Importance of Science, by Industry

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Educational and Experiential Knowledge in Entrepreneurial Firms:
Why are there differences between industries?
By
Jeffrey Funk
Martin Kenney
Donald Patton

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Educational and Experiential knowledge in Entrepreneurial Firms:
Why are there differences between industries?
Abstract
This paper addresses the types of knowledge that are needed in entrepreneurial firms
using a unique data base of executives and directors for all IPOs filed between 1990 and 2010.
Using highest educational degrees as a proxy for educational knowledge, it shows that 85% of
those with PhDs are concentrated in the life sciences and ICT (information and communication
technology) industries and second, that those in the ICT industries are concentrated at lower
layers in a “digital stack” of industries, ranging from semiconductors and other electronics at
the bottom layer to computing and Internet infrastructure at the middle layer and Internet
content, commerce, and services in the top layer. Third, industries with fewer PhDs have more
bachelor’s and MBA degrees suggesting that PhDs are being replaced by them and not M.S.
degrees. Fourth, age is higher for industries with the most PhDs thus suggesting a greater need
for experiential knowledge in industries with greater needs for educational knowledge. Fifth,
the number of Nobel Prizes tracks industries with high fractions of PhDs.
Keywords: innovation, startups, technology, science, PhDs, founders, Silicon Valley
JEL Codes: O31, O32, O33

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1. Introduction
How does knowledge impact on entrepreneurship, new technologies, and economic
growth? Early work emphasized case-based analyses of firms and countries (Rosenberg, 1974;
Mowery and Rosenberg, 1998, Mokyr, 2002), and analyses of R&D, productivity and survey
data to understand for example, the role of absorptive capacity (Cohen and Levinthal, 1989,
1990; Griffith et al, 2004; Aghion and Jaravel, 2015). Since the 1990s, analyses of knowledge
have focused on patents as a measure of innovation and academic papers as a measure of
knowledge (Narin and Noma, 1985; Narin et al, 1995, 1997). They have analyzed various types
of knowledge transfer such as the co-authoring of papers between public and corporate
researchers (Cockburn and Henderson, 1998), 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), and the temporal lags between scientific
papers and patents (Ahmadpoor and Jones, 2017).
This paper uses a different approach. It examines successful startups, which are a better
proxy for innovative products and services, and thus productivity improvements, than are
patents. Successful startups introduce novel products and services that impact on productivity
growth by providing new forms of value and lower costs for users (Solow, 1956, Gordon, 2016).
Although incumbents can also introduce novel products and services, startups have been
remarkably successful at introducing them, particularly in the U.S. where innovation has been
dominated by startups over the last 60 years.
This paper uses a unique database of successful startups to investigate the role of science
and other forms of knowledge in startups. This database includes the highest degree obtained
by top managers in startups, the disciplines of these degrees, the ages of the managers, and the
differences in these degrees, disciplines, and ages across industries. In doing so, it replaces
patents with startups as the measure of output, and papers with educational attainment,

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discipline, and age for top managers of startups as measures of knowledge. Educational
attainment is used as a measure of scientific knowledge and age as a measure of experiential
knowledge. The large data base (50,000 top managers in 5,000 startups) reveals large
differences between industries, reaching different conclusions about knowledge than do patent
analyses, finding that the impact of scientific knowledge is very dependent on industry. Some
industries require more PhD and M.S. degrees, particularly more science degrees, and they also
have more Nobel Prizes than do other industries all of which suggests they have higher
scientific intensities and thus require more absorptive capacity in incumbents than do other
industries. We also find that the science-intensive industries have higher average ages for the
top managers than do the non-science intensive industries, suggesting that education is a poor
substitute for experience.
The paper proceeds as follows. The literature review examines the different mechanisms
by which knowledge impacts on entrepreneurial activity. This includes the differences between
science and technology, the differences between educational and experiential knowledge, and
the technical structure of the ICT (information and communications technology) industries, i.e.,
a vertical stack of products and services. In the latter, lower layers involve science-intensive
industries such as semiconductors and fiber optics and higher layers involve sectors such as
Internet content, e-commerce, and online services. Second, the methods section describes the
data collection for the educational degrees, scientific disciplines, ages of startup executives and
directors, and Nobel Prizes. Third, the data analysis results are presented. Fourth, we explain
the results using theories of technological change, and fifth we explore the implications of the
above summarized research.
2. Literature Review
Advances in knowledge are an important part of entrepreneurship and economic growth
but identifying the sources of this knowledge and measuring these knowledge flows and their

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impact on economic growth are difficult (Rosenberg, 1974; Mowery and Rosenberg, 1998;
Mokyr, 2002). As noted in the introduction, patent analyses have become the primary research
methodology used to investigate knowledge and innovation in general. Since the earliest
studies of patents, 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).
Although many of these analyses focus on biotechnology (Fleming, 2001; Fleming and
Sorenson, 2001; Kotha, et al, 2013) and, to a lesser extent, semiconductors (Hall and Ziedonis,
2001) due to the central role of science in them (Pisano, 2006, Lim, 2004), recent analyses of
patents and academic papers have examined a broader set of industries and academic
disciplines respectively.
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 age are independent of industry, and that educational
attainment and other characteristics (age, specialization, and team size) of the innovators are
increasing over time. 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
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

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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, new knowledge is important for all industries
(Ahmadpoor and Jones, 2017), academic articles are an important method of knowledge
generation and transfer (Jones, 2009), and the Internet enables more and better academic
discourse (Agrawal and Goldfarb, 2008). 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 university research and absorptive capacity are increasing. Third, documented reductions in
corporate R&D spending cannot be due to falling scientific intensity because patent analyses
show strong linkages between patents and papers (Arora et al, 2015). Fourth, educational
attainment, age, and specialization (Jones, 2009) are rising in all industries, as are social skills
(Deming, 2017).
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).
Academic articles, as a measurement device, also have drawbacks and certainly are a 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).

