The Competitiveness of Nations
in a Global Knowledge-Based Economy
March 2003
Paula
E. Stephan †
The
Economics of Science
Journal of Economic Literature
Volume 34, Issue 3
Sept. 1996, 1199-1235.
1. Introduction
SCIENCE COMMANDS the
attention of economists for at least three reasons. First and most important, science is a source
of growth. The lags between basic
research and its economic consequences may be long, but the economic impact of
science is indisputable. Second, scientific
labor markets - and the human capital embodied in scientists - offer fertile
ground for study. Third, a reward structure
has evolved in science that goes a long way toward solving the appropriability problem associated with the production of a
public good. Another reason to study
science relates to the large amount of resources employed in the enterprise. In 1991, for example, more than 85,000 Ph.D.
scientists were engaged in research in the physical, environmental, and life
sciences in the United States (National Science Foundation 1994, table 10, p.
18). An undetermined but substantial
number of physicians were also engaged in research. Basic research budgets in these fields were
approximately 13 billion dollars, applied research budgets about 17 billion. [1]
Early work in the economics
of science focused almost exclusively on the relationship between science and
technology and the ways technology affects growth and responds to economic
forces. This work led to the realization
not only that science makes technological innovations possible, but that
science itself is affected by technology. For example, technology provides apparatus to
understand physical phenomena better. This
work also led to an appreciation that to a considerable extent the scientific
enterprise evolves in disciplines that from their beginnings have been closely
tied to fields of technology.
The enhanced respect with
which science emerged from World War II underscored the need to understand
better the workings of scientific labor markets. The advent of human capital models in the
early 1960s created a framework for
† Paula E. Stephan
Department of Economics and Policy
Research Center
Georgia State University
The author would like to
thank William Amis, David Audretsch,
Dave Boykin, Eileen Collins, Paul David, Ronald Ehrenberg, Alan Fechter, Julie Hotchkiss, Mary Frank Fox, Vincent Mangematin, Edwin Mansfield, Rubin Saposnik,
F. M. Scherer, Frank Stafford, Mary Beth Walker, Harriet Zuckerman, and two
anonymous referees for helpful comments.
Some of the ideas expressed in this essay have evolved from extensive
conversations and collaboration with Sharon G. Levin. The author, however, bears sole
responsibility for the opinions and conclusions expressed here. Stephen Everhart and Janet Keene provided
research assistance. This essay was
begun when the author was a visiting scholar at the Wissenschaftszentrum
Berlin fur Sozialforschung. Financial support was received from the
Andrew W. Mellon Foundation and the College of Business Administration, Georgia
State University.
1.
Research expenditures for these broad fields are estimated from data found in
National Science Board (1993).
1199
their study and a second line of inquiry concerning science-related
issues was firmly launched. A third line
of inquiry had its genesis in the work of sociologists, who, at a slightly
earlier time, had begun to study the reward structure in science and the
behavior that it engenders. This work
has provided economists with a basis for understanding how a reward structure
has evolved in science that encourages the production of the public good
“knowledge.” Other useful concepts and
ideas have also been imported to economics from the sociology of science, such
as the observation that processes of cumulative advantage operate in science.
This essay attempts to
bring together these (and other) lines of inquiry concerning science and to
incorporate into the discussion salient facts about science and scientists that
have been observed by colleagues working in other disciplines. We begin by discussing the public nature of
knowledge and characteristics of the reward structure. Special attention is given to the recognition
that priority of discovery is a form of property right. We then explore the winner-take-all nature of
scientific contests and the inequality that characterizes such contests. Efficiency considerations follow. This leads to a discussion of how the incentives
to disclose information in a timely fashion relate to the type of property
right sought. We demonstrate that, contrary
to popular belief, it is not uncommon for scientists in industry to publish,
nor is it unknown for scientists working in the nonprofit sector to “privatize”
information.
The second half of the
essay begins with a discussion of scientific labor markets. This includes an examination of life-cycle
models of the labor supply of scientists and empirical tests of life-cycle
models. A portion of the essay is
devoted to a discussion of the complexities underlying the production of
scientific knowledge. The importance
that resources play in this process leads to a consideration of attributes of
different funding regimes. The essay
ends with a discussion of empirical studies relating scientific research to
economic growth. We also argue that a
case can be made that science, by having endogenous aspects, figures
prominently in the new growth economics. We conclude by suggesting topics for further
study.
2. The Public Nature of Knowledge and
the Reward Structure of Science
In his 1962 article
concerning the economics of information, Kenneth Arrow discussed properties of
knowledge that make it a public good.
