Attributed to: Lebret, Hervé, Serial Entrepreneurs: Are They Better? - A View from Stanford University Alumni (August 21, 2012). Available at SSRN: http://ssrn.com/abstract=2133127 or http://dx.doi.org/10.2139/ssrn.2133127 Posted on Chaganomics.com

1. Electronic copy available at: http://ssrn.com/abstract=2133127
SERIAL ENTREPRENEURS: ARE THEY BETTER?
A VIEW FROM STANFORD UNIVERSITY ALUMNI
Hervé Lebret, Ecole Polytechnique Fédérale de Lausanne, Switzerland
ABSTRACT
More than 2700 start-ups and founders affiliated with Stanford University are analyzed to study if
serial entrepreneurs are more successful or not than novice entrepreneurs. The value creations are
measured through the acquisition values or market capitalizations of companies and compared.
The study also compares how serial entrepreneurs do in their new venture compared to the
previous ones. As an interesting complement, the author also analyzes how novice and serial
entrepreneurs access venture capital. It appears that serial entrepreneurs are not better than novice
entrepreneurs in their new ventures, but were more successful with their first venture. Serial
entrepreneurs also raise more money with their new ventures but less money with their first
venture than novice entrepreneurs.
INTRODUCTION
Serial entrepreneurs are often viewed as better than novice entrepreneurs thanks to the
experience accumulated with their previous ventures. But are those entrepreneurs any better if they
have succeeded or failed in their previous ventures and how do they compare to one-time
entrepreneurs? This paper studies the success of 445 serial entrepreneurs out of a group of more
than 2’700 entrepreneurs, more specifically through three different groups of start-ups linked to
Stanford University.
Among the reasons why companies succeed or fail, it is quite well-known that the uniqueness
and value of the technology used for the products, the dynamics of the market in which the
company is positioned and the execution by the management team are the most critical elements.
The experience of the management team is often considered as most important and the concept of
serial entrepreneur has become fashionable. If it is probably a strong asset to count experienced
people in the team which will develop a startup from incorporation to a (successful) development,
how important is the experience of the company founders? And more specifically, does a founder
who had a previous experience as a founder of another start-up increase the likeliness to succeed
in his or her new venture?
,

2. Electronic copy available at: http://ssrn.com/abstract=2133127
Prior Work
Serial entrepreneurship has not been very much studied, even less in high technology. A little
more than ten years ago, Wright and his colleagues did some pioneering work (Wright, Robbie,
and Ennew 1997; Westhead and Wright 1998). Wright, Robbie, and Ennew analyzed serial
entrepreneurship based on the data from 55 venture-capital firms. The study was however focusing
on the preference of venture capitalists (VCs) to use or back serial entrepreneurs. Westhead and
Wright made an analysis of a group of about 600 British entrepreneurs including a quarter of serial
entrepreneurs. More recently Lerner and his colleagues (Gompers, Kovner, Lerner, and
Scharfstein, 2010) have analyzed a group of entrepreneurs backed by venture-capitalists. The
authors analyzed 1’100 serial founders out of more than 10’000 entrepreneurs from the well-
known VentureSource database. A recent work under review (Bengtsson, 2008) analyzes the
repeated relationships of 637 founders with venture capitalists from the VentureXpert database.
Both studies focus on venture-backed founders. Finally Roberts and his colleagues (Hsu, 2007;
Roberts and Eesly, 2009) analyzed data on MIT alumni. Roberts and Eesly compared the impact
of about 1’000 one-time and 1’000 serial entrepreneurs. Hsu studied the impact of prior
experience on new venture valuation. Other works should be mentioned however, where serial
entrepreneurship has been studied for other reasons than performance, such as for example
motivation, opportunity recognition, public policy (Amaral and Baptista, 2006; Baird and
Morrison, 2007; Baron and Ensley, 2006; Sarasvathy and Menon, 2006; Ucbasaran, Westhead,
and Wright, 2009; Westhead, Ucbasaran and Wright, 2005a, 2005b; same authors with
Binks, 2004. Zhang analyzed access to venture capital linked to prior experience with this tool
(Zhang, 2009). The results of the studies are quite different. Wright and al. do not seem to
conclude that they are (major) differences between novice and serial entrepreneurs. They add that
venture capitalists do not have a preference in using serial ones. Lerner and his colleagues show
that VC-backed serial entrepreneurs have a higher likeliness (30%) to drive their second company
to an Initial Public Offering (IPO) if they succeeded in their first venture compared to those who
failed in their first venture (18%). Bengtsson notices that repeated relationships are uncommon
except when VCs have more information about one founder. Roberts shows that companies started
by one-time and serial entrepreneurs had an average of about 1’000 employees and $1 billion in
sales, but serial-entrepreneurs created much more value with an average of 3 times more
employees and sales per founder. Hsu could measure a high likeliness (21%) for a founder with
prior experience to raise venture capital. However most studies have their limitations: Wright is
not focusing on high-tech and his founders belong to a broad group including “intrapreneurs” who
buy-in their venture; Lerner and Bengtsson analyze founders who used venture-capital in all their
ventures and Lerner focused on IPOs as a success measure. Finally the author was recently
mentioned the doctoral work of De Cleyn (De Cleyn, 2011) based on 185 interviews of European
high-tech founders. De Cleyn comments that “the entrepreneurial experience (whether in a high-
tech venture or not) has a significant positive impact, but this effect disappears and becomes
negative for serial entrepreneurs.” Initially attracted by his interest to understand better some
characteristics of Silicon Valley (Lebret 2007), the author had built a database of 2’727 companies
spun-off from Stanford University or founded by alumni from that institution (Lebret 2010).
