The Competitiveness of Nations
in a Global Knowledge-Based Economy
March 2003
Paula E. Stephan
The
Economics of Science
7. The Market for
Scientists
Science emerged from World
War II with enhanced respect. Its
successes had shortened the war and led to reduced fatalities of American
troops. There was also a growing
appreciation for the important role science could play in stimulating economic
growth and employment in peacetime. In a
report prepared at the invitation of the White House, Vannevar
Bush (1945) argued that science provided an endless frontier and should be more
heavily supported by the government. One response to Bush’s report was the
formation of the National Science Foundation in 1950.
This groundswell of support
for science, heightened in the 1950s by the threat of Soviet scientific and
technological superiority, underscored the need to understand the workings of
scientific labor markets. Stellar talent
was drawn to this question. First, David
Blank and George Stigler (1957) published a book on the demand and supply of
scientific personnel; then Arrow and William Capron (1959) wrote an article
concerning dynamic shortages in scientific labor markets. Both studies set the
stage for work to come.
A.
A Description of Scientific Labor Markets
The majority of doctoral
scientists in the United States are employed in institutions of higher
education and in business and industry. A
distinct minority work at FFRDCs, the government, and
29.
Our knowledge of scientists working in industry comes largely from a number of
excellent case studies. These include
Alfonso Gambardella’s (1995) study of the pharmaceutical industry, Hounshell and Smith’s (1988) study of Du
Pont, Willard Mueller’s discussion of Du Pont (1962),
Nelson’s study of the development of the transistor (1962), and Robert Sobel’s study of RCA (1986).
1211
nonprofit institutions. Over
time, the sectoral composition has shifted substantially
as industry has employed proportionately more scientists and academe
proportionately fewer. This is shown in
Table 2 [HHC: not included].
Funding for research and
development in the United States comes primarily from the federal government
and business and industry. The
government’s rationale for supporting scientific research rests on several
principles: the importance of research and development to defense; the need to
subsidize the production of the public good knowledge; the desire to win what
Harry Johnson (1972) calls the “Scientific Olympics”; and the importance of
science to economic growth. Business and industry’s rationale relates to the
desire to innovate. In addition to
R&D considerations, the demand for scientists is influenced by the demand
for post-secondary education.
The elements underlying the
demand for scientists are far from stable, as indicated by Table 3 [HHC: not
included], which gives R&D expenditure data and undergraduate enrollment
data for the past 30 years. We see that
the proportion of GDP spent on R&D (Column 1) grew in the early 1960s and
then declined continuously until 1978. It
then began a steady increase, almost reaching 1960s proportions in the
mid-1980s. Since that time, the propor-
1212
tion has again declined. These changes are driven in large part by
decisions made at the federal level (Column 2). The growing importance of source of R&D
funding however, has softened the government swings in recent years. The table also indicates the enormous growth
that occurred in the number of bachelor degrees conferred in science and engineering
in the 1960s (Column 4), followed by no growth in the 1970s and minimum to no
growth in the 1980s. The supply of new
doctorates in science is also summarized in Table 3 and is expressed as the
ratio of Ph.D.s granted to U.S. citizens and permanent residents to the U.S.
population aged 25-34. We see that the
proportion in the 25-34 age category receiving a Ph.D. in both the physical
(Column 5) and life sciences (Column 6) increased throughout the
1213
1960s, declined in the 1970s, and was fairly stable in
the 1980s. We also see that growth was
slightly higher in the life sciences and the decline more extreme in the
physical sciences.
One other labor market
indicator is given in Table 3: the percentage of new Ph.D.s who have definite commitments for employment on for postdoctoral
positions whose commitment is for employment. Note that in recent years the proportion with
an employment commitment has declined by about 25 percent in the physical
sciences (Column 7) and by over 35 percent in the life sciences (Column 8). Stated differently, for approximately 50
percent of new Ph.D.s in the physical sciences a definite commitment now means
taking a postdoctoral appointment upon receipt of the Ph.D., while for almost
two-thirds of those in the life sciences the first position is as a postdoctorate. Although the postdoc
process provides the recipient time to accumulate publications that signal
future “grant worthiness,” the dramatic increase in the number of persons with
these positions (as well as the increase in the number of persons holding more
than one postdoctora1 position) is generally seen as an indication of the
softness of the market.
B. Studies of the Supply and Demand for
New Entrants to Science
A number of studies have
examined the market for new entrants to science. Larry Leslie and Ronald Oaxaca (1993) do an
excellent job of surveying this literature and summarizing the major findings,
as does Ehrenberg (1991, 1992). [30] The market variables that are usually found to affect the
supply of enrollees (or the number of graduates) in field j are salary
in field j, salary in an alternative occupation such as law or business,
and (for men) the draft deferment policy. These variables almost always have the
expected signs and are highly significant. The magnitude of the implied elasticities, however, varies considerably across studies,
even when field is held constant (Ehrenberg 1992). Another market variable often included in
predicting supply is some measure of concurrent, past, or future supply. Other things being equal, enrollments are
positively associated with present cohort size. Various lag structures are used in estimating
these models and it is common to assume some form of adaptive (or rational)
expectations. Supply variables generally
ignored by these studies (primarily because of a reliance on aggregated data)
include type of support available while in school, debt level upon graduation
from college, and average time to degree.
Demand equations prove more
difficult to specify, partly because we know so little about the behavior of
universities and governments (David Stapleton 1989). There is, however, convincing evidence that
demand relates to R&D expenditures and that these expenditures in turn
affect supply decisions. In a series of
equations, for example, Richard Freeman (1975) finds degrees at the B.S., M.S.,
and Ph.D. level in physics for the period 1950 to 1972 to be significantly
related to R&D expenditures. The
propensity of recent doctorates to work increasingly for industry is in part a
response to higher relative salaries in industry (Ehrenberg 1991). It also undoubtedly relates to the type of
academic jobs available. Most students
enter graduate school with the expectation of eventually working in the academic
sector and these preferences are reinforced while in school. The academic jobs they want, however, are not
at four-
30.
Most studies focus on long-run adjustments.
A few, however, examine the short-run responsiveness of the market by
also focusing on the movements of trained personnel between fields and sectors
(Blank and Stigler 1957).
1214
year institutions, but at research institutions where they
can have their own lab. When jobs are
scarce in this top sector (as they have been for a number of years), industry
becomes substantially more appealing.