Others (for example, Partha Dasgupta and David 1987, 1994; Harry Johnson 1972; Richard
Nelson 1959) have also commented on the public nature of knowledge: it is not
depleted when shared, and once it is made public others cannot easily be
excluded from its use. [2] Moreover, the
incremental cost of an additional user is virtually zero [3] and, unlike
the case with other public goods, not only is the stock of knowledge not
diminished by extensive use, it is often enlarged.
Economists were not the
first to note the public nature of knowledge. More than 180 years ago Thomas Jefferson
2.
Research findings only become a public good when they are codified in a manner
that others can understand. The
distinction, therefore, is often drawn between knowledge, which is the product
of research, and information, which is the codification of knowledge (Dasgupta and David 1994, p. 493).
3.
In reality, the marginal cost of use is greater than zero because users must
incur the opportunity cost of time as well as the direct cost of access to
journals or attendance at meetings.
Information, of course, is only of use to those who possess the
requisite intellectual framework. Michel
Gallon (1994) argues that the public nature of science is greatly overstated. Tacit knowledge (discussion to follow) can be more
costly to learn than knowledge that is codified.
1200
(1967 edition, p. 433, section 4045) wrote:
If nature has made any one thing less susceptible than
all others of exclusive property, it is the action of the thinking power called
an idea, which an individual may exclusively possess as long as he keeps it to
himself; but the moment it is divulged, it forces itself into the possession of
every one, and the receiver cannot dispossess himself of it. Its peculiar character, too, is that no one
possesses the less, because every other possesses the whole of it. He who receives an
idea from me, receives instruction himself without lessening mine; as he who
lights his taper at mine, receives light without darkening mine.
A cornerstone of economic
theory is that competitive markets provide poor incentives for the production
of a public good, because providers cannot appropriate the benefits derived
from use. This observation, however,
relates to rewards that are market-based. An important contribution of the
sociologists of science and the economists who have extended their work is the
demonstration that a non-market reward system has evolved in science that
provides incentives for scientists to behave in socially responsible ways. In the sections that follow, we analyze the
components of that reward system as well as the behavior it encourages.
A.
The Reward Structure of Science: The Importance of Priority [4]
As economists we owe a
substantial debt to Robert Merton for establishing the importance of priority
in scientific discovery. In a series of
articles and essays begun in the late 1950s, Merton (1957, 1961, 1968, 1969)
argues convincingly that the goal of scientists is to establish priority of
discovery by being first to communicate an advance in knowledge and that
the rewards to priority are the recognition awarded by the scientific
community for being first. Merton
further argues that the interest in priority and the intellectual property
rights awarded to the scientist who is first are not a new phenomenon but have
been an overriding characteristic of science for at least three centuries.
The recognition awarded
priority has varied forms, depending upon the importance the scientific
community attaches to the discovery. Heading
the list is eponymy, the practice of attaching the name of the scientist to the
discovery. Haley’s comet, Planck’s
constant, Hodgkin’s disease, the Copernican system are all examples. Recognition also comes in the form of prizes. Of these, the Nobel is the best known,
carrying the most prestige and the largest purse (approximately $1 million in
the early 1990s), but hundreds of others exist, a handful of which have purses
in excess of $300,000. [5]
Many countries also
have societies to which the luminaries are elected: the National Academies of
Science, Engineering, and Medicine in the United States, the Royal Society in
England, the Académie des Sciences in France.
Publication is a lesser
form of recognition, but a necessary step in establishing priority. A common way to measure the importance of a
scientist’s contribution is to count the number of citations to an article or
the number of citations to the entire body of work of an investigator. And while eponymy or a
prestigious prize are perceived by most to be beyond their reach, the
reward of publication is within the reach of most.
It is important to stress
that recogni-
4.
Parts of Sections A and B draw on joint work with Levin (Stephan and Levin
1992).
5.
Zuckerman (1992) estimates that approximately 3,000 prizes in the sciences were
available in North America alone in the early 1990s. This is five times the number awarded 20 years
earlier.
1201
tion in science depends on being first. [6] There are no awards for being second or third. The behavior such an incentive structure
elicits is one of the themes of this essay.
One consequence is the perceived need to rush work to a journal. It is not unknown for scientists to write and
submit an article in the same day. Neither
is it unknown to negotiate with the editor of a prestigious journal the timing
of publication or the addition of a “note added” so that work completed between
the time of submission and publication can be reported, thus making the claim
to priority all the more convincing (Stephan and Levin 1992). Another consequence of a priority-based
reward system is the energy scientists devote to establishing priority over
rival claims. Moreover, such practices
are not new. Merton (1969, p. 8) describes the extreme measures Newton took to
establish that he, not Leibniz, was the inventor of the calculus.
