Using the percent of top managers in IPOs (initial public offering) as a proxy for an industry’s/technology’s scientific intensity, this paper shows that the percentage of IPOs and of venture capital financing for science-based technologies has been declining for decades. Second, the percentage of PhDs among the top managers in science intensive industries is also declining, suggesting that their scientific intensities are falling. Third, the age of these top managers rose during the same period suggesting that the importance of experiential knowledge has increased even as the importance of PhDs and thus educational knowledge has decreased. Fourth, the numbers of IPOs and of venture capital funding are not increasing for newer science-based industries such as superconductors, solar cells, nanotechnology, and GMOs. Fifth, there are extreme diseconomies of scale in the universities that produce the PhD-holding top managers, suggesting that universities are far less effective at doing research than are companies. These results provide a new understanding of science and technology, and they offer new prescriptions for reversing slowing productivity growth.

1. 1
Are Fewer Science-Based Technologies Being Commercialized?
And if so, is ineffective university research the reason for the decline?
By
Jeffrey Funk
Independent Researcher
Martin Kenney
University of California, Davis
Donald Patton
University of California, Davis

2. 2
Are Fewer Science-Based Technologies Being Commercialized?
And if so, is ineffective university research the reason?
Abstract
Using the percent of top managers in IPOs (initial public offering) as a proxy for an
industry’s/technology’s scientific intensity, this paper shows that the percentage of IPOs and
of venture capital financing for science-based technologies has been declining for decades.
Second, the percentage of PhDs among the top managers in science intensive industries is also
declining, suggesting that their scientific intensities are falling. Third, the age of these top
managers rose during the same period suggesting that the importance of experiential
knowledge has increased even as the importance of PhDs and thus educational knowledge has
decreased. Fourth, the numbers of IPOs and of venture capital funding are not increasing for
newer science-based industries such as superconductors, solar cells, nanotechnology, and
GMOs. Fifth, there are extreme diseconomies of scale in the universities that produce the PhD-
holding top managers, suggesting that universities are far less effective at doing research than
are companies. These results provide a new understanding of science and technology, and they
offer new prescriptions for reversing slowing productivity growth.
Keywords: innovation, startups, technology, science, PhDs, entrepreneurs
JEL Codes: O31, O32, O33

3. 3
1. Introduction
Advances in science have long been considered key drivers of economic growth (Nelson,
1959; Salter and Martin, 2001; Mokyr, 2002). Early research emphasized case-based analyses
of science-based technologies such as vacuum tubes, radio and TV broadcasting, plastics,
synthetic fiber, penicillin, jet engines, nuclear power, integrated circuits, magnetic storage,
lasers, LEDs, and fiber optics (Rosenberg, 1974, 1982, 1992, 1994; Dosi, 1982; Mowery and
Rosenberg, 1998; Rosenberg and Nelson, 1994). Many of these technologies have subsequently
been defined as general purpose technologies because of their large impact on economic growth
(David, 1990; Bresnahan and Trajtenberg, 1995). R&D spending, surveys, and productivity
data were also used to understand the roles of external knowledge (Arora and Gambardella,
1990; Arora, Fosfuri, and Gambardella, 2001; Higgins and Rodriguez, 2006; Mowery, 2009),
tacit knowledge, and of absorptive capacity (Cohen and Levinthal, 1989, 1990; Griffith et al,
2004; Aghion and Jaravel, 2015).
More recent analyses have focused on patents. Since the earliest studies, they have been
used as a measure for innovation while academic articles cited in the patents are used as a
measure for advances in science (Narin and Noma, 1985). For example, a 2017 paper in Science
magazine (Ahdmapoor and Jones, 2017) demonstrated that most patents are linked to papers
either directly or indirectly by calculating a distance metric that measures the distance back
from patents to papers and the distance forward from papers to patents. The results from these
studies are consistent with the existence of increasing returns to scale in basic and applied
research (Romer, 1986).
On the other hand, research by a growing minority of scholars suggest science-based
technologies are not being commercialized as much as they were in the past. Some have
documented the rising cost of maintaining improvements in Moore’s Law or crop yields, or of
introducing new drugs (Bloom et al, 2017; Scannel et al, 2012) while others document overall
slowdowns in labor (Gordon, 2016) and corporate research productivity even while finding

4. 4
economies of scale in corporate research (Knott, 2016). Still others have questioned the
relevance of patents as a measure of innovation (Griliches, 1990; Roach and Cohen, 2013) and
academic articles as a measure of advances in science (Meyer, 2000; Nelson 2009).
This paper provides a different approach. It uses the percent of top managers in IPOs (initial
public offering) as a proxy for the scientific intensity of an industry, thus introducing a new
way to measure advances in science that is different from patent analyses and other measures
such as R&D as a percent of sales. It does this using a database of 3679 IPOs and 19,702
executives and directors with known educational backgrounds and industries between the years
of 1990 and 2010 (Kenney and Patten, 2017). After ranking industries by the percent of top
managers with PhDs and thus scientific intensity, the paper then shows that the percentages of
total IPOs and venture capital financing for these science-based industries has fallen. Third, the
age of these top managers rose during the same period suggesting that the importance of
experiential knowledge has increased even as the importance of PhDs and thus educational
knowledge has decreased. Fourth, the numbers of IPOs and of venture capital funding are not
increasing for newer science-based industries such as superconductors, solar cells,
nanotechnology, and GMOs. Fifth, there are extreme diseconomies of scale in the universities
that produce the top managers with PhDs, suggesting that universities are far less effective at
doing research than are companies.
The paper proceeds as follows. The literature review examines the different ways in which
advances in science and science-based technologies have been analyzed and the implications
of these analyses for reversing slowing productivity growth. Second, the methods section
describes the data collection for analyzing educational degrees, numbers of IPOs, venture
capital financings, and ages of startup executives and directors, all by industry and also the
universities that trained the PhDs. Third, the data analysis results are presented. Fourth,
possible reasons for fewer science-based technologies being commercialized are discussed and
fifth, the implications for theory are explored.