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This paper introduces a different approach to understanding the types of knowledge that
are necessary for entrepreneurship, new products and services, and thus productivity
improvements. This approach builds from the important distinction between science and
technology (Dosi, 1982), the impact of this distinction on different industries, and the
differences between educational and experiential knowledge. Advances in science refer to new
explanations of physical and artificial phenomena while the latter refers to artefacts, techniques,
and designs. Some industries (e.g., biotechnology and semiconductors) benefit more from
advances in science than do other industries (Lim, 2004), while other industries benefit far
more from new techniques and designs and thus require less of the extreme specialization that
characterizes individuals holding a PhD.
Advances in science are important because they often form the basis of new product and
service concepts (Balconi et al, 2012; Arthur, 2007, 2009) and universities make many of these
advances and report them in academic journals (Colyvas et al, 2002). Scientific advances
illuminate the mechanisms by which biological, physical, and artificial phenomena operate and
thus facilitate the development of products that are based on such mechanisms. For example,
new drugs may be developed through research on the mechanisms by which diseases begin and
spread, how drugs act on the diseases, and the method of synthesizing drugs (Pisano, 2006).
Understanding these mechanisms may be built upon decades of basic and applied research and
may involve large numbers of experiments of which the most important result in the awarding
of a Nobel Prize in Physiology or Medicine, which is the ultimate recognition for advances in
science. At least 14 of these prizes, often shared among multiple researchers, had contributed
to biotechnology by 2005 (The New Scientist, 2005)
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,

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photovoltaics, light modulation, optical loss in glass fiber, giant magneto resistance, and
information theory formed the basis for new product families 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. Thousands of experiments contributed to the explanation of the mechanisms for
these effects and the most important discoveries resulted in Nobel Prizes for the scientists
involved. To illustrate, at least two prizes since 1950 involved transistors and ICs (1956, 2009),
magnetic storage (1970, 2007), and LCDs (1991, 2000), and others involved lasers (2000),
CCD sensors (2009), fiber optics (2009), and LEDs (2014).
Advances in science also enable improvements in the performance and cost of new
technologies, 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
organic LEDs (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). At least one of the Nobel Prizes mentioned in the previous paragraph
involved the creation of new materials (blue LEDs) and other Nobel Prizes also involved the
creation of new materials (e.g., high temperature superconductors).
Sometimes, the causality runs in the opposite direction. Improvements in instruments such
as telescopes, microscopes, DNA sequencers, and nuclear magnetic resonance facilitated
scientific advances. Examples of instruments for which Nobel Prizes were recently received
include the LIGO detector in Physics and the cryo-electron microscope in Chemistry, both in
2017, fluorescence microscopy in 2014, and mass spectrometric analysis of macromolecules

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in 2002. Not surprisingly in each of these cases, new technological devices were required to
make the scientific discovery (von Hippel 1976).
Advances in science may be less important in other industries such as computers, software,
and Internet content, services, and commerce, where improvements in technologies such as
integrated circuits (e.g., Moore’s Law), disk drives, lasers, and many of the artefacts that make
up a computer or the Internet made many of them possible. Sometimes called general purpose
technologies (David, 1990; Bresnahan and Trajtenberg, 1995; Lipsey et al, 2006), these
technologies have had a large impact on the economics of computers (Cortada, 2003, 2005),
and economic growth (Oliner and Sichel, 2002; Olner, Sichel and Stiroh, 2007; Jorgensen et al,
200). For example, in many of the industries that have semiconductors as one of their key GPTs,
PhD-holding founders were unnecessary – in these the semiconductor was simply a modular
input (Funk, 2018). This also suggests that educational knowledge might be less important than
is experiential knowledge for computers, software, and Internet content, services, and
commerce
This is not to say that universities have not contributed towards the commercialization of
new technologies (Rosenberg, 1974; 1992; Rosenberg and Nelson, 1992) such as computers,
cloud computing, enterprise systems, robotics and smart phones. For example, universities did
much of the early research on parallel and distributed systems, software, and controls, along
with the development and improvement of prototypes (NAS, 2016). But the commercialization
may require fewer advanced degrees and thus less educational than experiential knowledge
than do more science-intensive industries such as biotechnology or semiconductors.
One way to understand the different knowledge requirements, particularly between science
and technology, is through recognizing that the ICT industries can be thought of as a vertical
stack of products and services in which new innovations can occur at any layer in the stack
(Messerschmitt and Szyperski 2003). Computers, phones, and the Internet consist of vertical
stacks of products and services with each layer connected to the ones above and below by

