The Evaluation and Reassignment in Science-Based Business: Theory and Evidence
Serguey Braguinsky, Yuji Honjo, Sadao Nagaoka, and Kenta Nakamura*
Abstract
The conventional view of science-based business emphasizes the inseparability of the
scientist generating the idea and the entrepreneur who must implement it. We propose a
model where ideas and entrepreneurial ability can be positively matched for evaluated good
ideas. If third party idea evaluators are available at a moderate cost, a constrained-optimal
decentralized equilibrium exists in which high-ability entrepreneurs whose ideas turn out to
be useless are reassigned to work on certified good ideas for the startups with low-ability
founders. We use novel data on Japan’s biotechnology and find evidence consistent with
the theory and with empirical studies of the U.S. biotechnology.
JLE classification numbers: O31, O32
Keywords:
Science-based
Business,
Biotechnology,
Start-ups,
Entrepreneurship,
Innovation
January 2012
*
Braguinsky (corresponding author), Department of Social and Decision Sciences, Carnegie Mellon
University ([email protected]); Honjo, Faculty of Commerce, Chuo University ([email protected]); Nagaoka, Institute of Innovation Research, Hitotsubashi University ([email protected]);
Nakamura, Graduate School of Economics, Kobe University ([email protected]). The first draft
of 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.
The traditional view of an innovative entrepreneur, dating back to at least
Schumpeter [1912], is that his function requires a special set of skills that are distinct from
those required to be a successful inventor. The advent of science-based business presents a
challenge from this viewpoint. In science-based business, “star scientists” are the “critical
resource” (e.g., Zucker, Darby, and Brewer [1998]), and the apparent inseparability of the
roles of the “inventor” who generates the core scientific idea and the “innovator”
(entrepreneur) who must implement it in the production process becomes a source of
concerns about its implications for corporate governance. (Zingales [2000], Pisano [2010]).
Recent empirical studies have shown, however, that founders are often replaced in
science-based startups, even though the underlying core technology remains the same. In
an early study using SPEC (Stanford Project on Emerging Companies) data, Hannan,
Burton, and Baron [1996, Table 1] estimated the cumulative first 4-year hazard rates of
founder-CEOs in young high-tech firms to be about 40 percent. More recently, Kaplan,
Sensoy, and Strömberg [2009] conducted an in-depth study of 49 highly successful
venture-backed startups from the business plan stage to the first annual report and found
that more than 60 percent of the companies had a non-founder CEO at the time of the first
annual report but only one company had changed its line of business.
The above evidence is not limited to the U.S. or to select samples of highly
successful firms.
Figure 1 compares three cohorts of Japanese startups in the
biotechnology industry. The first cohort is comprised of the startups formed from 1991-
2
1997, the period before Japan embarked on a wide-ranging set of reforms (discussed in
more detail in Section II below) closely modeled after the U.S. example and aimed at
stimulating innovative science-based startups. The second cohort represents the startups
that entered during the first four years of the reforms (1998-2001), while the third cohort
are the startups that entered from 2002-2005, during the late stage of the reform process and
the first years after it was completed.
Figure 1. Number of startups per year, fraction exploiting basic research and founders’ 4year replacement rates by entry cohorts in Japan’s biotechnology industry.
Source: authors’ estimates based on JBA (Japan Biotechnology Association) surveys data.
“Basic research” refers to research conducted in universities and/or public research
corporations.
As can be seen from Figure 1, the average annual number of startups increased
3
sharply and the fraction of startups with core technologies coming from ideas developed in
universities or public research corporations almost doubled from the pre-reform to postreform years. What is more surprising is the three-fold increase in early replacement rates
of founders by non-founder CEOs in the two later cohorts as compared to the pre-reform
cohort. In other words, more science-based later startups were more, not less likely to
quickly replace the founder with a non-founder CEO.
Figure 2. Capital in entry year (millions of yen) and founder replacement
by entry cohorts in Japan’s biotechnology industry.
Source: authors’ estimates based on JBA (Japan Biotechnology Association) surveys data.
Even more intriguing evidence pertains to the kind of startups experiencing
transition from founders to non-founder CEOs. Figure 2 employs the same Japanese data
to compare the amount of capital startups were able to raise in the year they entered the
4
industry (“initially raised capital,” hereafter). In all three cohorts initially raised capital is
much higher among the startups that subsequently replaced founders with non-founder
CEOs than among those where there was no such subsequent replacement. The picture
would look essentially the same if we used the initial size measured by the number of
employees instead of initially raised capital. It thus appears that high CEO turnover rates
among the startups examined by Kaplan et al. [2009] may also be related to their success.
A “stylized fact” in corporate governance literature associates CEO turnover with
subpar past performance (Weisbach [1988], Brickley [2003]). Much of the organizational
literature also leads us to expect that the disruptions to the founding team might be
associated with difficulties rather than with success (e.g., Beckman and Burton [2008]), and
the fact that the founder’s high-level human capital is the critical resource for a sciencebased business should in principle only exacerbate the problem. So why would better
rather than worse startups be more likely to replace their founders by non-founder CEOs?
And why would the increase in founder replacement rates coincide with increased entry by
genuinely science-based businesses? In this paper, we present a model of reassignment of
entrepreneurial talent that is intended to shed light on one possible story behind these
phenomena. Some alternative explanations are discussed in the concluding section.
Our model starts from a simple insight that since good inventors are not necessarily
good entrepreneurs, commercially promising inventions must somehow be transferred from
scientists-inventors with poor entrepreneurial skills to those with good such skills. We first
5
formulate and solve the optimal assignment problem that a social planner would face if
scientific ideas with high commercial value could be separated from their owners and
reallocated to high-ability entrepreneurs.
We then go on to show that under certain
conditions, a decentralized equilibrium exists that supports the optimal assignment, even
though ideas are inseparable from their generators.
We assume that all potential entrepreneurs in science-based business must come
from the scientific research community. This means that all of them, including those with
high entrepreneurial ability, initially have their own inventions/ideas to work with. To
induce a scientist with high entrepreneurial ability to abandon his own idea and to work on
an idea invented by someone else, the latter has to be shown to be much more promising
than the former.
