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Trends in the Production of Scientific Knowledge
Paula StephanGeorgia State University and NBER
July 2009
Overview
• Focus will be on production of scientific research in the university sector
• Draws from updated article “Economics of Science”
Role of Academic Sector
• Academic sector plays important role in knowledge production.
• In US, 74% of all articles (fractional counts) produced in academe; academe and PROs play similar role in Europe
• Role of academe in patenting is increasing, but considerably smaller– 4.5%--grown from 1.5% in U.S.– Similar order of magnitude in Europe but more
difficult to measure
Domain of Academe
• Academe historically been more focused on basic research
• Evidence that individuals who value independence in choosing research agendas are more likely to work in academe than they are in industry.
• Individuals working in industry generally place higher value on monetary rewards.
Examples of Research• Bertozzi’s lab (Berkely )has 20 PhD students,
10 postdoctoral fellows & 10 undergrad students. Senior staff scientist & research associate also work in lab as do 3 administrative staff & a biosafety facility director.
• High Performance Networking Group at Stanford, led by Nick Mckeown, includes 12 PhD students, two masters students, an administrative assistant, three visitors, three associates, and a research engineer.
• Fluid physicist David Quéré, (on the faculty of the Ecole Superieure de Physique et Chimie Industrielles of France) & research director at CNRS leads a CNRS research group composed of a researcher, seven graduate students and one postdoc.
• Research of hydrologist Elizabeth Screaton (University of Florida), which “investigates the interrelationship of fluid flow and deformation in subduction zones,” combines field work—on board drilling vessels—with lab work and numerical modeling.
Other examples• Caltech Observational Cosmology Group is
composed of 17 individuals: One professor (Andrew Lange), an administrative assistant, an electronics engineer, 6 postdocs, 5 graduate students, 1 undergrad student and two visiting associates. Group’s focus is development of novel instruments to “study the birth and evolution of the universe.” It has designed instruments that collect data at South Pole Viper telescope as well as at other locations.
• Susan Lindquist’s lab at MIT, which studies protein folding (and which we discussed in Chapter 3) has 37 members: 20 postdocs, 7 graduate students, 1 visiting scientist, 1 staff scientist, 3 technicians, 4 administrators and Lindquist.
• Zhong Lin (ZL) Wang’s Nano Research Group in the College of Engineering at the Georgia Institute of Technology includes two postdocs, two visiting scientists, six research scientists and 14 graduate students.
Commonalities and Differences
• All are doing science and engineering• All share certain common characteristics• But environments in which they work, the
importance of equipment in the research that they do and way in which their work is structured and supported varies considerably.
Production Function Approach• No one model of production fits all of science and engineering.• Mathematicians, chemists, biologists, high energy physicists, engineers,
and oceanographers share certain common characteristics in terms of production. – All require time and cognitive inputs. – In other dimensions there is considerable variability. – Way in which research is organized is case in point.
• Mathematicians & theoretical physicists rarely work in labs (although they may identify with a group and work with coauthors) while most chemists, life scientists, engineers and many experimental physicists do.
• Role of equipment provides another dimension. In some fields, equipment required to do research is fairly minimal, as in the case of certain areas of math, chemistry and fluid physics. In others, research is almost entirely organized and defined by equipment, as in the case of astronomy and high energy experimental physics. Materials also play a role. In vivo experiments require access to living organisms. For many biomedical researchers this means having—and taking care of—large numbers of mice, and, in recent years, zebra fish.
Production Functions• Long tradition in economics of studying production
processes—or functions. When auto and steel plants were important components of economies there were studies of the productivity of the industry and production processes within the firms.
• But when economists study science rarely think of how science is produced.
• Instead—like sociologists-- economists focus on people as unit of observation. Not surprising. People are faces—and brains—behind science.
• But important to think of science as having multiple inputs…not just inputs brought by people
How Science Is Produced
• K=f(Cg, R, t, e) – K is knowledge being produced– Cg= cognitive resources– R=other resources, such as equipment, materials, lab
assistants– t=time of researchers– e is some error term, encompassing among other things
serendipity and uncertainty.
Scientist(s)
• Effort– Science takes time; common observation is that
scientists work exceptionally long hours (52.6 hours per week in U.S.)
– Also requires motivation. “Informed observers have long described high-producing scientists as driving and indefatigable workers.” (Fox.)
