This paper discusses the problems with using patents as a measure of innovation and papers as a measure of science. It also uses data to show the problems. for example, the number of patent applications and awards have grown by six times since 1984 while productivity growth has slowed.

1. 48 ISSUES IN SCIENCE AND TECHNOLOGY
O
ver the past 20 years, patent analysis has
become a dominant method of studying
innovation. The number of papers con-
taining “patent citation” listed in Google Scholar
rose from fewer than 10 in 1998 to more than 900
in 2014. For papers published in seven journals of
the American Economic Association since 2010, 9
of the 20 judged most relevant to innovation by the
association’s search engine used patent analyses.
If papers that analyze sociolinguistic, behavioral,
consumer sentiment, and measurement innova-
tions are excluded, to focus exclusively on product,
process, and service innovation, the proportion
rises to 9 of 16. For Management Science, the lead-
ing management journal on innovation, 11 of its 15
articles most relevant to innovation and published
since 2010 used patent analyses.
For many of these papers, the focus is on the
relationship between innovation and advances in
science. Patents themselves are used as a measure
for innovation, whereas papers cited in the patent
applications are used as a measure for advances in
science. For example, Science, the world’s leading
generalist science journal and one that publishes
few social science papers, did so in August 2017
with an article, “The dual frontier: Patented in-
ventions and prior scientific advance.” This study
concluded that “most patents (61%) link backward
to a prior research article” and “most cited research
articles (80%) link forward to a future patent.”
Questions about these types of analyses have
been asked since long before they became the dom-
inant method of analyzing innovation. Strengths
and limitations of patent analyses as indicators of
innovativeness and scientific advance—such as the
fact that not all inventions are patentable and not
all innovations are patented—have been thoroughly
reviewed over the past few decades by scholars of
innovation such as Zvi Griliches and Martin Meyer.
But the mixed value of patent analysis seems to
have had little influence on the growing use of pat-
ent data by those studying innovation and science.
In my view this growing influence of patent analy-
sis reflects a fundamental bias in the social sciences
toward large-scale empirical analyses of quantifi-
able databases aimed at uncovering allegedly gener-
al economic principles, and away from studies that
can reveal the complexity, context, and processes of
technological innovation in the economy.
I argue that the growing dominance of patent
analysis not only fails to provide valuable and
reliable insight into innovation processes, but is
a smoke screen that prevents social scientists and
policy-makers from understanding real problems
and processes of innovation. Further, excessive fo-
cus by innovation scholars on studying patents, and
by academic scientists on pursuing patents, togeth-
er suggest the possibility that increased patenting
activity may contribute to a long-term slowdown
in productivity growth. We need an alternative
research agenda for studying the relations between
science and innovation, and I propose the outline of
such an agenda with a focus on productivity.
What good is patent analysis,
and what good are patents?
It has long been recognized that most innovations
are not patented and that many patents don’t rep-
resent important innovations. There are many rea-
Beyond Patents
JEFFREY FUNK
Scholars of innovation use patenting as an indicator of both
innovativeness and the value of science. It might be neither.

2. SUMMER 2018 49
patents
sons for this. One is that most innovations represent a
combination of ideas, some of which represent novel
patentable designs and some of which do not. Anoth-
er reason is that the benefits from patenting do not
exceed the costs for many types of innovations, and
thus firms do not apply for patents. Patent law offers
patent recipients protection in return for disclosure,
but not all innovators benefit from this trade-off. The
ones who do not apply for patents do not have their
innovative activities counted as innovations in patent
analyses, even though many of their activities are
highly beneficial to the users.
For example, consider the Wall Street Journal’s
“billion-dollar startup club,” new firms that are
valued at $1 billion or more. These start-ups can
certainly be defined as innovative, even if their values
are probably inflated. Table 1 shows the percentage
of start-ups by numbers of patents (as of February 1,
2018). Only 41% of the 170 start-ups had at least one
patent and only 20% and 9.4% had at least 10 and 50
patents, respectively. The percentages are particularly
low for e-commerce, financial services (fintech), and
consumer internet, categories that include highly val-
ued services such as ridesharing and room sharing,
e-commerce for fashion, social networking, peer-to-
peer loans, and mobile payments. Patent analyses
miss these types of innovations and thus represent a
highly skewed view of innovation in the US economy.