Thus, the key condition defining the existence and properties of a
reassignment equilibrium is the availability of reputable third-party “brokers” who can
evaluate and credibly certify the expected values of science-based innovations (Gans, Hsu,
and Stern [2002], Hsu [2004]). 1 Also, property rights over ideas must be clearly
established as otherwise the costs of using third-party evaluation can easily become
prohibitively high because of the risk of losing the idea (cf. Hellman and Perotti [2011]).
We show that if the costs of third-party idea evaluation are too high, all scientists,
regardless of their entrepreneurial ability, work on their own ideas of unknown quality,
1
It is not coincidental that the startups with high rates of hiring non-founder CEOs in the Kaplan et al. [2009]
are all VC-backed startups.
The role played by venture capital in evaluating and certifying ideas is
documented in various past studies (e.g., Lerner [1995], Kaplan and Strömberg [2001], Hsu [2004]).
6
echoing the pessimism about the potential inefficiencies of the vast majority of “new firms”
expressed, in particular, by Zingales [2000]. But in an environment with clearly delineated
property rights for ideas and readily available third-party evaluators, idea evaluation not
only leads to certification of good ones among them but also weeds out the ideas with no
business potential. When the latter outcome happens, it frees a potential science-based
startup founder to work for another startup that has a certified good idea, and unsuccessful
founders with high enough entrepreneurial ability take up this opportunity.2 Thus, thirdparty idea evaluation and certification create not just the demand for but also the supply of
high-ability scientists-entrepreneurs who happen to possess no idea to work on in their own
startups.
In the ensuing reassignment equilibrium, both private and social returns to
science-based businesses are higher.
Reallocation of businesses from less efficient to more efficient managers has been
studied in several past studies (Holmes and Schmitz [1990] and [1995], Jovanovic and
Braguinsky [2004]). However, none of these studies have addressed the situation specific
to “new,” science-based industries where the nature of the firm is such that ideas cannot be
separated from inventors (startup founders). For example, in Holmes and Schmitz [1990],
projects are sold by founders-entrepreneurs to business managers to free up the
entrepreneur who then goes on to invent a new project. In our model, on the other hand,
2
In the already mentioned study, Kaplan et al. [2009, Table 7] report that while founders were replaced in the
CEO positions in more than 60 percent of the biotechnology firms they examined by the time of the first
annual report, they still remained with the firm as top 5 managers or directors in 82 percent of the cases.
7
ideas must be evaluated before entrepreneurial talent can be freed to be reassigned to work
on those of them that are certified as good. Aghion, Dewatripont, and Stein [2008] also
offer a model of transferring promising scientific ideas from academia to the private sector
as the ideas get closer to the stage of commercial implementation. Their model is built
around the tradeoff between academic freedom and pecuniary compensation. In contrast, in
our model the tradeoff is between the opportunity cost of not knowing the potential of an
idea and the cost of learning its quality through third-party evaluators. We also present an
explicit characterization of a decentralized equilibrium that implements the optimal
reallocation of ideas from low-ability founders to high-ability entrepreneurs.
I. The model
We exploit two features inherent in the nature of science-based business. First,
startups are based on ideas developed through scientific research and all agents must
possess high-level specialized human capital. Second, failure rates are high because many
ideas that look promising turn out to be commercially not viable. Thus, even startups
established by inventors with high entrepreneurial ability often find themselves lacking a
good project after failing to develop their original idea into a commercializable product.
Such “projectless” entrepreneurs can under certain circumstances put their talents to work
with an idea invented by someone else.
The key issue is that this reassignment of
entrepreneurial talent requires a smoothly functioning system of third-party evaluation and
certification of ideas. If such a system is absent, or prohibitively costly, high-ability
8
entrepreneurs have few ideas to work on, resulting in sluggish growth and low values of
startups. In contrast, if the evaluation mechanism is readily available and not very costly,
ideas get evaluated and certified good ones are developed by the best entrepreneurs, leading
to high values of many startups. We develop a stylized model that formalizes these ideas.
Longer proofs are in the appendix.
1.1 The set-up
A science-based startup has a founder with idea z, the quality of which is equal to 1
with probability λ and 0 with probability 1-λ. The founder’s entrepreneurial ability is
denoted by x. For simplicity, we assume that entrepreneurial ability is known, independent
from z, and distributed among founders according to the cumulative distribution function
F(x) with strictly positive density over a finite support [ 0, xmax ] . The value of the startup is
equal to zx. Thus, the quality of the idea and the ability to develop it are complements.
A startup founder can hire another scientist-entrepreneur at a competitive wage to
develop the idea. If a founder with idea z hires an entrepreneur with ability x’, the value of
such a startup will be zx’. A startup founder can learn the quality of z and have it certified
by a reputable outsider (e.g., a VC fund or a consulting agency) by paying a one-time cost
C > 0. We assume that there is an infinitely elastic supply of such third-party idea
evaluators at price C. This price can be thought of, for example, as the real cost of time and
resources spent by third-party evaluators or as the transaction/bargaining costs if there is
ambiguity about property rights over ideas (Gans et al. [2002]), etc. All agents are risk-
9
neutral and maximize their private expected values with no discounting of the future.
1.2 Optimal Assignment
We first derive the optimal assignment, which would prevail if ideas could actually
be reallocated by a planner who had to pay cost C to know if any given idea is good or
useless.
We then show how this assignment can be implemented in a decentralized
equilibrium where ideas cannot be moved.
The planner’s goal is to maximize the total value of all ventures, net of
evaluation/certification costs. The strategy is to reassign certified good ideas from founders
in the lower tail of entrepreneurial ability distribution to entrepreneurs in the upper tail of
that distribution. The return to such reallocation is obviously bounded from above by the
ability of the best available entrepreneur, so if C is very high, it will be optimal not to
evaluate (and not to reassign) any ideas.
To derive the parameter restrictions that will make at least some evaluation optimal,
consider the planner’s expected net return from conducting the first evaluation. Since the
fraction of good ideas is ! , the expected cost of discovering the first good idea is C ! .
