Hitotsubashi University
Institute of Innovation Research

Science-Based Business: Knowledge Capitalor Entrepreneurial Ability?
Theory and Evidence from a Survey of
Biotechnology Start-ups

Serguey Braguinsky
Yuji Honjo
Sadao Nagaoka
Kenta Nakamura
IIR Working Paper WP#10-05

September 2010

Institute of Innovation Research
Hitotsubashi University
Tokyo, Japan
http://www.iir.hit-u.ac.jp

Science-Based Business: Knowledge Capital or Entrepreneurial Ability?
Theory and Evidence from a Survey of Biotechnology Start-ups
Serguey Braguinsky, Yuji Honjo, Sadao Nagaoka, and Kenta Nakamura*
Abstract
We present a model of science-based entrepreneurship where ideas initially produced byresearchers with high-level knowledge capital may be developed by high-ability
entrepreneurs.

With moderate investment costs, startups continuously managed byinventors-founders coexist in equilibrium with startups that experience entrepreneurial
turnover.

The model predicts that startups managed by non-founder entrepreneurs would

on average outperform the startups managed by their founders and that better functioning of
the market for entrepreneurial talent should result in more entrepreneurial turnover in
equilibrium which in its turn leads to more ideas being commercialized and higher rewards
to successful startups.

The predictions of the model are tested against a unique dataset

drawing upon a representative sample of biotechnology startups in Japan and are found to
be broadly supported in the data.
JLE classification numbers: O31, O32
Keywords:

Science-based

Business,

Biotechnology,

Start-ups,

Entrepreneurship,

Innovation
This version
2010/8/30*

Braguinsky (corresponding author), Department of Social and Decision Sciences, Carnegie Mellon
University(sbrag@andrew.cmu.edu);
Honjo,
FacultyofCommerce,
Chuo
University(yhonjo@tamacc.chuo-u.ac.jp); Nagaoka, Institute of Innovation Research, Hitotsubashi University(nagaoka@iir.hit-u.ac.jp);
Nakamura,
Graduate
Schoolof
Economics,
Kobe
University(knakamura@econ.kobe-u.ac.jp). This paper was written while Braguinsky was visiting Hitotsubashi
University Institute of Innovation Research (IIR), during which he participated in the research project on
biotechnology innovation collaboratively pursued by the IIR and the Japan Bio Industry Association.

1. Introduction
The nature of entrepreneurship in industries that place particularly strong demand on
highest-level knowledge and human capital has been examined in several recent
contributions (Bhidé [2000], Zingales [2000], Kaplan, Sensoy, and Stromberg [2009],
Braguinsky, Klepper, and Ohyama [2010]).

Biotechnology industry has attracted

particular attention as a primary example of such “science-based” industry (e.g., Zucker et
al. [1998]).

It has been argued that the role of human capital as a “critical resource” insuch industries changes the very essence of how economists define firms and theirinteraction with markets, and also requires the introduction of radically new management
concepts (Zingales [2000], Pisano [2010]).

Other studies, however, have found that more

“traditional” theories of the firm actually mesh surprisingly well with evidence from suchhigh-tech, science-based industries (Kaplan et al. [2009]).
In this paper, we present a simple theory of science-based entrepreneurship, todeepen our understanding of the factors of entrepreneurial success in industries such as thebiotechnology industry.

We then empirically probe some of the predictions of the theory

by using a hand-collected data set on biotechnology start-ups in Japan.
In our theory, the input from basic research is a “critical resource” in the sensethat an innovative startup is not possible without it.

There is another critical resource,

however, which we call “entrepreneurial ability” but which could be interpreted broadly as
including knowledge of the market and experience of doing business in a given industry.

This resource has repeatedly been found to be the most important predictor of success inhigh-tech industries of the past (e.g., Klepper [2002]) and we conjecture that in this sense,
science-based business is no different.

The expected value of a startup is thus a product of

two factors, the input from the basic research and the input in the form of entrepreneurial
ability.
In the model, startup founders are endowed with seeds coming from basic
research, the commercial values of which are not known. Founders are heterogeneous inentrepreneurial ability and those of them with low ability may hire those with high ability
to develop their seeds.

A founder either retains the seed for the future at no investment

cost, or he invests to learn the value of the seed.

In the latter case, if the seed turns out tobe no good, a high-ability founder becomes a “seedless” entrepreneur who may move toanother startup to develop a good seed.
The implications of the model shed light on several characteristics ofscience-based business that featured prominently in past studies.

First, the model

highlights the crucial role played by the “market for ideas” (see Gans, Hsu, and Stern[2002]).

Without such a market, which we model as the market where talented

entrepreneurs are matched with commercially promising seeds, the cost of investing tolearn the quality of the seed can easily become prohibitive even for seed owners with thehighest entrepreneurial ability, leading to an equilibrium characterized by sluggish growthand “dormant seeds” that never get a chance to be evaluated.

Second, the model implies3

that lowering investment costs results in fuller transfer of seeds from basic researchers tohigh-ability entrepreneurs as they reach the potential commercialization stage, which inturn leads to an equilibrium with fast selection of seeds to be either developed or discarded,and higher expected payoffs, in line with the model in Aghion, Dewatripoint and Stein[2008].

Third, in equilibrium with moderately high investment costs, there will bestartups continuously managed by original inventors alongside startups that experience achange in management.

Furthermore, the model predicts that high levels of investment

and better development of seeds will be primarily associated with the startups thatexperience a change in management.
We probe the predictions of the model using a unique hand-collected dataset on
the Japanese biotechnology industry.

While the United States remains the undisputed

leader in this area, since the late 1990s Japan has adopted a wide range of regulatory,financial and other measures to spur the growth of the domestic biotechnology industry(Honjo et al. [2009]).

Partly as a result of that, the number of biotech firms in Japan had

increased from 116 to 586 from 1994-2006 (Motohashi [2007], p. 2), making the Japanese
biotechnology industry the second largest in the world after the U.S. in terms of sheer
number of firms.

Japan continues to lag behind the U.S., however, in the number of IPOs

(around 20 IPOs as of 2006 compared to 330 in the U.S.) and the volume of sales (barely
over 10 percent of the U.S. level; see Motohashi [2007]).
Our theory implies that in order to catch up with the U.S. not just in quantity but

4also in the quality of startups, Japan needs to create conditions under which more seeds aretransferred to specialized entrepreneurs.

In line with the theory, we find high-investing

and faster-growing startups, as well as startups that successfully conducted IPOs tend to be
those that had experienced entrepreneurial turnover at some point after they were founded.
Our paper has important management and policy implications.

To the best of

our knowledge this is one of the first studies to demonstrate, both theoretically andempirically using a relatively large and representative sample of startups the importance of
having specialized entrepreneurs develop and commercialize seeds produced from basic
research.

High-level specific human capital may be important at the inception but it isrelegated to a supporting role when it comes to developing a commercially viable product.
Startups in countries where the market for entrepreneurial talent is not yet as developed as
it is in the U.S. (such as Japan), may also benefit from international cooperation.

In termsof policy, our findings suggest that the Japanese government should try not so much toemulate the early development stage of the U.S. biotechnology industry by supporting
university-based businesses operating in local business communities, but instead should
concentrate its resources on developing nation-wide market for entrepreneurial talent.
The rest of the paper is organized as follows.
discuss past research.

In the next section we briefly

No attempt is made to survey the fast-growing literature on thebiotechnology industry as a whole; instead, we focus on studies, where we think our paper
can contribute some new theoretical insights or interpretations of empirical evidence.