The importance accorded
priority and the response priority elicits bear a striking similarity to the
practice of offering an award to the first firm to complete successfully a
well-defined project (Brian Wright 1983). More generally, the race for priority can be
compared to patent races, the essence of which is described in work by Morton Kamien and Nancy Schwartz (1975). Both are extreme forms of winner-take-all
contests (Robert Frank and Philip Cook 1992) in which the winner is determined
solely on the grounds of being first. [7]
Two characteristics of
science account for the winner-take-all nature of scientific contests. The first is the difficulty that arises in
monitoring scientific effort (Dasgupta and David
1987; Dasgupta 1989). This class of problem is not unique to
science. Edward Lazear
and Sherwin Rosen (1981) have investigated incentive-compatible compensation
schemes where monitoring is costly. A
second characteristic of science that fosters a winner-take-all reward
structure is the low social value of the contribution made by the runners-up:
there is no value added when the same discovery is made a
second, third, or fourth time. To put it
sharply (and thus somewhat inaccurately), the winning research unit is the sole
contributor to social surplus. (Dasgupta and Eric Maskin 1987, p.
583)
B.
The Reward Structure of Science: Financial Remuneration and the Satisfaction
Derived from Puzzle Solving
Financial remuneration is
another component of the reward structure of science. Because the winner-take-all nature of the
race places much of the risk on the shoulders of the scientist, it is not
surprising that compensation in science is generally composed of two parts: one
portion is paid regardless of the individual’s success in races, the other is
priority-based and reflects the value of the winner’s contribution to science. While this clearly oversimplifies the compensation
structure, the role played by counts of publications and citations in determining
raises and promotions at universities is evident from the work of Arthur Diamond
(1986a) and Howard Tuckman and
6.
There are, of course, different levels of contests and repeated contests to
enter. Many scientists choose to play in
the minor leagues, working in the backwaters of science, or, as some would say,
functioning as ditchdiggers. See discussion page 1204.
7.
Substantial differences also exist between patent races and priority races. For example, there is no reward for reverse
engineering in science and consequently no incentive to play the type of waiting
game discussed by William Baldwin and Gerald Childs (1969).
8.
The inaccuracy of the quote relates to the fact that replication and
verification have social value and are common in science.
1202
Jack Leahy (1975).
The Diamond estimates, for example, suggest that the present value of
publishing another article for a 35-year-old mathematician is (in 1994 dollars)
about $6,750; the present value of an additional citation to a 35-year-old
physicist’s work is about $2,225. [9] Unfortunately, we know little about the
reward structure for scientists in industry or in government labs, particularly
as that reward structure relates to priority.
The flat profile of
earnings in science (at least for those employed in academe) is frequently
noted. Ehrenberg (1992), for example,
calculates that the average full professor in the physical and life sciences
earns only about 70 percent more than the average new assistant professor. This arguably relates to monitoring problems and
the need to compensate scientists for the risky nature of their work. On the other hand, if earnings are expanded to
include compensation outside the institution, the profiles are in all likelihood
not nearly as flat as is often assumed. A variety of extra-institutional rewards
awaits the successful scientist in the form of prize money and speaking and
consulting fees. Successful patents can
also generate a significant income stream for their scientific inventor, and in
recent years it has become standard practice for eminent scientists, particularly
in the life sciences, to serve as scientific advisors and directors of new
companies. Stephan and Stephen Everhart
(forthcoming) demonstrate that a handful of scientists realize extraordinary
returns from the stock they hold in such companies and that a substantial
number have the potential of realizing nontrivial sums of money by exercising
stock options. A fruitful area for
further research would be to investigate what happens to the earnings profile
when the definition of income is broadened to include these extra-institutional
forms of compensation.
The other reward often
attributed to science is the satisfaction derived from solving the puzzle. To quote Warren Hagstrom
(1965, p. 16), “Research is in many ways a kind of game, a puzzle-solving
operation in which the solution of the puzzle is its own reward.” The philosopher of science David Hull (1988,
p. 305) describes scientists as being innately curious and suggests that
science is “play behavior carried to adulthood.” This suggests that time spent in discovery is
an argument in the utility function of scientists. Robert Pollak and
Michael Wachter (1975) demonstrate that maximization
problems of this type are generally intractable, because implicit prices depend
upon the preferences of the producer. While
this provides a rationale for excluding the process of discovery from models of
scientific behavior, the failure of economists to acknowledge the puzzle as a
motivating force makes economic models of scientific behavior lack credibility.