Reassigning this first good idea to the best available entrepreneur (and letting go the worst
entrepreneur with x = 0) generates extra value given by xmax ! ! xmax . Thus, the expected
return is larger than the cost if and only if C < ! (1! ! ) xmax . We thus have
Proposition 1. If C ! ! (1" ! ) xmax , it is optimal to assign all startup founders to work on
their own ideas of unknown quality and not to evaluate/certify any ideas.
10
Assume now that C < ! (1! ! ) xmax .
The planner needs to choose the fraction of
ideas e to evaluate. The fraction ! e of those can be expected to turn out good and will be
assigned to entrepreneurs in the upper tail of the ability distribution. Let h ≡ F −1 (1 − λe)
denote the lowest ability of the entrepreneur in this upper tail that will be assigned a
certified good idea (hence, ! e = 1! F ( h ) is the fraction of entrepreneurs assigned to work
with certified good ideas). Let F(l) denote the fraction of founders in the lower tail of
entrepreneurial ability who will be left without any idea to work with as a result of this
evaluation/reassignment process (so that l is the lowest ability of a founder/entrepreneur
who still has an idea to work with). The planner chooses e, h, and l so as to solve
max V = ! !
h,l,e
xmax
0
x dF ( x ) + (1" ! ) !
xmax
h
l
x dF ( x ) " ! ! x dF ( x ) " Ce ,
0
(1)
subject to:
(1! ! )"#1! F (h)$% = ! F (l ) , and
(2)
! e = 1! F ( h ) .
(3)
The first term on the right-hand side of expression (1) is the total value of startups with no
reallocation of certified good ideas to high-x entrepreneurs. The second term is the net gain
from replacing ideas of unknown quality by certified good ideas in the upper tail of the
ability distribution (with x > h), while the third term is the expected loss resulting from
entrepreneurs in the lower tail of the ability distribution (with x < l) having no ideas to work
with. The last term is the total expected evaluation/certification cost of discovering and
11
certifying λe good ideas. Constraint (2) ensures that the expected “demand” for certified
good ideas to assign to high-ability entrepreneurs (on the left-hand side) is equal to the
“supply” of good ideas among low-ability entrepreneurs (on the right-hand side). Finally,
constraint (3) is the definition of e. We have
Proposition 2. If C < ! (1! ! ) xmax , the optimal assignment problem (1)-(3) has a unique
solution, consisting of a fraction of evaluated ideas e < 1, and certified good ideas assigned
to entrepreneurs with x > h, where h ! F "1 (1" ! e) . All entrepreneurs with x < l, where
l = h ! C ! (1! ! ) , are not assigned to work on any idea, while entrepreneurs with
intermediate ability levels (l ≤ x ≤ h) work on ideas of unknown quality. Moreover, the
fraction e and the total value of the startups V are both decreasing functions of C.
Proof: The Lagrangian is
L ( h, l, e, !1, ! 2 ) = V + !1 {" F (l ) ! (1! ! ) "#1! F ( h )$%} + ! 2 "#" e !1+ F ( h )$% .
The first-order conditions are given by
(1! ! ) h ! "1 (1! ! ) ! " 2 = 0 ,
!l ! "1! = 0 , and
C ! !" 2 = 0 .
The second-order derivatives in h and l are negative, the second-order derivative in e is zero
and so are the cross-partial derivatives. Hence, the Lagrangian is globally concave in the
vector (h,l,e). Combining the first-order conditions, we get l = h ! C ! (1! ! ) , so that l < h
as long as C > 0, meaning that the fraction of evaluated ideas e is less than one and
12
obviously decreasing in C. The last claim follows from the Envelope Theorem. [End of
proof.]
Intuitively, as l and h above converge closer, the marginal return to reassigning a
certified good idea from the entrepreneur of ability l to the entrepreneur of ability h tends to
zero. The evaluation/certification costs, however, remain positive and finite, which dictates
that a non-zero fraction of ideas should remain unevaluated and assigned to entrepreneurs
with ability in-between the optimal levels of l and h.
1.3 Decentralized Equilibria Where Ideas Cannot be Moved
In this section we show that the optimal assignment above can be supported as a
decentralized equilibrium with payoff-maximizing risk-neutral agents, where agents decide
whether or not to evaluate and certify their ideas, and then, given some wage functions,
may work on other ideas if there is mismatch between their ability and the type of idea. As
mentioned before, we assume that the evaluation/certification services are provided by
third-party “brokers” with infinitely elastic supply at price C.
Definition. A reassignment equilibrium consists of the pair of abilities h > l and the
assignment of agents to work with ideas which satisfy the following four conditions:
(i)
Payoffs to all agents with x > h (“high-type” agents hereafter) are maximized
when they work on ideas of certified good quality;
(ii)
Payoffs to all agents with l ≤ x ≤ h (“medium-type” agents hereafter) are
maximized when they work on ideas of unknown quality;
13
(iii)
Payoffs to all agents with x < l (“low-type” agents hereafter) are maximized
when they do not work on any ideas themselves and hire high-type agents to
work on their ideas of certified good quality;
(iv)
The supply of high-type agents to work with ideas of certified good quality
equals the demand for them coming from startups endowed with good ideas.
Reassignment equilibrium (if it exists) supports the optimal assignment in
Proposition 2. In particular, condition (ii) means that a fraction F(h) – F(l) of agents will
work on ideas of unknown quality in such an equilibrium, so that the fraction of ideas that
are going to be evaluated will be 1 – F(h) + F(l). Of these ! will be certified as good, so
that condition (iv) can be written as
1! F ( h ) = ! "# F (l ) +1! F ( h )$% ,
(4)
which can be easily seen to be the same as condition (2) in the planner’s problem above.
We also show below that l = h ! C ! (1! ! ) in equilibrium, as in the optimal assignment.
To construct a reassignment equilibrium, let w(x) denote the wage of a type x
entrepreneur if he works on a certified good idea and let wu(x) denote his wage if he works
on an idea of unknown quality. Let
w ( x ) = x ! h + C (1! ! ) , and
(5)
wu ( x ) = ! ( x ! h ) + C (1! ! ) .
