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APPENDIX D
THE IMPACT OF PUBLICLY FUNDED
BIOMEDICAL AND HEALTH RESEARCH:
A REVIEW1
Bhaven N. Sampat
Department of Health Policy and Management
Columbia University
I. INTRODUCTION AND BACKGROUND
New biomedical technologies trigger a number of major challenges
and opportunities in health policy. Among economists, there is
widespread consensus that new technologies are the major drivers of
increased healthcare costs but at the same time a major source of health
and welfare improvements (Murphy and Topel 2003). This has led to
discussion about whether technological change in medicine is “worth it”
(Cutler and McClellan 2001). The impact of new technologies on the
health care system has also been the subject of much debate among
health policy scholars more generally (Callahan 2009).
Public sector research agencies have an important role in the U.S.
biomedical innovation system. In 2004, federal agencies funded roughly
one-third of all U.S. biomedical R and D (Moses et al. 2005). The
National Institutes of Health (NIH) accounted for three-quarters of this
amount. Private sector drug, biotechnology, and medical device
companies provide the majority of U.S. biomedical R and D funding
(about 58 percent). This private sector research is, in general, focused
more downstream and tends to be closer to commercial application than
NIH-funded research.
1
I thank Pierre Azoulay, and participants in the National Academies’ 2011
Workshop on Measuring the Impacts of Federal Investments in Research, for
useful comments and suggestions.
153
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154 MEASURING THE IMPACTS OF FEDERAL INVESTMENTS IN RESEARCH
Donald Stokes (1997) observes that the public values science “not
for what it is but what it is for.” A perennial question in U.S. science and
technology policy is what benefits taxpayers obtain from publicly funded
biomedical research. Recent concerns about the clinical and economic
returns to NIH funding in the post-doubling era reflect this emphasis.
In this paper, we review the evidence on the effects of publicly
funded biomedical research. Reflecting Stokes’s observation above, the
review will focus on the health and economic effects of public research,
rather than measures of scientific outcomes. Given the prominence of
the NIH in funding this research, many of the published articles and
research focus on this agency. The evidence examined includes
quantitative analyses, and qualitative case studies, published by scholars
from a range of fields. While we have made efforts to be broad, the
references discussed should be viewed as representative rather than
exhaustive. This review takes stock of the empirical methodologies
employed and the types of data used; it also highlights common research
and evaluation challenges, and emphasizes where existing evidence is
more, or less, robust.
We proceed as follows. In Section II, below, we discuss a stylized
model of how public research funding affects health, economic, and
intermediate outcomes. As Kline and Rosenberg (1986), Gelijns and
Rosenberg (1994), and others have emphasized, the research process
cannot be reduced to a neat, linear model. While we recognize this fact
(and highlight it in our literature review) the simple model is still useful
in helping to organize our discussion of theory and data on the effects of
publicly funded research. In Section III, we discuss the empirical
evidence. In Section IV, we discuss common evaluation difficulties. In
Section V, we conclude. The empirical approaches, data sources, and
findings of many of the studies reviewed are also summarized in Tables
D1–D3.
II. PUBLIC SECTOR RESEARCH AND OUTCOMES: AN
OVERVIEW
Figure D-1 is a simple model illustrating how the literature has
conceptualized the health and economic effects of publicly funded
biomedical research (and publicly funded research more generally):
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155
APPENDIX D
Fundamental New Drugs,
Knowledge Devices
1
Instruments
and Techniques Health
Publicly Private Sector
Outcomes
Funded R&D R&D
2
(Costs?)