C.
Forecasting Scientific Labor Markets
Although models of
scientific labor markets have been somewhat successful in providing insight
into factors affecting demand and supply, reliable forecasts of scientific
labor markets do not exist, partly because of the unavailability of reliable
predictions of exogenous variables. While
this problem is endemic to forecasting in general, the ups and downs of federal
funding make forecasts of scientific labor markets particularly unreliable.
The failure of researchers
to successfully forecast labor market conditions in science (for anything
except the very near future) has been well documented by Leslie and Oaxaca
(1993). Their work should be required
reading for anyone who is tempted to enter this arena. Stapleton (1989) also
chronicles the issues involved, which, in addition to the problem of
forecasting federal R&D, include inadequate data, a poor understanding of
the behavior of educational institutions, and poor estimates of undergraduate
enrollments and degrees conferred. To
this list must be added the failure to come up with consistent estimates of elasticities (Ehrenbeng 1991). Despite these problems, forecasts of scientific
labor markets are somewhat common, in part because they are mandated by
Congress, supposedly in an effort to keep the U.S. competitive. In the recent past, forecasters predicted an
impending “shortage” of scientists. [31]
While some of this
was wishful thinking on the part of science advocates in the United States, it
also stemmed from the assumption that scientists would retire and be replaced
on a one-to-one basis. Such has not been
the case, in part because changes in the law permitted retirements to be
deferred; in part because tight budgets have limited the number of replacements
hired at universities.
8. Life-cycle Models
Ever since the
path-breaking work of Gary Becker (1962) and Theodore Schultz (1963),
economists have focused attention on the question of how behavior varies over
the life cycle in occupations where human capital plays an important role. The models developed predict that, due to the
finiteness of life, investment behavior declines (eventually) over time. [32] This decline may be hastened if the production of human
capital is non-neutral, meaning that time is more productive in the market than
in the production of human capital. These
models typically incorporate a depreciation rate for human capital that
produces a peaked profile. In the
presence of depreciation, earnings also peak, although at a later time than the
human capital profile.
Several authors have
adapted the human capital framework to develop life-cycle models of scientists
or academics. Like their first cousins,
these models are driven by the finiteness of life and investigate the implications
this has for the allocation of time to research over the life cycle. The models differ in the assumptions they make
concerning the objective function of the scientist but reach somewhat similar
conclusions. In its simplest form the
objective is the maximization of
31.
This was not the first time that a shortage was discussed. Talk of shortages in the early 1950s led Blank
and Stigler to examine alternative meanings of the term (1957, pp. 22-24). At other times the concern has been that an
“oversupply” exists.
32.
Sociologists generally use age as a proxy for experience (Zuckerman and Merton
1973) while economists, though interested in experience, focus on the idea that
age is a measure of time left in the career, or more generally, in life.
1215
income, itself a function of prestige capital (Diamond
1984). In a more complex form, the
objective is the maximization of a utility function that includes income as
well as research output (Levin and Stephan 1991). [33] The latter is included given the strong anecdotal
evidence that puzzle solving is part of the reward to science. [34] The implications of these models are that the stock of
prestige capital peaks during the career and then declines and that the
publishing profile declines over the life cycle. The addition of puzzle solving to the
objective function produces the result that research activity is greater at any
time, the greater is the satisfaction derived from puzzle solving; it also
produces the strong suggestion that the research profile is flatter, the larger
is the satisfaction derived from puzzle solving.
The implications of the
human capital models for science have been investigated in a number of
empirical studies. The dependent
variable is generally earnings on publishing activity. In a few instances researchers have adapted
the human capital model to study the acceptance of new ideas. The rationale behind the latter studies is
that scientists as they age become increasingly vested in their own ideas and
hence more and more resistant to alternative theories. In the discussion that follows we summarize
these empirical studies, organizing our discussion around the three variables
most frequently studied.
A.
Empirical Studies of Research Activity [35]
The research productivity
of scientists over the life cycle has received minimal attention from
economists, although there have been numerous studies by psychologists and
sociologists (e.g., Alan Bayer and Jeffrey Dutton 1977; Stephen Cole 1979;
Harvey Lehman 1953; and Zuckerman 1977). The only studies by economists that examine
the publishing activity of scientists in a life-cycle context are those by
Diamond (1986b), Weiss and Lillard (1982), and Levin
and Stephan (1991).
Several classes of problems
present themselves in studying research productivity in a life-cycle context. These include measurement, the confounding of
aging effects with cohort effects, and the availability of an appropriate
database.
Publication counts are
generally used as a proxy for research activity. This is justified on the grounds of the high
acceptance rates - often in excess of 70 percent (Lowell Hargens
1988) - that exist among scientific journals. The question of attribution in the case of
joint authorship is sometimes addressed by prorating article counts among coauthors,
despite the work by Raymond Sauen (1988) that
indicates that co-authors receive more credit for work than such a device would
suggest. Article quality is often proxied by weighting article counts by some type of
citation measure.
Because scientists of
different ages come from different cohorts, aging effects are confounded with
cohort effects in cross-sectional studies. One type of cohort effect is associated with
change in the knowledge base of the scientist’s field. If, for example, there is a secular
33.
The objective function can also include fame as an end in itself, not only as a
means for generating income. Levy (1988)
uses such a model to investigate what happens when the rewards to a field
change and fame becomes rewarded more handsomely in the market. He does not, however, draw implications for
life-cycle behavior.
34.
This way of dealing with the puzzle issue is not completely satisfactory
because it assumes that it is the product of discovery that enters the
utility function, not the input of time in discovery. Yet, it is the process of discovery
that is often reported as giving enjoyment to scientists.
35.
Parts A and B of the discussion draw on joint work with Levin (Levin and
Stephan 1991; Stephan and Levin 1992).
1216
progression of knowledge (to paraphrase Jacob Mincer
1974, p. 21), the latest educated should be the best educated and hence the
most productive, other things being equal. Another factor that affects research
productivity and varies by cohort is access to the resources that affect
research. Finally, in addition to
differences in the rate that knowledge becomes obsolete and differences in opportunities
that greet different cohorts over time, cohorts may vary in the level of
ability on motivation they bring to the fields or specialty areas they enter.