Persistence
• > 50% of physicists chose persistence from list of 25 adjectives of what it takes to be successful. No other quality came close.
• Many examples– Judah Folkman– Lorenz
Dimensions of Cognitive Resources• Ability: studies document that as a group scientists
have above average IQs.• Knowledge base: Important in choosing and solving
problems. – Education;– Does scientist keep up? – Raises possibility of obsolescence and related vintage
effects.• Public nature of knowledge intensifies races in
discovery.
Embodied or Disembodied Knowledge?
• Different types of research rely more heavily on one than the other.
• Nuclear physicist Leo Szilard, who left physics to work in biology, 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.”
Too Much Knowledge?
• One can be encumbered by “too” much knowledge
• One reason young may have an edge
Importance of Tacit Knowledge
• Difference between codified and tacit knowledge– Only way to acquire tacit knowledge is to work
with someone with the knowledge– Lab rotations as a mechanism– Visiting other labs– Transgenic mice as an example—need to have
“magic hands”
Collaboration
• Research rarely done in isolation• Often done in labs—common for individuals
to specialize• Staffing of labs varies across countries
– U.S. model relies on “temporary workers”—postdocs & doctoral students;
– European model: permanent staff—employees of CNRS, Max Planck, etc.
Responsibility for Funding
• U.S. faculty has responsibility for funding graduate students & most postdocs. Also faculty member’s time.– Grad student: $28,000 stipend plus $25,000
tuition.– Postdoc: $38,000
• Europe: permanent staff generally employees of state or PRO.– Graduate students receive stipend from state
Biological and Medical Sciences Postdocs by Source of Support
19Source: http://www.nsf.gov/statistics/gradpostdoc/
Full Time Biological and Medical Sciences Graduate Students in Doctorate Granting Departments by Mechanism of Support
20Source: http://www.nsf.gov/statistics/gradpostdoc/
Labs “belong” to faculty in U.S.
• Most have web pages• Lab is named for PI• Sometimes lab members are referred to using
PI’s name as in “Sharpies” for Philip Sharp’s students at MIT
Lab Structure: Example
• 415 labs affiliated with a nanotech center• Average lab has 12 technical staff, excluding PI• 50% are graduate students; 16% are postdocs
and 10% are undergraduates; rest are staff scientists, etc.
Team Behind Science’s 2008 Breakthrough of the Year: University of Wisconsin
James Thomson Lab Back View
J. Yu—first author
Amon Lab: Whitehead Institute
• Amon, HHMI investigator, works on cell division, focusing on how “cells make sure their
chromosomes separate in the right way.”
Christine White and Group: U. of Illinois, Chemistry
Interface & Company
ESCPI Quéré with group
Example from Science
Three-Dimensional Super-Resolution Imaging by Stochastic Optical Reconstruction Microscopy Bo Huang,1,2 Wenqin Wang,3 Mark Bates,4 Xiaowei Zhuang1,2,3*
Science, February 8, 2008
1 Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA.2 Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA.3 Department of Physics, Harvard University, Cambridge, MA 02138, USA.4 School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.
Members of team
• Xiaowei Zhuang, Prof of Chemistry & Chemical Biology, Prof of Physics, HHMI, Harvard
• Bo Huang, post-doctoral fellow in Zhuang lab.
• Wenqin Wang, graduate student, Dept of Physics, Harvard; member Zhuang lab
• Mark Bates, graduate student, Division of Engineering and Applied Sciences, Harvard; member of Zhuang lab
Birth origin of PI Matters in terms of lab staffing—at least in U.S.
• Korean-directed labs have 29% more Koreans than labs directed by U.S.-born PIs
• Chinese-directed labs have 38% more Chinese students than labs directed by U.S.-born PIs
• Indian-directed labs have 27% more Indians than in labs directed by U.S.-born PIs
• Turkish-directed labs have 36% more Turkish students than in labs directed by U.S.-born Pis.
Why?