One-time founders and serial founders
What is a founder? The definition may seem simple but it is not. A founder of a start-up is an
entrepreneur but the converse is not necessarily true. Even if many academic papers study
entrepreneurs, they often include managers with a key or early role in a company, but who were
not always active at the creation of a company. A founder is not a founding shareholder. A
founding investor in a start-up is generally not considered as a founder, neither are employees who

3. join even at the first day of incorporation. Some founders did not have the financial resources to be
a shareholder and sometimes had only stock-options. A founder does not have to be an employee
of the company. A founder may be a critical contributor to the company and may never become an
employee, or may be an employee for a limited period of time. Some founders leave their
company, others are fired. They remain founders. There is therefore no real definition of a
founder. I would however like to define the founders of a company as the group of people who
recognize themselves as such and they do not have to do it formally. This lack of a precise
definition is obviously a difficulty in analyzing whether serial entrepreneurs perform better or not.
Some examples, famous and less famous, may be helpful in illustrating the definition and
status of founder. Apple Computer’s founders, Steve Jobs and Steve Wozniak are famous, but the
company initially had a third founder Ronald Wayne (Wikipedia – Ronald Wayne, 2010). Wayne
renounced his Apple shares 10 days after the company incorporation. Sun Microsystems had four
founders who are nearly as famous: Scott McNealy, Andy Bechtolsheim, Vinod Khosla and Bill
Joy have become icons of Silicon Valley. However, Bill Joy is not considered as a founder of the
company in its IPO prospectus (Sun Microsystems, 1986). He was an early employee. Finally the
recent litigation between the founders of Tesla Motors shows the complexity of the issue
(Wikipedia – Tesla Motors, 2010): from the apparent initial two founders, a judge decided that
five individuals would be considered as the co-founders of the company.
The special case of professors who are at the origin of a start-up deserves some attention. Most
university professors never quit their academic position, which has often taken years to obtain.
They sometimes take sabbatical leaves when the university is flexible and they seldom work full-
time or even part-time for the company, but have a consulting role that the title of Chief Scientist
often represents. Stanford University President, John Hennessy has been the founder of three
companies, MIPS, Atheros and Tensilica. Hennessy took an 80% leave of absence for MIPS and
moved from Chief Scientist to Chief Evangelist before he came back to Stanford full time after a
few months. Hennessy helped in Atheros’ foundation, received founders’ shares and became the
chairman since inception, but he was not as active as he was with MIPS.
Founders therefore may have different roles and a different influence on the company
development. An inexperienced founder such as Steve Jobs or Bill Gates may stay as a leader and
charismatic CEO, but other ones such as Steve Wozniak (Apple) or Paul Allen (Microsoft) may
disappear much sooner from the company management teams. Some founders will lead their
company, even for a limited period of time; others may never have any management position. All
founders, however, because of their initial role, are entrepreneurs and will or should keep the
informal title of founder.
Serial founders are a special species. Robert Noyce and Gordon Moore belonged to the
founding team of Fairchild Semiconductor before founding Intel 10 years later. Jim Clark, a
Stanford professor, founded Silicon Graphics and then Netscape and Healtheon. His co-founder in
Netscape, Marc Andreessen then launched LoudCloud and Ning. Both are famous for repeating
success. These legends may be hiding less famous but also less successful serial entrepreneurs.
Knowing about the success of serial entrepreneurs may not be anecdotal only. It might be a
relevant study to consider if succeeding in high-tech entrepreneurship is correlated to experience
or… to luck. The broader subject of deciding if innovation is a process which can be managed or a
phenomenon merely linked to serendipity is similar in nature. The question is of interest both for
investors and policy makers. If serial entrepreneurs are more successful in their new venture than
one-time entrepreneurs, one may consider that experience matters.

4. Even such a conclusion would be debatable. A one-time or novice (we use the two terms for
the same status) entrepreneur may have had experience as a prior manager or employee. A serial
entrepreneur may be over confident with his second venture or so rich thanks to the first one that
he is not as committed with the second one. A serial entrepreneur who would have failed with his
first venture may feel a higher pressure to succeed and fail despite the experience gained in his
first company. Finally, as mentioned above, success is not only due to the management team but
also in part linked to the quality of the technology and products and to the good or bad timing and
dynamics of the market.