Source: Jeffrey Funk and Martin Kenney, “New Knowledge, New Industries, and Industry Evolution: Evidence from Entrepreneurial
Firms,” Working Paper
Table 1. Data on firms in the Wall Street Journal’s Billion Dollar Startup Club
Consumer internet
Software
E-Commerce
Fintech
Hardware
Biotech/Bioelectronics
Other
TOTAL
NUMBER OF
STARTUPS
PERCENTAGE OF STARTUPS WITH PATENTS
≥ 1 PATENT ≥ 10 PATENTS ≥ 50 PATENTS
52
50
26
18
9
8
7
170
20%
68%
2%
28%
100%
100%
100%
41%
12%
22%
0%
0%
67%
100%
43%
20%
6.1%
10%
0%
0%
45%
100%
29%
9.4%TOTAL
INDUSTRY
What economists really want to understand about
innovation is its impact on productivity, because in
the long run productivity growth is the most import-
ant issue for economies. Economists have long been
aware that there is little correlation between total fac-
tor (or labor) productivity and total patenting num-
bers. This led early researchers on innovation such
as Jakob Schmookler to recognize as far back as the
1960s that patents were a better index of innovative
“activity” than of the actual economic output from
this activity. He was concerned with what patents can
measure rather than what we would want them to
measure, a lesson that many innovation scholars have
forgotten.
Figure 1 plots the number of patent applications,
patent awards, and average productivity growth (five-
year averages) over time. The numbers of US patent
applications and awards were flat until the mid-1980s,
when patenting activity began its current period
of explosive growth. First noted by the economist
Brownyn Hall in 2004, patent applications increased
by more than three times between 1984 and 2003 and
more than six times by 2015. Patent awards also rose
by almost six times between 1984 and 2015. Reports
from the World Intellectual Property Organization
show similar trends; global patent applications qua-
drupled between 1980 and 2015 while global licensing
income from patents grew even faster, rising by more

3. 50 ISSUES IN SCIENCE AND TECHNOLOGY
patents
THOUSANDSOFPATENTS
than five times between 1980 and 2009 and by three
times in terms of percent of gross national product
(GNP).
Figure 1 also shows that US productivity growth
has not increased along with patent applications
and awards, suggesting that patents are not a good
measure of innovation. If patenting activities were a
good measure for innovation, productivity growth
should have also increased, probably with some type
of time lag. However, growth in US productivity has
slowed since 1970, and that slowing is in addition to
the slowing that has occurred since 1940. Other than
short-term decreases (1980) and increases (2000), the
average productivity growth after 1984 is not higher
than before 1984. Furthermore, Anne-Marie Knott
of Washington University has found that corporate
research and development (R&D) productivity has
been falling as well, with corporate revenues gener-
ated per dollar of corporate R&D spending having
dropped 65% in the past 30 years, even as patent
applications and awards have exploded. Understand-
ing the reasons for a slowdown in economic produc-
tivity growth and a fall in R&D productivity should
be major goals of the economics discipline, but don’t
expect much from patent analyses.
Looking at Figure 1, it is tempting to advance
a hypothesis that patent applications could be
one reason for the lack of productivity growth. In
addition to patents slowing the diffusion of infor-
mation, could patent applications be a distraction
for engineers, scientists, and their managers because
they require large amounts of administrative work
in applications and infringement cases? Since the
cost of applying for a patent is about $10,000, it is not
hard to imagine that if the cost of engineers, internal
lawyers, and other personnel are included, the cost
could easily reach $50,000 per patent. With 630,000
patent applications in the United States in 2015, this
suggests the US market for patent activities that year
was about $30 billion, equivalent to about half of
the entire federal investment in nondefense R&D.
Since economists have noted the negative impact
of environmental, health, and safety regulations on
productivity growth, why wouldn’t they take seriously
the possibility that the administrative work of patent
applications distracts the nation’s engineers and sci-
entists, including those at universities, from the real
work of innovation?