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standardized interfaces, thus enabling easy design and effective interaction (Baldwin and Clark
2000). Firms can operate in multiple layers or a single layer and a single layer often constitutes
one or multiple industries. When considered vertically (See Figure 1), certain products and
services occupy lower layers and others occupy higher lawyers in the stack. For example,
semiconductor-based integrated circuits, magnetic hard disks, communications modules, and
other electronic components occupy lower layers in personal computers and to a lesser extent
mobile phone stacks while operating systems and application software occupy higher layers
(Langlois, 1992; Baldwin and Clark, 2000). The Internet is organized in yet another set of
layers that in the physical layers encompass fiber optic cables and communications modules,
in the mid-layers are routers, servers, and software, and in the highest layers are Internet content,
commerce, and apps. The fiber optic cable includes glass fiber and semiconductor-based
integrated circuits, lasers, LEDs, and photo-sensors (Orton, 2009). Furthermore, some layers
in these vertical stacks serve as platforms and, if they are effective choke points, exert even
greater control and allow the firms controlling the choke point to extract greater profits than do
the firms at other layers (Cusumano and Gawer, 2002; Parker and Van Alstyne 2005).
By impacting on the types of opportunities that are made available for startups and
incumbents, the evolution of these stacks affects the type of knowledge necessary for
developing new industries and their products and services. It is possible to hypothesize that if
innovations occur higher up in the stack, then it could be expected that fewer managers with
advanced degrees or fewer science and engineering articles would be required. More
specifically, as computing, phones, and the Internet evolved over time, we believe that
entrepreneurial opportunities have been emerging higher-up in the stack. Effectively, the
overall opportunities are shifting from the lower layers to products and services higher up in
stack. In general, higher up in the stack there is less dependence on scientific or university
engineering advances, either with respect to scientific articles or PhD-layer scientists and
engineers. What enabled these changes were improvements in semiconductors and other

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electronic components (Bresnahan and Trajtenberg, 1995; Lipsey et al, 2006) and the
emergence of modular designs, standards (Langlois, 1992; Baldwin and Clark, 2000), and
vertically disintegrated industry architectures (Jacobides, 2005).
To summarize, through the literature review we have shown that knowledge can contribute
to product, startup, and industry creation, but that the type of knowledge varies by industry.
Advances in science are more important for some industries than others and this knowledge
can be called scientific or educational knowledge. Advances in technology are more important
for other industries and this knowledge can be called experiential knowledge. The former is
measured with educational attainment and the latter with age, both for top managers of
entrepreneurial startups.
3. Methods
This paper uses data on startups to test the relative importance of advance in science to
entrepreneurial startups in different industries. Startups are emphasized because they have been
remarkable successful in commercializing many of the most important new products and
services over the last 50 years from integrated circuits to electronic products and software,
many of which can be considered new industries. To examine startups, this paper uses data
from initial public offerings (IPOs) between 1990 and 2010 (Kenney and Patton, 2017). These
data cover the years in which many new industries were created including those of fiber optic
cable, communications modules, servers, routers, new types of operating system and
applications software, Internet content, Internet commerce, smart phones, and smart phone
apps.
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:

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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 41,223 directors and executive
officers in the database with known industry and IPO filing year. Industry, IPO filing years,
and educational data are known for 19,701 individuals.
Scientific or educational knowledge is measured using the highest educational degrees for
top managers within the IPOs. The degrees were calculated for PhD, MD, JD, M.S., MBA,
other master’s, and bachelor’s degrees and this order was used to avoid double counting. Two-
year and other professional degrees were ignored because their numbers were very small,
representing less than 0.1% of degrees. Averages were calculated for each degree, IPO filing
year (1990 to 2010), industry, and executives vs. directors of boards. No differences were found
between executives and directors.
Industries with large percentages and numbers of PhD and M.S. degrees were identified.
These industries include biotechnology, education and research, general and medical
instruments, semiconductors, electronic equipment and communications. Because the PhD
disciplines for education and research are mostly biological sciences, education and research
are combined with biotechnology (see supplementary file for more details). For industries
related to ICT, the firms were sorted into their layer in a vertical stack of products and services.
Semiconductors, electronic equipment, and communications were situated in the lowest layers
in the vertical stack. Computers, software, computer systems, and telephone and telegraph were
situated in the mid-layers in the vertical stack. Internet content, commerce, and services
represent the highest layer in the vertical stack. Individuals associated with this highest layer
were identified by carefully reading prospectus and placing in himself into an Internet dummy
variable in the database and an industry associated with content, commerce, or services. These
industries include retail and wholesale trade, services, business services, computer services,
information retrieval, securities, insurance, and real estate, advertising, employment, and
leasing, and broadcasting and services.

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The percentage of executives and directors with known educational data varies 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 are few
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 (see the supplementary file for more details).
Experiential knowledge is measured using the age of the executives and directors. Based
on the ages of 19,264 top managers, the average ages were calculated for industry and IPO
filing year (1990 to 2015). 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 use the differences by industry in formal schooling (percent of
PhDs among top managers) to analyze the age differences by industry. Assuming a PhD
requires five years of work, we can use the percent differences in PhDs to show that most of
the age differences between industries are due to something other than differences in formal
schooling.
Another measure of scientific advances is Nobel Prizes in Physics, Chemistry and

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Physiology/Medicine. These prizes represent the ultimate recognition for advances in science,
with others representing the ultimate recognition for Literature, Peace, or Economics. We
recognize that Nobel Prizes are not a perfect measure for advances in science partly since other
fields such as mathematics may also make fundamental breakthroughs. For example, Claude
Shannon’s work on information theory is often characterized as the basis for mobile phone
communication standards such as CDMA and for methods of digital compression such as MP3
(Gleick, 2011).
Nevertheless, most people recognize that Nobel Prizes are given to those advances in
Physics, Chemistry and Physiology/Medicine that are deemed most beneficial to humans
(Groopman, 2017). This is partly because Alfred Nobel said in his will that prizes should be
given to “those who, during the preceding year, shall have conferred the greatest benefit to
mankind (NobelPrize.org, 2017).” Exceptions occur when the Nobel Committee are unable to
identify three or fewer people who contributed to most of the advances. For example, this
occurred in anesthesia, where the benefits to reducing pain during surgery have been
considerable (Brookshire, 2017).
We focus on Nobel Prizes since 1950. For those industries for which there are IPOs, we
searched for Nobel Prizes that had direct implications for those industries. We relied on the
official site for the Nobel Prize (NobelPrize.org), an analysis of the top 100 living contributors
to biotechnology (The Scientist, 2005), analyses of Nobel Prizes related to instruments
(Marcovich and Shinn, 2017) and electronics (Electro.Patent-Invent.com, 2018), and the
authors’ science and engineering background to identify Nobel Prizes relevant to different
industries. One author has a bachelor’s degree in Physics, work experience in semiconductors,
and a master’s degree in mechanical engineering and a second author has extensively
researched biotechnology.
4. Results