5Section 3 presents our model and discusses its implications for markets and startups. In
Section 4 we describe the data and test several predictions of the model using these.
Section 5 concludes and contains further discussion of our findings.
2. Connection to past research
Zucker et al. [1998] examined the process of entry and development of the American
biotechnology industry and concluded that “the growth and location of intellectual humancapital was the principal determinant of the growth and location of the industry itself,” with
the industry itself being “a testament to the value of basic research.” (p. 302)

The

fundamental feature of the biotechnology industry as a science-based business and thedramatic impact its emergence has had on commercial activities by universities, such as
patenting, licensing and investing in spinouts has been confirmed

by numerous

subsequent studies (see, for example, Jensen and Thursby [2001], Thursby and Thursby[2002], Zucker and Darby [2006]).
Even though there is thus little doubt that specific high-level human capital is of
crucial importance for a potential biotechnology innovation, it is less certain that the same
specific human capital remains critical during the process of developing and implementing
an innovation.

In a careful study, Gittelman and Kogut [2003] find that importantscientific papers written by academic researchers and high-impact innovations are actuallynegatively correlated.

They conclude that science-based business in general and thebiotechnology industry in particular need separate organizational entities (small

6research-intensive firms) whose task would be to combine valuable knowledge and to selectscientific ideas to produce important technical innovations.

Similarly, Gans et al. [2002]

find that in the biotechnology industry, start-up innovators earn returns not by pursuingtheir businesses independently but by acting as upstream “suppliers” of technology, with animportant role played by third-party “brokers.”
This theme has been pursued further in the bulging literature on strategic
alliances in the biotechnology industry (see, e.g., Lerner and Merges [1998], Baum,Calabrese and Silverman [2000], Rothaermel and Deeds [2004], etc.).

In particular, it has

been found that forming alliances at an early stage (including the exploratory stage) helps
especially smaller startups (Rothaermel and Deeds [2004]).

Shan, Walher and Kogut[1994] and Baum, Calabrese and Silverman [2000] hypothesized that the relationshipbetween alliances and better performance of start-ups can be reciprocal but they only foundevidence that alliances affect performance positively but not vice versa.
The nature of entrepreneurship in science-based business and the biotechnology
industry in particular has been conceptualized in broadly two different ways.

According

to one approach, “the innovations which underlie biotechnology are properly analyzed interms of naturally excludable knowledge held by a small initial group of discoverers, theircoworkers, and others who learned the knowledge from working at the bench-science levelwith those possessing the requisite know-how.” (Zucker et al. [1998], p. 291).

Zingales[2000] and Rajan and Zingales [2001] argue that because of these features, the traditional7

theory of the firm, which associates this concept with strictly hierarchical control over awell-defined tangible critical resource may not be applicable to the biotechnology industry.
Having high-level specific human capital as the “critical resource” may lead to“deintegrating the firm”, potentially paralyzing its ability to function effectively.

In asimilar vein, Pisano [2010] concludes that new organizational forms, beyond the traditional
concept of a firm and even beyond the concept of traditional venture capital are required tomeet the challenges of science based-business.
Kaplan, Sensoy and Stromberg [2009] put these ideas to test using a dataset ofventure capital-backed startups in biotechnology and other high-tech industries.

They findthat, contrary to the perception of human capital being all-important, in virtually every firm,
there was little change in their core line of business from age 2 onward whereas there wasconsiderable turnover in the human capital, contradicting the perception that biotechnology
firms are crucially dependent on specific human capital.

Notably, all their firms

experience “dramatic growth in revenue, assets and market value (although they do not
become profitable).” (Kaplan et al. [2009], p. 77)

Hence, while their conclusion that thetraditional theory of the firm is still quite relevant even in science-based business ispersuasive with regard to the select sample of successful startups, it does not necessarily
contradict the conclusions in Zingales [2000] which, almost by construction, apply tosomewhat dysfunctional firms.
A theory proposed by Aghion, Dewatripoint and Stein [2008] hints at one

8possible way of reconciling the two approaches.

In their model, the initial basic research

stage of developing a potential high-impact innovation is carried out in academia and may
be characterized by the lack of focus and self-indulging behavior of scientists, but once theidea approaches the commercialization stage, the control over its implementation should begiven to the private sector and carried out with much greater focus.
One crucial thing not modeled in Aghion et al. [2008] is how this turnover
happens and who selects ideas for innovations.

A different kind of a critical resource isall-important at this stage, and this resource is the high-ability entrepreneurs (“brokers” inthe “market for ideas” as in Gans et al. [2002]).

Such entrepreneurs would normally also

possess high-level knowledge in biotechnology but they are not necessarily star scientists
while their biggest asset is the knowledge of the market that comes from experience in theindustry or in venture capital.

In the next section we construct a simple model that

captures the essence of this story and derive some implications about the nature ofinnovation in the biotechnology industry that will then be tested against the data.
3. The model
We exploit two features inherent in the nature of science-based business.

First, startups

are based on a seed developed through scientific research and startup founders must possessa high-level specialized knowledge in science.

Second, failure rates are high because

many seeds that look promising initially turn out to be commercially not viable.

Thus,

even startups established by founders with high entrepreneurial ability often find9themselves lacking a good project (seed) after failing to develop their original idea into acommercial product.

The key assumption (which finds support in evidence, including theevidence from Japan presented below) is that such a “seedless” entrepreneur can undercertain circumstances put his talent to work with a seed invented by someone else. This
“recycling” of entrepreneurial talent requires a functioning “market for ideas” (or market
for entrepreneurial talent, as the case might be) but it has the potential to dramaticallychange the nature of the game in science-based business.
The crucial role is played by investment costs involved in evaluating andpursuing the development of a seed.

If such investment costs are high (which may be theresult of poor protection of property rights in the market for ideas – see Gans et al. [2002],
or of poor development of capital markets), few or even no seeds will be transferred tohigh-ability entrepreneurs in equilibrium, resulting in sluggish development and low valuesof startups.

In contrast, in markets with low investment costs, all seeds get evaluated fastand good seeds are developed by high-ability entrepreneurs leading to high values ofstartups.

We develop a stylized model that formalizes these ideas.

Longer proofs are in

the appendix.
3.1 The set-up
We generalize and adapt the model in Jovanovic and Braguinsky [2004].

At the outset

startups differ in the entrepreneurial ability, x, of their founders.

Each startup is endowedwith a seed, and the quality of the seeds, z, differs over startups.

Some startup founders

10with high entrepreneurial ability may find themselves with useless seeds and vice versa.
The market for entrepreneurs then serves to re-deploy high-ability entrepreneurs fromuseless seeds to good seeds.

A startup’s value is xz.

Thus the quality of the seed, z, andthe entrepreneur’s ability to develop it, x, are complements.
Among startups, x ≥ 0 is distributed according to the cumulative distributionfunction F(x).

Seeds are either good or useless:.

A fraction λ of seeds is good,

and the fraction 1− λ is useless; also, we assume for simplicity that λ does not depend onx, so all startups are equally likely to have a good seed.
€

We assume that entrepreneurial ability x is known.

Evaluating and developing aseed is a full-time activity, so that an entrepreneur can handle only one seed at a time.

A

startup founder has two options: (i) retain the seed for possible future development at no
cost (hereafter “no-investment option”), or (ii) invest C > 0 to learn and certify the qualityof the seed (hereafter “investment option”).

All startups are risk-neutral and maximize thediscounted expected value, with the reservation value of exit normalized to zero.
Startups cannot change seeds.1 They can, however, hire entrepreneurs to develop
certified good seeds.

If a startup founder hires an entrepreneur with ability x’, the startup

uses its own seed, z, and the entrepreneurial ability of the entrepreneur it has hired, x’.
The value of such a startup will thus be x’z.

1

To the seed owner who hires an entrepreneur,

This assumption is made to concentrate on turnover of managerial talent, which is our focus here, but it is

not essential for the properties of the equilibrium below.