Evidence for the wastefulness of patenting can be
seen in the lack of correlation between patenting and
market capitalization (i.e., profitability) in Table 2.
Researchers have long recognized that patents have
little impact on firm profitability, and this problem
still exists. The World Intellectual Property Organiza-
tion reported in 2016 the top 100 organizations (both
firms and universities) in terms of patent applications
for four recent years (2010-2013), and PwC listed the
top 100 in terms of market capitalization as of March
30, 2013. Of the top 10 patent applicants, only three
were in the top 100 for market capitalization: IBM
was ranked 9th, Samsung was 19th, and Toyota was
27th. Expanding the leading patent applicants to the
top 100 adds only six more from the top 100 mar-
ket-capitalized companies, for a total of nine compa-
Figure 1. Patent vs. average productivity growth in the United States
1950 19701960 1980 1990 20102000 2020
0
1
2
3
4800
600
400
200
0
Patent AwardsPatent ApplicationsProductivity Growth
PRODUCTIVITYGROWTH

4. SUMMER 2018 51
patents
nies. Looking the other way, only four of the top 10
companies for market capitalization were among the
top 100 patent applicants, and they were ranked 9th
(IBM), 31st (GE), 45th (Microsoft), and 65th (Google).
Apple, Exxon Mobil, PetroChina, Walmart, and Nes-
tle are not even in the top 100 even though many of
them are in industries that do apply for many patents.
Proponents of patents would argue that patent
citations are a better predictor of profitability because
they show which patents are viewed as important by
other innovators, and thus Table 2 should rank firms
by the number of citations to their patents, and not
the number of their patent applications. But such logic
calls to mind the old adage (variously attributed to
Mark Twain, Niels Bohr, and Yogi Berra) that “pre-
diction is hard, especially about the future.” Patent
citations are the same. In retrospect one can identify
the important patents, but beforehand most of us have
great difficulty knowing which patents will end up
being heavily cited. So using patent citation to show
that patents are important indicators of profitability
is circular. If top companies aren’t patenting much,
they must not think that patenting is important for
profitability.
These observations suggest that scholars of innova-
tion need to take seriously not only the possibility that
patents are not important predictors of innovative-
ness, but that the increasingly intense focus by firms
and universities on patenting may be distracting them
from more important work.
What can patents tell us
about science?
As in the recent Science paper cited above, many
patent analyses treat citations of science papers in
patent applications as a measure of knowledge flows.
Yet scholars have long known that citations of science
and engineering papers in patents are not a very
good measure of knowledge flows, or of advances in
science, and that informal interactions between and
among university researchers and corporate engi-
neers may be more important. Patent citations do not
reflect the actual activities or thought processes of
the engineers and scientists who devise novel designs
and submit patents. Instead, they reflect the efforts of
many participants in the patent application process
to distinguish new ideas from prior ideas, including
prior patents and public information. Such efforts
amount to a complex sociopolitical dance among ap-
plicants and patent examiners to determine the scope
of patents. Specialist patent advisers, not the scientists
Table 2. Top companies in terms of patent applications and market capitalization
COMPANY COMPANY
TOP MARKET CAPITALIZATIONTOP PATENT APPLICANTS
PANASONIC
CANON
TOYOTA
AMSUNG
TOSHIBA
MITSUBISHI
HONGHAI PRECISION
IBM
OCEAN’S KING LIGHTING SCIENCE
& TECHNOLOGY
SHARP
APPLE
EXXON MOBIL
GOOGLE
BERKSHIRE HATHAWAY
PETROCHINA
WALMART
GENERAL ELECTRIC
MICROSOFT
IBM
NESTLE
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
RANKING RANKING

5. 52 ISSUES IN SCIENCE AND TECHNOLOGY
patents
and engineers themselves, are necessary because
expanding the scope of a submitted patent while
narrowing the scope of competing patents requires
a special set of skills. The patent examiner then tries
to understand the novelty and limits of the claimed
versus competing inventions. The result is that both
patent examiners and advisers add many of the papers
eventually included in patent applications, whereas
the engineers and scientists who did the work may
not have read many of the papers, or even have been
aware of them.