• Networks and role PI has in staffing lab• Efficient: language
Collaboration
• Common and growing in science• Within labs and across labs• Several ways of seeing trends
Evidence Concerning Teams
Figure 4--Mean Number of Authors per Paper, for PapersWith at Least One Author In the Top 110 U.S. Universities, 1981-1999:
Adams et al 2002
2.40
2.80
3.20
3.60
4.00
4.40
81 84 87 90 93 96 99
Year
Aut
hors
Per
Pap
er
Source: Adams et al
Wuchty, Jones & Uzzi
• Analysis of approximately 13 million published papers in S&E over the 45 year period 1955 to 2000 found team size to increase in virtually every one of the 172 subfields studied.
• On average team size nearly doubled, going from 1.9 to 3.5 authors per paper (Wuchty, Jones & Uzzi, 2006).
• Team size even increased in mathematics-- seen as the domain of individuals working alone and field least dependent on capital equipment:
Teams Increasingly Have Members from Another Institution
• Jones, Wutchy and Uzzi– Study 662 U.S. institutions which have received NSF
funding. – Find collaboration in S&E across these institutions,
which was rare in 1975, grew in each and every year between 1975-2005, reaching approximately 40 percent by 2005.
• My own work with Wolfgang Glänzel, Katholieke Universiteit Leuven, Steunpunt O&O finds similar results using Thomson Reuters ISI data for 1300 plus four year institutions in the U.S.
Percent of papers with U.S. author at another U.S. institution by tier
30.00
35.00
40.00
45.00
50.00
55.00
60.00
Pub Intra Percent
Tier 1, 2 & TopLib
Research 1
Rest of Tier 1
Tier 2
Top Liberal Arts
Rest of Tier 3
Work is joint with Wolfgang Glänzel, Katholieke Universiteit Leuven, Steunpunt O&O.
Percent of U.S. papers with an international author
0.00
5.00
10.00
15.00
20.00
25.00
30.00
Pub Inter Percent
Tier 1, 2 & TopLib
Research 1
Rest of Tier 1
Tier 2
Top Liberal Arts
Rest of Tier 3
Work is joint with Wolfgang Glänzel, Katholieke Universiteit Leuven, Steunpunt O&O.
Why Increase? Importance of interdisciplinary research
Systems biology is case in pointResearchers are arguably acquiring narrower expertise
and thus have more to benefit through collaborationVast amount of data that has become available—
Human Genome project; PubChem Increased complexity of equipment—accelerators and
telescopes are a case in point. CERN’s four colliders have combined team size of just under 6,000.
Rapid spread of connectivity decreases cost of collaboration
Spread of Connectivity: Examples from U.S.
• Twenty five years ago, only way to work with someone at another institution was to talk with them by phone, visit in person, or fax them material– Phone calls & travel were expensive. Cheapest
trip to Europe cost around 1800 in today’s dollars.• Internet, as we know it, did not exist; e-mail
not a possibility.• This changed with inauguration of BITNET.
BITNET• Conceptualized by the Vice Chancellor of University
Systems at the City University of New York (CUNY)• BITNET’s first adopters were CUNY and Yale in May
1981 (Bitnet history). • At its peak in 1991-1992, BITNET connected about
1,400 organizations (almost 700 academic institutions) in 49 countries (CREN).
• By the mid-1990s BITNET was eclipsed by Internet as we know it today and began to fade away.
• We have collected information on date of adoption of BITNET for 1300 four-year institutions in U.S.
Adoption of BITNET by Tier
BITNET replaced by Internet as we know it today
• Key requirement for efficient communication on internet was development of domain name system—such as gsu.edu.
• We have collected information on date that almost every 4-year institution in U.S. took a domain name.
Adoption of DNS by Tier
Concurrently, increased incentives to publish encourages collaboration
• Occurs at both the system level and at the individual level
• Budgets of universities and departments in certain countries depend heavily on publication and citation counts.
• Funding for research of individual scientists depends increasingly on publication track record.
• Bonus payments based on publications
Examples• UK—ranking of departments and allocation of funds based in part on
publications and citations. (Research Assessment Exercise).• Australia—funding of departments based in part on
publications/citations. • Flemish Science Foundation makes research awards based in part on
reputation of faculty as established through publication.• NIH in U.S. (with $29 billion budget) places considerable emphasis on
publication record of grant applicants.• Chinese researchers who place in top half of colleagues in terms of
bibliometric measures can earn three to four times salaries of co-workers. Some institutes pay cash bonus for publishing in Science, Nature or Cell.
Increased emphasis on networking encourages collaboration
• Government agencies have bought heavily into the importance of networks
• “Networks of excellence” funding in EU• Network funding at NIH through “glue” grants
and P01s.