DATA AND RESULTS
The author gathered data on three groups of start-ups related to Stanford University. The data
are described in more detail in the previously mentioned work (Lebret, 2010). The first one is the
group of start-ups which obtained a license from the Office of Technology Licensing (OTL) of
Stanford University (called the “spin-offs”). The second one is based on a study commissioned by
OTL (Leone et al. 1992). The third group known as the Wellspring of Innovation
(http://www.stanford.edu/group/wellspring) is a list of companies founded by Stanford Alumni
and was retrieved on February 6, 2009 (this web site is an ongoing project). The database gathers
2’727 unique companies. A start-up may have many founders and a founder may have created
many companies. This explains why the number of founders who are affiliated to Stanford
University as alumni or staff members is 2’711. Out of these, 167 are or were professors. The
number of founders who have created more than one company is 445, i.e. 16% of the total, as
illustrated in Table 1. More than one third (988) of the companies had at least one serial founder.
Figures 1a and 1b show respectively the fields of activity in which 2’727 companies and the 988
companies founded by at least one serial founder were active. A striking fact is the large
representation of Information Technology (IT) and Software (SW) companies with a serial
founder. All simulations were made using either R, the open-source statistical software (R Project,
2010) or Matlab (Matlab 2010).
Initial results
How do the new start-ups compare to the previous ones and to the companies who did not have
serial founders. Table 2 shows some initial basic results. The author gathered data by hand through
a number of diverse sources. Data include the status of the company: still private, acquired
(M&A), public (IPO) or cessation of its activities. The amount of money raised with investors,
mostly venture capital was also compiled. These elements of information are very difficult to find
and it can never be sure that they are accurate. So the data have to be considered as a best effort.
Given the number of companies considered, it is very unlikely that the information would be very
inaccurate, but it has to be recognized that this kind of data face some limitations. The average
values of Table 2 are self-explanatory (with the exception of the notation “wo 99-00” for the 3rd
serial, which me ans the companies funded in 1999 and 2000 have been excluded.) Serial
founders have a tendency to do better in their first venture than one-time founders, but their do
worse in their second venture and even worse with their third and fourth ones. They also raise less
money on average in their first company than in the following ones. However they raise more
money with time, with the exception of the 4th one. Table 3 provides the Students tests for the
means and mean differences of these data. It should be noticed that the data are highly non-
Gaussian. The author made additional q-q and Kolmogov-Smirnov (non-parametric) analyses:
figures 2a, 2b, 3a and 3b are examples which perfectly illustrate money raised and M&A value for
novice and serial entrepreneurs.

5. One-to-one comparison
The initial results show aggregated results only and it is of course more interesting to compare
the performance of the same founder in his prior and new ventures. However, it is not easy to
make a pure quantitative analysis as an M&A transaction may be higher without being better (if
for example the among of VC money raised is itself higher). The direct comparison of disparate
data such as M&A value, public value or cessation may also be invalid. In the section, the author
used an intermediate quality measure. Though he is aware it is not without weaknesses, it is the
best criterion he could imagine. Table 4 gives the relative performance from first to second and
from second to third start-up, both in terms of success and money raised. Table 5 adds a finer
analysis by comparing the serial founders who had a venture capital firm (VC) in common or not
in both ventures as well as the impact of a founder having founded his new venture before an exit
of the prior venture. Table 6 gives the Student tests of these results where the ranking goes from 1
(worst performance - nearly 0x) to 2 (less than 1x multiple), 3 (equivalent performance), 4
(between 1x and 2x) to 5 (best performance- bovex 2x multiple).
The final and as important analysis is to take into account the prior success measure in the
future success. Once again, it was not easy for the author to use a measure of the success so the
following measure was used. 5 means an exit over $50M with a ration of exit to investment above
5x, 4 means an exit to VC ratio above 2x, 2 means an exit to VC ratio below 1 and 1 means an exit
near $0M, and 3 is the complement. Tables 7 and 8 give the statistics and Student tests of this
success measure.
Regression Analysis
Our final analysis focuses on linear regressions on the available data. These should probably be
seen as preliminary results and the author is also aware that the variables which are available may
not be diverse or rich enough. This being said, it first appears that the presence of venture
capitalists has a positive impact on success, independently of the size of the amount invested. It
also seems that the success degrades with time and this could be linked to the Internet bubble and
the large increase of entrepreneurship in the late nineties. In combination with these two variables
or not, the fact of being a serial entrepreneur is not impacting the success (the reader should be
reminded that a serial entrepreneur in his first venture is not considered as serial). The previous
analysis comes from Table 9 whereas Table 10 shows the impact of the prior success on the future
success. Though it does not appear as important as the presence of venture capital, there is a
positive impact of the nature of the past experience, whether it was a success or a failure. This is
consistent with Tables 7 and 8. Table 11 shows the impact of having a common investor in the
prior and new venture. It looks positive but the confidence level is much lower however. The final
table shows the impact of the technical field. It does not include here the fact of being a serial
entrepreneur or not, but the presence of a VC is also included. Once again, the author prefers to be
careful about the conclusion, but it appears here that biotech, medtech and electronics are more
successful fields, whereas non-technical services is not.