This process was well illustrated in a detailed study
of patent applications by Martin Meyer, who used
interviews with scientists and engineers to better
understand where their ideas came from. The study
looked at patents for nanoscale technologies, an area
of innovation that probably benefited from advances
in science more than most technologies, yet the study
concluded that few of the ideas came from academic
papers, but rather from the independent work of the
engineers and scientists. Many of the papers added by
the patent examiners could not be recognized by the
scientists and engineers who devised the novel ideas
for the patent applications.
Nor are counts of citations of papers in patents a
good measure for advances in science. For example,
the types of journals cited in patents include not only
scientific journals but also engineering and even man-
agement journals. Patent citation studies typically use
the Science Citation Index (SCI) or the Web of Science
(which includes the SCI) to identify science journals,
but the SCI includes not only science journals but
also engineering and business journals; even Harvard
Business Review is included in the index. Including
engineering journals in the list of journals used to
count scientific papers overestimates the contribu-
tion from scientific journals. In a previous article in
Issues (Spring 2017), I looked at 143 members of the
billion-dollar startup club and found that most of the
papers cited in their patents were from engineering
journals published by groups such as the Institute of
Electrical and Electronics Engineers or the Associa-
tion for Computing Machinery, and not basic science
journals such as Nature and Science, nor standard
disciplinary journals. More specifically, although
18% of the 143 start-ups had patents that cited at least
one paper in the SCI, only 6% of them had patents
that cited a scientific as opposed to an engineering
journal. Lumping engineering papers together with
basic scientific papers as a surrogate for advances in
science so generalizes the phenomenon of “knowledge
flows” that it thoroughly undermines the value of that
concept for assessing the relations between scientific
advance and innovation.
I am arguing that research on patent databases
may not help us understand innovation. But I am also
suggesting that such research may rather be a smoke
screen, hiding the true issues, problems, and dynam-
ics of innovation behind an illusion that innovation
is booming and that rising scientific and patenting
activities are the reason for the boom. Many inno-
vation scholars seem to believe that patents are more
important indicators of innovation than are new
products, services, and processes and their contribu-
tion to productivity improvements and company prof-
its. Although some highly cited patents are of course
extremely important and valuable, and some scien-
tific papers are very influential, the patent obsession
makes us think that such cases are everywhere, and
are what drive an innovative economy, when in fact
“home runs” such as transistors are rare.
This type of research is also self-serving, plac-
ing academics at the center of the economy while
ignoring the other sources and forces behind new
products, services, processes, and the entrepreneurs
that introduce them. It enables university professors
to claim that they are doing what they are supposed
to do: writing papers that are cited in patents. It
enables Science magazine to claim that it is the driver
of innovation and productivity growth. More gen-
erally speaking, the emphasis on patents and papers
reflects a much larger problem in the social sciences:
an increasing obsession with large databases, so-
phisticated statistics, and elegant mathematics. This
quantification arms race has pushed and continues to
push the social science research on innovation toward
more analyses of large patent databases, yet it may
also be pushing it away from true understanding of
how innovation works in today’s complex and varied
economic settings. In part this is a classic problem
of looking for one’s lost keys under the streetlight. In
this case, if the keys are the complex dynamics of sci-
ence, innovation, and productivity, the streetlight is
patent data, and what keeps researchers looking in the
same place is the academic incentive system, where
publications are the sources of promotions, job secu-
rity, social and intellectual status, and pay increases.
A new agenda for innovation research
If patent analysis is a smoke screen that has prevented
scholars from improving their understanding of inno-
vative processes, how should they address innovation
instead? Although there are many possible alternative
avenues, here I focus on a new agenda for produc-
tivity that is being advocated by a small number of
scholars such as Robert Gordon and Tyler Cowen, but

6. SUMMER 2018 53
patents
is largely neglected by most innovation scholars. A
first set of questions involves the extent to which new
products, services, and processes have emerged and
are currently emerging. GNP data tell us some of this,
but not anywhere to the extent that is needed. We
need databases that better highlight these chang-
es, particularly in the early years of new products,
services, and processes. This includes science-based
technologies such as superconductors, quantum com-
puters, nanotechnology, synthetic food, glycomics,
and tissue engineering; new forms of digital products
such as augmented reality and drones; and new forms
of internet services including those that are free, such
as music and user-generated content.