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Table 1 ranks industries by the fraction of PhD degrees among top managers (executives
and directors) of IPOs filed between 1990 and 2015. Six industries have greater than or equal
to 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) sectors. If the
fraction with either PhD or M.S. degrees are considered, the number of industries with greater
than or equal to the average of 23% grows to ten. The new ones are agriculture,
communications, machinery, other, and computers of which the first, third, and fourth ones are
eliminated due to insufficient data points and the fifth one can be considered part of the
electronics sector. Looking further down the list, other industries that can be combined with
computers to form a category of computing and Internet infrastructure can be identified, all of
which have a combined PhD and M.S. fraction that is the same or higher than 19%; these
include computer programming, computer systems, and software.
Table 2 organizes these industries into sectors and compares sectors in terms of the
percentages of PhD, MS, and MD degreed executives and directors. The life sciences sector
has the largest percentage followed by the electronics, Internet infrastructure, and Internet
content, commerce, and services sectors. The percentage of PhDs drops from 30% for life
sciences to 16% for electronics, 8.0% for Internet infrastructure, and 4.2% for Internet
commerce, content, and services. The combined percentage for PhD and M.S. degrees also
drops, falling from 36% for both the life science and electronic sectors to 22% for the Internet
Infrastructure sector and 13% for the Internet commerce, content, and service sectors. Including
medical degrees increases the differences between sectors, with 46%, 37%, 22%, and 14% for
the life sciences, electronics, Internet infrastructure, and Internet content, commerce, and
service sectors respectively, reflecting the differential impacts of scientific advances in these
sectors. All these differences are significant at the .001 level for a chi-squared test of
proportional means.

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Table 2 also shows the percentage of total PhDs represented by the life science and ICT
industries. The life science sector has 52% of the total PhDs followed by 17% for the
electronics sector, and 12% for Internet infrastructure. The large percentage (81%) represented
by these three sectors suggests that advances in science only have a large impact on a small
number of industries. Comparatively speaking, there were few IPOs in new industries such as
nanotechnology, superconductors, or solar cells.
Table 3 shows the percentages of other degrees for the industries shown in Table 3. It shows
that the differences between industries in the percentages of PhD, MS, and MD degrees mostly
comes from differences in the percentages of bachelor’s degrees and MBAs. While 45% of the
life science executives and directors had bachelor’s and MBA degrees, this percentage rose to
56% for electronics, 69% for Internet infrastructure, and 74% for Internet content, services,
and commerce. This means that the differences between industries for percentages of PhDs are
the result of differences between the percentage of PhD and bachelor or MBA degrees than
between the percentage of PhD and M.S. degrees.
Table 4 focuses on the discipline of the PhDs by industry. It shows that the higher the
percentage of PhDs, the higher the percentage of scientific PhDs and the lower the percentage
of engineering PhDs, with an exception of semiconductors. These scientific PhDs include the
biological (both biology and organic chemistry) and physical (physics and physical chemistry)
sciences. The percentages decline from 93% for biotechnology to 25% for communications
equipment, reflecting the differing levels of scientific knowledge required in these industries.
Overall, the life sciences have higher percentages of science PhDs (89%) than do electronics
(29%), with many physical chemists needed for the life sciences.
Table 4 also shows that the engineering PhDs are mostly computer science and electrical
engineering with few “other” engineering disciplines such as mechanical, chemical, civil and
industrial engineering. These “other” engineering PhDs only comprised 12% of the PhDs in
the electronics sector and 4.3% of them in the life science sector. The percentage of these other

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engineering PhDs are only slightly higher than those for non-engineering and science
disciplines (e.g., social science, humanities, business, economics) in the life science (4.3% vs.
4.1%) and electronics (12% vs. 5.3%) sectors. Overall, these percentages show that PhDs are
highly represented among the executives and directors in only a small number of disciplines,
suggesting that it is not only a small number of industries that are impacted by advances in
science, it is also a small number of disciplines, mostly scientific ones, that are having most of
the impact on the creation of entrepreneurial startups.
The data presented in Tables 2 through 4 reflects different types of required knowledge.
Higher percentages of PhDs, lower percentages of bachelors and MBAs, and higher
percentages of scientific PhDs reflect a greater importance of scientific knowledge. For the ICT
industries, the different percentages for the electronics, Internet infrastructure, Internet
commerce, content, and service can be partly explained by a vertical stack of Internet and
computing (See Figure 1). Excluding general instruments, the other three industries
(semiconductors, electronic equipment, and communications equipment) in the electronics
sector represent the lowest layer in a vertical stack, including semiconductor-based integrated
circuits, lasers, and LEDs, other electronic components, communication modules, and fiber-
optic cable. Internet infrastructure represents a higher layer in the physical stack and it includes
computers, servers, routers, software, and Internet service providers. The Internet commerce,
content, and service sector is the highest layer in the stack, providing the information and
services that are consumed by Internet users. As one moves up the stack, the percentage of
PhDs falls, and the percentage of bachelors and MBA degrees rises.
Another way to look at the differing percentages of PhDs, bachelors and MBA degrees,
and of disciplines for the PhDs degrees can be found in Noble Prizes awarded since 1950 in
Physics, Chemistry, and Physiology or Medicine. Table 5 lists the Nobel Prizes that can be
associated with each industry. Many more Nobel Prizes are relevant for industries with high
percentages of PhDs among executives and directors than for other industries. This includes 16