5. 5
2. Literature Review
Most recent analyses of innovation have used citations to academic papers in patents to
study the impact of advances in science on innovation. These analyses have focused on various
types of knowledge transfer such as the co-authoring of papers between public and corporate
researchers (Cockburn and Henderson, 1998), the role of star scientists (Zucker et al, 2002),
the impact of science and engineering patents on new ventures (Agrawal and Henderson, 2002),
the increasing use of external knowledge by firms (Higgins and Rodriguez, 2006), the
educational attainment, age, team size, and specialization of patent recipients (Jones, 2009),
the corporate and academic reading patterns of scientific papers published by Elsevier (Plume
and Komalski, 2014), and the temporal lags between scientific papers and patents (Ahmadpoor
and Jones, 2017).
For example, a 2017 paper in Science (Ahmadpoor and Jones, 2017) analyzed all patents
and cited articles with a goal of understanding “The extent to which scientific advances support
marketplace inventions,” which in this case the “marketplace inventions” are patents; highly
cited patents are judged to be “home runs.” They demonstrate that by calculating a distance
metric that measures the distance back from patents to articles and the distance forward from
articles to patents most patents are linked to articles either directly or indirectly. The paper
concludes that “most patents (61%) link backward to a prior research article” though cited
patents and “most cited research articles (80%) link forward to a future patent.”
Another paper by Jones (2009) focuses on the patent histories of 55,000 innovators and
finds that educational attainment and other characteristics (age, specialization, and team size)
of the innovators are increasing over time and that educational attainment and age are
independent of industry. Jones explains this finding in the following way: “If technological
progress leads to an accumulation of knowledge, innovators and entrepreneurs will obtain
higher degrees over time.” He concludes that innovation is becoming increasingly difficult and

6. 6
more knowledge intensive and suggests to him a possible explanation for slowing productivity
growth. He explains this with Isaac Newton’s observation almost 500 years ago: if one is to
stand on the shoulders of giants, one must first climb up their backs, and the greater the body
of knowledge, the harder this climb becomes.”
Jones’ conclusions and those of other patent analyses summarized above have several
implications for policy makers. First, advances in science and other forms of knowledge are
becoming more important over time, and much of this knowledge is being generated in
academic articles (Jones, 2009), academic discourse (Agrawal and Goldfarb, 2008), and to a
lesser extent work experience. Second, companies are becoming increasingly dependent on
external knowledge for new ideas (Arora and Gambardella, 1990; Arora, Fosfuri, and
Gambardella, 2001; Higgins and Rodriguez, 2006; Mowery, 2009) and thus the importance of
tacit knowledge and absorptive capacity are increasing. Third, these increases in knowledge
confirm the importance of increasing returns to research (Romer, 1986). Fourth, reductions in
corporate R&D spending cannot be due to falling scientific intensity because patent analyses
show strong linkages between patents and papers, both of which are rising (Arora et al, 2015).
Fifth, educational attainment, age, and specialization (Jones, 2009) are rising, as are social
skills for workers (Deming, 2017), thus suggesting that complexity is increasing, complexity
that may be coming from the increasing importance of science and external knowledge.
Together, these conclusions suggest that productivity growth is slowing (Gordon, 2016)
because innovation and knowledge creation are becoming more complex and the solutions
include higher educational attainment (Jones, 2009) perhaps with greater teamwork (Kotha et
al, 2013).
Even as these types of patent and paper analyses have grown in importance, however,
other scholars have questioned their relevance. Most innovations are not patented, and many
patents don’t represent important innovations (Griliches, 1990; Roach and Cohen, 2013; Knott,
2016). Academic articles, as a measurement device, also have drawbacks and certainly are a

7. 7
limited measure for knowledge flows (Meyer, 2000; Nelson 2009). In part, this is because much
technology transfer is done outside either patents or academic articles because PhD graduates
and informal interactions are powerful knowledge conduits (Agrawal and Henderson 2002;
Kenney and Mowery, 2014).Academic articles are also a flawed measure of scientific advances
because most patent analyses treat scientific and engineering journals as equivalent when they
measure scientific advances and thus miss the important distinction between science (an
explanation) and technology (a way of doing something) (Dosi, 1982; Arthur, 2007, 2009).
Some scholars have focused on better measures of innovative activity such as
improvements in microprocessors or crop yields, new drugs, or new revenues from corporate
R&D. These studies find that the development cost of these improvements (Bloom et al, 2017),
new drugs (Scannel et al, 2012), or new revenues has risen, even as economies of scale continue
to exist in corporate R&D (Knott, 2016). Some qualitative studies have also concluded that
basic and applied research were done better in the past (Ness, 2013; Odumosu and
Narayanamurti, 2016). These studies suggest that real products and services, and revenues from
them, are better proxies for innovative activity than are patents.
This paper introduces a complementary approach to understanding the impact of advances
in science on innovation and whether this impact is becoming stronger or weaker. By focusing
on successful startups, this approach complements the analyses of integrated circuits, drugs,
and corporate revenues from R&D, which mostly involve incumbents, by looking at the
innovative activities of entrepreneurial startups. This approach also builds from the important
distinction between science and technology (Dosi, 1982) and the fact that many advances in
science are made by PhD-trained researchers often while they work in universities training new
PhDs.
Advances in science refer to new explanations of physical and artificial phenomena while
technology refers to artefacts, techniques, and designs. These new explanations often
illuminate the mechanisms by which physical and artificial phenomena occur and thus facilitate

8. 8
the development of designs that are based on these mechanisms (Arthur, 2007, 2009; Balconi
et al, 2012). For example, new drugs benefit from an understanding of the mechanisms by
which diseases begin and spread, how drugs act on the diseases, and the method of synthesizing
drugs (Pisano, 2006). Similarly, new explanations of physical or artificial phenomena also lead
to new products and services because they often form the basis of new product or service
concepts (Fleming, 2001; Fleming and Sorenson, 2001; Arthur, 2007, 2009). For example, new
explanations of physical or artificial phenomena such as PN junctions, optical amplification,
electro-luminescence, photovoltaics, light modulation, optical loss in glass fiber, giant magneto
resistance, and information theory formed the basis for new concepts such as transistors, lasers,
light-emitting diodes (LEDs), solar cells, liquid crystal displays (LCDs), optical fiber, a new
form of magnetic storage (Orton, 2009), and new forms of mobile phone transmission standards
respectively.
Advances in science also have an important role to play during the applied research phase,
long after the phenomenon and resulting concept were identified. For example, a better
understanding of organic materials enabled researchers to create materials that better exploit
relevant physical phenomena and thus improvements in the cost and performance of OLEDs,
organic transistors, and organic solar cells. Similar advances in other phenomena enabled
researchers to create better materials for superconductivity, quantum dots, and new forms of
integrated circuits and these research results supported double-digit annual improvements in
the pre-commercialization performance and cost of these technologies (Funk and Magee, 2015).
Many of these advances in science, whether they led to new concepts or to improvements
in cost and performance, have been achieved by scientists with PhDs and often as professors
or PhD students. The prevalence of these PhDs and other advanced degrees among university
scientists suggest that the existence of these degrees among top managers can be used to
measure the scientific intensity of a technology or industry. Such a measure is better than than
using R&D spending as a percent of sales to measure the scientific intensity of an industry