The presence of cohort
effects dictates a research design that uses a pooled cross section time series
data base. Such databases are not only
costly to create; issues of confidentiality can limit access to the ones that
do exist. Diamond uses a database he
assembled for mathematicians at Berkeley; Levin and Stephan develop a database
by matching records from the National Research Council’s biennial 1973-1979
Survey of Doctorate Recipients (SDR) with publishing information from the Science
Citation Index. Weiss and Lillard use a sample of Israeli scientists.
Levin and Stephan analyze
six areas of science. They find that,
with the exception of particle physicists employed in Ph.D.-granting
departments, life-cycle effects are present in the fully specified model that
controls for fixed effects such as motivation and ability. [36] For the fields of solid-state physics, atomic and molecular
physics, and geophysics the evidence suggests that publishing activity initially
increases but declines somewhere in mid-career. For particle physicists at FFRDCs,
as well as for geologists, the profile decreases throughout the career. The absence of life-cycle effects for particle
physicists at Ph.D.-granting institutions is not totally unexpected. Abstract theorists working on unification are
often depicted as involved in a “religious quest,” handed them by Albert
Einstein, or, as is commonly stated in the literature, the “search for the Holy
Grail.”
Diamond finds that the
publishing activity of Berkeley mathematicians declines slightly with age. Weiss and Lilland
use a pooled model to estimate the growth rate of publications for 1,000 Israeli
scientists. They find that the average
annual number of publications tends to increase in the early phase of the academic
careen and then decline. They also find
that, along with the mean, the variance of publications increases markedly over
the first ten to 12 years of the academic career.
The results of these (as
well as other) studies should not, however, be used to conclude that the human
capital model provides a satisfactory explanation of life-cycle research
activity. Despite the fact that some
indication of an age-publishing relationship is found, the amount of variation
explained is usually small. Diamond, for
example, reports R-squares of .09 or less for his research productivity
equations; Aloysius Siow (1994) reports R-squares
between .05 and .08. The low explanatory
power of these models suggests, at a minimum, that other important factors,
often ignored by economists, are at play in affecting productivity.
B.
Empirical Studies of the Acceptance of New Ideas
The notion that older
scientists are slow to adopt new ideas and may actually impede the progress of
science by blocking innovative work of younger scientists has been articulated
by several scientists and is consistent with a human capital
36.
Vintage variables cannot be included in a fixed-effects model because the
vintage variable is invariant over time for an individual. Equations were also estimated that included
vintage variables but excluded the fixed effects.
1217
model that age determines how vested a scientist is in a
particular idea. The concept is often
referred to as Planck’s Principle, because Max Planck stated in his
autobiography that
a new scientific truth does not triumph by convincing
its opponents and making them see the light, but rather because its opponents
eventually die, and a new generation grows up that is familiar with it. (1949,
pp. 33-34)
Planck’s Principle has been
tested by several researchers. The focus
of two of these studies is the plate-tectonic revolution that occurred in the
mid-to-late 1960s (Stewart 1986; Peter Messeni 1988).
Two others look at the acceptance of
Darwin’s ideas on evolution in the nineteenth century (Hull, Peter Tessner, and Diamond 1978; Hull 1988). The results of these studies suggest that the
effect of an additional year lowers the probability of acceptance by only a
tiny percentage. The general conclusion
is that “Age matters, but it does not matter much” (Diamond 1980, p. 841). A recent study by Levin, Stephan, and Walker
(1995) commenced with the goal of seeing whether this outcome was caused by the
failure of previous researchers to control for censoring. Their results concerning the acceptance of
Darwin’s ideas indicate that the age effects obtained after controlling for
censoring are statistically insignificant at conventional levels. They conclude that, at least for the theory
of evolution, age is a poor predictor of acceptance.
C.
Empirical Studies of Earnings Functions
Estimates of earnings
functions constitute the bread and butter of labor economics; thus an essay of
this length can hope to scratch only the surface of this literature, even when
we restrict the analysis to estimates of the earnings functions of scientists
and ignore the issue of the role of gender in the reward structure. In an effort to further focus discussion, here
we examine only earnings equations that use pooled databases and hence make an
effort to disentangle experience effects from cohort (or time-period) effects.
Five studies nicely fit
this bill. Four focus on scientists in
the United States (Diamond 1986b; John Laitner and
Stafford 1995; Lillard and Weiss 1979; and Weiss and Lilland 1978). One
studies scientists in England and Australia (John Creedy
1988). The five all find that the
earnings profile is concave from below, peaking (at the very earliest) in late
career. [37] This finding is fairly robust with regard to
specification, estimation technique, and database. Of particular interest is the finding by Laitner and Stafford (1995) that the profile remains
concave from below even when the earnings measure is expanded to include “other
earnings.” The parameter estimates of
experience (when the dependent variable is the natural log of real earnings)
are generally between .05 and .06; those for experience squared somewhere
between - .0008 and - .0005. The
R-squares (when the statistical techniques employed permit their computation)
are quite respectable, being in the neighborhood of .50.
Clearly the earnings of
scientists are related to experience or age. It would be imprudent, however, to suggest
that these robust results with regard to age/experience confer infallibility on
the human capital model in terms of an explanation of earnings. First, a number of other theories (e.g.,
principal agent/bonding/antishirking models;
efficiency wage models; and rank-order tournament models) predict a positive
relation-
37. A related question not addressed here is why in
cross-sectional data Michael Ransom (1993) finds a negative seniority wage
premium for faculty members but not for the population in general or for other
highly skilled occupations.
1218
ship between age/experience and earnings. Second, the driving force of the human capital
model is the finiteness of life. Yet
early in the career the present value of an investment declines only modestly
with age, unless the discount factor is quite large. It is only toward middle age that the
finiteness of life takes on substantial economic significance. [38]
D.
Does the Human Capital Model Come Up Short?
While it is an
overstatement to say that across the board the human capital model comes up
short when applied to scientists, it is fair to say that it does not come up
king. Especially with regard to
publishing activity and the acceptance of new ideas, the empirical results
(even when sophisticated estimating strategies are employed) fail to convince,
at least this observer, that the human capital approach provides the
cornerstone on which we should model the behavior of scientists. Neither does the human capital model provide a
ready explanation of why the publishing activity of a cohort becomes
increasingly unequal over time.