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for biotechnology, seven for general instruments, four for semiconductors, two for electronic
equipment, one for medical instruments, and one for communications. The number of these
Nobel Prizes roughly tracks the percentage of PhDs by industry. Biotechnology has the largest
number of Nobel Prizes and the highest percentage of PhDs followed by general instruments,
and semiconductors, with electronic equipment, medical instruments, and communication
having one or two. Not a single Nobel Prize has been awarded for advances directly relevant
for computers, routers, servers, software, or Internet content, commerce, or services nor can
new explanations of physical or artificial phenomena that are directly relevant to their designs
be identified from analyses of their fields and associated industries (David, 1990; Cortada,
2003, 2005; Gleick, 2011). The data in Table 5 and its interpretation are consistent with the
analysis of educational background for top managers in the IPO database (Tables 2, 3 and 4).
We now turn to a measure of experiential knowledge, average age of top managers at IPO
filing time. Table 6 shows the average age of executives and managers by the same sectors
analyzed in Tables 2 through 4. Top managers in biotechnology and life science were older
than those in Internet content, e-commerce, and online services, while those in electronics and
Internet infrastructural equipment firms were in the middle. This tracks the Ph.D. percentages
in each sector and suggests that part of the age differences may be correlated with education.
However, differences in the representation of PhDs cannot explain most of the differences in
age. As shown in Table 6, the adjusted age differences for biotechnology industry and the life
science sector are still several years higher than those in electronics and Internet related sectors.
The biggest difference for example is between the 47.9 years of the life sciences and 43.5 years
of Internet services, commerce, and content, or 4.4 years. This suggests that life sciences and
biotechnology require more of both experiential and educational knowledge than do industries
with smaller percentages of PhDs as top managers such as ICT.
5. Interpretation

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Some industries have much higher percentages of PhDs among startup executives and
directors at IPO filing time and this suggests that advances in science are more important to
some industries than others. The industries with the highest percentages of PhDs, namely
biopharmaceuticals, medical instruments, semiconductors, and electronic and communications
equipment, each depend strongly on advances in science (i.e., new explanations of biological
or physical phenomena) much more than advances in technology (i.e., new techniques or
artefacts); biopharmaceuticals has both the highest percentage of PhDs and the most university
licensing income (Markmann, Phan, Balkin, 2005; Ali and Gittelman, 2016). Many of the
advances in science illuminate new mechanisms, define new concepts for products and services,
and lead to Nobel Prizes for those who made the largest advances.
Within the ICT industries, the concept of a vertical stack helps us understand where the
impact of scientific and particularly engineering research is the largest. Lower layers in the
stack such as semiconductors, hard disks, fiber-optic cable, LEDs, lasers, LCDs, other
electronic components, and communication modules depend on advances in science and
engineering. The highest degrees obtained for founders, executives, and directors reflects the
knowledge necessary for entrepreneurial success and thus these startups are more likely to have
PhDs in leadership positions.
It is possible to speculate that at the higher layers of the stack such as computers, routers,
servers, and telephone services, the firms are primarily users of the scientific and engineering
research advances rather than authors of them. Therefore, there are fewer PhDs as founders,
executives, or directors, and fewer patents based on science and engineering articles. The
highest layers in the stack such as ecommerce, content, and online services depend the least
upon scientific and engineering research and thus have few executives, and directors with PhDs
and M.S. degrees. The firms in the highest layers of the stack leverage off the already existing
infrastructure including available cloud services for software, storage, and computing, they do
not to develop these technologies themselves. An understanding of the physical layers, the

20
lowest in the stack, is unnecessary for the design of Internet content, commerce, and services,
which are often assembled from off-the-shelf open source software modules and powered by
cloud services such as Amazon housed at locations such as GitHub (see, for example, Zysman
and Kenney 2018).
The data in Table 2 strongly support this interpretation. While 16% of the executives and
directors in the electronics industries have PhDs, at firms situated in higher layers in the stack,
the number of PhDs is far lower. The percent drops to 8.0% for Internet infrastructure and 4.2%
for Internet content, commerce, and services. The decline in the number of PhD and M.S.
degrees is even more prevalent at the highest layers. The percent dropped from 37% for
electronics to 22% for Internet infrastructure, and 14% for Internet content, commerce, and
services.
This does not mean that university disciplines such as electronic engineering and computer
science are not important to new firm, product, and industry creation. New types of computer
architectures and software have been developed at universities. Consider the now ubiquitous
Unix system, which was developed at Bell Laboratories and by computer scientists at UC
Berkeley (Kenney et al. 2014). And yet, for entrepreneurial firms, often M.S. and B.S. degrees
are sufficient to conceptualize and commercialize new products and service. This is best
illustrated by Google, where Sergey Brin and Larry Page developed the algorithm while PhD
students, but left prior to completion to launch Google.
Changes in the stack are continuously underway, and unsurprisingly the mix of
opportunities that exist for different layers may be changing. It might be useful to consider the
changes as reverse salients (Hughes, 1993), where changes in one part of the stack, say the
introduction of iOS or Android, then created opportunities at the next layer higher in the stack.
Or changes in the iPhone may demand better processors further down the stack, a duet that
Intel and Microsoft played during the PC era. New types of computers and the vertical stacks
based upon them were, in many respects, made possible by improvements in ICs and magnetic