Prototype
3 Drugs, Devices
Improved
Clinical Practice
Applied
Knowledge
Better Health
Behaviors
FIGURE D-1 Publicly Funded R and D and Outcomes, Logic Model
SOURCE: Sampat, 2011
The top arm of the model illustrates one important relationship:
publicly funded R and D yields fundamental knowledge, which then
improves the R and D efficiency of private sector firms, yielding new
technologies (drugs and devices) that improve health outcomes.2 This
conceptualization has been the essential raison-d’etre for the public
funding of science since Vannevar Bush’s celebrated postwar report,
Science, The Endless Frontier. For example, Bush asserted in 1945 that
“discovery of new therapeutic agents and methods usually results from
basic studies in medicine and the underlying sciences” (Bush 1945). It is
also the essential mechanism in several important economic models of R
and D (e.g. Nelson 1984). Importantly, this conceptualization generally
views publicly funded research as “basic” research that is not oriented at
particular goals, and thus yields benefits across fields. The influential
“market failure” argument for public funding of basic research is that
profit-maximizing, private-sector firms will tend to underinvest in this
type of fundamental, curiosity driven research, since they cannot
appropriate its benefits fully (Nelson 1959, Arrow 1962).
The channels through which publicly funded basic research might
influence private sector innovation are diverse, including dissemination
via publications, presentations and conferences, as well as through
informal networks (Cohen et al. 2002). Labor markets are another
2
Stokes (1997) and others have challenged this definition of “basic” research.
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156 MEASURING THE IMPACTS OF FEDERAL INVESTMENTS IN RESEARCH
channel, since public agencies may also be important in training doctoral
and post-doctoral students who move on to work for private sector firms
(Scherer 2000).
The second arrow illustrates another relationship. New instruments
and techniques that are by-products of "basic" research can also improve
private sector R and D (Rosenberg 2000). Prominent examples of
instruments and research tools emanating from academic research
include the scanning electron microscope, the computer, and the Cohen-
Boyer recombinant DNA technique.
Third, publicly-funded researchers sometimes develop prototypes
for new products and processes. Some of these are indistinguishable
from the informational outputs of basic research discussed above. For
example, when academic researchers learned that specific prostaglandins
can help reduce intraocular pressure this discovery immediately
suggested a drug candidate based on those prostaglandins, though the
candidate required significant additional testing and development. (This
academic discovery later became the blockbuster glaucoma drug,
Xalatan.) The public sector has also been important in developing
prototypes (Gelijns and Rosenberg 1995). Roughly since the passage of
the Bayh-Dole Act, in1980, publicly funded researchers have become
more active in taking out patents on these inventions and prototypes for
new products and processes, and licensing them to private firms
(Mowery et al. 2004. Azoulay et al. 2007).
While much of the discussion of publicly funded biomedical
research focuses on this more “basic” or fundamental research the public
sector also funds more “applied” research and development.3 For
example, about one-third of the NIH budget is for clinical research,
including patient oriented research, clinical trials, epidemiological and
behavioral studies, as well as outcomes and health services research.
Such research can be a useful input into the development of prototypes,
and may also directly inform private sector R and D. Clinical research
may also directly affect health behaviors. For example, knowledge from
epidemiological research about cardiovascular health risk factors
contributed to reductions in smoking and better diets (Cutler and
Kadiyala 2003). New applied knowledge can also influence physicians:
3
Stokes (1997) provides a thoughtful critique of conventional distinctions
between “basic” and “applied” research. Since much of the literature before and
since Stokes uses this terminology, we employ it in our review of this literature,
even while recognizing the importance of his argument.
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157
APPENDIX D
for example, by changing their prescribing habits (e.g. “beta-blockers
after heart attacks improve outcomes”) or routines (e.g. “this type of
device works best in this type of patient”). Importantly, as various
studies we review below will emphasize, negative results from clinical
trials—showing that particular interventions do not work — can also be
important for clinical practice and in shaping health behaviors.
While the discussion above assumes that new biomedical
knowledge and technologies improve health outcomes, this is a topic of
debate. The conventional wisdom is that while other factors (e.g. better
diet, nutrition, and economic factors) were more important for health
outcomes historically (McKeown 1976), improvements in American
health in the post-World War II era have been driven largely by new
medical knowledge and technologies (Cutler, Deaton, and Lleras-Muney
2006). The contribution of publicly funded research to these
developments is an open empirical question, discussed below.