The failure of the human
capital model to deliver is undoubtedly related to the fact that the production
of scientific knowledge is far more complex than the human capital model
assumes and that these complexities have a great deal to say about patterns
that evolve over the life cycle. Human
capital models also come up short in their failure to recognize the importance
that priority plays in the objective function of scientists, or to fully incorporate
puzzle solving as an argument in the objective function. In the discussion that follows, we focus on
the complexities of the production function and discuss what they mean for the
modeling process.
9. The Production of Scientific Knowledge [39]
Any new idea - a new conceptualization of an existing
problem, a new methodology, or the investigation of a new area - cannot be
fully mastered, developed into the stage of a tentatively acceptable
hypothesis, and possibly exposed to some empirical tests without a large expenditure
of time, intelligence, and research resources.
So Stigler described the
“production function” for knowledge in his 1982 Nobel lecture (1983, p. 536). Here we explore these components in more detail,
arguing that as economists we have focused most of our attention on the attributes
that the individual contributes directly to the process, ignoring the
importance of research resources.
A.
Time and Cognitive Inputs
Although it is popular to
characterize scientists as having instant insight, studies suggest science
takes time. Investigators often portray productive scientists
- and eminent scientists especially - as strongly motivated, with the “stamina’
or the capacity to work hard and persist in the pursuit of long-range goals”
(Mary Frank Fox 1983, p. 287). A strength of the human capital models described above is
their explicit recognition of the role time plays in discovery. These models also recognize the importance of
intelligence or, more broadly speaking, cognitive inputs.
Several dimensions of
cognitive resources are associated with discovery. One aspect of this is ability. It is generally believed that a high level of
intelli-
38.
It is interesting to note that studies by psychologists suggest that it is only
in the late forties that individuals begin to measure time in terms of years
left to live instead of years since birth (Bernice Neugarten
1968).
39.
Section A draws on joint work with Levin (Stephan and
Levin 1992).
1219
gence is required to do science, and several studies have
documented that, as a group, scientists have above average IQ’s. [40] There is also a general consensus that certain people are
particularly good at doing science and that a handful are superb. Another dimension of cognitive inputs is the
knowledge base the scientist(s) working on a project possess. This knowledge is used not only to solve a
problem but to choose the problem and the sequence in which the problem is addressed.
The importance knowledge
plays in discovery leads to several observations. First, it intensifies the race, because the
public nature of knowledge means that multiple investigators have access to the
knowledge needed to solve a problem. Second,
knowledge can either be embodied in the scientist(s) working on the research or
disembodied, but available in the literature. Different types of research rely more heavily
on one than the other. The nuclear
physicist Leo Sziland, who left physics to work in
biology, once told the biologist Sydney Brenner that he could never have a
comfortable bath after he left physics. “When he was a physicist he could lie in the
bath and think for hours, but in biology he was always having to get up to look
up another fact” (Lewis Wolpent and Allison Richards
1988, p. 107).
Third, the knowledge base
of a scientist can become obsolete if the scientist fails to keep up with
changes occurring in the discipline. On
the other hand, the presence of fads in science (particularly in areas such as
particle physics) means that the latest educated are not always the best
educated (Stephan and Levin 1992). Vintage
may matter in science, but not always in the way that Mincer’s
“secular progression of knowledge” would lead us to believe (Mincer 1974, p. 21).
Fourth, there is anecdotal
evidence that “too” much knowledge can be a bad thing in discovery in the sense
that it “encumbers” the researcher. There
is the suggestion, for example, that exceptional research may at times be done
by the young because the young “know” less than their elders and hence are less
encumbered in their choice of problems and in the way they approach a question.
[41]
Finally, the cognitive
resources brought to bear on a problem can be enhanced by assembling a research
team, or at a minimum engaging in a collaborative arrangement with another
investigator. [42] Because of spiraling specialization and an increased emphasis
on equipment that requires unique skills, teams have become increasingly
important in science. Andy Bannett, Richard Ault, and David Kaserman
(1988) suggest two other factors leading persons to seek coauthors. One is the desire to minimize risk by diversifying
one’s research portfolio through collaboration; the other is the increased
opportunity cost of time. An additional
factor is quality. The literature on
scientific productivity suggests that scientists who collaborate with each
other are more productive, often-
40. Lindsey Harmon (1961, p. 169) reports that Ph.D.
physicists have an average IQ in the neighborhood of 140. Catherine Cox, using biographical techniques to estimate the
intelligence of eminent scientists, reports IQ guesstimates of 205 for Leibnitz, 185 for Galileo, and 175 for Kepler. Anne Roe (1953, p. 155) summarizes Cox’s
findings.
41.
There is a literature suggesting that individuals coming from the margin – “outsiders”
if you will - make greater contributions to science than those firmly
entrenched in the system (Thomas Gieryn and Richard
Hirsch 1983). Stephan and Levin (1992)
argue that this is one reason why exceptional contributions are more likely to
be made by younger persons. In studying
Nobel laureates, they conclude that although it does not take extraordinary
youth to do prize-winning work, the odds decrease markedly by mid-career.
42.
Although teamwork and collaboration are used interchangeably here, Donald
Beaver (1984) suggests that teamwork is a step beyond collaboration.
1220
times producing “better” science, than are individual
investigators. [43]
One indication of the trend
toward collaboration in modern science is given in Table 4 [HHC – not included].
Panel A reports
the mean number of authors per authored source item in the Science Citation
Index. We see that in the short span of
15 years the mean number has increased by one, a factor of almost 40 percent. Not surprisingly, coauthorship
patterns vary by field and organizational setting. This can be seen from Panel B of Table 4,
which gives the average number of collabora-
43.
Frank Andrews (1979) and S. M. Lawani (1986) discuss
the relationship among quantity, quality, and collaboration in science. Other considerations are that collaborative
work is more likely to be based upon funded research and more likely to be
experimental rather than theoretical (Mary Frank Fox 1991).
1221
tors on articles written by respondents to the Survey of
Doctorate Recipients in four broad fields over the period 1972-1981. The large number of coauthors on articles
written by physicists working at FFRDCs reflects the
fact that time on the large particle accelerators must be shared and the
setting up of experiments at accelerators involves more specialized skills than
any single individual can possibly command. Indeed, there are stories in physics that it
is possible, on an experimental article, for the author list to be longer than
the article!
B.