Which grows faster: Lab size or collaboration across labs?
• Number of names on an article has increased by 50%
• Number of addresses has increased by 37%.• Suggests lab size growing slightly faster than
institutional collaboration
Equipment• Science heavily influenced by availability of technology• Exceptions exist but• Increasingly science requires access to complex equipment
– In genetics: DNA gene sequencer and synthesizer, protein synthesizer & sequencer comprise the technological foundation for contemporary molecular biology. Super Computers
– tunneling microscopy—key in nanotechnology– Accelerators– Cell lines– Mice—90% of all mammals used in research are mice—13,000
published
Equipment changes output of lab• 1990 best-equipped lab could sequence 1000 base pairs a day• January 2000 the 20 labs mapping human genome were
collectively sequencing 1000 base pairs a second, 24/7• Measured in base pairs sequenced per person per day, for
researchers operating multiple machines, productivity increased more than 20,000 fold from early 1990s to 2007, doubling approximately every 12 months.
• Costs per finished base pair fell from $10.00 in 1990 to roughly $.01 in 2007
Just beginning…
• New technology for sequencing emerged recently
• Does work of 100 earlier sequencing machines• Ads
– “A billion a day, soon a billion an hour. “ (A billion an hour is what it would take to do the human genome for $1000).
– “More applications lead to more publications”
– “length really matters”
Consequences of changing role of equipment
• Increasing sophistication of research tools suggests that capital-labor ratio is changing. (Under researched area)– Broad Institute and Vetner Institute fired staff working in sequencing
late 2008/2009.• Cost considerations (discussion to follow)• Also substantially changed nature of dissertation work.
– Example: in chemistry, nuclear magnetic resonance combined with x-ray crystallography and advanced computing power allows protein structures to be elucidated more rapidly. Result: a PhD thesis used to be focused on defining structure of a single protein domain; now a thesis in a similar field might examine and compare dozens of structures.
Re-emphasizes importance of the non-linear model
• Importance of equipment is one reason to stress non-linearity of scientific discovery
• Not just that science affects technology• Technology very much affects science:
– The history of science is the history of how important resources and equipment are to discovery. Theme in research of Nathan Rosenberg; Joel Mokyr.
Protein Structure Initiative
• Funded by NIH. To date, $765,447,000• Aim: (1) to increase number of sequence families;
(2) continue technology development, (3) facilitate use of structure by broad scientific community
• Assessment report published in December 2007
Concludes
• “The PSI centers have matured many new technologies, and the activity around the PSIs has led to impressive advances that have a broad impact and are much appreciated by the structural community.”
• Technologies developed are “increasingly used by the broader research community.”
• Specific advances include “construction of the pipeline that enables the entire process of going from sequence to solved crystal structure to be almost fully automated and capable of working at high throughput for amenable proteins.”
• Cost per structure is nearly $100,000.
Leroy Hood as an example
• Author of more than 500 papers• Winner of 1987 Lasker Award for Basic Medical Research• Winner of 2002 Kyoto Prize for Advanced Technology in
recognition of his inventions, including the automated DNA sequencer and an automated tool for synthesizing DNA
• Winner of 2003 Lemelson-MIT Prize for inventing “four instruments that have unlocked much of the mystery of human biology…”
Mentor played role
• Hood’s mentor at Cal Tech, William Dreyer, reportedly told Hood when he was a student, “If you want to practice biology, do it on the leading edge and if you want to be on the leading edge, invent new tools for deciphering biological information.”
Zhuang article as example
Abstract reads:
“Recent advances in far-field fluorescence microscopy have led to substantial improvements in image resolution, achieving a near-molecular resolution of 20 to 30 nanometers in the two lateral dimensions. Three-dimensional (3D) nanoscale-resolution imagining, however, remains a challenge. We demonstrate 3D stochastic optical reconstruction microscopy (STORM) by using optical astigmatism to determine both axial and lateral positions of individual fluorophores with nanometer accuracy.”
Cost
• Equipment is usually expensive• Extreme: New LHC accelerator--$8 billion• Examples: Sequencer such as Applied Biosystems’ 3730 model
is $300,000; tunneling microscope $1 million plus; • New generation sequencers--$5 billion world market• Mice are expensive:
– Off shelf mouse is $50; – Transgenic mice can be much more--$2,000 and
some carry tag of $15,000.