9. 9
because the “D” is always bigger than the “R” yet the “R” contains more science than does the
“D.”
We focus on startups because they they have been remarkable successful in
commercializing many of the most important new products and services and the technologies
they represent over the last 50 years from integrated circuits to electronic products and software.
We use the percent of top managers with PhDs in industries as a proxy for the science intensity
of an industry/technology and we use their average age as a proxy for the importance of
experiential knowledge. The next section describes the ways in which we operationalize these
variables in more detail.
3. Methods
This paper uses data from initial public offerings (IPOs) filed between 1990 and 2010
(Kenney and Patton, 2017) and venture capital financing by PwC Moneytree from 2002 to
2017. The IPO database is comprised of all emerging growth IPOs on U.S. stock exchanges
and filed with the Securities and Exchange Commission from January 1990 through December
2010, a total of 3,679 startups with known industries. Emerging growth means newly formed
firms that are not spin-offs from other firms. It excludes the following types of firms and filings:
mutual funds, real estate investment trusts, asset acquisition or blank check companies, foreign
F-1 filers, and firms that had gone public previously. There are 41,225 directors and executive
officers in the database with known industry and IPO filing year. Industry, IPO filing dates, and
educational data are known for 19,702 individuals.
The highest educational degrees were calculated for PhD, MD, JD, master’s, MBA, and
bachelor’s degrees. Two-year and professional degrees were ignored because their numbers
were very small, representing less than 0.1% of degrees. The above order was used to avoid
double counting, thus giving precedence for example to J.D. over master’s degrees, including
M.S., M.A., and MBAs. Averages were calculated for each degree, IPO filing year (1990 to

10. 10
2010), industry. and executives vs. directors of boards. No differences were found between
executives and directors.
We also investigated the extent of non-reporting of educational data in the IPO database
to check for biases in the data. The percentage of executives and directors with known
educational data was also calculated and found to vary by industry and filing year. It varies
from a low of 36% for telephone and telegraph to a high of 80% for advertising, leasing, and
employment. It also varies by year, mostly increasing over time from a low of 23% in 1992 to
a high of 89% in 2009. An easy way to separate out the effects of industry and time is to
consider the years 2000 to 2010, for which there is less increases over time. For these years,
the industries of biotech, semiconductors, instruments, electronic equipment, and
communication have a reporting rate of 82% while Internet infrastructure and content have a
reporting rate of about 60%. Since the former have much higher fractions of top managers with
PhDs than do the latter industries, this suggests that individuals with higher levels of education
lead to higher educational reporting. This is because, in some industries, such as retail and
banking, education is not considered significant for signaling quality, while in the technology
sectors education is valued more highly and thus more likely to be reported. Furthermore,
individuals with higher degrees are more likely to report their educational data, as a method of
signaling quality to potential investors. Thus, the differences between industries are likely to
underestimate the actual differences as we omit the “non-reporting” individuals from the
analysis.
This data was used to identify industries with large percentages of PhD and M.S. degrees
among top managers to test whether the number of science-based technologies being
commercialized has declined or not. The higher the fraction of PhD and M.S. degrees, the
higher the scientific intensity of the industry. After ranking industries according to their
scientific intensity, we analyzed the number of IPOs for the industries with the highest scientific
intensity. The analysis was done for both absolute numbers and for their fraction of total IPOs.

11. 11
We also combined industries to have enough data points for a reliable time series. For
example, machinery and other manufacturing industries were combined to increase their
sample size. Industries were also combined into sectors in order make comparisons easier. For
example, computer programming, computer systems, software, and computers were combined
to form a sector of computing and Internet infrastructure. The industry of education and
research was combined with biotechnology because most of the PhDs involved biological
sciences.
Data on U.S. venture capital financing was also used to test whether the number of
science-based technologies being commercialized has declined or not. Data on venture capital
financing is obtained from PwC Moneytree. It provides financing data on a quarterly basis from
2002 to 2017 for 21 different sectors and more than 100 different technologies. We compiled
time series data for science-intensive technologies to examine whether their financing, on an
absolute or fractional basis, has declined over the last 15 years. We also searched for new
science-based technologies such as superconductors, nanotechnology, GMOs, and solar cells,
some of which were classified as semiconductors or manufactured products in the IPO database.
The latter is a category that includes a diverse variety of technologies, each having small
numbers and thus was combined with machinery.
We also utilize the National Science Foundation’s annual report, National Science
Foundation’s Science and Engineering Indicators for data on VC financing. These reports are
published every two years and reports from 2008 to 2014 include data on VC financing for
science-based industries, some going back to 1980. Unfortunately, different reports provide
different data for the same year and thus we only use the data for the 1980s and 1990s (NSF,
2008) to complement the more reliable PwC MoneyTree data for 2002 to 2017.
To better understand why the number of science-intensive industries might be receiving a
lower percentage of VC funding and conducting a lower percentage of IPOs, we also analyzed
the average age of executives and directors. Based on the ages of 19,264 top managers, the

12. 12
average ages were calculated for IPO filing year (1990 to 2010) and each science-intensive
industry. We assume that top managers have spent their years either in formal schooling or in
jobs and thus higher ages reflect more years spent accumulating one or the other type of
knowledge.
We also investigated the universities that trained the PhDs in the IPO database. We
identified the number of PhDs per university along with the research expenditures by the
universities. Graduates with multiple IPOs are counted multiple times. Because there is a lag
between university research, the introduction of new products and services, and a resulting IPO,
that may be decades, we focused on the oldest research expenditures available from the
National Science Foundation (NSF, 2000), which is for 1991. Although we examined all
universities that produced IPO-filing PhDs, our statistical analysis focuses on 43 U.S.
universities that are either in the top 30 for IPOs or top 30 for research expenditures.
4. Results
The results are divided into four sub-sections. The first and second sections provide
evidence of a decline using IPOs and venture capital financing respectively, the third section
examines the rising age of managers, and the fourth section focuses on the universities that
trained the IPO-filing PhDs.
4.1 Evidence of Decline with IPOs
Table 1 shows the numbers and percentages of executives and directors by highest
educational degree attained by year. Bachelor’s degrees and MBAs were the highest degree
attained for 34% and 28% of the executives and directors respectively followed by 13%, for
both PhD and M.S. degrees. Table 1 also shows that the percentage with PhDs declined from
the mid-20s to the low teens between 1990 and 2010, a first sign that the number of science-
based technologies being commercialized may be falling.