(6)
We assume that events unfold according to the following time line:
Stage 1. A continuum of potential startup founders receives ideas of unknown quality.
14
Stage 2. A fraction e of potential founders incurs the cost C and learns the quality of
their ideas, which is publicly revealed and certified. If z is 0, startups do not form; if z
is 1, startups enter. The remaining potential startups (with unevaluated ideas) also enter.
Stage 3. The market for entrepreneurs clears at wages w(x) and wu(x) as in (5) and (6).
Stage 4. Ideas are developed and values are realized.
Lemma 1. If a reassignment equilibrium exists, then wage functions (5) and (6) support
conditions (i) – (iii) above, that is, high-type founders (with x > h) work only on ideas of
certified good equality, medium-type founders (with l ≤ x ≤ h, where l = h ! C ! (1! ! ) )
work only on ideas of unknown quality, while low-type startup founders (with x < l) do not
work on any ideas themselves but hire high-type entrepreneurs to work on their ideas of
certified good quality.
Proof: Substituting x = h into (5) and (6) we can easily see that w(h) = wu(h). Thus, the
marginal high-type agent h is indifferent between working on a certified good idea and on
an idea of unknown quality. For any x > h the wage in (5) is greater than the wage in (6)
and vice versa. Let l ! h " C ! (1" ! ) . Then (6) can be written as wu ( x ) = ! ( x ! l ) and it
can be seen immediately that the marginal medium-type agent l is indifferent between
working on an idea of unknown quality and not working on any idea. Moreover, for any x
> l, the wage obtained working on an idea of unknown quality in (6) is positive, while for
any x < l it is negative. Finally, expected earnings of low-type agents (with x < l) who hire
other agents to work on their ideas if they turn out to be good, net of evaluation costs, are
15
given by ! #$ x! " w ( x!)%& " C = ! h " C (1" ! ) " C = ! h " C (1" ! ) = !l > ! x , so that they
prefer this outcome to working on their own ideas of unknown quality. [End of proof]
As in the planner’s problem, too high C makes reassignment impossible.
Proposition 3 (No-reassignment equilibrium). If C ! " (1 # " ) x max , all startups develop
their own ideas of unknown quality in equilibrium.
Proof: We saw immediately above that, given the wage function (5), an agent who hires a
high ability entrepreneur to work on a certified good idea receives the net payoff equal to
!l = ! h ! C (1! ! ) . This expression has to be positive in order for the lowest opportunitycost entrepreneur (with x = 0) to be willing to incur the evaluation/certification cost. Since
h is bounded from above by xmax, this implies that C < ! (1! ! ) xmax is a necessary condition
for any idea evaluation to happen in equilibrium. [End of proof.]
Assume now that C < ! (1! ! ) xmax . By Lemma 1, given wages as constructed in (5)
and (6), all agents maximize their payoffs by following the optimal assignment rules in the
previous section. It remains to show that the market for entrepreneurial talent clears, that is,
that for each value of " and C that satisfy C < ! (1! ! ) xmax , there is a unique value of h
(and the corresponding value of l = h ! C ! (1! ! ) ) that solve (4). Rewrite (4) as
! F (!h ! C ! (1! ! )) = (1! ! ) (1! F ( h ))
(8)
The left-hand side of (8) is zero if h = C ! (1! ! ) , while the right-hand side is positive,
while if h = xmax the left-hand side is positive, while the right-hand side is zero. Since x
16
has strictly positive density over its support, the LHS of (8) is strictly increasing and the
RHS is strictly decreasing in h. Hence, exactly one intersection exists. We have thus
established
Proposition
4
(Existence
and
optimality
of
reassignment
equilibrium).
If
C < ! (1 " ! ) x max , there is a unique pair {h, l} ! ( 0, xmax ) , where h = l + C ! (1! ! ) such
that conditions (i) – (iv) are satisfied. Such a reassignment equilibrium implements the
optimal assignment in Proposition 2, with the fraction of ideas undergoing evaluation
equal to e = [1 − F (h)] λ .
It is interesting to note that while the pair {h,l} in Propositions 2 and 4 and the
corresponding fraction of evaluated ideas e are unique, which agents will have their ideas
evaluated is not pinned down.3 This is obvious from a planner’s perspective, as all ideas
are ex ante the same and the only thing that matters for the optimal assignment is to
evaluate the right fraction of available ideas and assign them to the best entrepreneurs, but
this is also true in a decentralized equilibrium. More specifically, we have
Proposition 5. In reassignment equilibrium, all agents are indifferent between incurring
the evaluation/certification cost C and working on an idea of unknown quality.
The
expected returns for high-type, medium-type and low-type agents are given, respectively, by
x ! (1! ! ) h , ! x , and !l .
Hence, high-type and low-type agents are better off with
reassignment than without it, while medium-type agents are neither better nor worse off.
3
We thank Matt Mitchell for pointing this out.
17
Proof: See Appendix for the first part, which also derives the payoffs for each type. The
last claim follows from x > h for high-type and l > x for low-type agents. [End of proof.]
The intuition behind this result is as follows. Given that all agent types are equally
happy to work on any ideas, provided only they fit their type, there is nothing that would
prevent a situation where a medium-type agent decides to incur the cost of evaluating his
idea and to move on to work on someone else’s idea of unknown quality. This insight
might be important in certain environments, for example, where third-party evaluators are
available only in limited localities (universities). Provided that evaluators still charge the
same price for their services (e.g., because of legal constraints or university TLO rules),
Proposition 5 implies that reassignment equilibrium will still be possible and have all the
qualitative features of the optimal assignment, even if evaluation/certification services are
available only to medium-type agents.