Exceptions
• Some experiments inexpensive• Quéré lab as example
– IKEA tape measures– Plastic dishes from retail store—costs 30 times
more from supplier– Paper clips– Toy guns– Sling shot– But cameras were expensive…
Mouse upkeep
• Mice are expensive to keep– Mouse upkeep (per diem) is high: $.05 to $.10 per day– Irving Weissman reports he was spending $800,000 to $1
million a year at Stanford to keep his mice.– Immune deficient mice cost more to keep– Mouse packages play a role in recruitment. Researcher
recruited from one institution to another when offered a mouse package that translated into $.036 per mouse per day.
Role of materials and equipment means exchange plays increasingly important role
• 75% of academics in the Walsh, Cho and Cohen sample made at least one request for materials in a two-year period (7 to academics; 2 to industry).
• Note: not everyone agreed to share. 19% of material requests made by sample were denied. Competition among researchers played a major role in refusal, as did cost of providing the material.
• Patents “signal to other scientists that you [are ] a valuable exchange partner…” (Murray)
Policies to Encourage Exchange
• Deposit banks for materials• Government agencies taking role in
encouraging availability– MOU from NIH for oncomouse as example– Requirement that if government funded, data be
made available
Who Benefits?
• Individuals at non-elite institutions• Similar findings found with regard to who
benefited from availability of IT
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Results for Collaboration
Space Matters
• Labs take space at universities• Expensive space!• How is physical size of lab determined?• Is lab space ever taken away?• Under researched questions
Serendipity• Like the prince of Serendip (a legendary ruler of
Ceylon, known for knack of chance discovery), researchers often find different, sometimes greater, riches than the ones they are seeking.
• Research often provides answers to questions not yet posed…– Examples: the tetrafluoresthylene cyclinder that gave rise to Teflon
was meant to be used in the preparation of new refrigerants. – Anti-AIDS druz AZT was designed as a remedy for cancer.” Eliel 1992– Patel and her colleagues, set out to study glucocorticoid effects on 15-
hydroxyprostaglandin dehydrogenase (15-OH-PGDH) and inadvertently produced evidence for the first mineralocorticoid receptor (MR) responsive gene. http://jcem.endojournals.org/cgi/reprint/84/2/393.pdf
Not all luck
• “Chance favors only the prepared mind”—Pasteur• Researchers admit to role of chance:• He (Richardson) obtained his PhD degree from Duke in 1966. His
thesis advisor was Professor Horst Meyer. In the Fall of 1966 he began work at Cornell University in the laboratory of David Lee. Their Research goal was to observe the nuclear magnetic phase transition in solid 3He that could be predicted from Richardson’s thesis work with Horst Meyer at Duke. In collaboration with Douglas Osheroff, a student who joined the group in 1967, they worked on cooling techniques and NMR instrumentation for studying low temperature helium liquids and solids. In the fall of 1971, they made the accidental discovery that liquid 3He undergoes a pairing transition similar to that of superconductors. The three were awarded the Nobel Prize for that work in 1996.”
Access to resources necessary condition for doing research
• In U.S. access comes first through a start-up package provided by the dean
• Thereafter equipment and funds to hire students and postdocs become responsibility of scientist; must apply by writing research proposals to funding agencies.
• No grant no lab• Increasingly, no grant no job• Emphasis on individual scientist to generate resources has not
been as strong in other countries, where researchers are hired into government funded and government run laboratories such as CNRS in France.
Grants take time to write and administer
• 2006 survey of U.S. university researchers• Report spending 42% of “research time” on administrative
tasks– Split between pre-grant (22%) and postgrant (20%).
• Most time consuming– Filling out grant progress reports– Hiring personnel– Managing laboratory finances
• Recent changes have increased time requirement – Health privacy laws– Institutional review boards– Accounting for “select agents” after 9/11
Decker: Northwestern University
Changes in funding
• Mix of funding in U.S. is for life sciences• Success rates on grants has been declining in
U.S. in recent years• Proportion going to young researchers also
declining• Europe has begun to shift from the institute
approach to the U.S. PI-driven approach
Why Biomedical Sciences—NIH—So Favored?