13. 13
Table 2 ranks industries by the percentage of PhD degrees among executives and directors.
Six industries have more than the average of 13% for all IPOs of which three of the industries
are in the life sciences (biotechnology, education and research, and medical instruments) sector
and three are in the electronics (general instruments, semiconductors, and electronic
equipment) sector. If the fraction with either PhD or M.S. degrees are considered, the number
of industries with more than the average of 23% grows to nine. The new ones are
communications, machinery, and computers of which the first one can be considered part of
the electronics sector.
Looking further down the list, we note there are five other industries that have a combined
PhD and M.S. fraction that is the same or higher than 19%. These are computer programming,
computer systems, software, and manufactured goods. The first four can be combined with
computers to form a category of computing and Internet infrastructure. The last one, which
contains wide variety of manufacturing industries, both high and low-tech, is combined with
machinery in order have a larger sample.
Table 3 shows the number of IPOs by industry and their fractions of total IPOs for the most
scientifically intensive of the industries. Biotechnology has the most, followed by software,
manufactured goods, and medical instruments. Including education and research with
biotechnology raises biotech’s number to 515 or 12.4% of the IPOs. The rest of the paper
includes education and research in the numbers for biotechnology.
Figure 1 shows the fraction of total IPOs for the industries/sectors with high scientific
intensity using a three-year moving average. After declining from about 14% in 1990 to about
4% in the mid-1990s, the fraction for biotech has risen to almost 40% suggesting that the
commercialization of new science-based technologies, products and services in this industry
has not declined but has increased dramatically, which is consistent with analyses of licensing
income (Markmann, Phan, Balkin, 2005; Ali and Gittelman, 2016). The other two sectors have
experienced declines. The fraction for medical instruments has gone up and down several times

14. 14
over the decades ending slightly lower in 2015 (7%) than in 1991 (10%). The fraction of IPOs
for electronics has dropped the most, falling from a high of 15% in 2001 during the Internet
bubble to a low of 2% in 2015.
For the medium scientifically intensive industries, there has also been declines (See Figure
2). The fraction for Manufactured Products and Machinery dropped from a high of 15% in
1992 to 1% in 2015. The fraction for Internet Infrastructure dropped from a high of 24% in
2000 to 12% in 2015, albeit the 2000 figure represents an increase from 11% in 1990. If we
consider all industries other than biotech, the percentages have dropped from 43% in 2000 to
22% in 2015, a drop of almost half.
Figure 3 shows the fraction of PhDs for top managers within the science intensive
industries over time. Except for a brief rise following the Internet bubble, the fractions for all
these industries declined between 1990 and 2010, suggesting that the contribution of basic
science to entrepreneurial firms in science-intensive industries also fell. The decline is
particularly large between 2000 and 2010 when the reporting rates for these industries were
very constant (see supplementary file). As shown in the regression results (not included yet),
the percentage of PhDs fell by between 20% and 80% for these industries with the biggest drop
for biotech and the smallest drop in electronics.
4.2 Evidence of Decline in VC Financing
We now consider venture capital financing. Because this financing occurs about 5 to 10
years before a specific IPO occurs, we can say that VC financing is more of a leading indicator
than are the percentages for IPOs. Figure 4 shows the fraction of VC financing for science-
based industries. The data is for two-year averages with the data point shown for the second
year. Other than biotech, all of them have experienced dramatic declines. Medical Instruments
fell from a peak of 8% in 2002/2003 to a low of 4% in 2016/2017. Electronics, including
semiconductors, general instruments, and electronic equipment, fell from a high of 8% in

15. 15
2002/2003 to a low of 1% in 2016/2017. Communication equipment fell from a high of 7% in
2002/2003 to a low of less than 1% in 2016/2017. Biotech has done much better rising from
14% in 2002/2003 to 23% in 2008/2009 before falling to 9% in 2016/2017. Combining all of
them except biotech, these percentages have fallen from 23% in 2002/2003 to 6% in 2016/2017.
The decline may be even more pronounced if older data are used. Data from the National
Science Foundation’s Science and Engineering Indicators (NSF, 2008) shows that
semiconductors and communication equipment received about 20% of VC financing in the
1980s and 1990s with a peak in the late 1990s of 25%, or substantially more than the 1%
reported for 2016/2017 by PwC Moneytree. Furthermore, going back even further might find
even higher percentages for semiconductors and communications equipment given the roots of
the name Silicon Valley, the world’s most famous region for startups. Anecdotal evidence
suggests that some of these technologies were commercialized by large numbers of PhDs. For
example, six of the traitorous eight who left Shockley to form Fairchild in 1957 had PhDs, and
this was before the rapid growth in PhD programs in the 1960s and 1970s. Subsequent hires by
Fairchild also included many PhDs including Andy Grove who later founded Intel with Robert
Noyce and Gordon Moore, both with PhDs. At least 65 companies can be traced to Fairchild
employees (Nuttal, 2007; Wikipedia, 2018).
Figure 5 shows the absolute values for VC financing of science-based technologies, none
of which have been adjusted for inflation. Here a decline is less obvious because part of the
decline in Figure 3 is from the increased financing of non-science-based technologies such as
Internet commerce, content, and services, including mobile apps such as Uber and Airbnb.
Nevertheless, a decline in the absolute levels of financing can also be seen for electronics
(semiconductors and general instruments) and communications in Figure 5. VC financing for
electronics and communications dropped from about $366 and $300 million respectively in
2003 to about $250 million and $91 million respectively in 2017.
We now consider new types of science-based industries that have received attention over

16. 16
recent decades such as superconductors, nanotechnology, GMOs, and solar cells. For IPOs,
there were five IPOs concerning solar between 1990 and 2015 of which only one was for
manufacturing. There were three for superconductors of which one was in 1991 and two were
in 1993. There were five for bio-fuels of which three were in 2006, one was in 2011, and two
were in 2012. There were two for GMOs, one in 2013 and one in 2015. For nanotechnology,
only one company associated with nanotechnology was found and its IPO was in 1997. None
of these anecdotes suggests that new science-based industries are emerging and instead they
are consistent with a decline in the commercialization of science-based technologies (outside
of biotech).
Venture capital financing data from Price Waterhouse Moneytree suggests the same. Price
Waterhouse’s database does not include a sub-category for any of these technologies and the
categories closest to these technologies also had little venture capital financing. Of the $75
billion of total VC financing in 2017, only $39 million went to crop production and $13 million
to animal production, which are the two closest technologies to GMOs. Renewables received
$540 million in 2017 vs. $3.1 billion in 2008 of which the percentage for new types of solar
cells (e.g., other than silicon), bio-fuels, or other types of science-intensive renewables is
unknown, but probably close to zero. Basic materials in the industrials category received $450
in 2017 of which the percentage for nanotechnology or other types of science-intensive new
materials is also unknown, but probably also close to zero. In summary, VC financing data does
not suggest that new science-based are emerging.
4.3 Increasing Age of Top Managers
We now consider other data in the database of IPOs that might suggest reasons for a
decline in science-based IPOs or VC funding of science-based technologies. We begin with
changes in the age of executives and directors at IPO filing time because research on patents
found that the average age of inventors in patent applications has risen, along with highest