Research Resources
The production of knowledge
also requires research resources. In the
social sciences this generally translates into a personal computer, access to a
database and one on two graduate research assistants. For physical scientists the resource requirements
are considerably more extensive, involving access to substantial equipment, and
the assistance of numerous graduate students and postdocs.
In the life sciences research also
requires access to subjects (both of the human and nonhuman variety) as well as
access to certain strains. It is common
practice in these disciplines to reward with coauthorship
colleagues who share such access. Thus
the authorship counts of Table 4 do not necessarily reflect the actual size of
the team involved in any one undertaking.
An appreciation of the
magnitude of equipment employed in academic research can be obtained by
studying the triennial reports issued by NSF on characteristics of
science/engineering equipment in academic settings. The most recent survey (National Science
Foundation 1991b) describes the 1988-89 stock of movable science/engineering
equipment in the $10,000 to $999,999 price range at the nation’s
research-performing colleges, universities and medical schools. It estimates that the aggregate purchase price
of the equipment was about $3.25 billion dollars, expended in the majority of
instances during the previous five years. The results of this survey are summarized in
Table 5 [HHC – not included]
The table provides information on incidence of equipment as well
as mean price. We see, for example, that
the mean price of an electron microscope was $119,600; the mean price of an NMR
was $146,000. And these averages may be
biased toward teaching equipment. Sophisticated
NMRs and mass spectrometers can easily cost in excess
of one million dollars and hence are excluded from the study. Other types of equipment are also excluded
because of cost. Accelerators and
telescopes, for example, often easily cost in excess of $10 million and are usually
shared across institutions. [44]
The importance of graduate
students and postdocs to the research process is
harder to document, but case studies of productive scientists lead to the
conclusion that, in most fields, they are a necessary component of research. It is common practice, for example, for a
chemist to have three to four graduate students and one to two postdocs working in the lab.
The overwhelming importance
of resources to the research process in science means that in many fields
access to resources is a necessary condition for do-
44.
It is important to note that technology is a source of much of the
instrumentation used in science, a fact often ignored by those who argue that
science is the engine of technology and thus a necessary condition for
technological change. This is a common
theme of Rosenberg (1982, 1994) and was masterfully articulated by Price (1986,
p. 247) in one of his last public lectures: “If you did not know about the
technological opportunities that created the new science, you would understandably
think that it all happened by people putting on some sort of new thinking cap...
The changes of paradigm that accompany
great and revolutionary changes may sometimes be caused by inspired thought,
but much more commonly they seem due to the application of technology to
science.”
1222
1223
HHC: Table 5 not displayed
ing research. It
is not enough just to decide to do research, as human capital models assume. At universities, equipment is provided by the
dean only in the first years of the career and usually only for equipment at
the low end of the cost scale. Thereafter,
it, and the stipends that graduate students and postdocs
receive, become the responsibility of the scientist. Scientists whose work requires access to “big”
machines off campus must also submit grants to procure time (e.g., beam time)
at the research facility. This means
that for a variety of fields funding becomes a necessary condition for doing
research, at least research that is initiated and conceived of by the scientist.
Scientists working in these fields take
on many of the characteristics of entrepreneurs. As graduate students and postdocs
they must work hard to establish their “credit-worthiness” through the research
they do in other people’s labs. If
successful in this endeavor, and if a position exists, they will subsequently
be provided with a lab at a research university. They then have several years to leverage this
capital into funding. If they succeed,
they face the onerous job of continually seeking support for their lab; if they
fail, the probability is low that they will be offered “startup” capital by
another university.
C.
An Alternative Approach to the Study of Scientists
This leads one to wonder if
we should not use our talents as economists to develop a different approach to
the study of scientists that stresses the importance of resources in the
process of discovery rather than the importance of the finiteness of life. A key component of such an approach would be
the recognition that past success is extremely important in determining funding
and hence future success. These models
could draw inspiration from empirical work done in the field of industrial
organization that examines the entrance of new firms and their survival over
time. [45] A common finding of this work is that, while entry may
be fairly easy, survival is not and depends upon reaching a critical size
within a certain time frame. An analogy
exists in science, particularly if we think of entry as occurring in graduate
school. The majority of entrants survive
this phase and a large number continue to the postdoctoral phase. Getting “startup” capital from a dean (or
other nonprofit entity) is far harder, and a significant number of scientists
never become independent researchers. For
those who do, the crucial issue then becomes whether
this capital can be used to attain (in a specified period of time) the
reputation required to attract resources in the form of grants. The process is made more difficult because
funding constraints and priorities, which are exogenous to the scientist,
change over time. Such a model, we
suspect, does a far better job of fitting the data than the human capital
models, which treat current effort (and hence outcome) as a function of years remaining
in the career, not as a function of past success and the attainment of a
critical mass. This approach, we might
add, is consistent with an increased variance in the research productivity of a
cohort over time, at least in the early years when scientists fail to get
permanent jobs in the research sector. Obviously
the approach draws heavily on the concept of cumulative advantage or more gener-
45.
Early studies linked firm size positively to the likelihood of survival. Later studies explicitly linked the startup of
new firms and their survival and growth to underlying technological regimes. Audretsch (1995)
summarizes this literature. The analogy
between scientists and firms is not limited to the concept of critical mass. It also relates to learning. As entrepreneurs gain experience in the market
they discover whether they have “the right stuff.” They also learn whether they can adapt to
market conditions and strategies employed by rival firms. Scientists, too, learn as their careers
unfold.
1224
ally the concept of path dependence articulated by Brian
Arthur (1990).
To sum up, a reasonable
case can be made that economists need to rethink the way we study the careers of
scientists. A parsimonious model, with
strong explanatory power, would portray scientists as having the objective of
directing their own labs or research agendas. Given the importance of resources to research
and the role past success plays in getting these resources, this means that
scientists must continue to do research if they want to keep their place in the
funding queue.