DISCUSSION AND CONCLUSION
One first discussion which arises is the time issue. Could there be an impact of the fact that
serial entrepreneurs do their successive start-ups at different times inducing different values linked
to the times? Serial entrepreneurs in their first ventures raised less money in the mid nineties but
raised more money in their second ventures during the same period. The numbers are clearly
illustrative of the 90’s and less of the prior decades given the higher representation of the

6. companies founded during that decade. The numbers show slightly lower VC amounts and slightly
higher M&A and public values. But the time effect does not seem to be critical.
A second point of discussion is the topic of field of activities. As figure 1b shows, serial
entrepreneurs are over-represented in internet and software technologies. There is an easy
explanation to this phenomenon: start-ups in information technology (IT) are much easier to
launch than for example start-ups in biotechnology or electronics because of the level of resources
required. It does not mean, these use fewer resources but the decision to launch such companies is
easier and the initial resources certainly lower. On average, they raise less money, and are slightly
less successful in M&A for the first venture, and similar in the second. Both fields and timing
were impacted by the Internet bubble but it is not clear whether this exceptional event had an
impact on serial entrepreneurship performance.
A third point of discussion is Stanford University itself. Could there be a specific bias linked to
the fact that the Stanford population would not be typical of the entrepreneur population? Worse,
could it be that the sample of entrepreneurs used in this study would be exceptional? It is clear that
Stanford has many more entrepreneurial alumni than the ones analyzed here. As of January 2012,
the Wellspring of Innovation counts 5’014 companies, which were founded by 4’548 members of
the Stanford community, but this increased set has not been fully studied yet and is still a work in
progress. The group was not only mentioned by self-registration but also by a systematic search on
the web. Then the author filtered and corrected the data which have been known to contain
mistakes. Still the average values of VC amounts, M&A values and public values look
exceptionally high.
The outliers in the three dimensions are also exceptional and do explain such high values;
outliers should not be extracted from the list though as they are an expression of the high value
creation of high tech start-ups. As Schumpeter mentioned it, “the benefits to society of important
innovations and the lavish profits accruing to winning entrepreneurs must be measured against the
total cost of time and money invested in the same industry by unsuccessful entrepreneurs as well.
They receive no returns for their efforts, but their competitive pressure spurs the winners to victory
– to the great benefit of society” (McCraw, 2007). Though very high, these values are certainly
interesting benchmarks for policy makers and for any expert in academic innovation.
In conclusion, serial entrepreneurs do not appear to be more successful than one-time
entrepreneurs and even worse they even seem to perform less and less well with time. It should be
noticed however that they seem to perform well in the second venture and then the performance
degrade with the third venture. The statistical analysis is not indisputable but the contrary would
be unlikely given the results on the data about the Stanford entrepreneurs. This study tends to
confront the general belief that serial entrepreneurs would be important because they would bring
an experience to new ventures that would be a factor for their success. Therefore the results would
on the contrary be closer to the belief that high-tech entrepreneurship would (also) belong to
young and inexperienced people and that the uncertainty linked to new or emerging markets would
have a large contribution in the start-up success.
First it has appeared from the analysis that serial entrepreneurs in their first venture have a
tendency to do better with fewer resources than both one-time entrepreneurs and serial
entrepreneurs in their new ventures. This would signal that serial entrepreneurs have been
identified as successful entrepreneurs with their first ventures. The self-confidence of serial
entrepreneurs after their first success as well as the confidence it gives to potential investors for
the new venture explains why they have access to more resources. As the confidence in their talent

7. may be over-estimated, it is not very surprising that their success is smaller in the new ventures. It
might be that their motivation is not the same after a number of ventures, making them less
efficient in their entrepreneurial efforts.
Second, there is a distinction to be made about the serial entrepreneurs in the new ventures
between those who have an investor in common and those who have never the same investor. The
first group appears to do better whereas the second group has worse results. This may mean that
the consistent investors had identified “good” entrepreneurs, that is entrepreneurs who were
successful and were not only lucky but showed qualities and motivation relevant for their investors
whereas new investors who judged only the success of serial entrepreneurs without assessing the
reasons of the success or the qualities of the entrepreneurs may bet on founders who did not have
the requested qualities and motivation.
Finally, the prior success of serial entrepreneurs seems to have an influence on the future
success. This goes against the view that experience independently of success or failure would
matter and that the learning process would not be as important as imagined. Our study therefore
does not prove that serial entrepreneurs are worse than one-time entrepreneurs but it certainly tend
to show that they are statistically not better. The simple reason might be that being a good
entrepreneur is not sufficient to be successful because high-tech entrepreneurship requires the rare
combination of technology features, market need and human skills to increase the chance of
success. Founders are not the only contributors but firms succeed thanks to teams of people,
whose experience, talent and motivation are also important. The qualities needed for founders may
not be sufficient and even worse, difficult to identify.