• Age distribution of U.S. Senate (100 members)– Median age is 62– Oldest is 91; youngest is 42– 41 over 65– Only 2 under 45
NIH Funding Billions of dollars$30
$25
$20
$15
$10
$5
$0 1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
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2002
2003
2004
2005
2006
2007
Current Dollars Constant Dollars
OER: NIH Budget over time
Number of NIH Competing R01 Equivalent* Applications, Awards and Percent Funded
(Success Rate)
-
5
10
15
20
25
30
Fiscal Year
Num
ber
of A
pplica
tions
(in
Thousa
nds)
0%
5%
10%
15%
20%
25%
30%
35%
Per
cent Funded
Reviewed Awarded Success Rate
R01 Equivalent* Includes R01, R23, R29 and R37NIH, OER: “Investment…”
Number of New and Established Investigators Receiving Competing and R01 and R01 Equivalent Grants to 1962 to 2004
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
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Fiscal Year
Nu
mb
er o
f G
ran
ts
0%
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10%
15%
20%
25%
30%
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40%
Per
cen
t G
ran
ts t
o N
ew In
vest
igat
ors
Established Investigators
New Investigators
Percent New Investigators
NIH, OER for AIRI
DISTRIBUTION OF INVESTIGATOR AGES
NIH Competing R01 Equivalent Awardees
6.4% 6.4% 6.2% 4.8% 4.0% 3.8% 4.5% 3.5% 3.8%
18.4% 17.1% 16.5% 15.6% 14.1% 13.8% 13.0% 13.1% 12.9%
24.9% 23.9% 24.6% 23.1%22.4%
21.4% 21.7% 20.9% 20.0% 20.1% 19.6%
20.8% 20.3% 20.3%20.5%
21.4%22.1% 21.7% 21.7% 21.1% 21.1% 22.2%
14.6% 15.2% 14.6% 15.7% 16.4%16.6% 16.3% 16.0% 17.4% 18.1% 18.2%
14.5% 15.8% 17.0% 17.9% 19.6% 21.7% 22.7% 23.9% 24.9% 27.5% 27.6%
3.4%6.2%
12.0%19.0%
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Fiscal Year
Per
cen
t o
f To
tal
35 and Younger 36 - 40 41 - 45 46 - 50 51 - 55 Over 55
NIH, OER for AIRI
Other Countries ExperienceStop-and-go Funding
• Italy and France had major hiring waves in the 1980s (France 1985; Italy 1980)
• Lissoni, Mairesse, Montobbio, and Pezzoni & find that individuals hired into a “wave” are less productive throughout their career.
Demand for scientists• Demand depends on
– K/L ratio for efficient production— depends on technology & relative costs of inputs
– But will also depend on “scale” of scientific operation. Substitution of equipment for labor is occurring but as scale of science continues to grow because costs of discovery are decreasing, could result in employing more scientists in labs.
– Also depends on relative prices but– Science may not be “efficient.” If there are funds for
students and not equipment may not be on efficient production path
Who will staff labs in U.S.?• Staffing labs with doctoral students and postdocs provides a
ready flow of “new” ideas and “temporary” workers.• Produces more than “absorptive” capacity of university; • Movement of scientists from academe to industry is a major
way in which knowledge is transferred from the public to private sector.
• But if industry and academe cannot readily absorb the production of new PhS there can be a problem of over supply.
• Can this staffing model persist?
Closing thoughts• Need to study labs: economists almost always approach
productivity issues by studying individuals scientists rather than the labs in which scientists work
• Need to think of production function for science• Once shift to study of labs, numerous questions invite
exploration:– Need to learn more about production function of the lab, degree of
substitution between capital and labor; whether capital-labor ratio has changed over time and scale effects
– How this affects labor market for scientists and engineers– How lab size is determined and to what extent economic factors come
into play?– How outcomes relate to funding
• Networking• Size of labs
Some under-researched questions/issues
• How is lab size determined in terms of numbers of people?
• Efficiency of multiple grants• Will research opportunities change as amount of
data increases: Jeremy Berg hypothesis?• How is capital labor ratio changing in labs?• How will this affect labor market for scientists and
engineers?• Cross country comparisons of production function—
input prices vary across countries.
Final Thought…
• “Know more about how to organize a car factory than how to organize science”
• And recently it doesn’t appear that we know all that much about cars!