17. 17
educational degree. Figure 6 shows that the average age has risen by about six years for the
scientifically intensive industries - life sciences and electronics – while it has remained constant
for Internet infrastructure, even as the percent of top managers with PhDs fell during the same
period. This suggests that the importance of experiential knowledge has increased for life
sciences and electronics even as the importance of educational knowledge has decreased.
Figure 7 explores one driver of increasing age. It shows that about half of the increase in
age for the life sciences and electronics is from an increasing time lag between the year of
founding and the year of the IPO for life sciences and electronics. Both saw increases of about
three years with the life sciences starting from about a two-year shorter lag meaning that
startups in the life sciences for the IPOs are being founded at an earlier age for top the top
managers than are electronics firms. Overall, however, the results are the same; increasing age
at IPO time even while the need for a PhD declines means that the relevance of advanced
degrees such as PhDs are declining.
4.4. Universities that Trained the PhDs
Figure 8 plots the number of graduates*IPOs vs. 1991 research expenditures for 43 U.S.
universities that are in the top 30 for either graduates*IPOs or 1991 research expenditures. This
figure shows there are not constant returns to scale in university research, and certainly not
increasing returns to scale, such as those that exist in corporate research. Unlike corporate
research for which there are greater returns for corporations that spend more on R&D (Knott,
2016), universities with higher research expenditures do not have higher numbers of PhD
graduates in IPOs per research dollar (or even in absolute terms), thus suggesting that university
research is far less effective than is corporate research, where more funding does lead to more
revenues.
The outliers may represent the most interesting part of Figure 8. Four universities have far
more PhDs in the IPOs than do the others. Stanford has 163, MIT has 151, UC Berkeley has

18. 18
119, and Harvard has 97 even though these four do not have the highest research expenditures,
ranking 6th
, 5th
, 13th
, and 16th
respectively. Their IPOs represented about 20% of total IPOs
while their research expenditures represented about 5% of total research expenditures. On the
other hand, Johns Hopkins was ranked 38th
for number of graduates*IPOs with 17 PhDs in
IPOs while ranked first for research expenditures with $710 million in 1991 or more than twice
that of Stanford, MIT, UC Berkeley or Harvard. Texas A&M was ranked 95th
for number of
graduates*IPOs with six PhDs in IPOs while being ranked 8th
for research expenditures with
$289. Similar numbers could be cited for other years and other outlier universities, but one
conclusion from Figure 8 is clear; some universities are much better at doing research whose
results are can be commercialized and at training PhDs for doing this than are other universities.
These top performing universities have also not changed over time, in spite of changes in
the distribution of research expenditures among universities. Because the numbers of IPOs for
universities other than the top four are too small to analyze over time, Figure 9 focuses on the
fractions of PhDs represented by the top 5, 10, and 25 universities. These top 5, 10, and 25 are
determined for all the years and not for each year meaning that Stanford, MIT, UC Berkeley,
and Harvard are in the top 5 for Figure 9, along with number five, Cal Tech. Figure 9 shows
that the fractions were fairly constant between 1990 and 2010 with an average of 22%, 31%
and 48% for the top 5, 10, and 25 respectively. The constant percentages suggest that the
distribution of universities among the PhDs in IPOs has not changed over time and thus a
changing distribution is probably not a reason for a decline in science-based IPOs or VC
funding of science-based technologies.
No changes in the distribution is surprising for many reasons. An emphasis on research
began spreading to second tier universities in the 1970s (Bok, 2015) and thus greater numbers
of PhDs from these universities in startups should have emerged by now. The Internet was also
expected to increased output from second-tier universities because it increases academic
discourse between them (Agrawal and Goldfarb, 2008). The distribution of research

19. 19
expenditures among universities has also changed with some universities rising in the rankings
for research expenditures. For example, for purposes of illustration, Duke University rose from
28th to 14th
, University of North Carolina from 37th to 21st
, and University of Pittsburgh from
43rd to 13th
in terms of research expenditures between 1991 and 2005 and yet they did not
experience any increase in PhDs for IPOs. Nor did they enter the top 40 for graduates*IPOs
having 12, 15, and 12 respectively and nor did the fraction of the top 5, 10, or 25 change in
Figure 8. This provides further evidence that some universities are much better at both doing
research whose results are can be commercialized and at training PhDs for doing this than are
other universities.
5. Interpretation
The results presented in the previous section suggest that the number of science-based
technologies being commercialized is declining, with the exception of biotechnology. Outside
of biotechnology, the fraction of science-intensive industries among IPOs and VC financings
have dropped over the last few decades, as has the fraction of PhDs among executives and
directors within these science-intensive industries, including biotechnology. These are
dangerous trends because these industries have driven Moore’s Law and similar improvements
in Internet speed, and together Moore’s Law and improvements in Internet speed have driven
new waves of hardware and Internet commerce, content, and services (Funk, 2018), and they
are expected to transform health care. A decline in the commercialization of science-based
industries will likely prevent many of these changes from occurring and thus understanding
why fewer science-based technologies are being commercialized is a crucial question for
academics and policy makers.
Because we are using startup activity to measure the activities of science intensive
industries, we cannot blame the short-sightedness of large corporations, which is the usual
reason given for any decline in activities with long pay-back times such as basic research or

