ISSN (Print) 0473-453X

Discussion Paper No. 1099

ISSN (Online) 2435-0982

DECLINING BUSINESS DYNAMISM
AMONG OUR BEST OPPORTUNITIES:THE ROLE OF THE BURDEN
OF KNOWLEDGE

Thomas Astebro
Serguey Braguinsky
Yuheng Ding

August 2020

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Declining Business Dynamism among Our Best Opportunities:
The Role of the Burden of Knowledge

Thomas Åstebro, Serguey Braguinsky, Yuheng Ding*

August 2020

Abstract
We document that since 1997, the rate of startup formation has precipitously declined for firms
operated by U.S. PhD recipients in science and engineering. These are supposedly the sourceof some of our best new technological and business opportunities. We link this to an increasing
burden of knowledge by documenting a long-term earnings decline by founders, especially lessexperienced founders, greater work complexity in R&D, and more administrative work. The
results suggest that established firms are better positioned to cope with the increasing burden
of knowledge, in particular through the design of knowledge hierarchies, explaining why new
firm entry has declined for high-tech, high-opportunity startups.

*Åstebro is the L’Oréal Professor of Entrepreneurship at HEC Paris, Braguinsky is associate professor at the
University of Maryland’s Robert H. Smith School of Business, College Park, Maryland, a research associate at
the NBER Productivity, Innovation and Entrepreneurship program and professor (cross-appointment) at Osaka
University Institute of Social and Economic Research, and Ding is a PhD student at the University of
Maryland’s Robert H. Smith School of Business, College Park, Maryland. e-mail addresses are astebro@hec.fr,sbraguinsky@rhsmith.umd.edu, and yuheng.ding@rhsmith.umd.edu. Åstebro is the corresponding author. The
use of NSF data does not imply NSF endorsement of the research methods or conclusions in the paper.

1. Introduction
In the U.S. and across many countries, business dynamism is receding, as seen in declining
firm entry rates and a falling employment share in new firms (e.g., Decker et al. 2014;
Akcigit and Ates, 2019a). Is this something to worry about? Some emphatically say “yes”:“A country cannot be great over a sustained period without a steady flow of great new firms”(Klepper, 2016, p. 62). However, the decline in the rate of startups and employment might
not be anything to be very concerned about as long as it applies mainly to mom-and-pop
stores and is explained by the increasing diffusion of efficient franchises, replacing
individually run enterprises with expert managers of franchises of multiunit firms, assuggested by Lucas (1978) as the normal evolution of industries.
Alternatively, it might be of grave concern if the rate of entry and the share of
employment by young firms are declining among high-opportunity firms from which most
new technologies and business opportunities typically originate (Schumpeter, 1942).
Although suggested that, for example, the supply of innovations is drying up (Gordon, 2016)
or at least has become more difficult to achieve (Jones, 2009; Bloom et al., 2020), we stillknow little about the trend in business dynamics among high-tech, high-opportunity startups.1
We employ the nationally representative Survey of Doctorate Recipients to show adecline over the past 20 years in both the rate of startups founded and the share of
employment at startups by the highest-educated science and engineering portion of the U.S.
workforce. The declines are wide-ranging and not driven by any particular founderdemographic category or geographic region or scientific discipline.
A potential source of this decline is the exponential increase in the amount ofscientific knowledge.2 Medicine is a poignant example: “It is estimated that the doubling time1

For evidence of the importance of high-growth young firms, see Decker et al. (2016a, 2016b).
We leave aside various other reasons for the general decline in business dynamics suggested in the literature(see e.g. Akcigit and Ates, 2019a; 2019b; Decker et al., 2016b) but we examine some potential alternative
explanations for the decline specific to high-tech, high-opportunity startups in the robustness/extensions section.

2

of medical knowledge in 1950 was 50 years; in 1980, 7 years; and in 2010, 3.5 years… What
was learned in the first 3 years of medical school will be just 6% of what is known at the endof the decade from 2010 to 2020” (Densen, 2011). The impact of the so-called increasing
burden of knowledge has previously been analyzed with respect to the organization ofscience (Jones, 2009). It has been argued to be a driving factor in the increase in teamwork
for research and patenting (Wuchty et al., 2007), the increasing age at which PhDs are
obtained, and an increased specialization of PhD work (Jones, 2009). The latter can be seenfor example by the decrease in the number of physicians entering practice as generalinternists from 35% during 1991-2000 to 12% during 2011-2015 (Dalen et al., 2017).
We argue and find that an increasing burden of knowledge also leads to fewer hightech high-opportunity startups. We argue and find that it also leads PhDs to amass greater
work experience before becoming a founder, to shoulder more R&D tasks as founders, andnot being rewarded for that extra work. Working for existing firms has, therefore, become
considerably more attractive. Our argumentation links insights from three strands ofliterature: the increasing burden of knowledge, the organization of knowledge, and on the
declining business dynamism, specifically at high-tech, high-opportunity firms.
The decline in startups is illustrated with the medical sector where 48.5 percent ofphysicians worked as independent practice owners in 2012, but where only 31.4 percent wereindependent owners in 2019 (Merritt Hawkins, 2019). Speaking of the shrinking gap in
earnings, John A. Bergfeld of Cleveland Clinic added, “it used to be my colleagues in private
practice made double to three times what I make. I do not think it is that way anymore.”3
Exploring the impacts of the increasing burden of knowledge, we compareindividuals with a PhD in science and engineering either as founders of incorporated startups

3

Press, Robert, 2011. ”Private practice vs. salaried employment: A complicated decision.” Healio, January 01.
https://www.healio.com/news/orthopedics/20120325/private-practice-vs-salaried-employment-a-complicateddecision.

3

or as workers employed by established companies. We examine unique microdata on theevolution of work experience, earnings and wages, the change in the returns to experience,the change in the number of R&D and other tasks performed, and the trends in the span ofcontrol (direct reports) and the depth of the hierarchy (indirect reports) for these two groups.
We find that the number of different R&D tasks has increased more for founders thanfor workers. And the returns to experience have increased over time for founders but not forworkers, highlighting the increasing need for a single person––the founder––to cope with theburden of knowledge in startups. Workers at established firms have, instead, comfortably
narrowed their span of control, employed more people indirectly under their control tosupport their work, and kept administrative duties low. They are also better rewarded fortaking on more diverse work and managerial responsibilities than founders. All evidence
suggests that established firms have coped more effectively with the increasing burden ofknowledge in science by better utilizing the division of labor in innovative work through reorganizing tasks and hierarchies.
The literature on efficient knowledge hierarchies (Caliendo and Rossi-Hansberg,
2012; Garicano, 2000; Garicano and Rossi-Hansberg, 2004) can be used to explain how largeorganizations cope with the increasing burden of knowledge. As more innovation work
exceptions are reported by lower-level workers, the firm responds by increasing the numberof layers to handle the increasing volume of reports, and by narrowing the variance in reports
to allow greater job specialization. Increasing the number of layers of management adds afixed cost of operations, and narrowing task diversity may lower wages. Caliendo and RossiHansberg (2012) show that larger firms are more likely to become hierarchically taller byadding more layers of management when the number of managerially actionable exceptions
passed up the hierarchy increases, as they can more easily absorb the added fixed cost.
Founders at startups appear not to have recourse to this mitigation strategy.