Without data on recent products, services, and
processes, it is difficult to understand what has
emerged in the past 20 to 40 years, the period that
is the most important to analyze. Fine-grained
sales data from this and previous periods can help
us understand the extent to which new products,
services, and processes have been emerging and help
us analyze the impact of changes in regulatory policy
and in university and government R&D policy on the
emergence of new products, services, and processes,
particularly recent ones. A possible source of data is
the growing number of market research organizations
that provide regularly updated data on new technolo-
gies. The challenge is to integrate these data with tra-
ditional GNP data because this integration requires
a different set of skills. Scholars must understand the
technologies that form the basis for the new products,
services, and processes, a research activity that is very
different from statistically analyzing large databases.
Second, once we know what is coming out, we
can address where these new products, services, and
processes come from. Do they come from existing
technologies or new technologies? If they come from
existing technologies, what enabled them to emerge
when they did? Was it changes in regulation, con-
sumer demand, or cost and performance of existing
technologies? If they are from new technologies, how
did these new technologies emerge? What were the
changes in cost and performance that enabled them
to emerge, and were there advances in science that
formed the basis for the new products or that helped
the improvements to occur? Addressing these ques-
tions will help us understand the overall processes
that lead to the emergence of new products, services,
and processes, along with the factors that influence
the processes. Currently, there is little agreement
about where new products, services, and processes
come from, a seemingly basic issue.
Third, researchers should be looking the opposite
way. How are the new types of products, services,
and processes that are economically important today
connected to the many types of scientific research
that have been funded over the past 20 to 40 years by
the National Science Foundation (NSF), the National
Institutes of Health, and other government agencies?
Most funding agencies and analyses of this research
merely focus on academic papers as an output, but
what matters is the research that eventually leads to
new products, services, and processes. This analysis
should go beyond the standard litany of anecdotes
(by now everyone knows that the Google algorithm
was developed by NSF-funded researchers at Stanford
University) and be able to trace the linkages between
specific advances in science and the eventual prod-
ucts, services, and processes, including the intermedi-
ate steps of new product concepts and improvements
in the cost and performance of the resulting technol-
ogies.
In a blog that accompanied the above-mentioned
Science paper, the authors illustrated the importance
of indirect linkages between science and technolo-
gy by invoking the necessity of Einstein’s theory of
relatively for the Global Positioning System and thus
Uber. But such high-level, general linkages to para-
digm-busting geniuses such as Einstein tell us almost
nothing useful about knowledge flows, innovation,
or science policy options. Einstein’s theory of relativ-
ity was published in 1916. What we need to know is
the extent to which recent advances in science affect
new products and services. We already know that
most of the world’s products and services depend on
past advances in science and that these advances are
cumulative. We want to know how many advances
made since 1980, 1990, or even 2000 have had a major
influence on new products and services. This type
of data is needed to understand the bottlenecks for
innovation.
Patent analyses suggest that advances in science
have been making direct contributions to every
new product and service, but this conclusion seems
unlikely given the successive productivity slowdowns
in the US economy since 1970, even as government
support for academic basic research increased more
than tenfold (after inflation) between 1950 and 1980.
These increases in basic science should have led to a
productivity boom in the late twentieth century, one
that rivals the late nineteenth century. Techno-op-
timists might argue that the boom is coming soon,
but we need better data and analyses to test such a
hypothesis. Among other things, we need such data
to analyze the effects of various policies (such as the
Bayh-Dole Act of 1980 that incentivized university

7. 54 ISSUES IN SCIENCE AND TECHNOLOGY
patents
patenting) on the linkages between advances in
science and new products, services, and processes.