At the same time, some scholars suggest that we may have entered
an era of diminishing returns, where new technologies are yielding
increasingly less value (Callahan 2009; Deyo and Patrick 2004). The
effect of new biomedical technologies on healthcare costs is a related
concern. There is general agreement among health economists that new
medical technologies are the single biggest contributor to the increase in
long-run health costs, accounting for roughly half of cost growth
(Newhouse 1992). Rising health costs strain the budgets of public and
private insurers as well as employers, and may also contribute to
generate health inequalities. The dynamic that exists between new
medical technologies and health costs in the U.S. may reflect a
"technological imperative," which creates strong incentives for the
healthcare system to adopt new technologies once they exist (Fuchs
1995; Cutler 1995). It may also reflect positive feedbacks between
demand for insurance and incentives for innovation (Weisbrod 1991).
Concern about the effects of technology on health costs has fueled
empirical work on whether technological change in medicine is "worth
it." Long ago, Mushkin (1979) noted (though did not share) “widespread
doubt about the worth of biomedical research given the cost impacts.”
A large literature in health economics suggests that new biomedical
technologies are indeed, in the aggregate, worth it. Cutler (1995) and
others suggest that, given the high value of improved health (current
estimates suggest the value of one additional life year is $100,000 or
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158 MEASURING THE IMPACTS OF FEDERAL INVESTMENTS IN RESEARCH
more), even very costly medical technologies pass the cost-benefit test.4
Nordhaus (2003) estimates that the value of improvements in health over
the past half century are equal in the magnitude to measured
improvements in all non-health sectors combined. Others (Callahan
2009) view these health cost increases as unaffordable, even if they
deliver significant value, and therefore ultimately unsustainable.
At the same time, not all medical technologies necessarily increase
costs. As Cutler (1995) and Weisbrod (1991) indicate, technologies that
make a disease treatable but do not cure it - moving from non-treatment
to "halfway" technology in Lewis Thomas's characterization-are likely to
increase costs. The iron-lung to treat polio is an example of this.
However, technologies that make possible prevention or cure ("high
technology") can be cost-reducing, especially relative to halfway
technologies. Thus the polio vaccine was much cheaper than the iron
lung. Consistent with this, Lichtenberg (2001) shows that while new
drugs are more expensive than old drugs, they reduce other health
expenditures (e.g. hospitalizations). Overall, he argues, they result in net
decreases in health costs (and improve health outcomes).5
As Weisbrod (1991) notes, "The aggregate effect of technological
change on health care costs will depend on the relative degree to which
halfway technologies are replacing lower, less costly technologies, or are
being replaced by new, higher technologies. " 6 One way to think about
the effects of public sector spending on costs would be to assess the
propensity of publicly funded research to generate (or facilitate the
creation of) these different types of technologies. However, since the
effects of these new technologies are mediated by various facets of the
health care and delivery system, it may be difficult conceptually (and
empirically) to isolate and measure the effects of public sector spending
on overall health costs (Cutler 1995).7
4
Cutler (1998) observes "Common wisdom suggests that rapid cost increases
are necessarily bad. This view, however, is incorrect. Cost increases are justified
if things that they buy (increases in health) are worth the price paid." (2)
5
See however, Zhang and Sourmerai (2007) for a critique of this finding.
6
The cost-effectiveness of these technologies also depends on the populations
on which they are used, as Chandra and Skinner (2011) emphasize.