3. Inequality in Science
A defining characteristic
of winner-take-all contests is extreme inequality in the allocation of rewards.
Science, too, has extreme inequality
with regard to scientific productivity and the awarding of priority. One measure of this is the highly skewed
nature of publications, first observed by Alfred Lotka
(1926) in a study of nineteenth century physics journals. The distribution that Lotka
found showed that approximately six percent of publishing scientists produce
half of all papers. Lotka’s
“law” has since been found to fit data from sev-
9.
These calculations assume that the rewards are incorporated into the base
salary and that the real interest rate is three percent.
1203
eral different disciplines and varying periods of time
(Derek de Solla Price 1986). [10]
Inequality in scientific
productivity could be explained by differences among scientists in their
ability and motivation to do creative research. But scientific productivity is not only
characterized by extreme inequality at a point in time; it is also
characterized by increasing inequality over the careers of a cohort of
scientists, suggesting that at least some of the processes at work are state
dependent. Yoram
Weiss and Lee Lillard (1982), for example, find that
not only the mean but also the variance of publication counts increased during
the first ten to 12 years of the career of a group of Israeli
scientists.
Merton christened his
explanation for inequality in science the Matthew Effect, defining it to be
the accruing of greater increments of recognition for
particular scientific contributions to scientists of considerable repute and
the withholding of such recognition from scientists who have not yet made their
mark. (1968, p. 58)
He argues that the effect
results from the vast volume of scientific material published each year, which
encourages scientists to screen their reading material on the basis of the
author’s reputation. Other sociologists
(Paul Allison and John Stewart 1974; and Jonathan Cole and Stephen Cole 1973,
for example) have argued that additional processes of “cumulative advantage”
are at work in science, such as the ability to leverage past success into
research funding as well as the “taste” for recognition that success engenders.
While we have yet to understand these
processes completely, a strong case can be made that a variety of factors are
at work in helping able and motivated scientists leverage their early successes
and that some form of feedback mechanism is at work (David 1994). This observation is consistent with other work
in winner-take-all contests. Frank and
Cook (1992, p. 31) observe that “in all their manifestations, winner-take-all
effects translate small differences in the underlying distribution of human
capital into much larger differences in the distribution of economic reward.”
4. The Choice of
Scientific Contests
The winner-take-all
character of scientific contests dictates that scientists choose the contests
they enter with care. The probability of
being scooped is a constant threat. This
is particularly true in the case of “normal” science where the accumulated
knowledge and focus necessary for the next scientific breakthrough is “in the
air.” [11] Young scientists, in particular, must choose their contests
with care if they are to successfully signal their ability or “resource worthiness”
and set in motion the processes of cumulative advantage described above (Alan
Garner 1979).
Scientists can minimize the
threat of being scooped by seeking ways to monopolize a line of research. During the seventeenth and eighteenth
centuries, discoveries in process were sometimes reported in the form of
anagrams for the
10.
Lotka’s law states that if k is the number of
scientists who publish one paper, then the number publishing n papers is
k/n2. In many disciplines this
works out to some five or six percent of the scientists who publish at all producing
about half of all papers in their discipline.
Although Lotka’s Law has held up well over
time and across disciplines, David (1994) shows that other statistical
distributions also provide good fits to observed publications counts.
11
Note the distinction between social and individual risk. Because accumulated knowledge is an important
input in the process of discovery, normal science is not especially risky from
the social point of view (Dasgupta and David 1987, p.
526; Arrow 1962). From the individual
investigator’s point of view, however, the risks can be substantial: being in
the air is entirely different from being in scientist X’s air.
1204
“double purpose of establishing priority of conception
and of yet not putting rivals on to one’s original ideas, until they had been
further worked out” (Merton 1957, p. 654). It was also not uncommon to deposit a sealed
and dated manuscript with a learned society to protect both priority and idea. More recently, the ownership of apparatus or
strains has proved to be a convenient way to monopolize a line of research. Scientists can also minimize the threat of
being scooped by choosing to work on problems that fall outside the mainstream
of “normal science” or by working in “the backwaters” of research (Stephan and
Levin 1992). The downside of such a
strategy is that, while the low number of competitors increases the probability
of being first, the contest that is won may be of little interest to the larger
scientific community and hence receive minimal recognition.