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The rest of the paper is organized as follows. In Section 2 we briefly describe the data,with more exhaustive information provided in the Online Appendix. In Section 3 wedocument a declining startup rate and a declining employment share at startups for U.S. PhD
recipients in science and engineering. In Section 4 we examine the impact of the increasing
burden of knowledge as a possible explanation for the decline in startup rates and show thatstartup founders have been hit much harder by this burden than workers at established firms.
Section 5 contains robustness checks and some extensions. Section 6 concludes.
2.

Brief data description

The Survey of Doctorate Recipients (SDR) is a nationally representative survey of PhD
recipients in science and engineering from U.S. PhD-granting institutions. We employ
restricted-use data from all available surveys conducted (for most part) biannually between
1995 and 2017. We limit the sample to those who reside in the U.S. and report being
employed full time with non-zero salaries, whose principal employer is either a private-sector
for-profit company or organization, or who are self-employed or business owners at their own
business at the time of the survey. Information about startups comes from answers to thequestion asking whether one’s principal employer “c[a]me into being as a new businesswithin the past five years.” This question was not asked in the 1995, 2006, and 2008 surveys.
Most of our analyses therefore start with the year 1997. We call businesses that did not comeinto being within the prior five years “established businesses.”
Incorporated businesses have been found to be considerably more entrepreneurial andpursue better business opportunities than nonincorporated ones (Shane, 2009; Hurst andPugsley, 2011; Åstebro and Tåg, 2017; Levine and Rubinstein, 2017). In this paper, wetherefore focus on owners of incorporated startups, whom we call “founders.” This term
refers to owners at the time observed in the data, whose startups were their full-time primary5employer and who drew non-zero salaries. We also sometimes use the term “startup rate” as ashortcut when referring to the share of founders among all incorporated business owners.
We further examine individuals’ engagement in R&D tasks. The SDR data containresponses to the question “Which of the following work activities occupied at least 10 percent
of your time during a typical week on the job?” The list of possible alternatives includes 14
different activities. See Table A2 (all figures and tables starting with the letter A are in the
appendix). We call these “tasks” and follow the classification suggested by the SDR to
identify four of those activities as “R&D” tasks: “basic research,” “applied research,”“development,” and “design.” We add up tasks in the R&D category to obtain the number of
R&D tasks performed by founders and workers. We also follow the SDR-suggested
classification to identify management and administration work activities (abbreviated as“management” tasks), which consist of five categories: “accounting, finance, contracts,”“human resources,” “managing or supervising people or projects,” “sales, purchasing,marketing, customer service, public relations,” and “quality or productivity management.”
As in recent empirical work on knowledge-based hierarchies (e.g. Caliendo et al.,2015; Guadalupe and Wulf, 2010; Tåg, 2013; Tåg et al., 2016), we infer firms’ hierarchicalstructure based on microdata from workers’ and founders’ work tasks at their firms.
Specifically, SDR provides data on the number of people supervised directly and indirectly
by each respondent. Following Guadalupe and Wulf (2010), we call the former “span ofcontrol” and the latter “depth of the hierarchy.”

3. The startup founding rate and the dynamics of the employment share at startups

Figure 1 displays the large decline over the past two decades in the share of founders among
PhDs in science and engineering. In 1997, 34 percent reported being a founder of a startup,
but by 2017 this rate had declined to 21 percent, a decline of 38 percent. This observation

6

alone would not be sufficient to conclude that high-tech high-opportunity startups are on the
decline because there could be fewer founding teams, but each could be getting larger.
However, as also shown in Figure 1, the employment share of science and engineering PhDs
at startups is also falling over time.4 The downward trend is not driven by any particular
category of PhDs. For example, figures similar to Figure 1 for males versus females, whites
versus non-whites, California versus the rest of the U.S., and for U.S. versus non-U.S. born
PhDs differ in levels but their dynamics are almost exactly the same as those in Figure 1. See
Figures A1-A6. The dramatically falling share of founders and employment at startups raises
the prospect of a drying up of high-tech, high-opportunity startups.
--- Figure 1 around here --4. Evidence of an increasing burden of knowledge

What factors can explain the decline in high-tech, high-opportunity startups? We
focus on the impact of an increasing burden of knowledge in science and engineering. If theincreasing burden of knowledge is especially hard to handle at startups, we would expect tosee a decline in the rate of startups. And if having more work experience is akin to prolonging
the training in preparation for shouldering the burden of knowledge as a founder (cf. Jones,
2009), we would expect to see greater work experience among founders. We would also
expect experienced founders to develop an earnings advantage over less experienced
founders.
Consistent with the burden of knowledge increasing for founders, the years of work
experience among founders shows a steady increase. The regression results imply that the
average founder had about 14 percent longer post-PhD work experience in 2017 than in 1997.
See Table A11. Further, Figure 2 illustrates a pronounced decline over time in the earnings of

4

Uniquely, the employment share of PhDs at startups increased from about 11 percent in 1997-99 to more than
15 percent in 2001, possibly reflecting the dot-com boom (see Hombert and Matray, 2019).

7

less experienced founders relative to their more experienced peers. Here, we separate less andmore experienced founders by the median number of years after PhD (13 years), althoughother reasonable cut-offs lead to similar results. Between 1997 and 2001, founders with postPhD experience at or below the median earned more than the rest of the sample (consistent
with Ouimet and Zarutskie, 2014), but by the 2010s, the situation is reversed, with lessexperienced founders earning on average 30-40 percent less than other founders. Prior work
experience apparently is becoming much more valuable for founders over time. The earningsof less experienced founders declined not just in relative but also in absolute terms. The
average inflation-adjusted earnings of founders with below the median post-PhD experience
were $72,616 in 1997, whereas 20 years later their earnings were $57,517, a decline of morethan 20 percent (Table A3 presents full survey data).
--- Figure 2 around here --Running a startup might constrain founders’ ability to organize its hierarchy
efficiently, at least until it has succeeded in growing well beyond its initial size. As science
accumulates more knowledge, we would therefore expect PhD founders to have to take on
more R&D tasks. The SDR sample offers a unique opportunity for pursuing this line ofinquiry using detailed, individual-level data on founder and worker tasks. We also explore
how startups and established firms differ in their organization of work, guided by the theoryof knowledge hierarchies (e.g., Garicano, 2000).