7
There is also some discussion about whether the public sector should be paying
attention to the cost-side consequences of its investment decisions. Weisbrod
(1991) notes: "With respect to the NIH, it would be useful to learn more about
the way the size and allocation of the scientific research budget are influenced,
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159
APPENDIX D
III. THE EFFECT OF PUBLICLY FUNDED RESEARCH: A
REVIEW OF THE EVIDENCE
Health
Measuring the health returns to publicly funded medical research
has been a topic of interest to policymakers for decades. In an early
influential study, Comroe and Dripps (1976) consider what types of
research (basic or clinical) are more important to the advance of clinical
practice and health. The authors rely on interviews and expert opinion to
determine the top ten clinical advances in the cardiovascular and
pulmonary arena, and identified 529 key articles associated with these
advances. They coded each of the key articles into six categories: (1)
Basic research unrelated to clinical problems; (2) Basic research related
to clinical problems (what Stokes later termed “use-oriented” basic
research); (3) Research not aimed at understanding of basic biological
mechanisms; (4) Reviews or syntheses; (5) Development of techniques
or apparatuses for research; and (6) Development of techniques or
apparatuses for clinical use. The authors find that 40 percent of the
articles were in category 1, and 62 percent in categories 1 or 2. Based on
this, the authors assert “a generous portion of the nation's biomedical
research dollars should be used to identify and then to provide long-term
support for creative scientists whose main goal is to learn how living
organisms function, without regard to the immediate relation of their
research to specific human diseases.” Comroe and Dripps also note “that
basic research, as we have defined it, pays off in terms of key discoveries
almost twice as handsomely as other types of research and development
combined” (1976).
A more recent set of studies examines the effects of publicly funded
research on health outcomes. Operationalizing the concept of “health” is
notoriously difficult. Common measures employed to account for both
the morbidity and mortality effects of disease include quality adjusted
life years (QALYs) and disability adjusted life years (DALYs) (Gold et
al, 2002). However, it is difficult to get longitudinal information on these
measures by disease. As a result, most of the analyses of the effects of
public funding on health examine more blunt outcomes, including the
number of deaths and mortality rates for particular diseases.
perhaps quite indirectly, by the health insurance system, through its impact on
the eventual market for new technologies of various types" (535).
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160 MEASURING THE IMPACTS OF FEDERAL INVESTMENTS IN RESEARCH
Numerous prominent academic studies (Weisbrod 1983, Mushkin
1979) aim to examine the health effects of biomedical research, and the
economic value of this impact, in a cost-benefit framework. One
important recent study in this tradition, Cutler and Kadiyala (2003),
focuses on cardiovascular disease—the disease area where there has been
the strongest improvement in health outcomes over the past sixty years.
Since 1950 mortality from cardiovascular disease decreased by two-
thirds, as Figure D-2 (reprinted from their paper) shows:
450
400
350
Deaths per 100,000
300
250
200
150
100
50
0
19 0
19 2
19 4
56
19 9
61
19 3
19 5
19 7
68
19 0
72
19 4
19 6
78
19 9
19 1
19 3
19 5
87
19 9
19 1
93
5
5
5
5
6
6
6
7
7
7
7
8
8
8
8
9
19
19
19
19
19
19
19
CVD Cancer Infections HIV Accidents, Homicide, Suicide Other
FIGURE D-2 Mortality by cause of death 1950-1994
SOURCE: Cutler and Kadiyala 2003
Cutler and Kadiyala, through a detailed review of the causes of this
advance (relying on epidemiological and clinical data, medical
textbooks, and other sources), estimate that roughly one third of this
cardiovascular improvement is due to high-tech treatments, one third to
low tech treatments, and one third to behavioral changes. Assuming one
additional life year gained is valued at $100,000, the authors compute a
rate of return of 4-to-1 for investments in treatments and 30-to-1 for
investments in behavioral changes. These investments include costs
borne by consumers and insurers, and estimates of public sector R and D
for cardiovascular disease.
Based on these figures, the authors argue that the rate of return to
public funding is high, though they don’t directly trace public funding to
changes in outcomes in their quantitative analyses. Interestingly, in their
qualitative account, the major public sector research activities
highlighted have an “applied” orientation, including the NIH’s role in
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161
APPENDIX D
sponsoring large epidemiological trials and holding consensus
conferences. This may reflect a traceability and attribution problem,
which is common to the evaluation of fundamental research: It is
difficult to directly link improvements in outcome indicators to public
sector investments in basic research, even in a study as detailed as this
one.
