.868
0.716
(0.309)
(0.257)
African citizen
1.929
1.619
(0.309)
(0.670)
Log likelihood
-4,065.23
-5,040.05
-4,281.47
-4,055.56
Number of obs.
1338
1559
1383
1338
Note: 630 failures; 15,099 years at risk; ***, **, *, + indicate statistical significance at the 0.1%, 1%, 5%,
and 10% levels, respectively.
44
Independent Variables
Table 6
Cox Hazard Rate Regressions
Dependent Variable = Second start-up founded
(subjects start being at risk at year of first firm founding)
Note: reported coefficients are hazard ratios
(6-1)
(6-2)
(6-3)
(6-4)
Initial Conditions Performance All + Industries
All Effects
0.995 (0.023)
1.003 (0.026)
1.007 (0.025)
0.749 (0.143)
0.745 (0.168)
0.719 (0.159)
0.948* (0.023)
0.936* (0.025) 0.935** (0.025)
0.681+ (0.137)
0.730 (0.190)
0.680 (0.167)
0.979 (0.230)
0.833 (0.238)
0.799 (0.225)
1.021 (0.187)
1.092 (0.239)
1.099 (0.239)
0.474* (0.156)
0.561 (0.229)
0.552 (0.222)
0.805 (0.198)
0.795 (0.239)
0.744 (0.235)
0.707 (0.219)
0.893 (0.344)
0.914 (0.341)
0.991 (0.273)
1.127 (0.345)
1.122 (0.334)
1.549 (0.442) 1.725+ (0.525) 1.806* (0.536)
0.969 (0.036)
0.942 (0.043)
0.945 (0.042)
1.501* (0.284) 1.630* (0.335) 1.597* (0.319)
1.098 (0.271)
1.025 (0.270)
1.072 (0.270)
1.005 (0.028)
0.899 (0.128)
0.924 (0.129)
0.999 (0.006)
1.004 (0.006)
1.003 (0.005)
1.324 (0.354)
1.144 (0.344)
1.180 (0.351)
1.003 (0.028)
1.025 (0.032)
1.025 (0.033)
0.999 (0.001)
0.999 (0.032)
0.999 (0.001)
0.681 (0.377)
2.362* (1.000)
1.467 (0.727)
2.105* (0.808)
0.948 (0.284)
1.304 (0.690)
1.234 (0.662)
0.500 (0.512)
0.972 (0.257)
1.321*** (0.119) 1.114 (0.107)
-1.239+ (0.139)
Age at 1st firm founding
Recession Year
Lag to First Firm
Held Patents
Team from Research
Team from Work
Idea from Research
Idea from Work
Idea from Networking
VC funded
Angel funded
L Initial Capital
Acquired
Public
L Revenues
L (Revenues)2
Out of Business
Operating Years
Operating Years2
Aerospace
Biomedical
Chemical
Telecommunications
Electronics
Energy, Elec. Utilities
Finance
Machinery
Publishing
Software
Industry R&D to sales
ratio
Industry Fixed Assets
1.503 (1.629) 0.741 (0.845)
-1.735 (2.110)
# Cofounders
1.242*** (0.084) 1.181 (0.082)
1.129 (0.094)
1.129 (0.092)
Log likelihood
-991.55
-822.47
-723.43
-728.62
Number of observations
719
320
482
480
Note: 756 failures; 17,947 years at risk; ***, **, *, + indicate statistical significance at the 0.1%,
1%, 5%, and 10% levels, respectively.
45