This is discussed further in the concluding section.

11

then, only the ability of the hired entrepreneur matters, while for the hired entrepreneur all
potential employers with good seeds have the same value (z = 1).

A competitive market

then ensures that entrepreneurs receive compensation commensurate with their marginalcontribution (ability x) while seed owners who hire them receive the same compensation q.
Events occur in four stages:
1. A continuum of startups forms.
2. Startups choose whether to incur the investment cost C to learn the quality of theseed or to hold on to the seed of unknown quality under the “no-investment option”.
The startups that paid C observe the quality of the seed z.

Those with z = 1 choose

whether to develop the seed on their own or hire a “seedless” entrepreneur todevelop the seed.

Those with z = 0 become “seedless” entrepreneurs that may be

hired to develop seeds owned by startups with z = 1.
3. The market for entrepreneurs clears with owners of good seeds who hire others todevelop their seeds receiving q and seedless entrepreneurs with ability x receiving x– q.

This market is Walrasian in that startups take prices as given and there are no

out-of-equilibrium transactions.
4. Startups with certified good seeds (z = 1) proceed to the development stage.
3.2 Expected values
If a startup chooses the no-investment option (i) it retains the ownership of the seed andwill privately observe its quality at some future date, at which point it will decide whether

12to pursue the seed’s development or exit the industry.

We assume that without incurringinvestment cost C a startup cannot certify the quality of its seed, so it will not be able tohire another manager.

Hence, the expected value of such a startup is given by,(1)

where β is the discount factor.
If a startup chooses the investment option (ii), there are two possible cases.
€Inthe first case a founder invests C to learn and certify the quality of the seed in anticipationthat he will hire a seedless entrepreneur to develop the seed if z = 1.

It is shown later thatsuch founders are never hired to develop other startups’ seeds in equilibrium so that theyreceive compensation q if and only if z = 1.

Thus the expected private value of the startup

for a founder in this case is
E I1 = λq − C .

(2)

Now consider a founder who chooses the investment option with a view to€developing a good seed on his own, while also expecting to be hired to develop a good seed

owned by someone else if his own seed turns out to be useless.
these expectations are fulfilled in equilibrium.

Again, we show later that

Hence, such entrepreneurs’ expectedprivate values are given by

E I 2 = λx − C + (1− λ )( x − q) = x − C − (1− λ)q .

(3)

3.2 The no-investment equilibrium€
Let the support of x be given by [0, x max ] , where xmax is finite. We have:€13

Proposition 1. If the investment costs are very high, specifically, if

C ≥ λ(1− βλ ) x max ,(4)

no startup chooses the investment option and no entrepreneurs develop others’ seeds.

In€
this case, R&D investment levels and startup value are low, and seeds are evaluated anddeveloped slowly, with relatively low payoffs even for good seeds.
Proof:The expected value of a startup founder who chooses the investment option and

expects to hire an entrepreneur to develop the seed in case z = 1 has to be non-negative in(2), implying that q ≥ C λ .

The expected value of a startup founder who chooses theinvestment option and expects to be hired to develop a good seed in case z = 0 is given by(3).

€
It is easy to see that under condition (4) it will be less than ENI in (1) for theentrepreneur with the highest x even if the transfer price q = C λ .

Hence, no inventor

chooses the investment option, rendering the transfer of seeds to more capable
entrepreneurs impossible.

€
The remaining claims follow immediately.

3.3 Equilibrium with the investment option and entrepreneurial turnover
Assume now that investment costs are not prohibitively high, so thatC < λ(1− βλ ) x max ,(5)Then there exists an equilibrium in which some or possibly all startups exercise the€investment option, and seedless entrepreneurs with high x are hired to manage good seeds
by startup founders with low x.

We illustrate one such equilibrium (in which some but not

all startups exercise the investment option) in Figure 1, and describe it below.

14

[Figure 1 around here]
The key feature of the equilibrium are two real numbers, xˆ and

.

These

numbers divide the set of x-values into at most three regions – top, middle and bottom.
€
Startups that invest C to learn and certify z and hire seedless entrepreneurs to develop theirseeds if z = 1 come from the bottom region, while startups that invest C and either develop
their seeds if z = 1 or are hired to develop good seeds for the startups from the bottom
region if z = 0 come from the top region.

Startups from the middle region (which might

be empty as shown below) choose the no-investment option and hold on to their seeds.
We start describing the equilibrium with an account of Stage-2 actions and Stage-3 private
values of startups in each region.
The Bottom Region: x ≤ x˜ , whereis the lower bound of the middle region in casethe latter is non-empty, x ≤ xˆ , where xˆ is the lower bound of the top region otherwise ---€Startup founders in this region pay C and certify the quality of the seed z.

If z = 1, they€€hire an entrepreneur from the top region and all receive the same transfer price q.

, founder

is indifferent between receiving q with probability λ , on the one hand,and choosing the no-investment option with the expected value

βλx˜ = λq − C .
If

If, on the other hand:€(6)

so that the middle range in empty, the founder xˆ is indifferent between€
receiving q with probability λ , on the one hand, and developing his own seed in case z = 1€
or being hired to develop someone else’s seed in case z = 0, on the other hand:€15λq − C = xˆ − C − (1− λ)q ,or€xˆ = q .

(7)

The Middle Region: x ∈ ( x˜ , xˆ ) if

, empty otherwise --- Startup founders in this€region choose the no-investment option.

Intuitively, startups in this region have too good€management to be willing to pay for development by another entrepreneur, but are not goodenough to choose the investment option.
The Top Region: x ≥ xˆ --- Startup founders in this region pay C and learn the qualityof the seed z.

If z = 1, they proceed to develop it.

If z = 0, they become seedless€entrepreneurs and sell their services to startups in the first region.

If, founder xˆ is

indifferent between the expected value of this option given by (3) and the expected value of€the no-investment option (1):βλxˆ = xˆ − C − (1− λ )q ,If on the other hand,(8)
so that the middle range in empty, xˆ is indifferent€
between developing someone else’s good seed and exiting, that is, we once again have€xˆ = q .

(7)

Demand = Supply. --- Demand for entrepreneurs must equal their supply:
€

.

(9)

Proposition 2. If condition (5) holds, the investment option and entrepreneurial turnoveroccur in equilibrium.
•

EitherFurthermore,
where

16

xˆ =

(1− λ)β x˜ +1− βλCλ (1− βλ)(10)

and q < C λ (1− β ) solves€€ C + (1− λ)q  λq − C 
λF 
 ,
 = (1− λ)1− F  βλ 
 1− βλ 


•
€

Or

(11)

and q ≥ C λ (1− β ) solves

λF (q) = (1− λ )[1− F (q)] .

(12)€
In the latter case the no-investment option does not occur in equilibrium.
€
Proof: see appendix.

Figure 2 present a numerical example of the equilibrium of the first type (with non-emtpy
middle region, hence, with no-investment option as part of equilibrium behavior).

Figure3 present a numerical example of the equilibrium of the second type, where theno-investment option is dominated by the investment option and subsequent hiring of
seedless entrepreneurs from the top region by founders in the bottom region.
[Figures 2 and 3 around here]
Comparative Statics – The comparative statics of the model are given by thefollowing
Proposition 3.

Other things equal, more seeds will be certified and transferred tohigh-ability entrepreneurs in equilibrium if investment costs C and the discount factor
are low, but the effect of the increase in the fraction of good seeds

is ambiguous.

Proof: see appendix.

17

Investment costs C are likely to be lower if property rights for ideas are welldefined, and if there is an abundant supply of venture capital (e.g., Gans et al. [2002]). Ourmodel suggests a caveat, however.