4.1. The number of R&D tasks is rising, but established firms take advantage of adivision of labor and knowledge hierarchies
Figure 3, Panel A, illustrates the dynamics of the total number of tasks reported by founders
and, separately, the number of R&D and management tasks. The total number of tasks (with atheoretical maximum of 14) is measured on the left-hand scale whereas the number of8management and R&D tasks, whose maximum is five and four, respectively, are measured on
the right-hand scale. The average number of all tasks for founders increased by about 15
percent from the beginning to the end of our sample, a statistically significant difference
using a double-sided t-test. Furthermore, although the number of both R&D and managementtasks increased, the increase is more pronounced in R&D, for which it rose by more than 50
percent from 1997 to 2017. Importantly, the increase in R&D tasks was equally pronounced
at all experience levels (column (1) in Table 1 below). As a result, R&D tasks that comprised
about 25 percent of all tasks conducted by founders in 1997 increased to 34 percent of alltasks in 2017. As can be seen in Figure 3, this was not accompanied by any decline inmanagement tasks, so the founders had to shoulder the burden of doing more R&D tasks
while also running the same or more administrative tasks.
--- Figure 3 around here --Panel B of Figure 3 presents the trends for workers at established firms. Workers werealso affected by the need to perform more R&D tasks, as the increase in the number of R&D
tasks from 1997 to 2017 is about 12 percent. Notably, however, as the figure reveals, workers
did not need to handle more management tasks, which remained relatively flat for them.
Also, founders had to deal with significantly more management tasks than workers in termsof levels: about 30-40 percent more at the beginning of the study period, increasing to 50
percent more at the end. The explanation for this difference likely lies in how the two types offirms differ in their organization of work, which we analyze below.5
Based on these initial observations, we conjecture that established firms were able totake better advantage of a division of labor in innovation and organize knowledge hierarchies
more efficiently. To examine this further while accounting for various other differences

5

Another potential explanation is that the breadth of tasks is better rewarded for founders than workers, as inLazear (2005). However, note that whereas founders take on more tasks, they are not rewarded for doing so.

9

between founders and workers, we turn to regression analysis. The estimation equation,
estimated separately on the samples of founders and workers at established firms, is
𝑦!" = 𝛼 + 𝛽# 𝑡 + 𝛽$ 𝑒𝑥𝑝!" + 𝜸 ∙ 𝑿!" + 𝜀!" ,(1)

where 𝑦!" denotes outcome variables for individual i at time t, which are (a) the number of
R&D tasks, (b) the span of control, and (c) the depth of the hierarchy; t is the time trend;
𝑒𝑥𝑝!" denotes experience (years after PhD is received) of individual i at time t; Xit is a vector
of controls, consisting of demographic, occupational, and geographic location dummies; and𝜀!" is the error term. Estimation results are presented in Table 1.6--- Table 1 around here --First, coefficients on the time trend variable in columns (1) and (2) in Panel A confirm
a positive time trend in the number of R&D tasks for both founders and workers at
established firms, with a somewhat stronger trend for founders. Second, coefficients on work
experience show that workers at established firms, but not founders, tend to perform fewer
R&D tasks as their experience grows. This suggests that established firms employ a greater
division of labor in innovation by assigning less experienced workers to R&D tasks and moreexperienced workers to team leadership roles. In Panel B we dig deeper into this by looking
at the span of control and the depth of the hierarchy. Neither the span of control nor the depthof hierarchy changes over time for founders. Although more experience is associated withsomewhat deeper hierarchies for founders, the coefficient is relatively small and statistically
only marginally significant. Needless to say, most startups are small, so opportunities torestructure startups to counter the increasing burden of knowledge must be inherently limited.
In contrast, both the span of control and the depth of the hierarchy (especially thelatter) strongly increase with experience for workers at established firms, in line with such

6

To ease interpretation and to conform with the specification of earnings regressions in the next section, we
report the results of estimations for log-transformed dependent variables using the inverse hyperbolic sine (IHS)
transformation. The results are qualitatively similar to those with non-log-transformed values.

10

workers performing fewer R&D tasks as they age, as shown in column (2) in Panel A.
Strikingly, the coefficient on the time trend for the span of control in column (4) is negative,
indicating that the span of control decreases for workers at established firms on average by
about 0.46 percent per year (statistically highly significant). However, the coefficient on thetime trend for the depth of the hierarchy in column (6) is positive, also statistically highly
significant, indicating that hierarchies deepen over time for workers at established firms on
average by about 0.32 percent per year. Together, the results suggest that established firms
cope with the increasing burden of knowledge on their workers by introducing additional
layers of hierarchy, while simultaneously reducing the number of employees who report
directly to managers, as suggested in the literature on efficient knowledge hierarchies.7
We probed the data further by looking at the sample limited to workers at firms with5,000 or more employees. These firms account for about half the workers at established firms
in our sample. If, as we conjecture, the increasing burden of knowledge can be mitigated byintroducing costly additional layers of management, large firms should have greater
opportunities to implement these organizational changes. This should lead to a higher numberof indirectly supervised individuals. Hence, we re-estimated equation (1) for workers at large
firms. The results, reported in Table A4, show that hierarchies deepen over time at twice therate for workers at large firms as for workers at all firms. In addition, for workers at large
firms, the number of R&D tasks increases by about half the increase for founders and alsosignificantly more slowly than for all workers. Thus, other things equal, large firms mitigate
the impact of the increase in the burden of knowledge better than small firms, and thisappears to be related to hierarchies that grow taller more quickly at large than at small firms.

7

Suppose that a firm initially consisting of two R&D teams with 10 members each added a second layer madeup of a single manager, promoted from a supervisory position on one of the teams. The newly promoted
manager had nine direct reports before the change but now has zero direct and 19 indirect reports. Her
replacement within her former team will now have eight, not nine, direct reports. More generally, reorganizing afirm by adding layers of hierarchy while holding the firm size constant can be seen as accompanied by adecrease in the number of direct reports and an increase in the number of indirect reports.