CONTACT: Hervé Lebret, herve.lebret@epfl.ch; (T): +41 21 693 7054; (F): +41 21 693 14 89;
EPFL, 1015, Lausanne, Switzerland
ACKNOWLEDGEMENTS
The author would like to thank Katarine Ku, head of the Office of Technology Licensing at
Stanford University for providing data on the spin-offs and other related data. He would like to
thank Prof. Marc Gruber, Chistopher Tucci and Anu Wadhwa and their teams at EPFL College of
Management of Technology for their valuable comments during a presentation of an early version
of this work.
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9. Table 1 – Number of companies created by one-time and serial founders
Founders
Nb of companies
founded
Nb of founders Nb of professor
founders
1 2266 123
2 317 24
3 82 11
4 27 3
5 10 4
6 6
8 2 1
16 1 1
Total 2711 167
% serial 16% 26%
Companies having
No serial founder 1739 63.8%
1 serial founder 890 32.6%
2 serial founders 83 3.0%
3 serial founders 10 0.4%
4 serial founders 5 0.2%
Total 2727
Figure 1a: fields of the 2727 companies
6%
6% 2%
4%
10%
7%
16%13%1%
1%
7%
9%
8%
5%
1%
3%
Biotech
Medtech
Computers
Semiconductor
Electronics
Telecom
IT & SW
Internet
Environment
Manuf.
Eng. Services
Non tech services
Finance
Consumer
Others
Unknown
Figure 1b: fields with serial founders
5%
6%
2%
4%
11%
4%
40%
8%
1%
1% 2% 5%
6%
2%
1%
1% Biotech
Medtech
Computers
Semiconductor
Electronics
Telecom
IT & SW
Internet
Environment
Manuf.
Eng. Services
Non tech services
Finance
Consumer
Others
Unknown

10. Table 2: Performance of companies founded by one-time only or serial entrepreneurs – VC money raised by companies, value of
acquisitions (M&A) or market capitalization.
Data on non-serial VC-backed M&A Public value in 2009 Public value at IPO Public value 12 m. after IPO Ceased
1739 Number Average Number Average Number Average Number Average Number Average
530 $33'707'000 265 $497'000'000 101 $5'145'000'000 175 $833'000'000 174 $906'000'000 371
Data on serial VC-backed M&A Public Public value at IPO Public value 12 m. after IPO
988 Number Average Number Average Number Average Number Average Number Average
441 $35'690'000 225 $639'000'000 56 $5'858'000'000 151 $522'000'000 151 $635'000'000 232
1st comp VC-backed M&A Public Public value at IPO Public value 12 m. after IPO
378 Number Average Number Average Number Average Number Average Number Average
149 $23'319'000 98 $865'000'000 24 $9'417'000'000 68 $480'000'000 68 $592'000'000 83
2nd comp VC-backed M&A Public Public value at IPO Public value 12 m. after IPO
399 Number Average Number Average Number Average Number Average Number Average
185 $39'589'000 81 $642'000'000 21 $4'032'000'000 56 $495'000'000 56 $686'000'000 90
3rd comp VC-backed M&A Public Public value at IPO Public value 12 m. after IPO
124 Number Average Number Average Number Average Number Average Number Average
59 $51'776'000 21 $149'000'000 6 $2'324'000'000 13 $1'104'000'000 13 $1'141'000'000 39
wo 99-00 45 $48'717'000 19 $82'250'000 6 $2'324'000'000 7 $350'000'000 7 $370'000'000
4th+ comp VC-backed M&A Public Public value at IPO Public value 12 m. after IPO
87 Number Average Number Average Number Average Number Average Number Average
48 $39'289'000 25 $152'000'000 5 $681'000'000 14 $293'000'000 14 $165'000'000 20
2+ serial VC-backed M&A Public Public value at IPO Public value 12 m. after IPO
610 Number Average Number Average Number Average Number Average Number Average
292 $42'002'000 127 $464'513'000 32 $3'188'000'000 83 $557'000'000 83 $669'000'000 149

11. Table 3: Student tests on means and mean differences, related to VC amounts, M&A transactions and market capitalizations.