One interpretation of the discount factor β is that“slow” approach taken by a startup can result in losing the seed because patent rights of the€inventor may expire or be successfully challenged in court (such cases were indeed

confirmed in our interviews with Japanese university startups as documented below).
Hence, to increase the transfer of good seeds to high-ability entrepreneurs, property rights
to ideas should be strong but at the same time short-lived, rendering unprofitable the optionof holding on to the seed by founders of low entrepreneurial ability.
Even though the effect of a higher fraction of good seeds

is theoreticallyambiguous, numerical simulations show that for reasonable parameter values it also leads
to more good seeds being transferred in equilibrium (details are available upon request).
Increased supply of better-quality (that is, more commercializable) seeds can be expected tocome from increased willingness of universities and basic research institutions in general tolicense their innovations and place more emphasis on the ties between university andindustry research, the pattern that has also been well-documented in studies on recent trends
in university entrepreneurship (e.g., Thursby and Thursby [2002]; Shane [2002]).
3.4 Testable predictions
We concentrate on the equilibrium that includes but is not limited to the no-investment
option and summarize empirical predictions that are then tested against the data.

18

Note first that improving the conditions for entrepreneurial talent to be “recycled”
should result in more startups, especially by scientists and those formerly employed inacademia with potentially commercializable seeds from basic research.

This prediction,of course, is not surprising and is not particularly specific to our model.

A morediscriminating prediction is that as the number of startups founded around basic researchideas increase, more of those startups will use specialized entrepreneurial talent comingfrom non-academia to develop their ideas.

Hence, the model predicts that the increase in

the number of startups founded by academic researchers will tend to happen together withthe decrease in the number of startups still managed by academic researchers after
experiencing management turnover.

We thus have:EP1. Better functioning of the market for entrepreneurial talent and lower investment costs(such as the introduction of the equivalent of the Bayh-Dole act) should result in more
startups created with core technology coming from basic research and founded by former
scientists and academia employees.

At the same time, startups experiencing changes inmanagement will tend to be managed less by academic researchers and more byentrepreneurs with prior experience in the industry.
We now turn to the predictions of the model regarding startups that experience
entrepreneurial turnover versus those that don’t.

The model predicts that startups that do

not change management will be comprised of two subgroups.

The first such subgroupwill consist of startups managed by founders with entrepreneurial ability in the middle

19

range, choosing the no-investment option and neither learning the quality nor developing
their seeds.

The other subgroup will be startups managed by high-ability

founder-entrepreneurs that exercised the investment option and learned that z = 1.

If thefraction of good seeds λ is low (as available evidence leads us to expect might be the case– see e.g., Pisano [2010]), we can expect the sample of surviving startups in the data that do€
not undergo entrepreneurial turnover to be pretty much dominated by the former subgroup.

In contrast, surviving startups that do change managers in charge will becomprised only of those that have a certified good seed.

Hence, a testable prediction of

the model is that an average startup that subsequently experiences entrepreneurial turnover
will be more likely to have a certified good seed to be developed at the outset than an
average startup that does not experience entrepreneurial turnover.

It is natural to expectthat a startup with a proven commercially viable seed will be able to raise a larger amountof start-up capital and will have a larger initial size.

Thus, we haveEP2. Entrepreneurial turnover should be associated with larger initial capital and larger
initial size of the startup.
Also, all startups that experience entrepreneurial turnover will be managed byentrepreneurs in the top part of the ability distribution and will have their seeds being
developed, while only a (relatively small) fraction of those that do not experience suchturnover will be so.

Hence, an average startup that does experience entrepreneurial

turnover will be more likely than its counterpart that does not experience such turnover to20

pursue an active R&D program and to generate higher shareholders’ value.
be reflected in higher amounts of raised capital and larger firm size.
value will also tend to be manifested in IPO events.

The latter will

Higher shareholders’Thus,

EP3. Entrepreneurial turnover should be associated with higher R&D investment, larger
amounts of raised capital, larger size of the firm, and higher probabilities of IPO.
In the model, the startups with z = 1 founded by entrepreneurs in the low part of theability distribution immediately hire high-ability entrepreneurs to develop the seed. In
reality the process of finding a suitable entrepreneur can take some time.

If the startup is

only able to start moving with developing its seed after a high-ability entrepreneur has been
hired, the time that elapses between entry and the time the startup hires a new high-ability
entrepreneur to develop its seed will not affect the flow of R&D investment, but it willaffect the amount of capital raised and the startup’s size because these increase only
gradually as the startup moves through the seed development stage.

Hence, we have one

more, rather discriminating prediction of our model:
EP4. The positive effect of entrepreneurial turnover on the amount of raised capital andstartup size will be an increasing function of the new entrepreneur’s tenure after
controlling for the startup age.

The positive effect on R&D expenditure, however, will be

independent of the new entrepreneur’s tenure.

21

4. Testing the model: entry and growth in the Japanese biotechnology industry
4.1 The data
The data come from two surveys of firms in the Japanese biotechnology industry conductedby Japan Bioindustry Association (JBA) with the support of the Hitotsubashi University
Institution of Innovation Research (IIR) for the questionnaire designs in 2008 and 2009.
JBA is a non-profit organization dedicated to the promotion of the Japanese biotechnology
industry.

In order to obtain information on new ventures in the biotechnology industry,

the so-called “bio-ventures,” JBA has been constructing the list of firms and conducting asimple yearly questionnaire survey since 2002.
The extended questionnaires designed jointly by JBA and IIR was sent out to 770
firms and 716 firms in 2008 and 2009 surveys, respectively.

In order to be included into

surveys, firms had to satisfy two criteria: (1) they had to be operating in the biotechnology
industry as classified by the Japanese Industrial Standard (JIS) classification, and (2) they
had to be small or medium enterprises under the Small and Medium Enterprise Basic Law
of Japan (300 or fewer regular employees or capital stock of no more than 300 million yen– about $3 million).
the surveys.

Firms that did not meet either of these criteria were excluded fromThe numbers of effective responses was 309 in both years, with response

rates of 40 and 43 percent for 2008 and 2009, respectively.

We further excluded fromanalyses some special types of firms, such as firms conducting solely export-import
operations and non-profit organizations established solely for R&D purposes. As a result,22the final sample is composed of 292 firms (2008 survey) and 281 firms (2009 survey).
The respondents were asked to provide information about the year the firm wasfounded as well as the year in which it entered the biotechnology industry.

Only about 7

percent of the firms reported that they had been founded prior to entry into thebiotechnology industry, so that 93 percent of the sample consists of de novo startups. A
lion’s share of firms (88 percent) were founded after 1990, and more than 70 percent of thesample were founded in 2000 or later, presumably because several policies for thepromotion of new ventures, such as tax reform for business angels and the setup of thetechnological license office (TLO), accompanied by the introduction of its equivalent of theBayh-Dole Act were introduced in Japan in the late 1990s.
The questionnaires also asked whether the startup was managed by its founder at
the time of the survey or if there had been a change of the CEO.

In the latter case, therespondents were also asked to report how many years the current CEO had been in charge.
Other questions requested information about the seed technology at the time of the startup
and the current seed technology (if different from the startup technology), R&D
expenditure, the amount of capital and the number of employees at the time the firm wasfounded and at the date of the survey, whether the firm had received venture capital funding,whether it had patents granted or applied for in Japan and in the United States and whether
it had successfully conducted or planned to conduct IPO in the near future (this latterquestion was asked in the 2008 survey only).

The variables derived from these responses

23are used to test the model below.
The data also contain information about the area of the main activity of thestartup.

Slightly less than two thirds of the startups in the sample reported that their mainarea of R&D activity was the development of new medical products (including developing
new drugs), the rest were conducting most activity in bioengineering, environment and newenergy resources and so on.