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4.2. Earnings dynamics of founders versus workers at established firms
As mentioned in Section 3, founders did not appear to receive higher earnings over time,
despite having to cope with an increasing number of R&D and management tasks. In Table 2
we examine this with an earnings regression. The estimation equation is similar to Equation(1) except that the outcome variable is now the (logged) salary of individual i at time t, while
the explanatory variables of interest include the (IHS-transformed) number of R&D tasks, thespan of control, and the depth of the hierarchy, and there is an interaction term between thetime trend and experience. We present results in Table 2 separately for founders and workers
at established firms. This avoids well-known specification problems associated with how
wages and entrepreneurial earnings are reported. Several things stand out.
Founders’ earnings decline on average by about 1.6 percent per year (column 1).
However, this is offset by an opposite time trend in returns to experience. The mean numberof years after founders receive a PhD is 15.9 years; hence, the coefficient on the interaction
term between experience and time implies that, at the mean work experience, the negative
baseline time trend is completely offset.
In stark contrast to founders, workers’ real earnings grow over time, although theincrease is relatively small, 0.4 percent per year (column 2). Earnings for workers at
established firms are concave in experience. The trend toward increasing returns toexperience is much weaker among workers than among founders; at the mean number ofyears after receiving a PhD (12.5 years), earnings grow extra by just 0.125 percent per year.
--- Table 2 around here --Performing more R&D tasks is associated with significantly higher earnings forworkers at established firms. Doubling the number of R&D tasks increases their earnings by
4.4 percent. The point estimate for founders is similar in magnitude, but the standard errors
are high so the most we can say in this case is that, controlling for all other factors (including12

the time trend, experience, the interaction of the two, etc.), more R&D tasks are tentatively
associated with higher earnings, but there is large variance in outcomes.
Finally, both a larger span of control and hierarchical depth seem to be associatedwith higher earnings for both founders and workers. Recall that Table 1 showed no increasein the span of control or hierarchical depth for founders over time. Nevertheless, a larger spanof control or hierarchical depth provides economic compensation. Hence, the lack of change
in these features for founders is likely explained by their inability to offset the increasing
burden of knowledge by more efficiently organizing tasks at their startups.

5. Robustness and alternative explanations
We wanted to gain insights about the dynamics of Schumpeterian startups, which are themost likely to contribute to innovation and growth in the economy. For that purpose, we
studied founders with a PhD in science and engineering and their incorporated startups. In
this section, we briefly examine what happens if we perturb that measure and also consider
some potential alternative interpretations of our findings.

5.1. Type of entrepreneurial opportunity
Following a series of articles showing that incorporated businesses pursue the highest
opportunities, we analyze only founders of incorporated startups. However, also including
founders of unincorporated startups in the analysis yields similar findings, provided thesample is limited to founders whose job and education are closely related (see Braguinsky etal., 2012; Ohyama, 2015). The details are in Appendix E.

5.2. R&D-focused individuals

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In addition to the list of the work activities on which individuals spent at least 10
percent of their time on the job, SDR asks the question “On which work activity did you
spend most of your time during a typical week on the job?” We call founders who listed
R&D as their primary work activity “R&D-focused” founders. We examine these founders(and workers) to see whether the results are, potentially, even more extreme for thesefounders.
Note that an R&D-focused founder and a startup engaged in R&D might not be thesame thing. A startup might be primarily engaged in R&D whereas the founder primarily
manages a team of researchers. An increasing burden of scientific and technologicalknowledge, leading to an increasing number of exceptions brought to the attention of thefounder, might change this, however, shifting the primary work activity of the founder frommanagement to R&D. In fact, that interpretation appears to be most consistent with the data.
The first thing to note is that R&D-focused founders as a share of all founders
increases over time––from 26.1 percent in 1997 to 31.6 percent in 2017, an increase of 21
percent (see Table A7). However, the share of R&D-focused business owners among all
business owners irrespective of firm age rises even faster, from about 12 percent in 1997 toover 30 percent in 2017, suggesting that the burden of knowledge is widespread. As a result,
the rate of R&D-focused startups steeply declines, and this decline, from 54 percent in 1997
to 20 percent in 2017, is larger in magnitude than the decline in the overall startup rate shownin Figure 1. Notably, the share of R&D-focused workers at established firms remains
basically flat over time, fluctuating at around 50 percent.
The increasing burden of knowledge is also reflected in a steeper earnings decline for
R&D-focused founders. In Table A8, we present the estimation results from the same
regression for which results were in Table 2, but with the sample limited to R&D-focusedfounders. The negative earnings time trend is much more pronounced for R&D-focused14founders than for all founders––the decline at the baseline is more than 4.4 percent per year
and statistically highly significant. Table A9 present changes in levels.
In summary, we find that R&D-focused owners are becoming more plentiful among
both startup founders and other business owners, the startup rate is declining more rapidly for
R&D-focused founders than for all founders, and earnings are declining more rapidly for
R&D-focused founders than for all founders.

5.3 Limited scope for the burden of knowledge?
Could it be that the burden of knowledge is limited to certain fields of science? For

example, since the bioengineering revolution created by the invention of rDNA in 1973 by
Herber W. Boyer and Stanley N. Cohen, the life sciences have raced forward, but mechanicalengineering has not. However, we have already provided evidence that the decline inbusiness dynamics is not driven by any particular category of PhDs. Further, our regression
specifications (Tables 1 and 2) include detailed occupational dummies, controlling fordifferences between bioengineers and mechanical engineers in their work.
In Appendix H, we use information about PhD specializations and redo our main
analyses. Specifically, we sort PhDs into two groups: those where science has built up rapidly
over the past forty years, and those that have not experienced such a rapid increase. Then, thelevels do indeed appear different. PhD founders who are more science based perform more
R&D tasks. However, the general time trends already reported are found to apply universally,
albeit with one major difference. The drop in the share of founders is more pronounced for
PhDs that are more science based. This result provides further support for the conclusion thatthe increasing burden of knowledge is driving the decline in business dynamics among hightech, high-opportunity startups.