10. Funding Regimes
The conventional wisdom
holds that because of problems related to appropriability,
public goods are underproduced if left to the private
sector. Although priority goes a long
way toward solving the appropriability problem in
science, this ingenious form of compensation does not insure that efficient
outcomes will be forthcoming. In
addition to problems caused by uncertainty and indivisibilities, as well as
other efficiency concerns raised in Section 5, there is the problem that
scientific research requires access to substantial resources. Unless priority can be translated into
resources, it cannot come close to generating a socially optimal amount of
research. Research must still be subsidized,
by either the government or philanthropic institutions. [46]
Many European countries
fund scientists indirectly by supporting the research institutes where they
work. This practice is less common in
the United States, especially for scientists working in academe. Instead, U.S. scientists are responsible for
raising their own funds through the submission of proposals to funding
agencies. This raises the question of
whether knowledge advances more rapidly under the peer-review grants system or
under the “institute” approach. The
issue, to the best of our knowledge, has been ignored by the economics
profession. It is, therefore, hoped that
the ad hoc discussion that follows will stimulate research on this important
topic.
The benefits of the
institute approach are several: it insures that scientists can follow a
research agenda (with an uncertain outcome) over a substantial period of time,
it exempts scientists from devoting long hours to seeking resources and it
minimizes administrative expenditures. These
benefits are not trivial.
The costs of the institute
approach are also substantial. Foremost is
the question of the research agenda. In
many institutes the agenda is set by the director, and younger scientists are
constrained from following leads they consider promising. The guarantee of resources also encourages
shirking; consequently, alternative methods of monitoring must be found. The institute approach also enhances
stratification in science and hence the possible waste of human resources. Most appointments are made early in the
career. If the
scientist does not succeed in getting an institute appointment (and tenure in
the job), the scientist will have minimal access to resources in that country
for the rest of the career. One
effect of this is that it encourages migration.
The grants system also has
its benefits. At the top of the list is
peer review, which promotes quality and the sharing of information. The system also encourages scientists to
remain productive throughout the life cycle, because scientists who wish to
have a lab must remain productive. To
the extent that success in the grants system is not completely determined by
past success, the system provides some opportunity for last year’s
46.
Callon (1994) proposes that public support of science
is needed to ensure that multiple lines of inquiry remain open.
1225
losers to become this year’s winners. The system also encourages entrepreneurship
among scientists and makes them somewhat disposed to explore the possibility of
technology transfer (Stephan and Levin 1996). It also provides younger persons the opportunity
to establish independent research agendas.
Just as some of the
benefits of the grants system are costs of the institute system, so, too, some
of the benefits of the institute approach are costs of the grants system. Grant applications divert scientists from
spending time doing science. A funded
chemist in the U.S. can easily spend 300 hours per year writing proposals. While some of this effort undoubtedly
generates knowledge, much of it is of a “bean counting” nature and adds little
of social value. The grants system also
encourages scientists to choose sure(r) bet short term projects that in the
longer run may have lower social value. The
system also implicitly encourages scientists to misrepresent their work or the
effort required to generate certain outcomes.
It is typical, for example, for scientists to apply for work that is
almost completed (yet not acknowledge that it has been performed) and to use
some of the proceeds of funding to support “unfundable”
work that is dearer to their hearts. [47]
11. Science, Productivity, and the New Growth
Economics
The foremost reason
economists have for studying science is the link between science and economic
growth. That such a relationship exists has long been part of the conventional
wisdom, articulated first by Adam Smith ([1776] 1982, p. 113). Technology, an intermediate step between
science and growth, has been the subject of extensive study by economists. More generally, the whole issue of the
research and development strategies of companies has occupied a significant
proportion of the profession during the past 50 or so years.
It is one thing to argue
that science affects economic growth or to establish that a relationship exists
between R&D activity and profitability. It is another to establish the extent that
scientific knowledge spills over within and between sectors of the economy and
the lags that are involved in the spillover process. To date, three distinct lines of inquiry have
been followed to examine these relationships. One inquires into the relationship between
published knowledge and growth. Another samples innovations with the goal of determining the
scientific antecedents of the innovation and the time lags involved. A third examines how the innovative activity
of firms relates to research activities of universities (and other firms). The studies suggest that spillover effects are
present and that the lags between scientific research and its market impact are
not inconsequential.
James Adams (1990) uses the
published-knowledge line of inquiry to examine the relationship between
research and growth in 18 manufacturing industries between the years 1953 and
1980. The study is ambitious; for
example, Adams measures the stock of knowledge available in a field at a
particular date by counting publications in the field over a long period of
time, usually beginning before 1930. He
creates industry “knowledge stocks” by weighting these counts by the number of
scientists employed by
47.
1t is not accidental that the two systems are found in countries which have
different attitudes toward education and social mobility. The institute approach is a logical outcome
of a culture that places heavy ‘emphasis on screening. The grants system, on the other hand, is a
logical extension of a culture that values (at least publicly) the opportunity
of a second chance and places less emphasis on screening. Ultimately, of course, the results of
research in both systems are judged by the international scientific
community, irrespective of how the research was funded.
1226
field in each of the industries being studied. He then relates productivity growth in 18
industries over a 28-year period to stocks of “own knowledge” and stocks of
knowledge that have flowed from other industries. Adams finds both knowledge stocks to be major
contributors to the growth of productivity. He also finds that the lags are long: in the
case of own knowledge, on the order of 20 years; in the case of knowledge
coming from other industries, on the order of 30 years.
A different way to study
the relationship between research and innovation is to seek the scientific and
technological roots of certain innovations. A 1968 study prepared for the National Science
Foundation by the IIT Research Institute does precisely this, tracing the key
scientific events that led to five major innovations (magnetic ferrites, video
tape recorders, the oral contraceptive pill, electron microscopes, and matrix
isolation). Of particular significance
is the finding that in all five cases non-mission scientific research [48] played a
key role and that the number of non-mission events peaked significantly between
the 20th and 30th year prior to an innovation. The study also finds that a disproportionate
amount of the non-mission research (76 percent, to be precise) was performed at
universities and colleges.
A somewhat related approach
to the question focuses on firms, instead of specific products, in an effort to
ascertain the role that university research plays in product development. Mansfield (1991) uses such a technique. He surveys 76 firms in seven manufacturing industries
to ascertain the proportion of the firm’s new products and processes commercialized
in the period 1975-85 that could not have been developed (without substantial
delay) in the absence of academic research carried out within 15 years of the
first introduction of the innovation. He
finds that 11 percent of the new products and 9 percent of the new processes
introduced in these industries could not have been developed (without substantial
delay) in the absence of recent academic research. Using sales data for these products and
processes, he estimates a mean time lag of about seven years. He also uses these data to estimate “social”
rates of return of the magnitude of 28 percent. In a follow-up study, Mansfield (1995) finds
that academic researchers with ties to the firms report that their academic
research problems frequently or predominantly are developed out of their
industrial consulting and that this consulting also influences the nature of
work they propose for government-funded research.