Detailed information about the current CEO’s background(such as education, age, and prior work experience) was also provided, and we use these as
controls where appropriate.
Arguably the biggest advantage of our data is that the sample is not limited tosuccessful startups (such as VC-backed startups or startups that had already conducted IPO).
Just slightly over 27 percent of the startups in our sample report having received financing
from venture capital and less than 10 percent had conducted IPO or at least had specificplans to conduct IPO at the time of the surveys.

In contrast, almost half of the respondents

reported having applied for or being granted patents in the United States and 70 percent had
patents applied for or granted in Japan.

There is also large variation in market

capitalization, R&D expenditure and the number of employees.

The data thus present uswith a unique opportunity to test the predictions of our theory on a relatively large andreasonably representative sample of biotechnology startups.

Table 1 presents the basic

summary statistics for the sample.

24

4.2 Entry cohorts and types of entrepreneurs
We examine how empirical predictions of our theory in Section 3 hold against the data. In
Table 2 we present evidence on the evolution of types of startups by four entry cohorts:prior to 1990, 1990-1999, 2000-2004, and 2005-2009.

As Japan introduced measures

aimed at improving conditions for science-based startups, EP1 predicts that the number ofsuch startups should increase but those firms that experience management turnover should
be managed more by entrepreneurs with non-academic background.
The data in Table 2 provide strong support for these predictions.

The sample ofall startups is divided into those managed by their founders at the time of the survey andthose managed by non-founder entrepreneurs at the time they were surveyed.

The data

show that both categories equally experienced a strong increase in the fraction of startups
whose core technologies draw on basic research (developed at universities or publicresearch corporations).

Paralleling this, among the startups managed by their founders thefractions of those founded by former scientists and individuals formerly employed inacademia increased dramatically (from less than 27 percent to more than 57 percent in theformer category, and from 6 percent to 37 percent in the latter category).
But among the startups already managed by non-founders, the fractions of those
whose entrepreneurs are former scientists and academic employees actually fell sharply
throughout the 1990s and the 2000s.

Hence, while policy measures aimed at promoting

startups based on academic research have resulted in a dramatic increase in the number of25

new startups in the biotechnology industry founded by scientists and individuals formerly
employed in academia, they seem to have also resulted in more frequent replacement offormer scientists and academics by specialized entrepreneurs in the startups experiencing
managerial turnover.
4.3 Turnover of entrepreneurs and startup size at the time of founding
We now turn to testing the predictions of the theory related to the effects of entrepreneurial
turnover.

We predict that startups experiencing entrepreneurial turnover should have

proven good seeds at inception, which will be manifested in higher initial amounts of raised
capital and larger initial size (EP2). To test this prediction we estimate the following
regression:log _ FS0i = α + β1 ETi + γX i + εi ,(13)

where log _ FS0i is the natural log of startup i’s size in the year it was founded, measured€
by either paid capital or the number of employees in actual estimations below, ETi is the€dummy equal to 1 if the startup was managed by a non-founder CEO at survey date andzero otherwise, and Xi is a vector of controls.

Since the amount of initial capital (andpossibly also the initial number of employees) will be strongly affected by the timing ofentry, we include 48 founding year dummies in the vector of controls Xi and also sevendummies corresponding to different areas of activity of the startup.
The baseline specification contains only the above controls, and the results arepresented in the first and third columns of Table 3 (with log initial paid capital and log

26initial number of employees as the dependent variables, respectively).

The mainprediction of our theory is that the coefficient β1 should be positive and significant (EP2),
and this prediction is very strongly supported.

The estimates indicate that startups that€experience subsequent entrepreneurial turnover have initial paid capital more than twice aslarge as startups that don’t experience such a turnover (exp(0.719)-1), and have 46 percent
more employees (exp(0.381)-1).

As expected, entry year dummies are jointly very highly

significant (F-statistic = 51.66 with log initial capital as the dependent variable andF-statistic = 18.17 with log initial number of employees as the dependent variable) but
areas in which startups conduct their primary activity are jointly not significant atconventional levels in either regression.
We then add more controls for firm, technology and the current CEO background,
and what may be thought of as the intrinsic quality of the seed.

To control for firm

background we use the dummy equal to one if the startup was founded before it entered thebiotechnology industry (hence, was a diversifying entrant) and zero otherwise, the dummy
equal to 1 if the core seed technology at the time of entry into biotechnology came frombasic research and zero otherwise.

To control for CEO background, we include twodummies, one equal to 1 if his previous job was in scientific research and 0 otherwise, andthe other equal to 1 if CEO’s previous job was that of a top manager and 0 otherwise (thebasis for comparison are CEOs whose pre-startup jobs were in other commercial activities,
such as sales, marketing, accounting and so on).

We further distinguish between CEOs27

who were previously employed in academia and in large corporations (with the basis forcomparison being small and medium-size firms and financial institutions) and we also
include in the vector Xi age, minus tenure in the startup as a proxy for prior labor market
experience and the dummies capturing the CEO’s educational attainment (one dummy
equal to 1 if the founder has a Ph.D. in science and engineering and 0 otherwise, and theother equal to 1 if the founder has B.A. or M.S in science and engineering and 0 otherwise).
Finally, to control for the intrinsic quality of the seed we also include the dummy equal to 1
if the startup has applied for or was granted a patent in the U.S. and 0 otherwise.
The results of estimating regression (13) in these specifications with log of initial
paid capital and the log of the initial number of employees as the dependent variables arepresented in the second and fourth columns of Table 3, respectively.

Including all these

extra controls has little effect on β1 : entrepreneurial turnover is still estimated to be
associated with 88 percent increase in initial capital and with 50 percent more employees at€
the time of founding, still statistically significant at 1 percent level.

Among the controls,core technology coming from basic research (that is, developed in a university or a publicresearch corporation) has a negative effect on initial capital, while patenting activity in theUS, as expected, has a strong positive impact on both initial paid and initial number of
employees.

The variables capturing CEO’s background and education, on the other hand,are jointly not statistically significant and the areas in which startups conduct their primaryactivity also remain jointly not significant.

28

4.4 Turnover of entrepreneurs and R&D expenditure
The prediction of the model is that entrepreneurial turnover should be associated withhigher R&D expenditure (EP3) and that, moreover, this effect should be independent of thenumber of years the new entrepreneur had been in charge (EP4).

To test this prediction

we estimate the following regression:log _ RDi = α + β1 ETi + β 2 tenurei + β 3 ET _ tenurei + γX i + εi ,(14)

where log_RDi is the natural log of R&D expenditure by startup i in the year of survey, ETi€
is, once again, the dummy equal to 1 if the startup was managed by a non-founder CEO atthe date of a survey and 0 otherwise, tenurei is the CEO’s tenure on the job, and ET_tenurei
is the interaction term between non-founder CEO and tenure.

The main prediction of thetheory is that the sign of β1 should be positive, while the coefficient β 3 should be close
to zero and should not change the impact of β1 .
€€The results are presented in Table 4. In the first two columns we limit the€vector of controls to seven dummies corresponding to different R&D activity areas and to

firm age and its square term.

The coefficient on the dummy capturing entrepreneurial

turnover is large and statistically significant, although only on 10 percent level, while thecoefficients on CEO tenure and its interaction term with entrepreneurial turnover are
statistically indistinguishable from zero, as predicted by theory.

When these two variables

are excluded (in the second column), the magnitude of the coefficient on the non-founder
CEO dummy barely changes, but standard errors go way down, making it statistically29significant at 1 percent level.