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5.4 Declining human capital?
A decline in the human capital of founders might explain the decline in high-tech,high-opportunity startups and their founders’ earnings. For example, using data in the U.S.
manufacturing sector, Ayyagari and Maksimovic (2017) find a declining trend in skill levels,
especially in cognitive skills among new entrants. We employ a standard approach (see Choiet al., 2019; Azoulay et al., 2020) with prior salaries as a measure of the human capital of
future founders. The results, presented in Figure A8, show that although the predicted priorsalaries of future founders tended to be lower than of those who stay in paid work in the
1990s, the ordering was reversed in the 2008 and 2010 surveys while the two were at about
the same level in the 2015 survey. Furthermore, we examined the share of founders who
graduated from top-ranked PhD programs. Consistent with the findings about pre-transition
salaries, around 5 percent more founders graduated from top-ranked programs in the 2010s
than in the late 1990s and early 2000s (Figure A10). Hence, both prior salaries and the qualityof education received by founders have increased over time. In short, we find no evidence
that the declining trend in the startup rate and in earnings of founders could be due to a fall in
the ability or education quality of founders over time. 8

5.5 Declining supply of PhDs?
Could the decline in the creation of high-tech, high-opportunity startups somehow be
driven by a decline in the number of PhDs? It appears not. The overall supply of recent
science and engineering PhD recipients has demonstrated a strong upward trend since 1997(see Figure A13). The number of PhDs awarded in science and engineering fields increased
from 28,486 in 1997 to 41,294 in 2017 (National Science Foundation, 2019).

8

Increasing human capital of founders is consistent with a ”selection effect” due to higher barriers to entry that,
according to our story, stem from the increasing burden of knowledge. Note that this does not have to lead to anincrease in average earnings of the remaining founders, because the increasing burden of knowledge reduces
earnings at all levels of human capital.

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5.6. Increasing Age of Founders?
Over the past few decades, the U.S. population has been aging. Since young people
tend to disproportionately found or join new businesses (Ouimet and Zarutskie, 2014), theaging of those receiving PhDs could explain the decline in Figure 1 (Hopenhayn et al., 2018;
Karahan et al., 2019). However, if we define “young” as those below the median age of 45(or any other reasonable cutoff, for that matter), the share of such young founders in the total
number of all founders was stable, about 35-40 percent throughout the period studied. In
Figure A7, we also show that the age at which individuals receive their PhDs in science andengineering had increased steadily until about 1993––the average recipient was 28.7 years
old in 1971 and 31.3 years old in 1995. Then, the average age of PhD recipients stopped
increasing and even declined after 2003––the average recipient in 2015 was 30.3 years old.
Apparently, the age of founders is not strongly associated with the decline in high-tech, highopportunity startups.9

5.7 Dot Com Bubble Effects?
Since the starting period of our data coincides with the advent of the dot com boom,
the decline could just be an anomaly. We therefore reconstructed Figure 1 excluding
computer-related occupations (Figure A14) and excluding individuals who performed
computer-related tasks (Figure A15). The trends without computer-related occupations are
the same as in Figure 1, suggesting a widespread decline that is not specific to computing.
Also, Table A12 shows that the overall share of PhDs in computer-related occupations isstable among incorporated business owners while it slightly increases over time among those9

In contrast, as mentioned above, accumulated work experience (years after PhD) prior to founding a startup did
tend to increase over time. Startup founders appear to have been receiving their doctorate degrees at a younger
age over time. See Appendix H, Table A11 and the discussion there.

17employed in industry. None of these two latter trends shows a clear relationship to thedeclining trend in startups, suggesting that the dot com boom and bust is not the culprit.

5.8. Declining Venture Capital?
A simple explanation for the decline in high-tech, high-opportunity startups is that theaggregate amount of venture capital for startups has declined. However, the supply of venture
funding has substantially increased since the dot com crash. For example, the amount of
global venture capital investments increased tenfold, from $25 billion in 2004 to $254 billion
in 2018 (National Venture Capital Association, 2019). More than 8,380 venture-backed
companies received $131 billion in funding in 2018, surpassing the $100 billion mark set atthe height of the dot-com boom in 2000. For more detail on how venture capital has shifted
over the years, see Lerner and Nanda (2020).
6. Discussion
We have established a couple of new facts regarding business dynamism among high-tech,high-opportunity startups run by PhDs in science and engineering. Since 1997, the share offounders in these startups has declined by around 38 percent, not limited to any particular
founder demographic or ethnic group or occupation, and this decline is widespread acrossregions of the United States. The share of workers at startups has followed the same path of
decline.
There is a significant trend toward an increasing amount of work experience among
founders (although not their age), pointing to the burden of knowledge as an explanation forthe decline in startup rates. Probing this idea in the data, we show evidence that founders are
rewarded for building experience, as they have had to perform an increasing number of R&D
tasks over time while also increasing the number of other tasks that they have to perform.

18

Nevertheless, founders’ earnings generally do not reflect that added workload, as average
earnings have been declining, especially for less experienced founders.
The data further suggest that established firms have an advantage over startups increating a division of labor in R&D, in particular by introducing more hierarchical layers,
reducing knowledge workers’ span of control, and allocating more experienced workers topositions with greater managerial responsibility. Further, established firms compensate
workers for performing more R&D tasks and supervising more individuals. Thesedevelopments are not seen among founders. The differences follow from the natural limits
imposed by running a small firm with less division of labor and a high amount ofmultitasking by the founder. The largest firms are even more active in reorganizing job tasks,
increasing the depth of hierarchy at twice the rate of all established firms.
Why don’t founders hire more specialized workers and start with bigger teams andbuild a richer hierarchy, as established firms do? Although we do not address this follow-on
question, we can at least speculate. Some of the answers might be that startups face highuncertainty about the viability of their businesses and thus start small, and either grow or exit
as uncertainty diminishes; and/or most startups are financially constrained and cannot afford
to hire larger teams. We leave it to future research to explore these ideas.
As noted in the introduction, a steady flow of great new high-tech firms is generally
agreed to be necessary for an economy to remain vibrant in the long run. In this sense, ourfindings present cause for concern. It is not immediately clear what the remedy is. Typicalregulatory actions, such as changing taxation rates or restricting or enlarging businesses’
operating conditions would likely have no effect. And restricting established firms’ ability tomake organizational design changes seems highly unlikely to pass any legislature. Ourfindings suggest that if the goal is to restore business dynamism in the high-tech sector,
alleviating the burden of knowledge should be front and center in the strategy to attain it.