20. 20
the commercialization of science-based technologies. To claim that short-sightedness of
startups is the source of the decline, we must identify changes in the behavior of venture capital,
entrepreneurs, IPOs, and other aspects of the startup process. For example, is the process
becoming more risk averse? Given the increases in VC funding overall over the last 15 years
and the continued funding and low profitability of biotechnology (Ernst & Young, 2016),
greater risk averseness is unlikely, so we must search for other reasons.
We first address the decline in the fraction of PhDs among executives and directors within
science-intensive industries, even as average age, i.e., experiential knowledge, has risen. The
former suggests that these sectors have become less scientifically intensive over time, a
phenomenon that may occur in most sectors and industries. After all, life cycle analyses
(Klepper, 1996) find that the number of opportunities decline over time and thus it is not
surprising to find that the opportunities for new scientific advances also decline as an industry
matures. The increasing age of executives and directors and thus experiential knowledge may
also be from increasing difficulties with finding new opportunities, which is consistent with
both life cycle models and other research that finds rising development costs for new drugs
(Scannel et al, 2012) and integrated circuits (Bloom et al, 2017) and falling marginal revenues
from corporate research spending in an individual industry (Knott, 2016).
The drop in the fraction of science-intensive industries among IPOs and VC financings is
harder to explain, as is the lack of new technologies among the IPOs and VC financings. The
last section mentioned superconductors, nanotechnology, GMOs, and solar cells as examples
of new science-based technologies that have not experienced growth. But the real question is
why any new type of new science-based technology has not recently emerged to the extent that
semiconductors did for example after the 1950s. The lack of new technologies is truly
astounding when we consider the long list of science-based technologies that emerged in the
early to mid-20th
century and that were mentioned in the early part of this paper. Clearly
something unusual is happening, such as a falling output of basic and applied research from

21. 21
universities and corporations (Odumosu and Narayanamurti, 2016).
Figures 8 and 9 suggest that ineffective university research is one reason for the decline.
Although neither figure shows the types of temporal changes that may have led to fewer
science-based technologies, the diseconomies of scale evident in Figure 8 does suggest that
university research is much less effective than is corporate research and this suggests that the
large increase in government funding of university R&D in the 1960s and 1970s probably did
not lead to large increases in innovative activity. By moving research and its associated talent
from corporations to universities, it may have caused fewer science-based technologies to be
developed and later commercialized. Furthermore, this movement of resources from
corporations to universities and the resulting drop in research productivity may also be
impacting corporations, causing less publishing by corporate researchers in basic research
journals (Arora et al, 2015) and falling revenues from corporate R&D (Knott, 2016).
Why might university research be less effective than corporate research and why might
the effectiveness of this university research be falling. We speculate about three major trends,
all impacting on the basic routines of university researchers (Nelson and Winter, 1982). First,
there appears to be fewer interactions between basic and applied researchers than there were in
the past, partly because basic research has moved from large corporations to universities over
the last 70 years. This move has increased the barriers between basic and applied research as
university researchers and funding agencies actively discourage applied research and thus the
construction of prototypes and other devices that are necessary for commercializing new
technologies and that were often built by basic researchers at industrial labs such as Bell Labs,
GE, and RCAin the past (Odumosu and Narayanamurti, 2016). This move has also discouraged
the exchanges of tacit knowledge that are so necessary for commercializing new technologies
(Salter and Martin, 2001), and for professors and PhD students to understand the needs of the
private sector. For example, analyses of five University of California campuses, perhaps the
most collaborative of all public university systems, focused on examples mostly from the 1970s

22. 22
and 1980s (Kenney and Martin, 2014) thus begging the question what has been happening since
1990 or 2000?
One reason protypes, applied research, and collaboration between university and
corporate researchers is probably decreasing is that the pressure to publish papers, which is a
second big trend over the last 50 years in universities, has been pushing university professors
away from collaborative work with companies and towards work with PhDs students and other
university professors. From the 1970s, emphasis on research began to intensify leading to large
increases in PhDs programs (Bok, 2015). PhD students focus on reading academic papers,
summarizing them in literature reviews, and then reporting the research in academic papers.
Not only do these activities distract PhD students and professors from work with companies,
they also may not provide graduates with the skills they need to find and commercialize new
ideas as entrepreneurs, or even as researchers in large corporations. This may also be why the
educational premium of PhDs over M.S. recipients is not very high and may have declined
(Economist, 2010). Evidence of falling relevance can be seen in the downloading of papers by
corporate authored papers. Elsevier reports that the ratio of corporate vs. university-authored
papers downloaded by corporate researchers is about 0.62 even though university researchers
publish about 10 times more papers than do corporate researchers (Stephan and Ehrenberg,
2007), and the number written by corporate researchers is falling (Arora et al, 2015).
How might corporate-authored papers be more relevant and practical than are academic-
authored papers? One example can be found in an analysis of cost and performance
improvements for new technologies before they were commercialized (Funk and Magee, 2015).
These improvements often depend on the creation of new materials in for example LEDs and
reductions in the scale of features in for example new types of integrated circuits. Although the
ability to do both depends on the state of science, we can speculate that corporate researchers
might focus more on improvements than on scientific explanations and for academic
researchers, the reverse is probably true. This could be one reason corporate-authored papers

23. 23
might be considered more practical than academic-authored papers.
A third and related trend is that the increases in government funding has brought large
administrative burdens to universities that are less prevalent in corporations and these burdens
have steadily increased (Ginsburg, 2013). These burdens include writing proposals and
progress reports, managing PhD students, and writing recommendation letters for the students
(Ness, 2014), activities that distract university researchers from careful thinking about
breakthrough ideas. Evidence for this hypothesis can be seen in the doubling of submitted
proposals to the National Institute of Health between 1997 to 2011 (Howard and Laird, 2013)
even as real funding barely increased (Boadi, 2014), the increasing numbers of authors on
academic papers and patents (Jones, 2009), and the diseconomies of scale in university research
shown in Figure 8. Focusing on the number of authors, perhaps this is one result of greater
numbers of PhD students and not increasing technological complexity, and the increasing
number of PhD students and authors represent increasing administrative burdens, a rising
burden that could be reduced through changes to America’s R&D system. It could also be that
the Bayh-Dole Act inadvertently increased the administrative burden of universities through
encouraging more patents and their associated paper work even as some evidence suggests it
decreased the commercialization of university research by large corporations (Knott, 2016).
To further understand these three trends, it is also useful to consider the four universities
with the largest number of graduates*IPOs in Figure 8 - Stanford, MIT, UC Berkeley and
Harvard. Why have these universities been able to train more PhDs for successful startups than
have other universities, even as they faced many of the same pressures as other universities?
Although our data set on IPOs only goes back to 1990, it is likely that these four universities
have been training many PhDs for executives and directors in startups since the 1950s (or
earlier), when Silicon Valley and its less famous Massachusetts rival, Route 128, began to
create startups on a large scale. Their early success with startups and incumbents may have
involved routines (Nelson and Winter, 1982) that have continued to flourish.