Researchers must choose not
only a line of research. They must also
choose a research strategy, because more than one method can be used to address
the same question (Dasgupta and David 1994). Here, too, uncertainty enters the equation. The use of a novel method, for example, can
prove rewarding, but the risk of coming up empty-handed can be quite large when
an unorthodox approach is employed. [12] The uncertainty associated with the process of
discovery also can be substantial. The
outcome may not have been envisioned, neither may the outcome relate to the
original objective of the researcher. In
the process of trying to solve some very practical problems concerning
fermentation and putrefaction in the French wine industry, Pasteur established
the modern science of bacteriology (Nathan Rosenberg 1990). [13]
Basic research often
provides answers to unposed questions. [14] Consequently, the risk associated with such research can be
lessened by shifting goals during the course of research. Nelson (1959) argues that this strategy is
more appropriate for scientists working in a nonprofit-based environment than
for scientists working in the profit sector because the former can more easily
capture the rewards regardless of where the research leads. On the other hand, companies having a broad
technological base can benefit from research that is not directed to a specific
goal. At the time General Electric
developed synthetic diamonds, for example, it was the most diversified company
in the United States.
A number of institutional
arrangements have evolved in science to help minimize risk or provide some
insurance against risk. Some of these,
such as the ability to monopolize a line of research, have already been noted. Others include the adoption of a research
portfolio that contains projects with varying degrees of uncertainty, the
formation of research teams and networks and the practice of “gift giving”
whereby scientists, by acknowledging intellectual debts to their colleagues
(via citations), pay “protection money” to insure that those colleagues “won’t
deny their grants, spread slander,
12.
A consequence is that rival teams often select highly correlated research
strategies. From a social point of view,
highly correlated research strategies produce inefficiencies by failing to provide
the kind of portfolio diversification that society would choose if it were
allocating resources in a way to maximize the probability of success (Dasgupta and David 1994). The gains to society from sponsoring multiple
lines of independent research are examined by Scherer (1966).
13.
Serendipity plays a role in discovery when in the course of research an
unintended outcome is observed. The
following up, of course, is not accidental. Chance, according to Pasteur, favors only the
prepared mind. Bernard Barber and Renée
Fox (1962) discuss the role played by serendipity in science.
14.
The unpredictable nature of scientific discovery is explored by Michael Polanyi (1962).
1205
or – worst of all - ignore their work altogether” (Steve
Fuller 1994, p. 13).
5. Efficiency Considerations
A.
The Functional Nature of the Reward System
The socially desirable
properties attached to a reward system that is priority-based are substantial. Shirking is rarely an issue in science. The knowledge that multiple discoveries are
commonplace makes scientists exert considerable effort. [15]
A reward structure based on priority requires that
scientists share information in a timely fashion if they are to establish
priority. Such a process in turn permits
peer evaluation, which discourages plagiarism and fraud and builds consensus in
science (John Ziman 1968; Dasgupta
and David 1987). The process also
provides scientists the reassurance that they have the capacity for original
thought (Merton 1957) and encourages scientists to acknowledge the roots of
their own ideas, thereby reinforcing the social process. Reputation also serves as a signal of
“trustworthiness” to scientists wishing to use the results of another in their
own research without incurring the cost of reproducing and checking the
results. It also serves as a signal of
trustworthiness to foundations. As such,
reputation provides an answer to the agency problem (Stephan Turner 1994) posed
by Ronald Coase. [16]
From an economist’s point
of view, the most appealing attribute of a reward system that is rooted in
priority is that it offers non-market-based incentives for producing the public
good “knowledge.”Dasgupta and David (1987, p. 531),
the first to make the observation, say it well: “Priority creates a
privately-owned asset - a form of intellectual property - from the very act of
relinquishing exclusive possession of the new knowledge.” Arrow (1987, p. 687), commenting on their
work, articulates the cleverness of such a system:
The incentive compatibility literature needs to learn
the lesson of the priority system; rewards to overcome shirking and free-rider
problems need not be monetary in nature; society is more ingenious than the
market. [17]
A reward system based on
reputation also provides a mechanism for capturing the externalities associated
with discovery. The more a scientist’s
work is used, the larger is the scientist’s reputation and the larger are the
financial rewards. It is not only that
the reward structure of science provides a means for capturing externalities. The public nature of knowledge encourages use
by others, which in turn enhances the reputation of the researcher (Stephan and
Levin 1996).
B.
Are There Too Many Contestants in Certain Contests?
The conventional wisdom
holds that because of problems related to appropriability
a public good such as knowledge will be underproduced
if left to the private sector. [18] A common rationale for government laboratories and government grants for
research rests squarely on this
15.
