#35 from R&D
Innovator Volume 2, Number 5
and the Quest for the Ribbon
Stephan is professor of economics and senior associate, Policy
Research Center, Georgia State University, and Dr. Levin is
professor of economics at the University of Missouri, St. Louis.
They published Striking
the Mother Lode in Science: The Importance of Age, Place, and Time
(New York: Oxford University Press, 1992) from which the following
piece was adapted.
is a young person's game: This is a view to which many
scientists subscribe, although few take as extreme a position as
the physicist P.A.M. Dirac, who said, only half-jokingly, that a
physicist is "better dead" than living past 30.
We have examined
the relationship between age and scientific productivity.
Our quest was motivated in part by the sobering realization
that the recent slower pace of science has caused the typical
researcher of the 1990's to be significantly older than were
researchers 15 to 20 years ago.
While our research focused on academic
scientists, where success is measured by publications, the
same conclusions generally apply to industrial scientists, where
success is measured by patents and trade secrets, as well as
initially on explanations for an age-productivity relationship
that might exist. Many
of the explanations we examined hinge on the factors that motivate
scientists to do science. These
are best summarized as the puzzle, the ribbon and the gold.
A scientist hopes for intrinsic rewards from solving the
puzzle, and for extrinsic rewards from recognition (the ribbon)
and financial remuneration (the gold).
for the Ribbon
How can the quest
for the ribbon cause a relationship between age and productivity?
By the time scientists take their first professional
position, they have a fair idea of what they need for recognition:
publication in respected journals (or, in industry,
recognition for successful innovation).
In addition to
effort, there are other determinants of success, particularly
talent and luck. Just
as some musicians have a special gift that sets them head and
shoulders above other musicians, some scientists have an innate
ability. Luck also
enters the picture: a
serendipitous event in the lab, a referee or supervisor partial to
a particular scientist's research, a competitor who hasn't had
At the outset of
their careers, scientists believe they have a high probability of
illustrative purposes, consider scientist Jones, who believes her
probability of success is 70 per cent.
This is close enough to 100 percent to motivate her to work
hard, but not close enough to make her think that success will
Jones selects a
question to study, organizes the research, and performs the
experiments, but the experiment does not yield positive results.
She tries again, designing a new experiment.
This time Jones is successful and submits the results to a
prestigious journal, only to find that a competitor had submitted
a similar article two weeks earlier.
Things are not looking good for Jones.
focus, begins a new experiment, and spends six months in the lab,
only to find that a basic assumption has been proved invalid. Three research projects, no acceptances.
Surely it's time for her to reevaluate the probability of
doing research that can be published in a top journal.
(Her industrial counterpart has been unable to solve
various assigned projects.)
We could write a
different scenario for Jones:
Her first experiment was successful and the resulting paper
was accepted by a top journal, which led her to do additional
work, which was also published in top journals.
By this time, with several significant pieces of research,
she gains a small following, and is invited to conferences, where
she learns who is working on what questions, which is a great help
if she is doing research in a competitive area.
Do I Detect a
By this stage of
her career, colleagues are referring to Jones's work and deferring
to her as an expert. Now,
when she writes another paper, even if a referee is doubtful about
certain points, the referee may defer to Jones’s
when she applies for research funds, the review board will be
quicker to approve her project than if a comparable proposal has
come from an “unknown.” (The
industrial researcher who has solved key problems is, in a similar
fashion, more likely to be listened to by her colleagues and
longer Jones works, the better idea she has of the probability of
doing research deemed worthy of a top journal.
In the worst-case scenario, she begins to wonder whether 70
percent chance of success is a gross overestimation.
Not only does failure and rejection lead her to reevaluate
the odds of success, it also makes her question whether winning is
all that important. As
a result, she becomes less motivated to spend time doing research.
She finds, as her career unfolds, that the “game” of
research is not what she expected while in college.
In the best-case
scenario, Jones learns that she has a probability of achieving
success that’s far higher than 70 percent, and this encourages
her to spend even more time in the lab.
Why? All those
requests for preprints and invitations to conferences whet her
appetite for even more recognition, until she becomes a
connoisseur of recognition. Jones begins to dream of honors
science refer to this self-reinforcing process as cumulative
advantage, and argue that this process means that the recognition
for a specific piece of research may depend on the reputation of
the scientist. From
the point of view of journal referees and the broader scientific
community, research that is really only average may—depending on
the investigator’s reputation—appear more like an outstanding
for productivity implicit in cumulative advantage and
reinforcement theory are clear.
For the individual, scientific productivity is consistent
over time: Scientists who are productive early on remain
productive later on, while those who are unproductive at an early
date are likely to be less productive later on.
Some succeed for a while, it’s true, only to fail later.
But on the whole, success breeds success.
speaking, studies confirm this pattern:
Later productivity is heavily influenced by recognition of
early work. These
results could be explained by the “sacred spark”
hypothesis—that the population of scientists is heterogeneous:
Those with the spark are always productive, those without
it are not. No one
would argue that talent is equally distributed among scientists.
But research gives credence to the cumulative advantage
hypothesis without totally discrediting the sacred spark
differential distribution of talent does not appear large enough
to account for the vast inequality in research output that exists
There are other
reasons besides cumulative advantage for thinking that a negative
relationship exists between age and productivity. Suffice it to say they involve the quest for gold and the
desire to solve the puzzle. Another
factor that comes into play is the relationship between age and
creativity. We leave
that discussion for another time.
The main lesson
here for R&D managers is this:
Let your staff feel that they are continually increasing
their value. Otherwise,
they will get discouraged about their career potential and will
have less enthusiasm for productive research.
You have to determine an appropriate level of
responsibility for each individual, making it such that he or she
believes the ribbon to be within reach.