24. 24
These routines probably go beyond opening technology transfer or licensing offices,
creating public relation departments, incubators or Washington DC offices, or hiring proposal
writers or startup consultants, activities that most research universities pursue and that may
distract them from more pursuing more useful routines. The truly useful routines probably
involve problem solving in all phases of R&D including basic and applied research, monitoring
new technologies and their products and services, collaboration with incumbents and VCs, and
choosing and pursuing research that can be commercialized. Because the latter routines are
harder to implement than the former ones, the expansion of university research funding in the
1960s and 1970s may not have brought these useful routines to a broader set of universities or
even to the National Science Foundation or National Institute of Health that were providing the
funding. Routines associated with the mission-based approach of the Department of Defense
(Sarewitz, 2016b), which is often mentioned in connection with the development of transistors,
integrated circuits, lasers, and fiber optics in the 1950s and 1960s, were probably not adopted
by NSF or the NIH.
How might Stanford, MIT, UC Berkeley and Harvard have learned useful routines? We
speculate that these routines were borrowed from the research environment that existed in
corporate laboratories in the 1950s and 1960s (or earlier), at the same time as startups began to
flourish in Silicon Valley and Route 128 (Etzkowitz 2002; Gibbons 2000; Saxenian 1994). The
research environment of the 1950s and 1960s was defined by large corporate laboratories such
as AT&T, GE, RCA, IBM, and Motorola that did the basic research for transistors, integrated
circuits, lasers, LEDs, and displays often in collaboration with Stanford, MIT, UC Berkeley
and Harvard. Some accounts claim these laboratories emphasized ideas, prototypes, and
practicality much more than papers (Odumosu and Narayanamurti, 2016) and they often hired
engineers with practical backgrounds in farming, ranching and industry (Gertner, 2013).
Researchers had little administrative work because they were not required to train PhDs and
thus had more time for the deep thinking necessary to create and pursue new scientific

25. 25
explanations and breakthrough ideas. Stanford, MIT, UC Berkeley and Harvard may have
copied these routines as they expanded their research activities in the 1950s and 1960s.
The university research environment probably changed as government funding expanded
in the 1960s and 1970s thus pushing universities towards the routines of proposal writing,
publishing papers, and expanding PhD programs, all of which expanded administrative work.
Ironically, these new routines may have also made it more difficult for universities to do the
basic research that the funding was intended to encourage. Some have argued that a greater
emphasis on publishing papers has increased quantity at the expense of quality (Sarewitz,
2016a) and has also increased administrative work, specialization (Jones, 2009), and insularity,
thus making it harder for researchers to develop new ground-breaking scientific explanations
(Ness, 2014). After all, most leading researchers now spend large percentages of their time
managing graduate students, obtaining research funding, and helping their graduate students
find jobs, all of which reduces the amount of time for the careful thinking needed to find and
exploit new scientific explanations.
6. Discussion
This paper began with a discussion of the literature on science and technology, and what
these analyses reveal about slowing productivity growth. Patent analyses have become the
primary research methodology used to investigate these issues, but patents as a measure for
innovation (Griliches, 1990; Roach and Cohen, 2013) and papers as a measure for advances in
science (Nelson 2009) have been criticized by many.
This paper introduces a new approach for evaluating the scientific intensity of
technologies and industries. It uses the number of PhD and MS degrees among top managers
of IPOs as a measure of the scientific intensity of technologies/industries and based on this
measure, it finds different results from those found with patent analyses. The first big difference
with patent analysis is the scientific intensity of industries is falling. By showing the declining

26. 26
percentage of PhDs in science-intensive industries and the declining percentage of these
industries in IPOs and VC financing overall, this paper suggests that the science intensity of
America’s economy has fallen and with it the need for companies to access university
knowledge through academic papers and thus build absorptive capacity (Cohen and Levinthal,
1989, 1990; Griffith et al, 2004; Aghion and Jaravel, 2015). This conclusion is consistent with
falling corporate (Knott, 2016) and sector specific research productivity (Scannel et al, 2012;
Bloom et al, 2017), and the falling scientific intensity is also a likely reason for slowing
productivity growth.
A second big difference can be found in the analysis of age, the role of age in obtaining
knowledge, and in the interplay between educational and experiential experience. Patent
analyses find that both educational and experiential experience analyses are increasing over
time (Jones, 2009), suggesting that complexity is increasing and that more education is needed.
This paper finds that the importance of experiential experience is rising even while educational
experience is falling. Consistent with other interpretations (Knott, 2016), entrepreneurs are
spending more years both searching for opportunities and making them financially viable, thus
suggesting education is becoming less relevant. This means that ambitious people should be
careful about returning to school particularly for a PhD, that governments should think
carefully about encouraging their return through subsidies or research funding, and that the
falling relevance of education may be a reason for slowing productivity growth.
A third big difference with patent analysis is the diseconomies of scale in university
research. Unlike corporate research (Knott, 2016), increases in university research
expenditures are not leading to greater numbers of PhDs among startup top managers per
research dollar, or even greater numbers of PhDs among top managers in the startups, and this
could be another reason for slowing productivity growth. The expansion in university research
during the 1960s and 1970s may have diverted resource from corporations to universities and
thus reduced the overall research productivity of the U.S. economy. Possible reasons for the

27. 27
lower output of university than corporate research are greater administrative burdens, greater
emphases on papers, and less collaborative work with customers, suppliers, and manufacturing
facilities, and all three problems seem to be growing.
In summary, this paper provides a very different perspective on the productivity
slowdown than do patent analyses. While patent analyses point to a greater need for education,
this paper’s analyses point to a falling need for education because it is becoming less relevant.
The scientific intensity of the U.S. economy is falling because PhD research is becoming less
relevant and the solution is to rethink current approaches to university research including the
need for large PhD programs, their emphasis on academic papers, and their weakening ties with
the private sector.