The prevalence of multiples in science is discussed below. Mary Frank Fox (1983) and Hull (1988) discuss
the effort and work patterns of successful scientists.
16.
This is not to say that the reward structure is without problems. Fraud and misconduct occur with some frequency
in science (Alexander Kohn 1986). Susan Feigenbaum and David Levy (1993) discuss the market for (ir)reproducible results; Mary
Frank Fox and John Braxton (1994) discuss other issues related to fraud. There is also the considerable issue that the
reward structure in science appears to have favored white men over women and
members of minority groups.
17.
Merton (1988, p. 620) also makes the connection when he speaks of reputation,
saying that in science “one’s private property is established by giving its
substance away.”
18.
Uncertainty and indivisibilities provide two other reasons why knowledge will
be underproduced (Arrow 1962).
1206
premise. The
production of knowledge can also be stimulated through the granting of property
rights to the discoverer. With rare
exception, patents have been the primary form of intellectual property rights
that economists have examined, arguing that patents provide for appropniability while placing knowledge (eventually) in the
public domain. [19] Moreover, it has been shown (Dasgupta
and Joseph Stiglitz 1980) that under a wide array of
circumstances social inefficiency results from patent races among rival groups.
This inefficiency manifests itself in
“excessive duplication of research effort (or)… too fast a pace of advance of
the frontiers of knowledge” (Dasgupta and David 1987,
p. 532).
The recognition that
priority is a form of property rights leads to the question of whether there
are “too many” contestants in certain scientific contests. Would the social good be served by having
fewer? In a classic speech delivered at
a conference commemorating the 400th anniversary of the birth of Francis Bacon,
Merton detailed the prevalence of what he called “multiples” in scientific
discovery. And Merton was not the first
to note their presence. In what Merton
calls a “play within a play,” he gives 20 “lists” of multiples that were
compiled between 1828 and 1922. Moreover, Merton is quick to point out that
the absence of a multiple does not mean that a multiple was not in the making
at the time the discovery was made public.
This is a classic case of censored data where scooped scientists abandon
their research after a winner is recognized. Indeed, Merton argues that “far from being odd
or curious or remarkable, the pattern of independent multiple discoveries in
science is in principle the dominant pattern rather than a subsidiary one”
(1961, p. 356).
The presence of multiple
discoveries is due in part to the free access scientists have to knowledge and
in part to the fact that uncertainty associated with who will make a discovery
leads scientists to choose research portfolios that are correlated (Dasgupta and Maskin 1987). [20] The knowledge that multiples exist keeps scientists from
shirking and moves the enterprise of science at a rapid pace. Such observations invite the question of
whether science moves at too rapid a pace and whether certain contests attract
too many entrants. Dasgupta
and David (1987, p. 540) argue that the priority system can create excesses,
just as the patent system does, provided the “reward to the discoverer... is
tempting enough.” [21] They make no effort to define the boundary of temptation,
but one wonders if the general knowledge that certain contests deserve the
Nobel Prize does not attract an excessive number of scientists. [22]
19.
While neither goal is perfectly achieved by the patent process, the goal of
disclosure arguably suffers the most.
“The imperfections we have examined in the patent as a device for
rewarding disclosures of knowledge are not at all surprising; a stone flung at
two birds really ought not be expected to make a clean strike on either” (Dasgupta and David 1987, p. 534).
20.
Despite the popularity of patent race models, multiples are arguably more
common in science than technology. The
reason is that science is concerned with laws and facts, while technology is
looking for practical ways to solve problems. Hence, while there is often only one answer to
a scientific question, there usually are a variety of distinct ways of solving
the practical problem.
21.
Another efficiency concern relates to whether scientists direct excessive
amounts of time to research as opposed to teaching. The fact that only a handful of scientists
contribute the lion’s share of output suggests that substantial inefficiencies
arise when yeomen scientists devote long hours to research. Other efficiency concerns exist. One is discussed in footnote 12. Another concerns whether the process of cumulative
advantage excludes talented individuals from making contributions. Dasgupta and David
(1994, pp. 506-07) discuss additional efficiency issues.
22.
On the other hand, the common lament of interest groups that there are not
enough entrants in certain races of apparent Nobel proportions (e.g., a cure
for breast cancer) leads one to be cautious in making broad generalizations. It is, of [course,
possible that such groups are expressing the concern that victory is
undervalued by the community. It is also
possible that a cure is not “in the air” and applying more resources to the contest
would be inefficient.]