A paper by Heidenreich and McClellan (2003) is similarly
ambitious, looking at sources of advance in the treatment of heart
attacks. The authors focus on this treatment area, not only because of the
large improvements, but also because it is a "best case" for attributing
health outcomes to particular biomedical investments. Specifically, these
authors go further than Cutler and Kadiyala by attempting to link
changes in clinical practice to changes in specific R and D inputs. The
authors focus here on clinical trials, not basic research. This is not
because they believe that basic research is unimportant, “but because it is
much easier to identify connections between these applied studies and
changes in medical care and health.”
Based on detailed analyses of MEDLINE-listed trials and health
outcomes, the authors argue that medical treatments studied in these
trials account for the bulk of improvement in AMI outcomes. The
authors associate changes in clinical practice and outcomes to research
results reported in trials through analysis of timing of events, and
detailed clinical knowledge of how the trial results, clinical practices, and
health outcomes relate.
One interesting result from this paper is that clinical practice often
“leads" formal trials, challenging the “linear” model embodied in Figure
D-1 (above). The authors also emphasize that an important role for trials
is negative: telling clinicians what doesn't work, and stopping the
diffusion of ineffective technologies. While the sample they examine
represents a mix of publicly funded and privately funded trials, the
authors do emphasize a particularly important role for the public sector
in funding trials on drugs off patent, where private firms have fewer
incentives to do so.
Philipson and Jena’s (2005) study of HIV-AIDS drugs is another
paper that examines the value of increases in health from new medical
technologies. Though this study does not explicitly focus on the role of
the public sector, it estimates that HIV-AIDS drugs introduced in the
1990s generated a social value of $1.4 trillion, based on the value of the
increments to life expectancy created from these drugs (here again, using
the estimate of $100,000 per life year). This study is relevant because of
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162 MEASURING THE IMPACTS OF FEDERAL INVESTMENTS IN RESEARCH
the important role of public sector research in the development of HIV-
AIDS drugs, which is observed in several of the empirical studies
discussed below.
A recent paper by Lakdawalla et al (2011) employs a similar
approach to assess the benefits from cancer research. The authors find
these benefits to be large, estimating the social value of improvements
from improvements in life expectancy during the 1988-2000 period to be
nearly $2 trillion. The authors note that this compares to investments of
about $80 billion dollars in total funding for the National Cancer Institute
between 1971 and 2000. As with the HIV studies discussed above, the
authors do not calculate a rate-of-return on publicly funded research
explicitly, but do argue that the social benefits from cancer research in
general far exceed research investments and treatment costs.
A large share of the benefits in the cancer arena, according to this
work, results from better treatments. Lichtenberg (2004) also suggests
that new drug development has been extremely important in progress
against cancer.8 Public sector research may have been important to the
development of these drugs: various studies (Stevens et al. 2011,
Chabner and Shoemaker 1989) suggest an important role for the public
sector in cancer drug development.9
Each of the studies discussed so far focuses on particular disease
areas. In a more "macro" approach Manton (2009) and colleagues relate
mortality rates in four disease areas to lagged NIH funding by the
relevant Institute, over the period 1950-2004. They find that for two of
the four diseases (heart disease, stroke) there is a strong negative
8
Cutler (2008) also emphasizes progress in the “war on cancer” – though
highlights the role of screening and personal behavior changes, and notes the
high costs of treatment. Sporn (2006) and Balilar and Gonik (1997) offer less
sanguine assessments, emphasizing that progress against cancer has been highly
uneven. Long-standing debates in assessments of the War on Cancer include the
disagreements on the relative importance of treatment versus prevention, and of
basic versus applied research. The literature also suggests it is difficult to
evaluate the extent of progress in cancer, for two main reasons. First, advances
in screening increase incidence. The second is competing risks: for example, the
reduction in mortality from cardiovascular disease, discussed above, increased
cancer cases. See Cutler (2008) for a review.