21
storage, even while the continuing improvement in Microsoft operating system called forth
new generations of Intel chips (Yoffie and Cusumano, 2015). These improvements enabled
mini-computers, personal computers, work stations, and data centers (Baldwin and Clark 2000),
personal digital assistants, mobile phones, and then smart phones (Funk, 2018; 2013), and
tablet computers and later pads to emerge, each with their own vertical stack of ICs, operating
systems, and application software. Most of these improvements emerged at the higher layers
in the stack, even as these vertical stacks became more fine-grained with a greater number of
layers (Langlois, 1992; Baldwin and Clark, 2000; Messerschmitt and Szyperski, 2005).
A similar story, albeit much more complex, can be told for the Internet. Improvements in
semiconductors (e.g., Moore’s Law), hard disk drives, fiber optic cable, LEDs, lasers, and
communications modules continue to emerge fueling improvements in Internet speed and cost.
In turn these improvements in Internet speed and cost enable the emergence of opportunities at
higher layers such as new software and types of e-commerce, content, and services (Kenney et
al, 2014). This is also occurring in the mobile Internet in which the emergence of smart phones
created opportunities in mobile apps and software, neither of which depend directly on
advances in university science or engineering research (Funk, 2018).
Data on average age also provides support for the above interpretation. Higher age, even
when adjusted for differences in the percentage of PhDs, tracks the percentage of PhDs.
Executives and directors in biotechnology have higher ages than do those in other sectors such
as the electronics, computing and Internet infrastructure, and Internet commerce, content, and
services sectors. The higher average ages in sectors with high percentages of PhDs suggest
executives and directors need both more educational knowledge and experiential knowledge
than do other sectors. What this experiential knowledge constitutes is uncertain, but it seems
to be different than educational knowledge. It may constitute technical knowledge that is
difficult to obtain in PhD programs or it may constitute other types of knowledge such as
business or technical knowledge. In any case, it suggests that PhD programs are not providing

22
graduates with the best possible skills.
6. Discussion
This paper began with a discussion of the literature on knowledge. Although it has long
been recognized that advances in knowledge are an important part of entrepreneurship and
economic growth, it has also been recognized that measuring these knowledge flows and their
impact on economic growth are highly uncertain and can be confused (Rosenberg, 1974;
Mowery and Rosenberg, 1998; Mokyr, 2002). This is particularly true with patent analyses,
which have become the primary research methodology used to investigate knowledge and
innovation in general, 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 knowledge flows and their impact
on economic growth and it finds different results. The first key difference with patent analyses
is that PhDs and thus advances and science are important for a small number of industries.
Patent analyses conclude that scientific knowledge is important for all industries (Jones, 2009,
Ahmadapoor and Jones, 2017), that this intensity has not dropped even as basic R&D spending
by corporations have fallen (Arora et al, 2015), and that companies are relying more on external
knowledge from universities than in the past (Arora and Gambardella, 1990; Arora, Fosfuri,
and Gambardella, 2001; Higgins and Rodriguez, 2006; Mowery, 2009).
By showing the extent to which PhDs occupy leadership positions in entrepreneurial
startups, we show that the impact of scientific advances and training strongly depend on the
industry. They have a much larger impact on the life science and electronics sectors than on
other sectors. We explain these differences by focusing on the definitions of science and
technology, the number of Nobel Prizes by industry, and by introducing the technical structure
of the ICT industry, i.e., the vertical stack. Advances in science are the basis for new concepts

23
in the life science and electronics sectors and less so for other sectors and this distinction is
reflected in the awarding of Nobel Prizes.
Vertical stacks represent the breakdown in work among related industries and we focused
on the vertical stack in the ICT industries. In the lowest stack layers, which are physical, there
is the greatest dependence on highly trained Ph.D. scientists and engineers, while the higher
layers depend less on advances in engineering and science. This means that the prevalence of
PhDs is highest among leadership positions at the lowest layers in the stack and declines with
firms positioned higher up in the stack.
This suggests that knowledge accumulation is most important when it enables industry
creation in higher layers in a vertical stack. These higher layers provide new types of products
and services, but they depend on the accumulation of knowledge in lower layers and the rapid
improvements enabled by this knowledge creation. In the case of the digital stack, knowledge
accumulation in semiconductors, communications, and electronics enabled a plethora of new
products and services in higher layers in the vertical stack.
A second big difference with patent analyses is found in the analysis of age, the role of
age in obtaining knowledge, and in the interplay between educational and experiential
experience. Educational experience refers to the knowledge obtained in formal schooling and
thus can be measured by the highest educational degree obtained by top managers. Experiential
experience refers to the knowledge obtained through work and can be measured by the number
of years in this work. Patent analyses (Jones, 2009) find that both are increasing over time,
suggesting that complexity is increasing and that this increasing complexity may be a reason
for slowing productivity growth.
We find differences in age between industries, a conclusion not found in previous studies.
Because the differences in formal schooling explain only a small part of these differences in
average ages among industries, we conclude that most of the differences between different
industries come from different numbers of years working in jobs and thus resulting in different

24
amounts of acquired experiential knowledge. Thus, the life science and electronics sectors not
only require more educational knowledge but also require more experiential knowledge,
knowledge that is acquired working in industry. This has implications for the design and
support of PhD programs.