While there are thus multiple (indeed, infinitely many) decentralized reassignment
equilibria that support the optimal assignment, one of those stands out as a natural
candidate to prevail in any practical implementation. It is clear from the argument in the
previous paragraph that evaluation/certification by medium-type agents entails an
unnecessary extra step in the reassignment process. If only low-type and high-type agents
evaluate, medium-type agents never have to move away from the ideas they had originally
been endowed with. Thus, if evaluation services are equally accessible to all types of
agents, it is natural to expect that only high-type and low-type agents will use those services
18
and any reassignment of entrepreneurial talent will be limited to high-type agents moving
to work on ideas of certified good quality owned by low-type founders. Formally, define a
reassignment equilibrium to be “regular” if it satisfies conditions (i)-(iv) above and, in
addition, involves the smallest possible number of reassignment moves where agents leave
their own startups to work on ideas generated in some other startups.
Proposition 6. The regular reassignment equilibrium is unique. In this equilibrium, only
high-type and low-type agents incur evaluation costs in Stage 2 above, while all startups
entering with ideas of unknown quality are those founded by medium-type agents.
Moreover, the only market that opens in Stage 3 above is the market for high-type freeagent entrepreneurs.
Proof: See Appendix.
The equilibrium behavior in the regular equilibrium is illustrated in Figure 3.
Figure 3. Equilibrium behavior by startup type in Corollary 1
z=1
Evaluating, then hiring
Evaluating, then developing
Not
Evaluating, then exiting
0
l
evaluating
Evaluating, then being hired
h
x
19
Figure 4 illustrates expected values and a regular equilibrium in a parametric example.
Figure 4. Expected values and equilibrium ( " = 0.15 , C = 6, xmax = 100, F(x) uniform)
!
In most practical cases we only observe the startups that were actually formed and
their performance, including CEO turnover (if any) but not the evaluation/certification
process itself. We cannot therefore test our reassignment theory directly, but we can probe
its qualitative predictions for equilibrium outcomes (cf. Fox [2010]). The most interesting
such prediction is given by the following
Proposition 7. Startups that subsequently experience CEO turnover (i.e., where a startup
founder hires another agent to work on his idea) will be able to raise more funds from
capital markets than startups that don’t experience CEO turnover already at the time of
entry, that is, before the CEO turnover actually takes place.
20
Proof: Consider a regular reassignment equilibrium as in Proposition 6. Startups that are
managed by their founders and do not experience CEO turnover will be comprised of two
types; one such type will be founded by high-type agents who discovered that their ideas
were good, and the other will be founded by medium-type agents with ideas of unknown
quality. The average market value of startups that do not hire non-founder CEOs will thus
be given by
h
xmax
! ! l " x dF ( x ) + (1" ! ) ! h
where ! !
x dF ( x ) ,
(9)
#$ F ( h ) " F (l )%&
. In contrast, startups which hire non-founder CEOs
#$ F ( h ) " F (l )%& + " #$1" F ( h )%&
will come exclusively from those with certified good ideas, so their market value will be
given by
!
xmax
h
x dF ( x ) , which is clearly greater than the expression in (9). [End of Proof.]
In practice, if ! is small, while C is relatively high, the fraction ! of startups with
low valuation in (9) will be high, presenting a sharp contrast at the time of entry between
startups that change CEOs later and startups that don’t.
II. A Look at the Data: Some Evidence From Japan’s biotechnology
Our model was motivated by the evidence, presented in the introductory part, that
showed a sharp increase in founder-CEO turnover rates in the Japanese biotechnology
industry starting from the late 1990s and that this turnover was robustly associated with
higher amount of capital raised from investors already at the time the startups entered the
21
industry. In this section we take a slightly more formal look at these data and also discuss
how the institutional reforms introduced in Japan at the turn of the century played a role in
switching the environment from the one with very high costs of third-party idea evaluation
to the one where such third-party evaluator services became more widely accessible. We
do not aim to present a full-scale test of our theory or to rule out alternative interpretations,
the task is merely to illustrate the practical usefulness of the concepts introduced in the
previous section.
2.1. An outline of institutional changes
While the United States remains the world leader, Japan’s biotechnology industry
has been growing fast and recently became the second largest in the world in terms of the
number of university-based startups (Kneller [2007]), some of which have achieved
considerable success even by global standards.4 This followed a series of broad legal and
regulatory reforms in the late 1990s – early 2000s, three aspects of which were particularly
relevant from the viewpoint of establishing property rights over basic research ideas and
creating the institution of third-party evaluation and support for science-based startups.5
First, the Japanese government decisively shifted the focus of its industrial policy
4
For example, AnGes MG, based on genetic research conducted at Osaka University, conducted the first
successful IPO in the biotechnology industry in Japan in 2002 and has subsidiaries in the U.K. and the U.S.;
MediNet, pursuing advanced immune-cell therapy for cancer and based on research conducted at the
University of Tokyo (IPO in 2004), and so on.
5
There are several good sources in English that describe the reforms more generally: Rowen and Toyoda
[2002], Schaede [2005], Kneller [2007].
22
from supporting established companies to promoting innovative entrepreneurship. The
most important changes were the 1998 Law for Facilitating the Creation of New Businesses,
closely modeled after the SBIR initiative in the United States, the creation in 2000 of a
National Forum for Business Startups and Ventures and the 2004 merger of several
separate public corporations into the new publicly-run Organization for Small and Medium
Enterprises and Regional Innovation (SMRJ), which has been given the task of providing
one-stop support for innovative start-ups. Given much less development of venture capital,
especially hand-on type venture capital in Japan as compared to the U.S., these government
agencies emerged as important third-party “brokers” where science-based startups could get
outside expert evaluation and certification of their ideas that would be noted by capital
markets. By 2003 the sheer number of consultations conducted by the government regional
consulting organizations totaled 95,000 cases (Schaede [2005]).
Second, U.S.-style “hands-on” venture capital funds and angel investors also started
emerging,6 following the enactment of the Limited Partnership Act for Venture Capital
Investment in 1998, a series of Angel Tax Incentives measures first introduced in 1997 and
greatly expanded in 2000 and 2003 and the New Business Financing Program in 2001 that
enabled the provision of loans without collateral to new high-tech businesses.
Finally, a series of legal changes drastically changed the university-industry
relationship.