28. 28
Table 1. Number of Top Managers by Highest Educational Degree Attained and Year of Filing
Year
Total
Number
Education
Known PhD MD JD MS
MB
A MA BS BA
%
PhD
1990 421 113 24 3 6 19 27 3 22 9 21%
1991 1110 256 57 21 21 0 45 15 47 31 22%
1992 1494 265 68 15 15 25 51 8 59 24 26%
1993 2504 528 86 9 31 65 103 14 135 85 16%
1994 2751 637 105 29 47 50 121 30 146 109 16%
1995 3472 947 120 27 56 129 218 33 234 130 13%
1996 5579 2312 362 84 161 229 626 68 440 339 16%
1997 3843 1815 237 51 134 177 480 49 418 262 13%
1998 2475 1160 90 14 91 138 316 34 290 183 7.8%
1999 4820 3193 214 35 251 345 998 107 706 537 6.7%
2000 3590 2753 437 93 170 365 782 56 524 323 16%
2001 762 420 59 17 33 47 128 16 63 56 14%
2002 614 242 34 6 24 14 82 4 38 40 14%
2003 684 430 59 15 40 33 134 16 69 63 14%
2004 1666 1113 155 75 88 109 342 27 175 141 14%
2005 1666 853 65 39 85 73 261 24 180 126 7.6%
2006 1647 1013 120 54 98 64 328 23 146 179 12%
2007 1169 912 112 48 79 92 272 23 170 112 12%
2008 130 110 3 8 11 8 34 3 19 24 2.7%
2009 197 161 13 11 15 13 49 3 31 25 8.1%
2010 631 469 65 21 48 50 126 11 81 0 14%
Total 41225 19702 2485 675 1504 2045 5523 567 3993 2798 21%
Percentages 48% 13% 3.4% 7.6% 10% 28% 2.9% 20% 14%

29. 29
Table 2. Industries Ranked by Percentage of Top Managers with PhD Degrees
Percentage
SIC Codes Industry Name
Numbers
PhD PhD, MS PhDs MS
35% 41% 2830-2839 Biotechnology 791 289
33% 40% 8200-8299, 8730-8739 Education & Research 346 72
24% 38% 3820-3829 General Instruments 104 61
18% 41% 3674 Semiconductors 158 189
15% 31% 3600-3659,3670-3673, 3675-3699 Electronic Equipment 79 87
13% 24% 3840-3849 Medical Instruments 159 141
13% 23% AVERAGE FOR ALL INDUSTRIES 2485 2045
13% 17% 0-999 Agriculture 6 2
11% 32% 3660-3669 Communications 86 159
11% 25% 3500-3569,3580-3599, 3700-3799 Machinery 32 38
11%
17% 1800-1999,3830-3839, 6600-6711,
6740-6789, 6800-6999,7300-7309,
7390-7499 Other 10 5
10%
16% 7500-7999,8100-8199,
8200-8299,8300-8729 Services 72 42
10%
20% 2200-2829,2840-3499,
3800-3819,3850-3999 Manufactured Goods 74 64
8.9% 21% 7371 Computer Programming 51 72
8.9% 14% 8000-8099 Health Services 35 24
8.4% 29% 3570-3579 Computers 50 123
7.8% 21% 7373 Computer Systems 34 55
6.6% 17% 1500-1799 Construction 5 8
6.3% 20% 7372 Software 136 291
6.0% 12% 5000-5199 Wholesale Trade 23 24
5.2% 15% 4800-4829 Telephone & Telegraph 27 50
5.1% 18% 7370, 7374,7376-7379 Computer Services 43 106
5.0% 14% 7375 Information Retrieval 26 44
4.7% 8.4% 6000-6199 Finance 20 16
4.2%
8.8%
6200-6599
Securities Insurance and
Real Estate 28 30
4.1% 13% 7320-7329,7340-7349,7380-7389 Business Services 35 73
3.6% 3.6% 2000-2199 Food and Tobacco 5 0

30. 30
3.1% 14% 1000-1499 Oil Gas and Mining 10 34
2.2%
7.6%
7310-7319,7330-7339, 7350-7369
Advertising, Employ.
and Leasing 9 22
2.1% 4.2% 6719-6725,6790-6797,6799 Holding and Investment 1 1
1.5% 1.2% 4830-4899 Broadcasting & Services 3 18
1.4% 6.6% 4700-4799 Transportation Services 3 11
2.5% 7.3% 5200-5999 Retail Trade 20 39
0.0%
1.5%
4900-4999
Electricity Gas and
Sanitation 0 18
Table 3. Number and Percent of IPOs for Scientifically Intensive Industries
Industry Name Percent of Top Managers Number and Percent of IPOs
PhD PhD, MS Number Percent
Biotechnology 35% 41% 392 9.4%
Education & Research 33% 40% 123 3.0%
General Instruments 24% 38% 62 1.5%
Semiconductors 18% 41% 113 2.7%
Electronic Equipment 15% 31% 122 2.9%
Medical Instruments 13% 24% 198 4.8%
Communications 11% 32% 122 2.9%
Machinery 11% 25% 98 2.4%
Manufactured Goods 10% 19% 267 6.4%
Computer Programming 8.9% 21% 88 2.1%
Computers 8.4% 29% 132 3.2%
Computer Systems 7.8% 21% 88 2.1%
Software 6.3% 20% 387 9.3%

31. 31
0%
10%
20%
30%
40%
1990 1995 2000 2005 2010 2015
Green Medical Instruments;
Red: Electronics; Blue: Biotech;
Figure 1. Percentages of Highly Scientificaly
Intense Industries in IPOs
0%
10%
20%
30%
40%
1990 1995 2000 2005 2010 2015
Black: Internet Infrastructure
Orange: Manufactured Prod & Machinery
Figure 2. Percentages of Medium Scientificaly Intense
Industries in IPOs

32. 32
0%
10%
20%
30%
40%
50%
1990 1995 2000 2005 2010
Figure 3. Percentages of Top Managers with PhDs
Biotech: Blue
Electronics: Black
Medical Instruments: Red
Manufactured Products and Machinery: Green
Internet Infrastructure: Orange
0%
4%
8%
12%
16%
20%
24%
2002 2006 2010 2014 2018
Electronics, including
semiconductors,
general instruments
Communication
Equipment
Medical
Instruments
Biotech
Figure 4. Declining VC Investments in Science-Based Industries

33. 33
0
500
1000
1500
2000
2003 2005 2007 2009 2011 2013 2015 2017
Medical Instruments
Biotech
Figure 5. VC Financing (M$) of Science-
Based Technologies (Two-Year Averages)
Semiconductors
Communications
40
44
48
52
56
1990 1995 2000 2005 2010
Figure 6. Increasing Age at IPOs (3-Year Moving Averages)
Life Sciences: Blue
Electronics: Orange
Internet Infra: Green

34. 34
4
8
12
16
1990 1995 2000 2005 2010 2015
Life Sciences: Blue
Electronics: Red
Internet Infrastructure: Black
Figure 7. Increasing Time Lag Between Founding and IPO
0
40
80
120
160
0 200 400 600 800
Figure 8. IPOs*Graduates vs. 1991 Research Expenditures ($M)

35. 35
0%
20%
40%
60%
1990 1995 2000 2005 2010
Figure 9: Share of IPOs for Top 5, 10, 25 Universities
(3-Year Moving Averages)

36. 36
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