9
A National Cancer Institute (NCI) “Fact Sheet” asserts that “approximately one
half of the chemotherapeutic drugs currently used by oncologists for cancer
treatment were discovered and/or developed at NCI.”
http://www.cancer.gov/cancertopics/factsheet/NCI/drugdiscovery
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APPENDIX D
correlation, but find weaker evidence for cancer and diabetes. Several
issues arise here that will re-emerge in other quantitative analyses
discussed below. First, linking funds to disease areas is difficult. As with
other studies we will consider below, the authors here rely on the disease
foci of Institutes within the NIH. More importantly, the counterfactual is
hard to prove: It is difficult to make the case that the relationships
estimated are causal, since Institute-specific funding is not exogenous. In
particular, diseases where there is highest expectation of progress (even
absent funding) may be more likely to get funds. Finally, competing risks
also complicate interpretation of health outcomes. For example, part of
the reason cancer mortality has increased rather than decreased over the
period studied is that people no longer die of heart attacks, due to
advances in the cardiovascular arena.
Private Sector R and D
Another set of studies relates publicly funded research to private
sector R and D and productivity. These include econometric analyses
relating public sector and private sector funding, surveys of firm R and D
managers, and studies examining the geographic dimension of spillovers
from public sector researchers.
Several papers relate NIH funding by disease area to later private
sector funding. One motivation in these studies is to assess if public and
private sector R and D are substitutes or complements, an issue of
perennial interest in science and technology policy (David, Hall, and
Toole 2000). The econometric analyses generally find a positive
association between public sector and private sector funding. Toole
(2007) uses data from the NIH’S Computerized Retrieval of Information
on Scientific Projects (CRISP) database, covering NIH basic and clinical
research funding across seven therapeutic classes (between 1972 and
1996), and data from the Pharmaceutical Manufacturers of America
(PhRMA) on private sector R and D in these same areas (between 1980
and 1999) to examine the relationships between the two. This study finds
a 1 percent increase in basic research funding associated with a 1.7
percent increase in private sector funding, though the elasticity for
clinical research is much smaller (.4 percent). In a similar analysis, Ward
and Dranove (1995), using PhRMA data on R and D spending and NIH
data on funding by Institute (similar to that used in the Manton et al 2009
study discussed above) find that a 1 percent increase in NIH research
support in a disease area is associated with a .76 percent increase in
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182
Authors Question Empirical Approach Measures/Data Results
Comroe and What types of research Interviews, expert Top 10 clinical advances 41 percent of all work
Dripps (clinical vs. basic) are opinions used to “Key articles” associated judged to be essential or
(1976) important in the advance of determine of top 10 with these advances crucial for later clinical
clinical practice, health? clinical advances in Coding of whether the advances was not
cardiovascular and key articles are clinical clinically oriented at the
pulmonary arena or non-clinical time of research
Content analyses of key
articles
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TABLE D-2 Public Funding and New Drugs, Devices: Summary of Selected Studies
Authors Question Empirical Approach Measures Results
Cockburn and How does public Case studies of 15 clinically Qualitative Of 15 drugs, public sector
Henderson sector research affect important drugs determinations of roles
research made key enabling
(1996) pharmaceutical of public sector in drug
discovery for 11
innovation? development Public sector involved in
synthesis of major compound
in 2 cases
Ward and How do MEDLINE Panel regressions articles in NIH data on R and D Strong relationship between
Dranove “drug” articles a disease area to NIH R and by institute NIH funding and later
(1995) respond to NIH D by relevant institute MEDLINE data on MEDLINE articles
funding? publications by disease Indirect effect (from research
area outside disease area) stronger
than direct effect
Sampat and What are the roles of Examine share of new FDA approved NMEs Direct effect: public sector
Lichtenberg the public and private molecular entities where 1988-2005 owns key patent for 9% of
(2011) sectors in drug public sector developed Orange Book patents drugs
development? patent (direct effect) and on these drugs Indirect effect: Public sector
where private sector patents Government interest patents or publications cited