6
Prior to these changes, university researchers had to use a loophole in
See, for example, http://www.ntvp.com/english.html, http://www.sip-vc.com/english/mission/index.html.
23
regulation that allowed them to transfer rights to commercially exploit their inventions to
industry in exchange for research donations. Companies were expected to pay only token
royalties, thus, the cost of having an idea evaluated for its commercial potential was thus
very high (Kneller [2007]). The reforms began by the introduction of the 1998 TLO Law
which legitimized contractual transfers of university discoveries to industry, followed the
1999 Japan Bayh-Dole Law with its 1980 U.S. counterpart as the blueprint. The Law to
Strengthen Industrial Technology enacted in 2000 allowed national university researchers
to create and manage companies without having to resign their academic positions, while
the 2004 University Incorporation Law gave independent legal status to public universities,
putting them under the jurisdiction of the Bayh-Dole Law. In its totality, these measures
amounted to a serious strengthening of bargaining positions of researchers in universities
and public research corporations vis-à-vis third parties.
2.2. Entry time, ideas exploiting basic research and CEO turnover
The logic of our model suggests that an increase in entry by new science-based
businesses following the introduction of the third-party evaluation system should be
accompanied by an increase in early CEO turnover rates among post-reform startups
compared to early CEO turnover rates among pre-reform startups.
Table 1 presents the estimation results of a probit regression with the dependent
variable the probability of the startup founded based on an idea coming from a university or
public research corporation and a Cox proportional hazard regression with the dependent
24
variable the annual hazard of CEO change. 7 Each firm-observation in the data was
assigned a 1-0 dummy variable depending on the entry cohort it belonged to (see also
Figure 1 above).
Table 1. Entry cohorts, source of core technology and CEO change hazard rates
Cox proportional hazard
Probit regression
Dependent variable: core
regression
Dependent variable: annual
technology from basic research hazard rate of CEO change
Entry cohort dummies (baseline cohort: entrants before 1998)
2002 and
Coefficient
0.307 ***
5.931 ***
later
St.Error
0.080
2.173
From 1998-
Coefficient
0.242 **
4.657 ***
2001
St.Error
0.088
1.693
R&D area controls
Included
Included
Number of observations
276
1996
Number of startups
276
290
-182.211
-538.622
Log likelihood
Note: Authors’ estimates using JBA survey data. Robust standard errors clustered at the
startup level are reported. *** and ** indicate that the coefficients are significant at the 1
percent and 5 percent levels, respectively. Probit regression coefficients show marginal
effects.
In the probit regression, the probability of core technology coming from basic
7
The data come from two representative surveys conducted by the Japan Bio Industry Association (JBA), a
non-profit organization dedicated to the promotion of the Japanese biotechnology industry, with the support
from the Hitotsubashi University Institute of Innovation Research (IIR) in 2008 and 2009. See Braguinsky et
al. [2010] for details.
25
research is estimated to be 30.7 percent higher for the latest cohort of entrants than in the
baseline cohort of pre-reform entrants and 24.2 percent higher for the 1998-2001 cohort.
The difference between the two coefficients themselves is not statistically significant. In
the hazard regression, annual hazard rates of CEO change are 5.9 times higher for the
entrants in the post-2002 cohort than in the baseline entry cohort and 4.7 times higher in the
1998-2001 cohort.
The difference between the two later cohorts is once again not
statistically significant at conventional levels.
2.3. CEO Turnover and Capital Raised at the Time of Entry
As already mentioned, perhaps the most interesting qualitative prediction of our
theory is that the initially raised capital should be higher among the startups that
subsequently experience CEO turnover compared to the startups that continue to be
managed by their founders (Proposition 7). The future CEO (who may not be appointed for
several years after entry) can, of course, have no direct “causal” effect on the capital raised
by the startup at the time of entry. Hence, a positive relationship between subsequent CEO
turnover and higher amount of initially raised capital would be an indicator of positive
selection on the certified quality of the startup. To test this, we conduct the “pre-program”
regression, in which capital in entry year is regressed on subsequent replacement of founder
by a non-founder CEO. We expect to find a positive estimated coefficient on the dummy
reflecting subsequent CEO change. 8
8
Jovanovic and Moffitt [1990] use the same method to test for the presence of selection in intersectoral labor
26
Table 2. Capital at entry and entrepreneurial turnover
Dependent variable: Log capital at entry
Non-founder CEO dummy
Coefficient
0.738
***
0.747
***
St.Error
0.248
0.247
Other startup characteristics at foundation time (1-0 dummies):
Core technology exploiting
Coefficient
-0.394
basic research
St.Error
0.263
Founder largest shareholder
Coefficient
-1.174
***
St.Error
0.261
VC financing
Coefficient
-0.185
St.Error
0.560
Patent activity in the US
Coefficient
0.733
***
St.Error
0.263
Entry year and R&D activity area dummies
Included
Included
Constant
Coefficient
2.119
***
3.038
***
St.Error
0.610
0.654
Number of observations
233
218
Number of startups
233
218
Adjusted R-squared
0.051
0.190
Note: Authors’ estimates using JBA survey data. *** indicates that the coefficient is
significant at 1 percent level.
Table 2 presents the estimation results. We regress the (log of) capital in the year of
entry on the dummy equal to 1 if by the time of the survey, the startup had replaced its
founder and 0 if it had not. The identifying assumption is that all startups are equally likely
to replace founders for reasons unrelated to our theory. We control non-parametrically for
firm age by including all 34 entry year dummies and also for R&D areas. In addition, the
specification in the second column includes the 1-0 dummies capturing other entry-year
characteristics of the startup, which may reflect its capital-raising ability; the source of core
movement.
27
technology, whether the founder is the largest shareholder, VC financing at the time of
entry and patents applied for or granted in the U.S.
The results are consistent with the qualitative predictions from the theory.
Subsequent change from the founder to a non-founder CEO is associated with 109 percent
(exp(0.738)-1) higher entry-year capital in the first column and with 111 percent higher
entry-year capital in the second column. The estimated coefficient on the CEO turnover
dummy is practically unaffected by other characteristics included in the specification in the
second column. The effects of those characteristics themselves are for most part in line with
expectations; having a patent granted or applied for in the U.S. roughly doubles the initial
capital-raising capacity, while having the founder as the largest shareholder reduces this
capacity by about 2/3.