The fit of the regression also improves, so we concentrateon the specification that excludes these variables from now on.
The magnitude of the coefficient β1 estimated in the second column of Table 4
indicates that entrepreneurial turnover is associated with 112 percent (exp(0.750)-1)€
increase in R&D expenditure by an average startup.2

Not surprisingly, the areas ofactivity in which startups conduct their R&D (coefficients not shown) are now jointly
highly significant (F-statistic = 16.56) and so are firm age and its square term.
Estimations presented in the third column of Table 4 add more controls.
Specifically, vector Xi now includes also our proxy for the quality of the seed (the dummyreflecting patenting activity in the US) and all the dummies (not shown) reflecting firm andCEO backgrounds, education and experience as in the previous subsection.

None of these

extra controls are either separately or jointly significant at conventional levels (areas ofactivity dummy remain jointly statistically significant at 1 percent level), and including
them affects the coefficient on the non-founder CEO dummy only marginally.

It is only

when we also add the control for the startup size (which itself is strongly positively
associated with entrepreneurial turnover in our theory and in estimations presented in thenext subsection) in the fourth column that the coefficient on the non-founder CEO dummy
is reduced in magnitude by about 1/3 and becomes slightly less statistically significant.
2

The difference looks even more striking in raw data: startups that experienced entrepreneurial change

spend on average 4.25 times more on R&D than startups that did not (3,815 million yen in the former
category versus 897 million yen in the latter category).

30

4.5 Turnover of entrepreneurs, firm size, and IPO process
The model predicts that the amount of capital raised by the firm and its size (measured by
the number of employees) should be positively associated with entrepreneurial turnover(EP3), but only after the new entrepreneur has had enough time to move the startup along
the development stage (EP4). To probe this we estimate the following regression:log_ FSi = α + β1 ETi + β 2 tenurei + β 3 ET _ tenurei + β 3 STi + γX i + εi ,(15)

where FSi is the firm size at the time of survey, measured by paid capital or by the number€of employees, while all other variables are the same as in regression (14) above.

As theamount of raised capital and possibly also the number of employees will be strongly
influenced by the whole firm history, including the timing of entry, we once again control
for firm age non-parametrically in regression (15) by including 48 startup founding year
dummies.

The theoretical prediction is that the coefficient β 3 in regression (12) should

be positive and significant.
€
The estimation results in Table 5 show a pattern that is consistent with theory,

regardless of whether paid capital or the number of employees are used as a measure offirm size.

In both cases, the coefficient on the non-founder CEO by itself is statisticallynot significant at conventional levels (it is actually indistinguishable from zero when log
paid capital is the dependent variable and is negative 0.3 but with comparable standard
errors when log number of employees is the dependent variable).
should be positive and significant holds in both regressions.

The prediction that β 3

In fact, even the magnitude€31

of this coefficient is the same, although it is statistically significant only at 10 percent levelin the regression with log paid capital as the dependent variable (at 5 percent level with log
number of employees as the dependent variable). The estimated magnitude implies that
one extra year of tenure of a new CEO increases paid capital and the number of employees
by about 11 percent.

Since the median tenure of a non-founder CEO in the sample is 3

years, while the mean is 4.47 years, we estimate that entrepreneurial turnover increases thefirm size (measured either by capital or by the number of employees) by 33 percent at themedian and by almost 45 percent at the mean.
Not surprisingly, initial size and patenting activity in the US are strong
independent predictors of larger size at survey time (the latter especially so with regard toraised capital).

Among other controls (not shown), founding year dummies and main

activity area dummies are jointly highly statistically significant, but startup backgroundvariables are jointly statistically insignificant.

CEO background characteristics, on theother hand, are jointly statistically significant at 5 percent level in both regressions.
Finally in Table 6 we look at the stages of the IPO process as a measure of astartup’s success in bringing high value for its shareholders.

The respondents to the 2008

survey were asked to answer the question about their IPO event or intention.

The fourpossible answers were (i) “has already conducted IPO”, (ii) “intend to conduct at IPO at a
specific date and market” (specifying the date and the market), (iii) “consider IPO but nospecific plans at the moment”, and (iv) “have no IPO plans”.

Before presenting the32regression results, the top part of Table 6 shows the summary statistics.

Firms managed

by non-founders are more likely to have already conducted IPO or to have specific plans to
conduct IPO than firms managed by founders, and they are also less likely to have nointention of conducting IPO at all.

The statistical power of the mean comparison t-test islimited because of limited number of observations, but with regard to having no intentionof IPO the one-sided test is statistically significant at 5 percent level and the two-sided test
is significant at 10 percent level (in the two other cases only the one-sided test is
statistically significant at 10 percent level).
The bottom part of Table 6 presents the results of estimating an ordered probit
regression where the four answers above are classified as four different stages of IPO
process, with (i) being the highest and (iv) the lowest stage.

Both with and without extra

controls for changes in core technology, patenting activity in the US, log initial capital andventure capital financing, the non-founder entrepreneur dummy is positively andstatistically significantly (at 5 percent level) associated with the startup being further along
its way to conducting an IPO.

Areas of activity are jointly statistically significant at 1

percent level, but founding year dummies are jointly statistically insignificant.

Amongother controls, US patenting activity dummy and venture capital financing are predictably
strongly positively associated with IPO, but including these controls does not affect thecoefficient on entrepreneurial turnover (if anything, it makes it even somewhat larger).

33

5. Conclusions and discussion
We have presented a model where science-based businesses started by academic
researchers with relatively low entrepreneurial ability may be taken to thecommercialization stage by high-ability entrepreneurs who do not have a project (seed) of
their own.

An equilibrium exists where such entrepreneurial turnover coexists withstartups that are continuously managed by their academic founders.

The theory predicts

that the startups that experience entrepreneurial turnover will tend to outperform thestartups that are managed by their founders in terms of R&D expenditure, firm size andultimate success rates (such as IPO events), with capital raised but not R&D expenditurealso influenced by how long the new entrepreneur had been in charge.
Startups that are developed by high-ability entrepreneurs invest to learn thequality of their seed and discard useless seeds.

This insight is somewhat similar to the“cash-out or flame-out” idea explored by Arora and Nandkumar [2009].

But in our model

it is not just entrepreneurs with the highest opportunity cost who choose the investment
option, but entrepreneurs with the lowest opportunity cost also do the same, in anticipationof hiring high-ability entrepreneurs to develop their seed if it turns out to be good.
The basic framework of the model is closely related to the ideas first explored in
Jovanovic and Braguinsky [2004].

In that paper we assumed, however, that projects weretraded in the market, and not entrepreneurial talent.

It should be emphasized that all whatreally matters is the idea that good seeds are matched with high-ability entrepreneurs, and34

this can equally happen through seeds changing hands if the market for seeds were to
replace the market for entrepreneurs in our model, without changing the nature of theequilibrium in Section 3. Our decision to adopt the market for entrepreneurs rather thanthe market for seeds for the purposes of this paper was driven partly by theory and partly byempirical considerations.

From a theoretical perspective, we agree with the concept (e.g.,

in Zingales [2000]) that while projects may indeed more easily change hands thanmanagement teams in traditional types of business, in science-based business the tacitknowledge of the founder might be essential also at the development stage, making it
difficult to move the seed.

And on the empirical side, our data indicate that even though

entrepreneurial turnover is quite common, the controlling stake in the firm for most part isretained by the founder despite the change in the CEO (Honjo et al. [2009]).
When tested against the data on a representative sample of biotechnology startups
in Japan, the main predictions of the theory are strongly supported.

Both theory andempirical findings supporting it indicate that at the stage of commercialization,science-based businesses may after all be subject to many of the same kind of regularities
as non science-based businesses, so that the emergence of biotechnology and otherscience-based industries in the past couple of decades does not necessarily require
drastically changing the established theory of the firm.
Our study leads to important policy and managerial implications.