cite public sector statements/assignment by 48% of drugs
patents/publications in patents Both direct and indirect
(indirect effect) Backward citations in effects more pronounced for
patents to public sector most clinically important
patents, MEDLINE drugs (17%, 65%)
183
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184
Authors Question Empirical Approach Measures Results
articles acknowledging
public sector funding
Sampat (2007) On how many drugs Examine share of drug FDA approved NDAs 72 of 1546 NDAs have an
do academic approvals where academic 1988-2005 academic patent
institutions own and public sector Orange Book patents 10.3 percent of NMEs
patents? institutions own key patents on these drugs 5.9 percent of non-NMEs
USPTO data on patent 19.2 percent of priority NMEs
ownership have an academic patent
Azoulay-Sampat
concordance of
academic assignees
Keyhani et al Do drug prices Regression analyses relating 180 drugs listed in the Government supported
(2005) reflect development drug prices to measures of Federal Register clinical trials for 6.6 percent
time and government government support between 1992 and 2002 of the drugs
investment? Federal Register data Government owned or
on their patents supported patents for 7.2
Information on percent of the drugs
government assignees
and government
interest statements for
these patents
Data from NIH clinical
trials database and
FDA on whether NIH
trials supported FDA
approval
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Authors Question Empirical Approach Measures Results
Stevens et al On how many drugs Examine number of drug FDA data on drug and 153 FDA-approved drugs
(2011) and vaccines approvals in-licensed from biologic approvals discovered by public sector
emanate from public PSRIs (excluding licenses Orange Book data on institutions over past 40 years
sector research to platform technologies) FDA approved drugs (102 NMEs, 36 biologics, 15
institutions? AUTM data on vaccines)
academic patents and 13 percent of NMEs (21
licenses percent of priority NMEs)
rDNA data on licensing licensed from public sector
transactions research
Virtually all important
vaccines introduced over past
25 years come from public
sector
Broad correlation between
NIH Institute budgets and
therapy classes with public
sector drugs
Kneller (2010) How important are Examine place of 252 FDA approved Overall 24% of drugs from
new employment of inventors on drugs 1998-2007 universities
companies/universiti key patents for drugs Data on patents from By novelty: 31% of most
es (and other actors) Orange Book, Merck scientifically novel drugs
in drug discovery? Index, other sources By priority: 30% of priority-
Data from concurrent review drugs
publications and from
interviews on
inventors’ places of
185
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186
Authors Question Empirical Approach Measures Results
employment
Morlacchi and What were the Longitudinal case study of Interview data NHLBI contracts important in
Nelson (2011) sources of innovation the development of the Information from key spurring firm formation and
behind development LVAD patents and evolution in 1960s/1970s
of the left-ventricular publications on LVAD NHLBI important in
assist device sponsoring conferences,
(LVAD)? How centers to promote diffusion
important was the of best practice among
NIH? academics and industry
Public funding of key trials
and development of
component technologies also
important
Application led scientific
understanding; basic
understanding of heart failure
remains weak
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Dorsey et al Are new drug Correlations of NIH 1995-2000 FDA drug Despite a rise in NIH (and
(2009) approvals by funding data with future and approvals, mapped other funding), drug approvals
therapeutic area drug approvals to nine disease areas flat overall
associated with NIH NIH funding by Within class analyses of drug
funding in those Institute; allocated to approvals also show little
areas? disease areas based on correlation with research
Congressional inputs
justifications
Note: Also estimate R
and D by
biotechnology firms,
medical device firms,
pharmaceutical
companies, non-profits
Blume-Kohut How does NIH Panel regression CRISP and Some evidence of
(2009) funding in a disease RePORTER data on responsiveness of Phase I
area relate to the NIH grants/funds 1975- trials: elasticity .25-.31
number of drugs 2004 No evidence of
subsequently in Grants associated with responsiveness of Phase III
Phase I and Phase III disease areas using trials
trials in that area? parsing of abstracts,
keywords, concordance
with MeSH thesaurus
PharmaProjects data on
drugs in development,
by phase and category
187
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188
TABLE D-3 Public Funding and Private R and D, Patenting: Summary of Selected Studies
Authors Question Empirical Approach Measures Results
Ward and How does industry Panel regressions PhRMA data on R and A 1 percent increase in NIH