VC Data N Mean Lower absolute bound of confidence interval of difference of mRelated p-value on difference of means
$M Serial 1st Serial 2nd Serial 3rd Serial 4th Serial 2-4 Serial 1st Serial 2nd Serial 3rd Serial 4th Serial 2-4
One-time 474 $36M $33M $39M $0.1M *** $0.8M * $2.8M * -$7.5M $1.6M ** 0.9% 6.7% 6.5% 36.3% 2.0%
Serial 1st 147 $28M $24M $33M $3.4M*** $5.8M ** -$0.2M $5.6M *** 0.1% 1.7% 10.5% 0.0%
Serial 2nd 202 $42M $36M $48M -$3.5M -$7.8M 16.0% 35.4%
Serial 3rd 57 $54M $35M $74M -$2.6M 13.7%
Serial 4th 23 $39M $26M $52M
Serial 2-4 282 $44M $38M $50M
M&A Data N Mean Lower absolute bound of confidence interval of difference of mRelated p-value on difference of means
$M Serial 1st Serial 2nd Serial 3rd Serial 4th Serial 2-4 Serial 1st Serial 2nd Serial 3rd Serial 4th Serial 2-4
One-time 253 $520M $355M $685M $14M * -$123M $48M * $90M *** -$187M 9.2% 28.6% 5.5% 0.1% 49.7%
Serial 1st 120 $900M $459M $1'342M -$104M $144M ** $96M *** $9M * 17.4% 1.6% 0.4% 9.4%
Serial 2nd 93 $617M $385M $850M $42M ** $99M *** 3.1% 0.2%
Serial 3rd 18 $277M $85M $471M -$48M 18.3%
Serial 4th 13 $165M $74M $257M
Serial 2-4 124 $521M $343M $699M
Market Cap. N Mean Lower absolute bound of confidence interval of difference of mRelated p-value on difference of means
Data $M Serial 1st Serial 2nd Serial 3rd Serial 4th Serial 2-4 Serial 1st Serial 2nd Serial 3rd Serial 4th Serial 2-4
One-time 102 $4'929M $1'773M $8'085M -$1'258M -$1'606M -$540M $1'028M ** -$824M 13.8% 26.3% 14.3% 4.1% 18.1%
Serial 1st 30 $11'935M $1'681M $22'188M $415M * $1'482M * $452M ** $980M * 8.9% 6.5% 4.3% 7.6%
Serial 2nd 20 $3'371M $698M $6'044M -$1'798M -$191M 31.5% 11.8%
Serial 3rd 6 $2'324M -$639M $5'287M -$1'283M 25.7%
Serial 4th 3 $1'109M -$1'745M $3'964M
Serial 2-4 29 $2'920M $1'038M $4'803M
***, **, * indicates significance at 1%, 5% and 10% level
Bold indicates that the confidence intervals do not intersect at given significance level whereas italics shows lower bounds overlap at 10% significance level
interval ($M)
90% confidence
interval ($M)
90% confidence
interval ($M)
90% confidence

12. Figure 2a and 2b – q-q chart of VC amounts for novice and serial entrepreneurs
$1
$10
$100
$1 $10 $100
M
illio
n
s
Millions
One‐time
Serial 1st
Serial 2‐4
1
10
100
1 10 100
Millions
Millions
One‐time
Serial 1st
Serial 2nd
Serial 3rd
Serial 4th
Figure 3a and 3b – q-q chart of M&A amounts for novice and serial entrepreneurs
$1
$10
$100
$1'000
$10'000
$1 $10 $100 $1'000
M
illio
n
s
Millions
One‐time
Serial 1st
Serial 2nd
Serial 3rd
Serial 4th
Table 4 – One-to-one comparison multiple performance of serial founders
From1st to 2nd From2nd to 3rd Total From1st to 2nd From2nd to 3rd Total
At least 2x 59 10 69 120 18 138
Between 1xand 2x 18 2 20 32 15 47
Equivalent 102 22 124 12 0 12
Less than 1x 54 16 70 30 10 40
Nearly 0x 58 10 68 29 1 30
Total 291 60 351 223 44 267
At least 2x 20% 17% 20% 54% 41% 52%
Between 1xand 2x 6% 3% 6% 14% 34% 18%
Equivalent 35% 37% 35% 5% 0% 4%
Less than 1x 19% 27% 20% 13% 23% 15%
Nearly 0x 20% 17% 19% 13% 2% 11%
Total 100% 100% 100% 100% 100% 100%
Success Money raised

13. Table 5 – One-to-one comparison multiple performance with special situations
No common VC One common VC Founder out Founder in
At least 2x 15 18 18 27
Between 1xand 2x 11 23 16 11
Equivalent 22 1 45 43
Less than 1x 24 14 29 42
Nearly 0x 18 8 25 36
Total 90 64 133 159
At least 2x 17% 28% 14% 17%
Between 1xand 2x 12% 36% 12% 7%
Equivalent 24% 2% 34% 27%
Less than 1x 27% 22% 22% 26%
Nearly 0x 20% 13% 19% 23%
Total 1 1 1 1
Table 6 – Student tests for one-to-one comparison multiple performance of serial founders