19

7. References
Akcigit, Ufuk, and Sina T. Ates. 2019a. “Ten Facts on Declining Business Dynamism andLessons from Endogenous Growth Theory.” NBER Working Paper 25755.
Akcigit, Ufuk, and Sina T. Ates. 2019b. “What Happened to U.S. Business Dynamism?”
NBER Working Paper 25756.
Åstebro, Thomas, and Joacim Tåg. 2017. “Gross, Net, and New Job Creation by
Entrepreneurs.” Journal of Business Venturing Insights 8 (November): 64–70.
Ayyagari, Meghana, and Vojislav Maksimovic. 2017. “Fewer and Less Skilled? Human
Capital, Competition, and Entrepreneurial Success in Manufacturing.”
https://ssrn.com/abstract=3093443. Accessed June 16, 2020.
Azoulay, Pierre, Benjamin F. Jones, J. Daniel Kim, and Javier Miranda. 2020. “Age andHigh-Growth Entrepreneurship.” American Economic Review: Insights 2 (1): 65–82.
Bloom, Nicholas, Charles I. Jones, John Van Reenen, and Michael Webb. 2020. “AreIdeas Getting Harder to Find?” American Economic Review 110 (4): 1104–1144.
Braguinsky, Serguey, Steven Klepper, and Atsushi Ohyama. 2012. “High-Tech
Entrepreneurship.” Journal of Law and Economics 55 (4): 869–900.
Caliendo, Lorenzo, Giordano Mion, Luca David Opromolla, and Esteban RossiHansberg. 2015. “Productivity and Organization in Portuguese Firms.” NBER
Working Paper 21811.
Caliendo, Lorenzo, and Esteban Rossi-Hansberg. 2012. “The Impact of Trade on
Organization and Productivity.” Quarterly Journal of Economics 127 (3): 1393–1467.
Choi, Joonkyu, Nathan Goldschlag, John Haltiwanger, and J. Daniel Kim. 2019.
“Founding Teams and Startup Performance.” https://ssrn.com/abstract=3481850.
Accessed June 16, 2020.
Dalen, James E., Kenneth J. Ryan, Joseph S. Alpert. 2017. “Where Have the Generalists
Gone? They Became Specialists, Then Subspecialists.” The American Journal of
Medicine 130 (7): 766-768.
Decker, Ryan A., John Haltiwanger, Ron S. Jarmin, and Javier Miranda. 2016a.
“Declining Business Dynamism: What We Know and the Way Forward.” American
Economic Review 106 (5): 203–207.
Decker, Ryan A., John Haltiwanger, Ron S. Jarmin, and Javier Miranda. 2016b.
“Where Has All the Skewness Gone? The Decline in High-Growth (Young) Firms in
the U.S.” European Economic Review 86 (July): 4–23.
Decker, Ryan, John Haltiwanger, Ron Jarmin, and Javier Miranda. 2014. “The Role ofEntrepreneurship in US Job Creation and Economic Dynamism.” Journal of
Economic Perspectives 28 (3): 3–24.
Densen, Peter. 2011. “Challenges and Opportunities Facing Medical Education.”Transactions of the American Clinical and Climatological Association 122: 45-58.
Garicano, Luis. 2000. “Hierarchies and the Organization of Knowledge in Production.”
Journal of Political Economy 108 (5): 874–904.
Garicano, Luis, and Esteban Rossi-Hansberg. 2004. “Inequality and the Organization of
Knowledge.” American Economic Review 94 (2): 197–202.
Gordon, Robert J. 2016. The Rise and Fall of American Growth. Princeton: Princeton
University Press.
20

Guadalupe, Maria, and Julie Wulf. 2010. “The Flattening Firm and Product Market
Competition: The Effect of Trade Liberalization on Corporate Hierarchies.” American
Economic Journal: Applied Economics 2 (4): 105–127.
Hombert, Johan, and Adrien Matray. 2019. “Technology Boom, Labor Reallocation, andHuman Capital Depreciation.” https://ssrn.com/abstract=3496625. Accessed June 16,
2020.
Hopenhayn, Hugo, Julian Neira, and Rish Singhania. 2018. “From Population Growth toFirm Demographics: Implications for Concentration, Entrepreneurship and the Labor
Share.” NBER Working Paper 25382.
Hurst, Erik, and Benjamin Wild Pugsley. 2011. “What Do Small Businesses Do.”Brookings Papers on Economic Activity, 73–118.
Jones, Benjamin F. 2009. “The Burden of Knowledge and the ‘Death of the RenaissanceMan’: Is Innovation Getting Harder?” Review of Economic Studies 76 (1): 283–317.
Karahan, Fatih, Benjamin Pugsley, and Ayşegül Şahin. 2019. “Demographic Origins of
the Startup Deficit.” NBER Working Paper 25874.
Klepper, Steven. 2016. Experimental Capitalism: The Nanoeconomics of American HighTech Industries. Princeton: Princeton University Press.
Lazear, Edward. 2005. “Entrepreneurship.” Journal of Labor Economics 23(4): 649-680.
Lerner, Josh, and Ramana Nanda. 2020. “Venture Capital’s Role in Financing Innovation:
What We Know and How Much We Still Need to Learn.” NBER Working Paper
27492.
Levine, Ross, and Yona Rubinstein. 2017. “Smart and Illicit: Who Becomes an
Entrepreneur and Do They Earn More?” Quarterly Journal of Economics 132 (2):963–1018.
Lucas, Robert E. 1978. “On the Size Distribution of Business Firms.” Bell Journal of
Economics 9 (2): 508–523.
Merritt Hawkins. 2019. 2019 Physician Inpatient/Outpatient Revenue Survey. Dallas,
Texas: Merritt Hawkins.
National Science Foundation, National Center for Science and Engineering Statistics.
2019. Doctorate Recipients from U.S. Universities: 2018. Special Report NSF 20-301.
Alexandria, VA. Available at https://ncses.nsf.gov/pubs/nsf20301/.
National Venture Capital Association. 2019. 2019 Yearbook. Washington, DC: National
Venture Capital Association.
Ohyama, Atsushi. 2015. “Entrepreneurship and Job-Relatedness of Human Capital.”
Economica 82 (328): 740–768.
Ouimet, Paige, and Rebecca Zarutskie. 2014. “Who Works for Startups? The Relation
between Firm Age, Employee Age, and Growth.” Journal of Financial Economics
112 (3): 386–407.
Schumpeter, Joseph A. 1942. Capitalism, Socialism and Democracy. New York: Harper &Brothers.
Shane, Scott. 2009. “Why Encouraging More People to Become Entrepreneurs Is Bad Public
Policy.” Small Business Economics 33 (2): 141–149.
Tåg, Joacim. 2013. “Production Hierarchies in Sweden.” Economics Letters 121 (2): 210–
213.
21

Tåg, Joacim, Thomas Åstebro, and Peter Thompson. 2016. “Hierarchies andEntrepreneurship.” European Economic Review 89 (October): 129–147.
Wuchty, Stefan, Benjamin F. Jones, and Brian Uzzi. 2007. “The Increasing Dominance ofTeams in Production of Knowledge.” Science 316 (5827): 1036–1039.