HHC: [bracketed] displayed on page 1208 of original.
1207
C. The Incentive to Share Knowledge in a Timely
Fashion
Despite the similarities
between priority rights and proprietary rights such as patents, they differ
markedly in the incentives they provide to disclose research findings in a
timely fashion. On the one hand, the
quest for priority requires scientists to share discoveries quickly,
because it is only by sharing that priority rights can be established. The quest for proprietary rights, on the other
hand, discourages the rapid sharing of information, because the very purpose of
proprietary rights is to provide a means for capturing the economic rents attached
to a new product or technology. And,
while some forms of proprietary rights require the sharing of knowledge in
recognition of its public nature (e.g., the patent process), incentives to
divulge the knowledge quickly are not present. [23]
The distinction is so
crucial that Dasgupta and David (1987, p. 528) argue
that the two types of property rights, and the implications they hold for appropriability and disclosure, differentiate science from
technology.
If one joins the science club, one’s discoveries and
inventions must be completely disclosed, whereas in the technology club such
findings must not be fully revealed to the rest of the membership.
This distinction between
science and technology often leads to the (erroneous) conclusion that science
is done by scientists at universities and public labs and results in published
knowledge, while the focus of scientists working in industry is the development
of proprietary technology (Nelson 1982). While location does correlate with the
incentive to share knowledge in a timely fashion, the relationship is far from
perfect. Some firms make the results of
their research public; some academics engage in practices that lead to the “privatization”
of knowledge. In many instances agents
can eat their cake and have it too, selectively publishing research findings
while monopolizing other elements with the hope of realizing future returns. Rebecca Eisenbeng
(1987) argues that such behavior is more common among academics than might
initially be presumed because they can publish results and at the same time
keep certain aspects of their research private by withholding data or failing
to make strains available upon request. [24]
The ability to eat one’s
cake and have it too is not only facilitated by the fact that publication is
not synonymous with replicability. It is also facilitated by the fact that techniques
can often be transferred only at considerable cost, in part because their tacit
nature makes it difficult, if not impossible, to communicate in a written form
(or codify). [25] This pri-
23.
Many nations require publication a year or so after the application for
a patent. In most nations patents cannot
be obtained if publication has occurred prior to application.
24.
Eisenberg (1987) suggests that the patent process may be more congruent with
the scientific norms of disclosure and replication than the publishing process
in certain areas of the life sciences. This
is because patents in the biological sciences require that the material in
question be placed on deposit. This is
not a requirement for publication; neither are the materials themselves part of
the published text.
25.
Some aspects of technical knowledge have a strong tacit component, meaning that
they cannot be completed codified and made explicit in the form of blueprints
or instructions, but instead must be learned through practice. Nelson and Sidney Winter (1982) discuss tacit
knowledge, particularly as it relates to skill. Dasgupta and David
(1944) use the term tacit somewhat differently to connote knowledge that, for
whatever reason, is not codified and argue that the boundary between what is
codified and what is tacit is not simply a question of epistemology. Rather, as suggested above, the boundary is “a
matter, also, of economics, for it is determined endogenously by the costs and
benefits of secrecy in relation to those of codification” (p. 502).
1208
ate aspect of technology is a major reason patents are
not a necessary condition for successful research and development and underlies
the willingness of industry to share knowledge through publication.
There are other reasons why
firms engage in disclosure. Foremost
among these is recruitment of talent. Scientists
and engineers often see the ability to publish as a condition of employment in
industry, knowing that if they are not permitted to do so their career path
will be severely restricted and they may fail to achieve prestige among their
peers. The reputation of the lab, which
is directly related to publication activity, also affects the ability of the
company to hire scientists and engineers (Scherer 1967); it may also affect its
ability to attract government contracts (Frank Lichtenberg 1986). Stephan’s work on biotechnology (1994)
suggests that a firm’s publications can also play a role in signaling capital
markets. Diana Hicks (1994) explores a
number of other factors leading companies to opt for disclosure through
publication. She points out that a
critical element in this process is the company’s ability to screen the
material that is published, thereby insuring that its proprietary interests are
maintained. In the process, however, the
firm must be mindful that delays can lower morale among research scientists. David Hounshell and
John Smith (1988, p. 369) describe the loss of morale that occurred at Du Pont when research managers implemented what turned out
to be a de facto moratorium on publishing.