Dranove funded R and D relating private R and D D by field research associated with .76
(1995) respond to NIH R in a disease area to NIH NIH data on R and D by percent increase by private sector
and D? R and D by relevant institute over next seven years (direct)
institute Controls for disease A 1 percent increase in NIH
burden, drug research associated with 1.7
development, time percent increase by private sector
over next seven years (indirect)
Contemporaneous correlations
highest
Cockburn How does interaction Panel regression models MEDLINE data from Statistically significant association
and with public sector relating productivity to35,000 articles on firms’ between propensity to co-author
Henderson science within firm variation inco-authorship, with academics and important
(1996) (collaboration, hiring interaction with public publication by “star” patents/dollar
of “star” scientists) sector, with firm fixed scientists for 10 firms, Statistically significant association
affect firm-level R effects 1980-1988 between share of publications
and D productivity Data on “important” from “star” scientists and
patents/R and D for important patents/R and D dollar
these firms
Toole (2007) Does public Panel regression models CRISP data on NIH Public and private sector research
scientific research relating pharmaceutical basic and clinical complements
complement private R and D by to NIH research mapped to 7 A 1 percent increase in basic
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Authors Question Empirical Approach Measures Results
R and D investment? funding across disease therapeutic classes, research funding associated with a
areas, over time 1972-1996 1.7 percent increase in private
PhRMA data on private sector R and D
sector R and D in these A 1 percent increase in clinical
classes, 1980-1999 research funding associated with a
.40 percent increase in private
sector R and D
Azoulay, Do elite life scientists Panel regression models Data on 10,450 elite life Professional transitions lead to a
Graff Zivin, benefit local firms? examining geography of science researchers decrease in citations (in patents
Sampat citations to scientists’ (most publicly funded) and articles) to movers’ pre-move
(2011) work before and after Historical information patents at original location
they move on productivity, Weaker evidence of increase in
employment locations of citations from firms at destination
each scientist location
MEDLINE data on their
publications
ISI data on citations to
their publications
USPTO data on their
patents
USPTO data citations to
their patents and
publications
Zucker, How important was Panel regression models 337 “star” scientists Presence of stars and their
Darby and academic science in relating location of new (based on articles, collaborators – “intellectual
Brewer the creation of new biotechnology firms to genetic discoveries in capital” – in an area has a
189
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190
Authors Question Empirical Approach Measures Results
(1998) biotech firms? number of “star” Genbank) statistically significant and
scientists in area Data on their positive relationship with the
collaborators number of new biotechnology
Location and affiliation enterprises later formed in that
of stars (from journal area
articles
Data on biotechnology
firms and firm formation
form North Carolina
Biotechnology Center
and Bioscan
Cohen, What are the roles of Survey 1994 Carnegie Mellon Pharmaceutical industry an
Nelson, public sector Survey of Industrial R outlier: reports public research the
Walsh research on industrial and D managers most important source of new
(2002) R and D? What are Merged with publicly project ideas and contributing to
the channels through available data on project completion
which public respondents Medical instruments industry R
research affect and D projects less frequently use
industrial R and D? any of three outputs of public
research than other industries
Drug industry makes use of public
research much more frequently
Top three fields contributing to R
and D in pharmaceuticals:
Medicine, Biology, Chemistry
Top three fields contributing to R
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Authors Question Empirical Approach Measures Results
and D in medical instruments
industry: Medicine, Materials
Science, Biology
Mansfield How important is Survey Survey results from 77 Percent of new products that could
(1998) academic work for firms not have been developed (without
industrial substantial delay) in absence of
innovation? recent academic research, 1986-
1994: 31 in drugs/medical
products (15 across all industries)
Percent of new processes that
could not have been developed
(without substantial delay) in
absence of recent academic
research, 1986-1994: 11 in
drugs/medical products (11 across
all industries)
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