25
Table 1. Industries Ranked by Fraction of Top Managers with PhD Degrees
Fraction
SIC Codes Industry Name
Numbers
PhD PhD, MS PhDs MS
0.35 0.41 2830-2839 Biotechnology
0.33 0.40 8200-8299, 8730-8739 Education & Research 346 72
0.24 0.38 3820-3829 General Instruments 104 61
0.18 0.41 3674 Semiconductors 158 189
0.15 0.31 3600-3659,3670-3673, 3675-3699 Electronic Equipment 79 87
0.13 0.24 3840-3849 Medical Instruments 159 141
0.13 0.23 AVERAGE FOR ALL INDUSTRIES 2485 2045
0.13 0.17 0-999 Agriculture 6 2
0.11 0.32 3660-3669 Communications 86 159
0.11 0.25 3500-3569,3580-3599, 3700-3799 Machinery 32 38
0.11
0.17 1800-1999,3830-3839, 6600-6711,
6740-6789, 6800-6999,7300-7309,
7390-7499 Other 10 5
0.10
0.16 7500-7999,8100-8199,
8200-8299,8300-8729 Services 72 42
0.09
.019 2200-2829,2840-3499,
3800-3819,3850-3999 Manufactured Goods 74 64
0.089 0.21 7371 Computer Programming 51 72
0.089 0.14 8000-8099 Health Services 35 24
0.084 0.29 3570-3579 Computers 50 123
0.078 0.21 7373 Computer Systems 34 55
0.07 0.17 1500-1799 Construction 5 8
0.063 0.20 7372 Software 136 291
0.06 0.12 5000-5199 Wholesale Trade 23 24
0.052 0.15 4800-4829 Telephone & Telegraph 27 50
0.051 0.18 7370, 7374,7376-7379 Computer Services 43 106
0.050 0.14 7375 Information Retrieval 26 44
0.047 0.08 6000-6199 Finance 20 16
0.042
0.09
6200-6599
Securities Insurance and
Real Estate 28 30
0.041 0.13 7320-7329,7340-7349,7380-7389 Business Services 35 73
0.04 0.04 2000-2199 Food and Tobacco 5 0

26
0.03 0.14 1000-1499 Oil Gas and Mining 10 34
0.022
0.08
7310-7319,7330-7339, 7350-7369
Advertising, Employ.
and Leasing 9 22
0.02 0.04 6719-6725,6790-6797,6799 Holding and Investment 1 1
0.015 0.12 4830-4899 Broadcasting & Services 3 18
0.014 0.06 4700-4799 Transportation Services 3 11
0.025 0.07 5200-5999 Retail Trade 20 39
0.00
0.15
4900-4999
Electricity Gas and
Sanitation 0 18

27
Table 2. Number and Percentage of Advanced Degrees by Industry and Sector
Number of
PhDs
% with
PhD
% with PhD
or MS
% with PhD,
MS, or MD
% of Total
PhDs
Biotech 791 35% 41% 53% 32%
Education & Research 346 33% 40% 47% 14%
Medical Instruments 159 13% 24% 32% 6.4%
Sub-total, life science
sector
1296 28% 36% 46% 52%
General Instruments 104 24% 38% 40% 4.2%
Semiconductors 158 18% 41% 41% 6.4%
Electronic Equipment 79 15% 31% 31% 3.5%
Communications Equip 86 11% 32% 33% 3.2%
Sub-total, electronics
Sector
427 16% 36% 37% 17%
Computer Programming 51 8.9% 22% 22% 2.1%
Computers 50 8.4% 29% 20% 2.0%
Computer Systems 34 7.8% 20% 21% 1.4%
Software 136 6.3% 20% 20% 5.5%
Telephone & Telegraph 27 5.2% 15% 15% 1.1%
Sub-total, Internet
Infrastructure
298 8.0% 22% 22% 12%
Computer Services 29 5.1% 16% 19% 1.2%
Information Retrieval 22 4.7% 4.7% 13% 0.9%
Retail & Wholesale Trade 16 4.5% 12% 12% 0.6%
Finance, Broadcasting,
Transport, Securities,
Insurance, Real Estate.
8 2.6% 11% 12% 0.3%
Business and Other Services 26 4.0% 12% 12% 1.0%
Advertising, Employment,
Leasing
7 2.9% 9.4% 9.4% 0.3%
Sub-Total, Internet Content,
Services, and Commerce
108 4.2% 13% 14% 4.3%