Knowledge spillovers can
also be studied by examining the relationship between some measure of innovative
activity of firms and the research expenditures of universities. This line of inquiry ignores the lag
structure, but focuses instead on the extent that such spillovers exist and are
geographically bounded. The rationale
for expecting them to be bounded is that tacit knowledge is difficult to
communicate in writing, but instead is facilitated though face to face
communication. The approach is not restricted
to examining the relationship between innovation and university research, but
often includes a measure of private R&D expenditure in the geographic area
to determine the extent that spillovers occur within the private sector. Sometimes the measure of innovative activity
used is counts of patents (Adam Jaffe 1989); sometimes it is counts of innovations
(Zoltan Acs, Audretsch, and Maryann Feldman 1992). In either case, measured at the
geographic-industry
48.
The study defined non-mission research to be research “motivated by the search
for knowledge and scientific understanding without special regard for its
application” (p. ix).
1227
level, innovative activity is found to relate to the
expenditure variables of university units in the geographic area doing research
in scientific disciplines that relate to the industry as well as to the R&D
expenditures of other firms in the same geographic area. There is some indication that these
spillovers, particularly those coming from universities, are more important for
small firms than for large firms (Acs, Audretsch, and Feldman 1994). [49]
Despite the crudeness of
the measures and the problems inherent in the various approaches, [50] these
studies go a long way toward demonstrating that the spillovers between scientific
research and innovation are substantial, as are the lags. We cannot, however, leave the growth story
here. Recent work suggests that knowledge
spillovers are a major source of long-term growth and that these spillovers are
set in motion by endogenous forces. The story goes something like this: In an
effort to seek rents, firms engage in R&D. Public aspects of this R&D then spill over
to other firms, thereby creating increasing returns to scale and long-term
growth (Paul Romer 1994). The work of Jacob Schmookler
(1966) and Scherer (1982), which demonstrates the responsiveness of R&D to
demand factors, is consistent with this concept of endogenous growth. So is the work of
Jaffe (1989) and Acs, Audretsch,
and Feldman (1992), which suggests that firms appropriate the R&D of other
firms. Empirical work summarized above
also implies that scientific research conducted in the academic sector of the
economy spills over to firms.
Does this mean that
research in the academic sector is an important component of the new growth
economics? The answer depends upon the
extent that scientific research in the academic sector is endogenous. [51] If it is not, spillovers from universities to firms are important, but
not as a component of the new growth economics. Five aspects of science that we have developed
in this essay lead us to argue that an endogenous element of academic research
exists. First, profit-seeking companies
support academic research, and this support is growing. Second, the problems that academic scientists
address often come from ideas developed through consulting relationships with
industry. Third, markets direct, if not
completely drive, technology, and technology affects science (Rosenberg 1982
and Price 1986). [52] For example, instrumentation, which often comes from technology,
has proved to be extremely important in ushering in new scientific discoveries.
Fourth, government supports much of
university research, and the level of support available clearly relates to the
overall well-being of the economy. Finally,
there is evi-
49.
The actual mechanism by which spillovers occur has not been studied. Without a trail linking the
knowledge-producing center with the firm using the knowledge, it is difficult
to know if this type of knowledge transfer is indeed geographically
bounded. The Mansfield (1995) and Audretsch and Stephan (1996) studies represent first steps
in this direction. Future work should
also focus on the role mobility within the industrial sector plays in
facilitating spillovers. Scientists
sometimes become mobile, joining other firms or starting their own firms in
order to appropriate the value of their human capital.
50.
David, David Mowery, and Edward Steinmueller (1992)
offer a good critique. They emphasize
the limitations inherent in cost-benefit approaches for evaluating the
contribution of basic research and propose an alternative information-theoretic
approach for identifying the economic benefits.
They also note the importance of non-findings as well as findings in
guiding applied research and development.
51.
It goes without saying that the science performed in companies is endogenous
and spills over to other companies. A
good portion of this essay has been devoted to demonstrating that profit-seeking
companies hire scientists, direct them to do basic research, and often allow
(encourage) them to share their research findings with others.
52.
This counter thesis of “technology push” is also important. That is, in many cases the invention of a new
technology leads to new demands.
1228
dence that relative salaries and vacancy rates affect the
quantity and quality of those choosing careers in a field. “Hot fields” like biotechnology have attracted
a disproportionate number of people in recent years when the rewards (at least
for a few) have been extraordinary. The
impact on academic research has been substantial. [53]
One could even argue that
university researchers have become too responsive to economic incentives for
the good of science, or for the long-term good of the economy (Stephan and
Levin 1996). A common theme is that a
host of factors are leading university-based scientists in certain fields
increasingly to “privatize” knowledge, trading what could be thought of as reputational rights for proprietary rights and the
financial rewards attached to these rights.
Among the factors
encouraging increased secrecy is a change in the law that enables universities,
nonprofit institutions, and small firms to own patents resulting from sponsored
research, an entrepreneurship spirit that grantsmanship
fosters, and a time collapse in fields such as the life sciences that
dramatically shortens the lag between basic discovery and application
(Gambardella 1995). While the move to
“privatize” can do much to foster knowledge spillovers, basic science is also
affected by the process. Privatization keeps
knowledge from being available in a codified form (Dasgupta
and David 1994) and by-passes the peer-review system that helps to monitor
quality and produce consensus in science.
12. Conclusion
This essay suggests several
areas of inquiry in which economists have added significantly to an
understanding of science and the role that science plays in the economy. Some of these draw heavily on observations
made by sociologists of science and demonstrate the continued need to approach
the study of science from an interdisciplinary perspective.
First, we have begun to
quantify the relationship between science and economic growth, both in terms of
payoff and lag structure. We have also
achieved a better understanding of how science relates to growth, as a result
of two threads of research coming together. One demonstrates that firms benefit from
knowledge spillovers. The other suggests
that knowledge spillovers are the source of growth and that these spillovers are
endogenous. Although the authors
of the new growth economics focus on the role that the R&D activities of
firms play in this spillover process (both as creator of spillovers and recipient
of spillovers), a good case can be made that research in the nonprofit sector
spills over and has endogenous elements that are set in motion by
profit-seeking behavior.