2.4. CEO Turnover and Subsequent Performance
In our theory, higher entrepreneurial ability in startups with non-founder CEOs
implies that they will also have higher ultimate realized values than those with founder
CEOs. However, since the only startups to survive to Stage 4 are those with z = 1, the
model also implies that the gap between the Stage-4 values of the two groups should be less
than the gap between their Stage-2 values.
In the data we can use entry-year capital raised as a reasonable proxy for Stage-2
expected values in the theory. Using capital at the date of the survey as a proxy for Stage-4
realized values is more problematic. First, some startups, especially younger ones, will still
28
have ideas of unknown quality. Second, investment in developing good ideas in reality
happens in stages, so that startups with certified good ideas will keep raising more capital
as they move forward, increasing rather than decreasing the gap with the startups with ideas
of unknown quality. Nevertheless, it is instructive to take a look at the differences in
capital observed at the time the startups were surveyed between these two groups of
startups, if only because it presents an opportunity to clearly refute our theory. Namely, we
may not be able to find evidence of a narrowing gap between the capital raised by the
startups that had experienced CEO turnover by the time the startups were surveyed and
those that hadn’t as compared to entry-year capital. But if we find that this gap disappears
or is even reversed, that would be clearly inconsistent with the model. Also, once we
control for entry-year capital, we would expect the effect of CEO turnover on
contemporaneous capital to be much weaker than without such control.
Table 3 presents the estimation results. The first column, which does not control
for the initially raised capital indicates that capital at the time of the survey remains
strongly positively associated with the non-founder CEO dummy controlling for firm age
and areas of R&D. The magnitude of the coefficient on the non-founder CEO dummy in
the second column (where entry-time capital is controlled for), on the other hand, is about
1/3 of the coefficient in the first column and it is statistically not significant at conventional
levels. Once again, the results would look almost the same if we used employment or R&D
expenditure rather than capital at the time of survey as the dependent variable.
29
Table 3. Entrepreneurial turnover and contemporaneous capitalization
Dependent variable: Log capital at time of survey
Coefficient
0.609 ***
Non-founder CEO dummy
St.Error
0.217
Coefficient
Log capital at entry
St.Error
Other startup characteristics
Coefficient
Core technology from basic
research dummy
St.Error
Founder the largest shareholder Coefficient
dummy
St.Error
Coefficient
Venture capital financing
dummy
St.Error
Coefficient
Patent activity in the US
dummy
St.Error
Entry year and activity area dummies
Included
Coefficient
2.741 ***
Constant
St.Error
0.667
Number of startups
277
Adjusted R-squared
0.267
Note: Authors’ estimates using JBA survey data. *** indicates that
0.229
0.197
0.237 ***
0.057
0.018
0.202
-0.738 ***
0.207
1.117 ***
0.223
0.573 ***
0.206
Included
2.279 ***
0.664
246
0.506
the coefficient is
significant at 1 percent level.
III. Discussion and Conclusions
We proposed a theory where institutional regime changes that create opportunities
for early evaluation of science-based ideas and their certification by reputable outsiders
give rise to endogenous emergence of both the supply of and the demand for high-ability
scientists-entrepreneurs to be reassigned to work on certified good ideas. The mechanism
of third-party evaluation allows good ideas to be taken to commercial implementation stage
by capable managers even if startup founders (original inventors) have low entrepreneurial
ability. Absent such a mechanism, however, startups have to be managed by their founders,
30
leading to a lot of dysfunctional firms, as predicted by Zingales [2000]. Thus our theory
provides a unified framework that encompasses the opposing views on the nature of “new”
science-based firms expressed in the past studies. It also lays out explicitly the conditions
under which either one or the other view corresponds to reality.
Our model is general enough that it can accommodate a number of possible more
specific stories linking the positive relationship between capital raised at the time of entry
and the subsequent CEO turnover, especially in science-based ventures. For example,
institutional reforms (such as those that took place in Japan at the turn of the century or in
the U.S. some 20 years before that) leading to increased formation of university-based
startups, may by themselves also increase CEO turnover because new startups will likely
lack the knowledge of industry and management skills. Our model is consistent with this,
and it also shows that new CEOs can only be hired if ideas have already undergone some
third-party evaluation before that – providing an explanation also for why these startups are
valued higher by the market even before the founder is replaced.
Projects that raise more capital initially may be able to do so because they receive
VC funding, which has been repeatedly shown in the literature to be also related also to
founder replacement by outside CEOS (see, e.g., Lerner [1995], Kaplan and Strömberg
[2001]). Once again, our model is consistent with this story if “third-party brokers” happen
to provide not just idea evaluation but also help startups that use their services gain access
to capital markets and hire capable new managers at the same time. As long as the cost of
31
all these services is reflected in the value of the parameter C, our analysis goes through
without any modifications. But our model also shows that the economic functions of thirdparty idea evaluation and those of helping startups gain access to capital and managerial
markets can be separated, at least theoretically, and what is important to make the latter two
markets function is the presence of idea evaluators.
In this sense, the costly idea
evaluation proposed in our model can be thought of as one of the most important
components of the “standardization” process necessary to secure outside financing on the
part of a highly differentiated human-capital-intensive firm (cf. Rajan [2012]). We can see
empirical support for the importance of keeping the above functions at least potentially
distinct in Table 2 above which shows that VC participation in Japan was neither
economically nor statistically significantly associated with higher initially raised capital by
science-based startups, and, nevertheless, startups that had raised more initial capital were
likely to replace founders by outside CEOs.
It can be argued that higher-quality startups tend to hire specialized entrepreneurs
simply because they have more to gain from specialization. This can indeed present an
alternative explanation for a positive relationship between initially raised capital and
subsequent founder replacement. The specialization story by itself, however, does not
explain our finding in the Japanese data that post-reform entrants had significantly higher
founder replacement rates than pre-reform entrants even after controlling for both entry size
and the source of the core technology.