Student tests N Mean t-value p-value Table
Quality from 1st to 2nd 291 2.9 2.7 3.1 36.2 <0.01% 4
VC amount from 1st to 2nd 223 3.8 3.6 4.1 37.8 <0.01% 4
Quality from 1st to 2nd and 2nd to 3rd 351 2.9 2.7 3.1 39.9 <0.01% 4
VC amount 1st to 2nd and 2nd to 3rd 267 3.8 3.6 4.1 42.7 <0.01% 4
Quality when no VC in common 90 2.8 2.4 3.2 19.6 <0.01% 5
Quality when one VC in common 64 3.5 3.0 3.9 19.4 <0.01% 5
Quality if founder out after exit 159 2.7 2.4 3.0 25.1 <0.01% 5
Quality if founder out before exit 133 2.8 2.5 3.1 25.5 <0.01% 5
99% conf. interval
Table 7 – Impact of prior success on new success
Impact of initial success VC-backed
if prior was 1 2 3 4 5 NR N= Total if prior was 1 2 3 4 5 NR N= %
then new is 1 35% 40% 31% 25% 21% 16% 161 26% then new is 1 24% 39% 24% 17% 18% 13% 62 21%
2 10% 15% 10% 11% 15% 7% 69 11% 2 16% 14% 15% 17% 19% 13% 49 17%
3 15% 2% 23% 3% 11% 10% 78 13% 3 24% 4% 11% 0% 8% 9% 29 10%
4 2% 2% 4% 17% 7% 5% 33 5% 4 3% 4% 4% 28% 9% 16% 25 8%
5 9% 21% 11% 11% 22% 5% 82 13% 5 19% 32% 24% 22% 26% 19% 71 24%
NA 28% 21% 22% 33% 24% 56% 187 31% NA 14% 7% 24% 17% 21% 31% 59 20%
N= 99 53 131 36 175 116 610 N= 37 28 55 18 125 32 295
% 16% 9% 21% 6% 29% 19% % 13% 9% 19% 6% 42% 11%
Table 8 – Student tests on impact of prior success on new success
Value of new (all data) success when prior is known Value of new (VC-backed) success when prior is known
Prior N Mean New Prior N Mean New
1 71 2.15 1.88 2.43 1 32 2.71 2.27 3.16
2 42 2.35 1.91 2.80 2 26 2.73 2.12 3.34
3 102 2.41 2.18 2.64 3 42 2.85 2.42 3.28
4 24 2.66 2.10 3.22 4 15 3.26 2.54 3.98
5 133 2.91 2.68 3.14 5 99 3.07 2.80 3.33
90% conf. interval 90% conf. interval

14. Table 9 – Basic regression analyses
beta se t p DFE Dev
Success (0/1) vs. -1.708 0.105 -16.333 0.000 1516 1694
vc exists (0/1) 1.274 0.127 10.044 0.000 ***
Success (0/1) vs. -0.210 0.105 -1.997 0.046 698 932
vc size ($M) 0.000 0.000 -2.698 0.007 ***
Success (0/1) vs. 0.255 0.163 1.566 0.117 1522 1767
period (1-8) -0.237 0.032 -7.471 0.000 ***
Success (0/1) vs. 78.132 11.472 6.811 0.000 1522 1776
Year of foundation -0.040 0.006 -6.889 0.000 ***
Success (0/1) vs. 0.000 0.177 0.002 0.999 1499 1563
vc exists (0/1) 1.805 0.150 12.003 0.000 ***
period (1-8) -0.412 0.039 -10.623 0.000 ***
Success (0/1) vs. 1.698 0.337 5.036 0.000 697 895
vc size ($M) 0.000 0.000 -1.682 0.093 *
period (1-8) -0.359 0.060 -5.958 0.000 ***
Success (0/1) vs. -0.909 0.066 -13.757 0.000 1538 1835
Serial (0/1) -0.073 0.128 -0.568 0.570
Success (0/1) vs. 0.016 0.179 0.089 0.929 1498 1563
vc exists (0/1) 1.807 0.151 12.010 0.000 ***
period (1-8) -0.410 0.039 -10.566 0.000 ***
Serial (0/1) -0.091 0.140 -0.650 0.516
Success (0/1) vs. 1.661 0.339 4.901 0.000 696 894
vc size ($M) 0.000 0.000 -1.749 0.080 *
period (1-8) -0.360 0.060 -5.980 0.000 ***
Serial (0/1) 0.158 0.173 0.917 0.359
Table 10 – Regression analyses on prior success
beta se t p DFE Dev
Success (0/1) vs. -1.917 0.313 -6.134 0.000 370 427
Priorsuc (1/5) 0.283 0.081 3.491 0.001 ***
Success (0/1) vs. -2.950 0.398 -7.408 0.000 361 373
Priorsuc (1/5) 0.220 0.087 2.536 0.011 **
vc exists (0/1) 1.733 0.318 5.453 0.000 ***
Success (0/1) vs. -1.107 0.523 -2.118 0.034 357 349
Priorsuc (1/5) 0.261 0.092 2.836 0.005 ***
vc exists (0/1) 2.291 0.372 6.163 0.000 ***
Period -0.453 0.097 -4.649 0.000 ***
Table 11 – Regression analyses on prior success and common VC
beta se t p DFE Dev
‐0.4514 0.0853 ‐5.2905 0
Ser (0/1) ‐0.0646 0.1733 ‐0.3728 0.7093
SameVC 0.5539 0.3169 1.748 0.0805 * 811 1087
‐0.4206 0.2266 ‐1.8557 0.0635
Prior1 ‐0.7747 0.4683 ‐1.6543 0.0981 * 209 279
Prior5 ‐0.0816 0.3207 ‐0.2545 0.7991
SameVC 0.5564 0.3466 1.6055 0.1084 .