22

Figures and Tables
Figure 1. Dynamics of the share of PhDs who are employed at or founders of startups

Notes: The figure reports the share of PhDs in science and engineering who are employed full-timewith non-zero salaries in new (five years old or less) private for-profit companies (startups) compared with
PhDs in science and engineering who are employed full-time with non-zero salaries in all private for-profit
businesses. “Employed full-time” means working at least 48 weeks per year and at least 30 hours per week. The
figure also reports the share of PhDs in science and engineering who are founders of startups in the total numberof PhDs in science and engineering who are self-employed or owners of established businesses full-time with
non-zero salaries. “Founders” are owners of/self-employed at an incorporated startup at the time observed in thedata. The question of whether the business was a startup was not asked in the 1995, 2006, and 2008 surveys, so
the data for those surveys are missing.

Figure 2. Dynamics of relative earnings of founders with below the median years after PhD

Notes: The figure reports the ratio of earnings of full-time incorporated startup founders with non-zerosalaries and years after PhD at or below the median of 13 years, to earnings of full-time incorporated startup
founders with non-zero salaries and 14 or more years after PhD among PhDs in science and engineering. The
question of whether the business was a startup was not asked in the 1995, 2006, and 2008 surveys, so the data
for those surveys are missing.

23

Figure 3
Panel A. The number of all, R&D, and management work activities among founders

Panel B.
The number of all, R&D, and management work activities among workers at established firms

Notes: The sample consists of full-time founders of incorporated startups with non-zero salaries among PhDs inscience and engineering. A startup is a firm that is five years old or less. Work activities that occupied at least 10
percent of their time during a typical workweek on their jobs. R&D tasks consist of “basic research,” “appliedresearch,” “development,” and “design.” Management (management and administration) tasks consist of“accounting, finance, contracts,” “human resources,” “managing or supervising people or projects,” “sales,purchasing, marketing, customer service, public relations,” and “quality or productivity management.” The total
number of work activities includes, in addition to the above, computer programming, employee relations,production, operations and maintenance, teaching as well as other work activities. See also Online Appendix
Table A2.

24

Table 1.
Panel A. Factors that affect the number of R&D tasks.
DV: IHS-transformed number of R&D tasks

Time
Experience (years after PhD)
Constant
Other controls: gender, ethnicity,
occupation, U.S. state of employment
Observations
R-squared

Founders (1)0.0109***(0.002)-0.0021(0.002)0.905***(0.196)

Workers at established firms (2)0.0075***(0.000)-0.0052***(0.000)0.953***(0.048)

Included

Included

2,101
0.345

65,4850.274

Panel B. Factors that affect the span of control and depth of the hierarchy

Time
Experience (years after PhD)
Constant
Other controls: gender, ethnicity,
occupation, U.S. state of employment
Observations
R-squared

DV: span of control (IHStransformed # of individuals
directly supervised)
Workers at
Founders (3)established firms(4)
0.0001-0.0046***(0.004)(0.001)-0.00100.0064***(0.002)(0.001)1.058***
0.686***(0.405)(0.081)

DV: depth of hierarchy (IHStransformed # of individuals
indirectly supervised)
Workers at
Founders (5)established firms(6)-0.0027
0.0032***(0.004)(0.001)0.0051*0.0101***(0.003)(0.001)0.717*-0.054(0.413)(0.099)Included

Included

Included

Included

2,101
0.208

65,485
0.218

2,101
0.264

65,4850.470

Estimation method: OLS. ***, **, and * denote statistical significance at the 1 percent, 5 percent, and 10 percent levels,
respectively. Because there are observations with a zero number of R&D-related work activities as well as a zero span ofcontrol and depth of hierarchy, we use inverse hyperbolic sine (IHS) transformations, defined as 𝑦 = 𝑙𝑛%𝑥 + √𝑥 ! + 1*.

25

Table 2. Earnings of founders and workers at established firms
DV: Log(real earnings)-0.0163***(0.006)0.0121(0.009)-0.0005**(0.000)0.0011***(0.000)0.0488(0.046)0.0498*(0.029)Workers at
established firms(2)
0.0039***(0.001)0.0277***(0.001)-0.0005***(0.000)0.0001***(0.000)0.0437***(0.005)0.0466***(0.002)0.0820***(0.024)10.0238***(0.455)0.0461***(0.003)
10.5638***(0.042)

Included

Included

2,101
0.138

65,485
0.221

Founders(1)
Time
Experience (years after PhD)
Experience squared
Experience x time
IHS-transformed number of R&D tasks
Span of control (IHS-transformed # of individuals directlysupervised)
Depth of hierarchy (IHS-transformed # of individuals
indirectly supervised)
Constant
Other controls: gender, ethnicity, occupation, U.S. state of
employment
Observations
R-squared

Estimation method: OLS. Robust standard errors in parentheses. ***, **, and * denote statistical significance at the 1 percent,
5 percent, and 10 percent levels, respectively. Because there are observations with zero numbers of R&D-related work
activities as well as a zero span of control and depth of hierarchy, we use inverse hyperbolic sine (IHS) transformations, defined
as 𝑦 = 𝑙𝑛%𝑥 + √𝑥 ! + 1*.

26

Online Appendix
Declining Business Dynamism Among Our Best Opportunities:
The Role of the Burden of Knowledge
Appendix I: Further description of the data set
SDR. The Survey of Doctorate Recipients (SDR) gathers information from a representative sample ofindividuals who obtained a doctorate in science or engineering from a US PhD-granting institution,
conducted every two to three years. It is a longitudinal survey that follows respondents until age 76,
and at each observation point, new PhD recipients are added while some previously followed
individuals are dropped (because of their age or for other reasons). A significant number ofindividuals were followed over the period from 1993 to 2013, but the sample was almost entirely
redrawn in 2015. See http://www.nsf.gov/statistics/srvydoctoratework/ for a detailed description of
the target populations and other technical information about SDR.
Startup. The SDR questionnaire classifies individuals as working for startups (new businesses) if
they affirmatively answered the question “Did your principal employer come into being as a new
business within the past 5 years?” This question was asked in eight surveys, those conducted in 1997,
1999, 2001, 2003, 2010, 2013, 2015, and 2017.
Sample. We use restricted-use SDR data (available through NSF [National Science Foundation]
licensing process) from the 1995, 1997, 1999, 2001, 2003, 2006, 2008, 2010, 2013, 2015, and 2017
surveys, all of which are currently available with exception of the 1993 survey, which does notcontain some key information (e.g., whether the respondent worked full time). For the analysis ofstartups, we use eight surveys in which this question was asked, as noted above. We restrict the
sample to doctorate recipients who resided in the U.S. and reported being employed full time (defined
as 48 weeks or more per year and 30 hours or more per week) with non-zero salaries, whose principal
employer was either a private-sector for-profit company or organization or who were self-employedor business owners in their own (incorporated or non-incorporated) businesses. We exclude all
retirees and those working part time or not drawing positive salaries, as well as those employedoutside the private-sector for-profit sector. SDR surveys provide weights on individual observations torecover population numbers, and we employ those weights in calculating all the summary statistics
and in regression analysis. As mentioned in the main text, using unweighted observations leads tovery similar results. Table A1 provides the number of observations in the samples that are used in themain text.