Second, economists have
examined how a priority-based reward system provides incentives for scientists
to behave in socially beneficial ways. In
particular, it can be demonstrated that the reward of priority encourages the production
and sharing of knowledge and thus goes a long way toward solving the
appropriability dilemma inherent in the creation of
the public good knowledge. While this
line of inquiry was established by the sociologist Merton, Dasgupta
and David, as well as several other economists, have done much to extend
Merton’s observation that priority is a special form of
53.
This is not to argue that outcome X is endogenous, but merely that the growth
of knowledge has an endogenous component.
At any point in time constraints clearly exist to discovery, either
through the technology that is available to address the problem or because of
lack of fundamental knowledge in an area necessary to the inquiry. Many of these constraints must be viewed as
being exogenously determined, at least over a specific period of time
(Rosenberg 1974).
1229
property rights. Surely
this is interdisciplinary fertilization at its very best!
Third, science is not only
about fame; it is also about fortune. Another contribution of economists is the
demonstration that many of the financial rewards in science are a consequence
of priority: salary, for example, is positively related to both article and
citation counts. Because the financial
rewards often come in the form of consulting and royalty income, we will never
know the full extent of the relationship until we have reliable data on nonsalary dimensions of the income of scientists. There is also the suggestion that reputation
matters to industry. We know, for
example, that some firms encourage scientists to publish. We also know that startup companies
use highly cited scientists as a signal of quality to financial markets.
Fourth, economists have a
reasonably good understanding of the way scientific labor markets function,
although the estimates of elasticity are not as robust as one would like. Neither can we forecast market conditions with
much accuracy. We should not accept responsibility
for this failure, however, because much of the problem rests on the impossibility
of predicting the whims of Congress.
Economists have also
contributed to an appreciation of how the finiteness of life affects behavior
of an investment nature. Human capital
models have led to the prediction that earnings, research productivity and
receptivity to new ideas of scientists will decline in late career. Much effort has been allocated to testing
these models. The empirical results, especially
with regard to publishing activity and the acceptance of new ideas, lead this
observer of science to conclude that the human capital approach does not
provide the cornerstone on which we should model the behavior of scientists. Neither does the human capital model provide a
ready explanation of why the productivity of a cohort of scientists becomes
increasingly unequal over time. The
failure of the model is undoubtedly related to the fact that the production of
scientific knowledge is far more complex than the human capital model assumes,
and that these complexities have a great deal to say about patterns that evolve
over the life cycle. This leads us to conclude that economists need to rethink
the way we study the careers of scientists. A parsimonious model, with strong explanatory
power, would portray scientists as having the objective of directing their own
lab or research agenda.
There are other ways
economists could contribute to a better understanding of the workings of
science. Eight are mentioned here. First, economists have a comparative advantage
in understanding and analyzing the role that risk and uncertainty play in
science. We can, for example, explain
why risk aversion on the part of funding agencies dissuades scientists who are
by disposition willing to take risk from engaging in this kind of research. We have the tool kit required to understand
choices as outcomes of games and the possibility of using experimental
economics to better understand how outcomes depend on rewards and funding.
Second, economists can
continue to contribute to a discussion of efficiency questions: Are there too
many entrants in certain scientific contests or, more generally, too many
scientists? A related question concerns
whether science is organized in the most efficient way, particularly in the
nonprofit sector. Is the demand for
graduate students as research assistants and subsequently as postdocs so strong that it masks market signals concerning
the long-run availability of research positions and encourages inefficient
investments in human capi-
1230
al? [54] Could other kinds of personnel (e.g., individuals with
terminal masters degrees) substitute for graduate students and postdocs in the lab? [55]
Third, economists can
contribute to an understanding of science by extending to the study of science
approaches that have proved fruitful in the study of firms. We have already suggested, for example, that
work in industrial organization that examines the entrance and survival of new
firms could provide a framework for studying the careers of scientists. Another possibility is to view the production
of scientists through the lens of an evolutionary model (Nelson and Winter
1982). Diversity and selection - the
heart of evolutionary economics - are clearly present in the way in which
scientists are trained, promoted and rewarded.
Fourth, economists can
contribute to a better understanding of how the reward structure of science
leads some scientists to behave in socially irresponsible ways. Issues here concern the fragmentation of
knowledge that a focus on article counts encourages and the temptation to
engage in fraudulent behavior.
Fifth, given the role that
resources play in scientific discovery, it is important to understand more
fully how scientific outcomes relate to the way governments and philanthropic
organizations provide resources. Several
governments abroad are currently experimenting with new approaches for planning
research support, evaluating program performance, and using the results of
evaluation in subsequent decisions. Research
concerning the effectiveness of different approaches is clearly needed.
Sixth, as a discipline we
need to pay considerably more attention to understanding the way scientific
effort is organized, monitored, and rewarded in industry. We also need to study
how knowledge spillovers are transmitted to industry.
Seventh, the question of
how increased opportunities for entrepreneurial behavior affect the practice of
science bears further exploration. When millions of dollars are at stake, for
example, are journal editors less inclined to declare a winner and more
inclined to declare a tie, as anecdotal evidence would suggest?
Eighth, we need to
understand more fully how science relates to patterns of international trade. Although knowledge is a public good, it has
exclusive aspects once it is embedded in traded goods. Work by Ralph Gomory
and William Baumol (1995) and George Johnson and
Stafford (1993) suggests that the lessons of David Ricardo concerning the gains
to trade may fail to be realized in a world where developing countries
appropriate the technological advances made by others.
In short, economists have
accomplished a reasonable amount in our study of science; but other issues
await investigation. It is hoped that
this essay will encourage that process.
54.
In its most extreme form this question asks if the current system of graduate
education is fundamentally a pyramid scheme in which graduates recruit new talent
in order to keep the system going.
55.
The need to restructure graduate education and postdoctoral training in math
and the physical sciences was the topic of a June 1995 NSF conference. The summary report (John Armstrong 1995)
stresses the need to broaden graduate education and make increased use of
periods of off-campus experience. A
report on the graduate education of scientists and engineers issued by the
National Academy of Sciences (1995) made similar recommendations with regard to
providing a broader range of options to graduate students.
1235
The Competitiveness of Nations
in a Global Knowledge-Based Economy
March 2003