6. Scientists in
Industry
Firms engage in basic
research for a variety of reasons. [26] In some instances, basic research is a by-product of the development of a
new product or process (Rosenberg 1990). In other instances the production of generic
knowledge is, itself, the goal and is motivated by the belief that a particular
new product or process innovation will result from that knowledge. In still other instances basic research is
needed if the company is to stay abreast of developments in relevant scientific
fields and more readily absorb the findings of other scientists (Wesley Cohen
and Daniel Levinthal 1989). Sometimes firms are motivated by the
expectation that fundamental research will provide a scientific foundation for
the company’s technology. Firms have
even been known to engage in basic research because of a concern that the fundamental
knowledge required for the industry to advance is lacking and unlikely to be
forthcoming from the academic sector. When
Charles Stine made his presentation to the Executive Committee of Du Pont in 1926, for example, he argued that fundamental
research was necessary because “applied research is facing a shortage of its
principal raw materials” (Hounshell and Smith 1988,
p. 366). [27]
This means that the
research of some scientists and engineers in companies like IBM, AT&T, and Du Pont is virtually indistinguishable from that of their
academic counterparts. Not surprisingly,
a number have received the top honors that their field can bestow. Bell Labs, Du Pont,
IBM, Smith Kline and French,
26.
The demand for scientists in industry relates to the demand for research and
development. Here we focus on the narrower
issue of the demand for basic research.
27.
The payoff to a firm’s performance of scientific research often takes the form
of first-mover advantages (Rosenberg 1990). Thus, even if the research findings eventually
spill over to competitors or cannot be protected through proprietary rights,
the firm performing the research has the opportunity of being the first to use
the information for the basis of decisions, new products, etc. Despite the evidence
concerning the effects of basic research on productivity (Mansfield 1980), recent
years have seen a notable reduction in the amount of basic research supported
by industry.
1209
Sony, and General Electric have each been the research home
to scientists who have subsequently won the Nobel Prize. In 1994, 3.8 percent of the 2,088 members of
the National Academy of Sciences came from industry. Twenty-four of the members were at AT&T
Bell Laboratories.
Table 1 [HHC: not
included] gives the institutional origin of authors of U.S. scientific and
technical articles published in 1991 for eight fields of science and
engineering. While the vast majority of
articles are authored by scientists working in the academic sector, industry
produces a sixth of the literature in chemistry and physics and a fourth of it
in “engineering and technology.” The
blurred boundary between academics and industry is further indicated by the
fact that 35 percent of articles with an industry address have a coauthor from
the academic sector (see last column). Moreover,
this proportion grew by more than 50 percent between 1981 and 1991. This trend undoubtedly relates to the
increasing number of research alliances that have been formed between industry
and academe since Monsanto in 1977 gave Harvard $23 million in research funds. Such alliances are particularly prevalent in
biotechnology (David Blumenthal et al. 1986). [28]
28.
1 also points out the important research role that the nonprofit sector plays
in clinical medicine and that the federal sector plays in biology and earth and
space sciences. The Na-[tional Institutes of
Health and NASA are important government research sites for these fields,
respectively. The importance of
Federally Funded Research and Development Centers (FFRDGs)
in physics is also clearly demonstrated.
These include Fermi National Accelerator Laboratory in Chicago,
Brookhaven National Laboratory on Long Island, and the Stanford Linear
Accelerator in Palo Alto.]
HHC: [bracketed]
displayed on page 1208 of original.
The reasons for industry to publish research findings, as well as the economic incentives for adopting a basic research agenda, have been noted above. This should not, however, be taken as an indication that economists (or others, for that matter) have adequately studied scientists in industry doing “science.” Many questions remain unanswered and, perhaps even more fundamental, unposed. [29] For example, why do companies adopt compensation strategies that impair the productivity of scientists by tying salary increases to the assumption of managerial responsibilities? Does the strategy adopted by IBM and DuPont of creating well paid research fellow positions help alleviate the problem? What role do publications play in facilitating movement between the industrial and the nonprofit sector? There is also the question concerning how basic research in industry is monitored. The unpredictable nature of research, as well as the belief that creativity requires freedom of choice, suggests that success is hampered if managed too closely. Yet firms can ill afford to fund research that has little promise of (eventually) relating to the company’s objectives. Scherer (interview) reports that Bell Labs solved this problem by giving “the glassy-eyed stare” to scientists who were seen as straying too far from the Labs’ purpose. Recipients knew that they had the choice of either modifying their research or being ostracized. Finally, given the collaborative nature of science, there is a need to study the laboratory as a unit of analysis, instead of focusing exclusively on individual scientists.
The Competitiveness of Nations
in a Global Knowledge-Based Economy
March 2003