32
Science-based businesses present a growing and very important part of economic
activity, and one would hope that startups in those areas could benefit from efficient
reassignment of entrepreneurial talent to increase the returns to good ideas and welfare
more generally. The U.S. introduced institutional reforms to foster third-party evaluation
and certification of ideas starting more than 30 years ago (see e.g., Jensen and Thursby
[2001]). It is therefore not surprising, in view of our theory, that the best U.S. sciencebased startups have high founder replacement rates and nevertheless show little signs of
suffering from corporate governance problems stemming from the nature of human-capitalintensive firms. Japan has followed the U.S. example relatively recently, and the thirdparty evaluation/certification system in this country is still much less developed. But the
more recent Japanese experience has valuable lessons in it both because it presents the first
major such experiment outside of the U.S. context and because of its implications for other
advanced economies (such at the European Union). Our theory identifies reducing the
costs of third-party evaluation of the business potential of ideas stemming from basic
research as the key element of a policy whose task is to promote globally competitive
science-based industries.
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Appendix
Proof of Proposition 5.
High-type agents: If a founder whose x > h chooses to incur the evaluation/certification cost
C for his idea he either works on his own certified good idea or he abandons it and earns
wage w(x), working on someone else’s certified good idea. In the former case his return is
x and the probability of this outcome is ! ; in the latter case his return is w(x) and the
probability is 1! ! . Hence the expected return is given by
! x + (1! ! ) w ( x ) ! C = x ! (1! ! ) h ,
where the right-hand side is obtained by substituting the wage function (5). If, on the other
hand, a high-type founder decides not to incur cost C he leaves his own idea of unknown
quality to be managed by some type y (where in equilibrium l < y < h) and works for
another certified good idea. The expected return in this case is given by
w ( x ) + ! y ! wu ( y) = x ! h + C (1! ! ) + !l = x ! (1! ! ) h ,
in view of the relationship l = h ! C ! (1! ! ) . Thus, high-type founders are indifferent
between evaluating and not evaluating their ideas provided they always work on ideas of
certified good quality.
Medium-type agents: If a founder with l ≤ x ≤ h pays the evaluation cost, he will hire a
high-type agent y to develop a certified good idea (which occurs with probability ! ) and
work himself on another idea of unknown quality. The expected return is given by
37
! "# y ! w ( y)$% + wu ( x ) ! C = ! h ! !C (1! ! ) + ! x ! !l ! C =
! x + ! "#h ! l ! C ! (1! ! )$% = ! x
.
If, on the other hand such a founder chooses not to evaluate his idea, the return, of course,
is given by ! x . Hence, medium-type agents are also indifferent between evaluating and
not evaluating their ideas.
Low-type agents: If a founder with x < l chooses to evaluate, he will hire a high-type agent
y to develop a certified good idea (which occurs with probability ! ) and will exit otherwise.
The expected return is given by
! "# y ! w ( y)$% ! C = ! h ! !C (1! ! ) ! C = !l
Alternatively, such a founder may hire another agent with ability y to work on his idea of
unknown quality, earning the return equal to
! y ! wu ( y) = ! h ! C (1! ! ) = !l
Thus, low-type agents are also indifferent between evaluating and not evaluating their ideas.
Note that since l > x for this type, hiring another agent to work with an idea of unknown
quality is a better option for these agents than working on their idea themselves. [End of
Proof]
Proof of Proposition 6.
We first show that the regular equilibrium as defined in the main text is indeed the one with
the minimum number of moves across startup boundaries. To this effect, consider a nonregular reassignment equilibrium such that at least one medium-type agents evaluates his
38
idea. Since the fraction of evaluated ideas is the same in all reassignment equilibria, this
also means that at least one high-type or low-type agent does not evaluate his idea. There
are four possible cases to consider.
(i)
A high-type agent leaves his idea unevaluated and the idea evaluated by a mediumtype agent turns out to be useless. In this case the free-agent medium-type
individual will be hired by the high-type agent with an unevaluated idea for wage
wu(x), while the high-type agent will move to work on a certified good idea owned
by a low-type agent for wage w(x). Thus, there is an extra move across the startups
boundaries (medium-type agent moving to work on an idea of unknown quality
owned by a high-type agent) as compared to the regular equilibrium.
(ii)
A high-type agent leaves his idea unevaluated and the idea evaluated by a mediumtype agent turns out to be good. In this case the high-type agent with an
unevaluated idea will move to work on the medium-type agent’s certified good idea
for wage w(x), and will hire the medium-type agent to work on his idea of unknown
quality for wage wu(x). Thus, once again there is an extra move across the startups
boundaries (medium-type agent moving to work on an idea of unknown quality
owned by a high-type agent) as compared to the regular equilibrium.
(iii)
A low-type agent leaves his idea unevaluated and the idea evaluated by a mediumtype agent turns out to be useless. In this case the free-agent medium-type
individual will be hired by the low-type agent with an unevaluated idea for wage
39
wu(x). Again, there is an extra move across the startups boundaries (medium-type
agent moving to work on an idea of unknown quality owned by a low-type agent)
as compared to the regular equilibrium, where the low-type agent with a useless
idea simply does not form a startup.
(iv)
A low-type agent leaves his idea unevaluated and the idea evaluated by a mediumtype agent turns out to be good. In this case a high-type agent whose evaluated idea
turned out to be useless will move to work on the medium-type agent’s certified
good idea for wage w(x), while the medium-type agent will move to work on an
idea of unknown quality owned by a low-type agent for wage wu(x). Once again,
there is an extra move across the startups boundaries in this case (medium-type
agent moving to work on an idea of unknown quality owned by a low-type agent)
as compared to the regular equilibrium.
Hence, in all possible scenarios a non-regular reassignment equilibrium entails extra moves
of agents across startup boundaries. This proves that a regular equilibrium minimizes the
number of such moves. This equilibrium is also unique because only high-type and lowtype agents evaluate ideas and their numbers are pinned down uniquely by the market
clearing condition (8). [End of Proof]
40