28
Table 3. Percentage of Other Degrees by Industry and Sector
Industry PhD, M.S.,
MD
J.D. M.A. MBA BS, BA
Biotech 53% 6.4% 1.4% 21% 17%
Education & Research 47% 8.3% 3.1% 22% 19%
Medical Instruments 32% 5.7% 2.2% 30% 29%
Sub-total, life science
sector
46% 6.7% 2.0% 24% 21%
General Instruments 40% 5.8% 1.9% 23% 29%
Semiconductors 41% 4.6% 2.1% 26% 27%
Electronic Equipment 31% 5.8% 2.8% 25% 35%
Communications Equip 33% 5.0% 2.5% 25% 34%
Sub-total, electronics
sector
37% 5.1% 2.3% 25% 31%
Computer Programming 22% 8.2% 2.6% 29% 38%
Computers 20% 3.9% 1.5% 36% 29%
Computer Systems 21% 8.0% 3.7% 26% 41%
Software 20% 4.9% 3.4% 29% 43%
Telephone & Telegraph 15% 10% 3.5% 37% 34%
Sub-total, Internet
Infrastructure
22% 6.1% 3.1% 29% 40%
Computer Services 19% 9.9% 2.3% 32% 37%
Information Retrieval 13% 12% 3.4% 34% 37%
Retail & Wholesale
Trade
12% 9.2% 2.5% 39% 35%
Finance, Broadcasting,
Transport, Securities,
Insurance, Real Estate.
12% 11% 4.6% 37% 36%
Business and Other
Services
12% 7.5% 3.9% 32% 40%
Advertising,
Employment, Leasing
9.4% 6.7% 4.1% 37% 40%
Sub-Total, Internet
Content, Services, and
Commerce
14% 8.9% 3.4% 36% 38%

29
Table 4. Percentage of Scientific Disciplines for PhDs by Industry
Industry %
PhDs
Biological
Sciences
Physical
Sciences
Total
Science
Computer
Science and
Engineering
Other
Engi-
neering
Other
Discipline
s
Bio-
technology
35% 78% 15% 93% 0.8% 2.8% 3.2%
Education &
Research
33% 73% 15% 88% 2.7% 3.0% 1.3%
General
Instruments
24% 33% 46% 79% 9.9% 7.7% 0%
Semi-
conductors
18% 0% 29% 29% 60% 6.3% 4.9%
Electronic
Equipment
15% 5.9% 28% 34% 35% 24% 7.3%
Medical
Instruments
13% 26% 12% 38% 5.9% 7.5% 1.2%
Communi-
cations
Equipment
11% 2.7% 22% 25% 61% 11% 4.1%
Sub-Total
Life Sciences 28%
73% 16% 89% 2.6% 4.3% 4.1%
Sub-Total
Electronics
16% 2.1% 27% 29% 54% 12% 5.3%

30
Table 5. Nobel Prizes Relevant to Industries
Industry Nobel Prize Discipline Year
Biotechnology Mechanistic Studies of DNA Repair Chemistry 2015
RNA Interference Medicine 2006
Genetic Regulation of Organ Development Medicine 2002
Key regulators of cell cycle Medicine 2001
Specificity of the cell mediated immune defense Medicine 1996
DNA-Based Chemistry Chemistry 1993
Cellular origin of retroviral oncogenes Medicine 1989
Genetic principle for generation of antibody diversity Medicine 1987
Biochemistry of nucleic acids and base sequences Chemistry 1980
Mapping Structure and Function of DNA Chemistry 1980
Origin and Dissemination of Infectious Diseases Medicine 1976
Tumor Viruses and Genetic Material of Cell Medicine 1975
Interpretation of genetic code Medicine 1968
Messenger RNA Medicine 1965
Molecular structure of nucleic acids (DNA) Medicine 1962
Means of DNA molecules duplicated in bacterial cell, Medicine 1959
General
Instruments
Cryo-electron microscope Chemistry 2017
Laser Interferometer Gravitational-wave Observatory Physics 2017
Super-resolved fluorescence microscopy Chemistry 2014
Measuring and manipulation of individual quantum
systems
Physics 2012
Mass spectrometric analyses of macromolecules Chemistry 2002
Cool and trap atoms with laser light Physics 1997
Neutron Spectroscopy Physics 1994
Particle detectors Physics 1992
Nuclear magnetic resonance spectroscopy Chemistry 1991
Ion Trapping for hydrogen maser, other atomic clocks Physics 1989
Scanning Tunneling & Electron Microscope Physics 1986
High Resolution electron and laser spectroscopy Physics 1981
Holographic Method Physics 1971
Phase Contrast Microscope Physics 1953
Semicon-
Ductors
Blue LEDs Physics 2014
Imaging Semiconductor Circuit - the CCD sensor Physics 2009
Information & Communication Technology Physics 2000
Quantized Hall effect Physics 1985

31
Tunneling Phenomena in semiconductors Physics 1973
Quantum electronics Physics 1964
Semiconductors and Transistor Effect Physics 1956
Electronic
Equipment
Conductive Polymers Chemistry 2000
Liquid Crystals and Polymers Physics 1991
Medical
Instruments
Magnetic Resonance Imaging Medicine 2003
Computer Assisted Tomography Medicine 1979
Communi-
Cations
Transmission of Light in Fibers for Optical
Communication
Physics 2009
Internet
infrastructure
None
Internet
Content,
Commerce
None
Table 6. Average Age of Top Managers by Sector
Industry or Sector Percent PhD and
MD
Average Age Adjusted for Greater
Fraction of PhD and MD
Biotechnology 47% 49.1 46.7
All Life Sciences 38% 48.7 47.9
All Electronics 17% 48.1 47.2
Internet Infrastructure 8.0% 44.4 44.0
Internet Services, Commerce,
and Content
4.5% 43.3 43.5

32
Application Software
Computer Design
Operating System
Semiconductors and Other
Electronic Components
1a. Computers and Other Electronic Products
Figure 1. Examples of Vertical Stacks
1.b Internet
Content, Commerce, Services: Retail and
Wholesale Trade; Business, Health and
Computer Services: Information Retrieval;
Finance; Securities, Insurance and Real Estate,
Advertising
Internet Infrastructure: Computers (Routers,
Servers), Computer Programming and
Systems, Software, Telecom Services
Semiconductors, Lasers, Fiber Optics,
Communications, Other Electronic Equipment

33
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