In terms ofpolicy, establishing a smoothly functioning market for entrepreneurial talent, including35

lowering the costs of investing into learning the commercial potential of a basic researchseed and reducing incentives for academic researchers to hold on to their seeds for too long,
may be at least as important as supporting the basic research to produce such seeds in thefirst place.

Our findings also imply that science-based businesses should be proactively

seeking experienced managers if they want to evaluate and develop their seeds with a viewto generating high returns.

Zingales [2000] was right when he conjectured that firms

organized around high-level human capital might well be unstable and dysfunctional.

In

contrast to his view, however, our theory and evidence show that such an outcome is notinevitable.
The nature of science-based business and its implications for the theory of thefirm and industrial organization have been the subject of a hot debate in the literature.
Our paper does not aim to resolve this debate once and for all.

But it suggests that“traditional” theories of markets and firm can be fruitfully applied to understanding andanalyzing science-based businesses just as they have been applied to more conventional
types of businesses and industries.
References
Aghion, Phillippe, Mathias Dewatripont, and Jeremy C. Stein, 2008. “Academic Freedom,
Private-Sector Focus, and the Process of Innovation,” Rand Journal of Economics, 39(3), 617-635.
Arora Ashish and Anand Nandkumar, 2009. “Cash-out or Flame-out! Opportunity Cost and36Entrepreneurial Strategy: Theory and Evidence from the Information Security
Industry,” NBER Working Paper 15532, Cambridge, Massachusetts.
Baum, Joel A.C., Tony Calabres, and Brian S. Silverman, 2000. “Don’t Go It Alone:
Alliance

Network

Composition

andStartups’

Performancein

Canadian

Biotechnology,” Strategic Management Journal, 21 (3), 267-294.
Bhidé, Amar V., 2000. The Origin and Evolution of New Business, Oxford University
Press:

Oxford, England.

Braguinsky, Serguey, Steven Klepper and Atsushi Ohyama, 2010. “High-Tech
Entrepreneurship,” Working Paper, Carnegie Mellon University.
Gans, Joshua S., David H. Hsu, and Scott Stern, 2002. “When Does Start-up Innovation
Spur the Gale of Creative Destruction,” Rand Journal of Economics, 33 (4), 571-586.
Gittelman, Michelle and Bruce Kogut, 2003. “Does Good Science Lead to Valuable
Knowledge? Biotechnology Firms and the Evolutionary Logic of Citation Patterns,”
Management Science, 49 (4), 366-382.
Honjo, Yuji, Sadao Nagaoka, Kenta Nakamura, Setsuo Morishita, and Yumi Shimizu, 2009.
“Biobencha-no sannyu-to seicho (Entry and Growth of Bioventures)”, Working paper,
Hitotsubashi University, Institute of Innovation Research (in Japanese).
Jensen, Richard and Marie Thursby, 2001. “Proofs and Prototypes for Sale: The Licensingof University Inventions,” American Economic Review, 91 (1), 240-259.
Jovanovic, Boyan and Serguey Braguinsky, 2004. “Bidder’s Discounts and Target Premia

37

in Takeovers,” American Economic Review, 94 (1), 46-56.
Kaplan, Steven N., Berk A. Sensoy, and Per Strömberg, 2009. “Should Investors Bet on the
Jockey or the Horse? Evidence from the Evolution of Firms from Early Business
Plans to Public Companies,” The Journal of Finance, 64 (1), 75-115.
Klepper, Steven, 2002. “The Capabilities of New Firms and the Evolution of the US
Automobile Industry,” Industrial and Corporate Change, 11 (4), 645-666.
Lerner, Josh, and Robert Merges, 1998. “The Control of Technology Alliances: An
Empirical Analysis of the Biotechnology Industry,” The Journal of Industrial
Economics, 46 (2), 125-156.
Motohashi, Kazuyuki, 2007. “Comparing Biotechnology Startups’ Activities Between
Japan and US,” available on-line at http://www.mo.t.u-tokyo.ac.jp/
Pisano, Gary P., 2010. “The Evolution of Science-Based Business: Innovating How We
Innovate,” Industrial and Corporate Change, 19 (2), 465-482.
Rajan, Raghuram G., and Luigi Zingales, 2001. “The Firm as a Dedicated Hierarchy: A
Theory of the Origins and Growth of Firms,” Quarterly Journal of Economics, 116(3), 805-851.
Rothaermel, Frank T. and David L. Deeds, 2004. “Exploration and Exploitation Alliancesin Biotechnology: A System of New Product Development,” Strategic Management
Journal, 25 (3), 201-221.
Shan, Weijan, Gordon Walker and Bruce Kogut, 1994. “Interfirm Cooperation and Startup

38

Innovation in the Biotechnology Industry,” Strategic Management Journal, 15 (4),387-394.
Shane, Scott, 2002. “Selling University Technology: Patterns from MIT,” Management
Science, 48 (1), 122-137.
Thursby, Jerry G., and Marie C. Thursby, 2002. “Who Is Selling the Ivory Tower? Sourcesof Growth in University Licensing,” Management Science, 48 (1), 90-104.
Zingales, Luigi, 2000. “In Search of New Foundations,” Journal of Finance, 55 (4),1623-1653.
Zucker, Lynne G., and Michael R. Darby, 2006. “Movement of Start Scientists andEngineers and High-Tech Firm Entry,” NBER Working Paper, No. 12712,
Cambridge, Massachusetts.
Zucker, Lynne G., Michael R. Darby, and Marilynn B. Brewer, 1998. “Intellectual Human
Capital and the Birth of U.S. Biotechnology Enterprises,” American Economic
Review, 88 (1), 290-306.

39

Figure 1. Equilibrium behavior by startup typez

1

Investing, then hiring

Investing, then developing

Not investing

Investing, then exiting

Investing, then being hired

x˜

0

€

xˆ

x

€

40

Figure 2. An example of equilibrium, case (i) in Proposition 2.

Parameter values: λ = 0.15 , C = 2, β = 0.95 , xmax = 40, q = 25.

€

€

41

Figure 3. An example of equilibrium, case (ii) in Proposition 2.

Parameter values: λ = 0.15 , C = 0.5, β = 0.7 , xmax = 40, q = 25.

€

€

42

Table 1. Some summary statistics
Fraction founded:
Before 1990

7.54

1990-1999

20.70

2000-2007

71.75

Fraction with:
Startup technology from basic research

52.94

Seed turnover

27.98

Entrepreneurial turnover

37.07

Firm age (years)
Years since CEO change (if any)
R&D expenditure (mln. yen)Amount of capital raised (mln. yen)
Number of employees

Mean

10.35

Standard Deviation

9.19

Mean

4.33

Standard Deviation

3.92

Mean

1,911.3

Standard Deviation

7,336.7

Mean

3,580.8

Standard Deviation

9,872.7

Mean

18.0

Standard Deviation

26.6

43

Table 2. Testing EP1
Cohorts of entry

Before 1990

1990-1999

Number of startups

43

118

276

133

Fraction in total

7.54

20.70

48.42

23.33

•

2000-2004

2005-2009

Startups with founder CEOs at the time of survey

Fraction with startup
technology from basic research

23.08

32.08

51.22

73.20

26.67

47.37

51.47

57.55

Academia

6.25

12.28

21.13

37.38

Large corporations

18.75

54.39

38.73

30.84

Other non-academia

75.00

33.33

40.14

31.78

Fraction with CEOs
Former scientists
Formerly employed in:•Startups with non-founder CEOs at the time of survey

Fraction with startup
technology from basic research

26.32

37.78

61.96

62.50

52.63

47.83

35.85

25.00

Academia

0.00

24.44

17.31

5.88

Large corporations

45.00

48.89

49.04

58.82

Other non-academia

55.00

26.67

33.65

35.29

Fraction with CEOs
Former scientists
Formerly employed in:

Note: Academia includes universities and public research corporations.