‐1.0278 0.3739 ‐2.749 0.006
Priorsuc 0.4492 0.3379 1.3293 0.1837 210 280
SameVC 0.1356 0.0976 1.3891 0.1648
Period values are 1 if company founded before 1969, then 2 ≤ 1979, 3
if 1984, 4 if 1989, 5, if 1994, 6 if 1998, 7 if 2001, 8 otherwise. Years
correspond to high-tech cycles of Silicon Valley.

15. Table 12 – Impact of fields (also including VC backing) in success.
1 2 3 4 5 6 7 8 9 10 11 12 13
Field 'Biotech' 'Medtech' 'Electronics' 'Consumer' 'Finance' 'Healthcare' 'Internet' 'NA' 'Non tech services' 'OtherTech' 'Software' 'Tech Services' 'Telecom/IT'
N= 160 140 436 145 219 36 362 76 310 104 341 121 277
Interc ‐0.9931 0.0000 ‐0.9767 0.0000 ‐1.0327 0.0000 ‐0.9299 0. ‐0.9050 0.0000 ‐0.9357 0.0000 ‐0.8906 0.0000 ‐0.9052 0.0000 ‐0.8607 0.0000 ‐0.9158 0.0000 ‐0.8833 0.0000 ‐0.9093 0.0000 ‐0.9093 0.0000
Field 0.7779 0.0001 0.7642 0.0007 0.4574 0.000 0.0291 0. ‐0.8442 0.0293 0.4502 0.3203 ‐0.2333 0.1336 ‐101.6608 1.0000 ‐2.4227 0.0000 ‐0.4979 0.1855 ‐0.3207 0.0543 ‐0.8084 0.051 ‐0.8084 0.0511
*** *** *** ** *** * * *
Interc ‐1.7288 0.0000 ‐1.7260 0.0000 ‐1.7932 0.000 ‐1.7404 0. ‐1.6948 0.0000 ‐1.7145 0.0000 ‐1.6598 0.0000 ‐1.6740 0.0000 ‐1.5937 0.0000 ‐1.7179 0.0000 ‐1.6586 0.0000 ‐1.6979 0.0000 ‐1.7118 0.0000
Field 0.5557 0.0087 0.5306 0.0232 0.4116 0.003 0.5336 0. ‐0.1879 0.6545 0.3954 0.4233 ‐0.4378 0.0067 ‐101.2683 1.0000 ‐1.7379 0.0036 0.1553 0.6900 ‐0.3977 0.0219 ‐0.1908 0.6555 0.0412 0.8085
Vcexists 1.2359 0.0000 1.2479 0.0000 1.2617 0.000 1.3000 0. 1.2606 0.0000 1.2753 0.0000 1.3162 0.0000 1.2520 0.0000 1.1625 0.0000 1.2829 0.0000 1.2862 0.0000 1.2649 0.0000 1.2707 0.0000
*** ** ** *** *** **
DFE 1510 1510 1510 1510 1510 1510 1510 1510 1510 1510 1510 1510 1510
Dev 1687 1689 1685 1692 1694 1693 1686 1681 1680 1694 1688 1694 1694
beta se t p DFE Dev beta se t p DFE Dev
0 ‐1.6010 0.2182 ‐7.3366 0.0000 1507 1650 ‐1.8635 0.2847 ‐6.5447 0.0000 1506 1648
Vcexists 1.0899 0.1410 7.7284 0.0000 *** VC exists 1.0938 0.1413 7.7393 0.0000 ***
Biotech' 0.5498 0.3019 1.8210 0.0686 * Biotech 0.8090 0.3516 2.3010 0.0214 **
'Medtech' 0.5344 0.3170 1.6856 0.0919 * Medtech 0.7938 0.3647 2.1763 0.0295 **
'Electronics' 0.3367 0.2508 1.3427 0.1794 Electronics 0.5966 0.3091 1.9301 0.0536 *
'Finance' ‐0.2817 0.4607 ‐0.6115 0.5408 Consumer 0.7100 0.4386 1.6186 0.1055
'Internet' ‐0.3151 0.2673 ‐1.1791 0.2384 Finance ‐0.0192 0.4957 ‐0.0388 0.9691
'Non tech services' ‐1.7260 0.6274 ‐2.7509 0.0059 *** Internet ‐0.0557 0.3224 ‐0.1727 0.8629
'Software' ‐0.3123 0.2731 ‐1.1435 0.2528 Non tech services ‐1.4638 0.6535 ‐2.2399 0.0251 **
'Tech Services' ‐0.2539 0.4685 ‐0.5418 0.5879 Software ‐0.0525 0.3274 ‐0.1605 0.8725
'Telecom/IT' 0.0730 0.2726 0.2678 0.7889 Tech Services 0.0079 0.5028 0.0158 0.9874
Telecom/IT 0.3325 0.3269 1.0171 0.3091