Linking advances in science with real products
and services requires a completely different form
of research than is currently done in patent analy-
ses. Rather than do large-scale empirical analyses,
one must understand many intermediate linkages
through detailed case studies. What types of new
explanations did these advances entail? What types
of new concepts or what types of performance and
cost improvements in the resulting technologies did
the explanations enable? What types of products,
services, and processes emerged from these tech-
nologies, and what were the time lags? This type of
research changes the focus from statistical analysis
that searches for perhaps nonexistent general trends,
to a real-world, case-based understanding of science
and the emergence of new products and processes,
such as the work pioneered decades ago by innova-
tion scholars such as Kenneth Flamm on computers,
Nathan Rosenberg and David Mowery on aircraft,
Yujior Hiyami and Vernon Ruttan on agriculture,
and Richard Nelson on transistors.
We also need to understand from which scientific
disciplines the new products, services, and processes
have emerged if we are to make better funding deci-
sions and if we are to help engineering and science
students make better career decisions in an ocean of
hype about the value of science, technology, engi-
neering, and mathematics (STEM) education. The
current system makes little attempt to help students
understand what types of innovations are occurring,
what fields of science are making the most useful
contributions to innovation, and thus which courses
of study offer the most opportunities.
A fourth set of questions revolve around why
some sectors have faster productivity growth than
do others. Because most economic and management
research focuses on organizational factors such as
employee performance measures, incentives, and
skills, and uses patents as a surrogate for innovation,
the reasons for the differences between sectors is
largely being missed. For example, the mechanics
of Moore’s Law, the role of smaller scale, and the
impact of this smaller scale on rapid improvements
in cost and performance of information processing
technologies are well documented, but little system-
atic effort has been made to look across industrial
sectors and classes of technology to understand the
overall impact of Moore’s Law and new materials
on differences in productivity, or to identify other
specific pathways of innovation on productivity.
Patent analyses gloss over these details and leave us
with a vague feeling that innovation is occurring, science
supports this innovation, and as long as we have more of
both, everything will be okay.
Innovation scholars should be trying to better under-
stand the reasons for the productivity slowdown and how
it can be fixed. Identifying and analyzing these reasons
will require scholars to consider multiple types of data
and information, much of which cannot be placed in a
spreadsheet and analyzed with sophisticated statistics,
and will not likely be found in academic journals. Schol-
ars will have to get their hands dirty, understanding the
specifics of new advances in science, new technologies,
and their resulting new products and services. They will
have to make judgments, create new definitions, identify
new linkages, and begin building new bodies of data.
Just as Charles Darwin left home to understand the real
world, innovation scholars need to do the same, leav-
ing the safety of existing databases and theories so that
they can start collecting new data and generating new
hypotheses. Ultimately, such research can help inform a
more constructive discussion about fostering economic
opportunities across all levels of society.
Jeffrey Funk, an independent technology consultant,
formerly taught at the National University of Singapore,
Hitotsubashi University, and Kobe University.
Recommended reading
Mohammad Ahmadpoor and Benjamin Jones, “The
Dual frontier: Patented inventions and prior scientific
advance,” Science 357, no. 6351 (2017): 583-587.
Jeffrey Funk, “What Does Innovation Today Tell Us
about the US Economy Tomorrow?” Issues in Science
and Technology 34, no. 1 (2017): 29-36.
Robert Gordon, The Rise and Fall of American Growth
(Princeton, NJ: Princeton University Press, 2016).
Zvi Griliches, “Patent Statistics as Economic Indicators:
A Survey,” Journal of Economic Literature 28, no. 4
(1990): 1661-1707.
Brownyn Hall, “Exploring the Patent Explosion,” The
Journal of Technology Transfer 30 (2005): 35-48.
Adam B. Jaffe and Gaétan de Rassenfosse, “Patent
Citation Data in Social Science Research: Overview
and Best Practices, Journal of the Association for
Information Science and Technology 68, no. 6 (2017):
1360-1374.
Martin Meyer, “Does Science Push Technology? Patents
Citing Scientific Literature,” Research Policy 29, no. 3
(2000): 409-434.
Michael Roach and Wesley M. Cohen, “Lens or Prism?
Patent Citations as a Measure of Knowledge Flows
from Public Research,” Management Science 59, no. 2
(2013): 504-525.