44

Table 3. Estimations of regression (13)Dependent variable

Log paid capitalLog number of employees inin founding year

founding year

Non-founder

Coefficient

0.719 ***

0.629 *** 0.381 ***0.405 ***

CEO

St. Error

0.331

0.177

0.125

Basic research

Coefficient-0.678 ***-0.191core technology

St. Error

0.186

0.123

Diversifying

Coefficient-0.637 *0.410 *

entrant

St. Error

0.355

0.213

US patent

Coefficient

0.582 ***

0.207 *

activity dummy

St. Error

0.218

0.118

Constant

0.118

Coefficient

0.992

0.430

0.875 ***

0.826 ***

St. Error

0.626

0.648

0.137

0.236

Founding year

Founding year

dummies,dummies,

R&D activity

All other

R&D activity

All otherarea dummies

controls tooarea dummies

controls too

Number of observations

403

366

379

347

Number of firms

275

262

260

248

Adjusted R-squared

0.094

0.186

0.110

0.147

Other controls

Note: *** indicates that the coefficient is significant at 1 percent level, ** at 5 percent level,
and * at 10 percent level. Robust clustered standard errors. Other controls (notshown) in columns 2 and 4 include CEO prior job and prior occupation dummies,
CEO’s education dummies, seven area of activity dummies and 48 founding year
dummies.

45

Table 4. Estimations of regression (14)Dependent variable

Log R&D expenditure in survey year
Coefficient

0.714 *

0.750 ***

0.731 ***

0.475 **

St. Error

0.375

0.217

0.242

0.212

Coefficient-0.006CEO tenure

St. Error

0.032

Excluded

Excluded

ExcludedNon-founder CEO

Coefficient

0.000

inter. w/tenure

St. Error

0.047

Excluded

Excluded

Excluded

Coefficient

0.102 **

0.095 ***

0.058 *

0.004

St. Error

0.043

0.027

0.031

0.029

Coefficient-0.002 **-0.002 ***-0.001 *-0.001

Firm age squared

St. Error

0.001

0.001

0.001

0.000

US patent activity

Coefficient

1.021 ***

0.848 ***

dummy

St. Error

0.236

0.229

Log number ofCoefficient

0.734 ***

employees

St. Error

0.104

Non-founder CEO

Firm age

Coefficient

1.861 ***

1.858 ***

2.010 ***

0.874 *

St. Error

0.278

0.267

0.457

0.470

Activity area

Activity area

All other

All otherdummiesdummies

controls too

controls too

Number of observations

298

298

276

273

Number of firms

211

211

201

198

Adjusted R-squared

0.269

0.274

0.355

0.479

Constant
Other controls

Notes: *** indicates that the coefficient is significant at 1 percent level, ** at 5 percent
level, and * at 10 percent level. Robust clustered standard errors. Other controls (notshown) in columns 2 and 4 include CEO prior job and prior occupation dummies, CEO’s
education dummies, dummy equal to 1 if entrant from a related industry and zero otherwise,and dummy equal to 1 if the startup technology came from basic research and zerootherwise.

46

Table 5. Estimations of regression (15)
Log paid capital in

Log # of employees insurvey year

survey year

Coefficient-0.005-0.297

St. Error

0.422

0.268

Coefficient

0.042-0.022CEO tenure

St. Error

0.047

0.023

Non-founder CEO inter.

Coefficient

0.108 *

0.108 **

w/tenure

St. Error

0.063

0.043

Change in core technology

Coefficient-0.0570.063

since founding

St. Error

0.195

0.137

Coefficient

0.762 ***

0.217 *

US patent dummy

St. Error

0.207

0.113

Log initial paid capital (#Coefficient

0.287 ***

0.307 ***

of employees)

St. Error

0.102

0.105

Coefficient

0.941

0.972 ***

St. Error

0.712

0.308

Dependent variableNon-founder CEO

Constant

Activity areas, CEO and startup background
Other controls

dummies, 48 founding year dummies

Number of observations

361

339

Number of firms

259

243

Adjusted R-squared

0.426

0.373

Notes: *** indicates that the coefficient is significant at 1 percent level, ** at 5 percent
level, and * at 10 percent level. Robust clustered standard errors. Other controls (notshown) include 48 founding year dummies, seven areas of main activity dummies, CEOprior job and prior occupation dummies, CEO’s education dummies, dummy equal to 1 if
entrant from a related industry and zero otherwise, and dummy equal to 1 if the startup
technology came from basic research and zero otherwise.

47

Table 6. Effect on stages of IPO
Firms managed by: Non-founderStages of IPO:Founder

Fractionst-test (mean comparison)One-sided Two-sided

Have conducted IPO

0.064

0.024

0.081

0.162

Have specific IPO plan

0.096

0.049

0.090

0.180

No IPO intention

0.383

0.500

0.966

0.068

94

164

Number of observations
Dependent variable

IPO stage (ordered probit)
Coefficient

0.420 **

0.488 **

Non-founder CEO

St. Error

0.192

0.222

Core technology

Coefficient-0.299changed since founded

St. Error

0.218

US patenting activity

Coefficient

0.523 **

dummy

St. Error

0.216

Coefficient-0.027

St. Error

0.078

Coefficient

1.097 ***

St. Error

0.219

Log initial paid capital
VC financing dummy
Other controls

Area of activity and 48 founding year dummies

Number of observations (firms)

221

200

Pseudo R-squared

0.168

0.269

Notes: P-values in mean comparison t-test with unequal variance. ** indicates that thecoefficient is significant at 5 percent level. “IPO stage” refers to four answer categories as
described in the main text.

48

Appendix
Proof of Proposition 2.
The proof proceeds by construction.

To begin with, notice that for q close to 0 theleft-hand

zero,

side

of

(11)

tends

to

(1− λ)[1− F (C λ(1− βλ))] > 0 , since

while

the

x max > C (1− βλ)right-handside

by assumption.

tends

to

Also theleft-hand side of (11) is strictly increasing in q, while the right-hand side is decreasing in q.
There are two possible cases.
€--- Case (i).
q = qˆ .

€There is some qˆ < C λ (1− β ) such that (11) is satisfied with equality at
Conditions (10) and (11) yield, after some manipulations, thatat sucha qˆ .

Then (9) says that the number of entrepreneurs from the top region moving to€work with seeds owned by the startups with z = 1 in the bottom region isλF ( x˜ ) = (1− λ )[1− F ( xˆ )] . Together with (10) this implies that (11) is indeed the€€desired equilibrium.
--- Case (ii).

€Assume now that at q = C λ (1− β ) there is still excess supply of seedlessentrepreneurs (the left-hand side of (11) is smaller than the right-hand side).

Thismeans that q has to increase further. But if q > C λ (1− β ) , the no-investment€
option becomes less attractive than paying C and letting another entrepreneur develop
the seed if z = 1 for founder , so that only the bottom region x ∈ [0, xˆ ) and the top€
regionremain as part of an equilibrium (notice that
if and only
if q ≥ C λ (1− β ) ).

€€The number of entrepreneurs from the top region moving to€work with seeds owned by the startups with z = 1 in the bottom region isλF ( xˆ ) = (1− λ )[1− F ( xˆ )] , while founder xˆ is indifferent between paying C andreceiving q with probability λ , on the one hand, and choosing the investment option
with the expected private value EI as in (2), on the other hand. Hence, we must have€
λq − C = xˆ − C − (1− λ)q ⇒ xˆ = q , so that if equilibrium exists, (12) must be satisfied.
€
But since the left-hand side in (12) tends to 1 as q becomes high, while the right-handside tends to zero (because xmax is finite), an equilibrium will always exist.

€

Proof of Proposition 3.
To be completed.

49