27

Table A1. Sample number of observations and their distribution across surveys
Founders
Unweighted
Weighted
1997
177
2,971
1999
150
3,111
2001
128
2,903
2003
229
5,433
2010
234
6,217
2013
208
5,828
2015
465
6,746
2017
510
6,967
Total
2,101
40,176
Source: National Science Foundation data.

Workers at startups
Unweighted
Weighted
966
16,199841
16,493
1,192
23,795627
14,770506
13,701455
13,078991
15,802
1,289
17,5096,867
131,347

Workers at established firms
Unweighted Weighted
7,359
121,885
6,850
137,897
6,696
139,7545,656
131,493
5,964
158,632
5,852
167,431
12,858
193,087
14,254
195,057
65,4891,245,235

Work activities: In each survey the respondents were asked to check all work activities that occupied
at least 10 percent of their time in a typical week on the job. Table A2 presents the list and moredetailed description of all work activities and the number of individuals among founders and workers
at established firms who checked the respective boxes. The first four activities listed (basic research,
applied research, development, and design) are R&D activities in the definition used in this paper; thenext five activities are management activities.
Table A2. Work activities
SDR abbreviationand name
WABRSH ––Description of work activity

Founders
Unweighted Weighted

Workers at established firms
Unweighted
Weighted

Study aims to gain scientific knowledge forits own sake
Basic research
WAAPRSH––
Study aims to gain scientific knowledge tomeet a recognized need
Applied research
Using knowledge gained from research for
WADEV––
the production of materials and devices
Development
Design of equipment, processes, structures,
WADSN––and models
DesignWAACC––Accounting, Accounting, finance, contracts
finance, contracts
Including recruiting, personnel development,
WAEMRL––training
Human resources
WAMGMT––
Managing or supervising people on projects
Management
WAQM––
Quality or productivity management
Quality management
Sales, purchasing, marketing, customer
WASALE––
service, public relations
Sales
Production, operations, maintenance (e.g.,
WAPROD––
chip production, operating lab equipment)
Production
WASVC––
Professional services (e.g., health care,counseling, financial services, legal services)
Professional Services
WACOM––
Computer programming, systems, orapplications development
Computer applications
WATEA––
Teaching
Teaching
WAOT––
Other
Other
Source: National Science Foundation data.

28

419

8,012

16,827

319,956

1,108

21,18844,185840,152

1,043

19,94539,521

751,469

821

15,69930,047571,326

1,110

21,226

12,607

239,715686

13,11817,335

329,615

1,366

26,121

41,766794,156

587

11,225

15,379292,423994

19,00812,931

245,875

302

5,775

8,729

165,977952

18,204

10,933

207,885736

14,07425,027475,874

400

7,649

7,121

135,402

141

2,696

4,130

78,530Appendix II: Supporting analysis
A. Decline in startup formation and demographics
We present four figures that split the sample by gender and white/non-white race as well as a figurethat limits the sample to PhDs employed in California. The levels are different, but time trends arevery similar to those observed in Figure 1. California, in particular, exhibits a decline in the rate ofstartups as large as that in the rest of the country (cf. Decker et al., 2014).
Figure A1. Dynamics of the share of male PhDs employed at or founders of startups

Source: Author calculations using weighted National Science Foundation data.

Figure A2. Dynamics of the share of female PhDs employed at or founders of startups

Source: Author calculations using weighted National Science Foundation data.

29

Figure A3. Dynamics of the share of white PhDs employed at or founders of startups

Source: Author calculations using weighted National Science Foundation data.

Figure A4. Dynamics of the share of non-white PhDs employed at or founders of startups

Source: Author calculations using weighted National Science Foundation data.

Figure A5. Dynamics of the share of PhDs residing in California employed at or founders of startups

Source: Author calculations using weighted National Science Foundation data.

30

Figure A6. Dynamics of the share of non-U.S. born PhDs employed at or founders of startups

Source: Author calculations using weighted National Science Foundation data.

B. Age at which the PhD is received
In Figure A7 we plot the dynamics of the age at which individuals received their PhDs. Sample
attrition in this case causes those with PhDs received in earlier years to drop out of the sample at ahigher rate than those who received their PhDs later, so, rather than pooling all observations, we lookat pictures at two points in time, using the 1997 and 2017 surveys. We also limit the sample in eachcase to those who received their PhD within the 25 years preceding the cutoff year of observations
and between ages 20 and 40, to avoid going past the common retirement age.
Figure A7. Average age when the PhD is received
Panel A. 1971-1995 PhD recipients in the 1997 survey

Source: Author calculations using National Science Foundation data. Bars represent 95 percent confidence intervals.

31Panel B. 1991-2015 PhD recipients in the 2017 survey

Source: Author calculations using National Science Foundation data. Bars represent 95 percent confidence intervals.

Both Panels A and B (as well as in other years, not shown) show that the average age at which
PhDs are received had been increasing until around 1993. Average PhD recipients were about 2.6
years older in 1993 than they were in 1971. Starting in the mid-1990s, however, the average age ofreceiving a PhD is flat or even declining.
C. Inflation-adjusted annual earnings of founders and workers at established firms
Table A3 presents the dynamics of inflation-adjusted real earnings of founders and workers at
established firms. Consistent with Figure 2 in the main text and the regression estimation results inTable 2, we can see that earnings of less experienced founders are declining not just in relative but
also in absolute terms. Both more and less experienced workers at established firms, by contrast,
increase their earnings over time, as do also more experienced founders.
Table A3. Annual real earnings of founders and of workers at established firms (in US $)Founders

Workers at established firms

Survey YearAll

≤13 years afterPhD>13 years after
PhD

All

≤13 years afterPhD>13 years after
PhD

1997

71,094

72,616

69,95879,50966,37591,846

1999

81,31081,85380,90980,006

68,261

92,460

2001

76,40180,781

70,676

83,82373,496

95,248

2003

87,27880,691

93,39884,812

74,57996,936

2010

86,353

71,464

100,780

88,46976,078100,849

2013

84,572

63,16598,45387,07473,614

98,341

2015

77,656

61,73488,001

90,762

76,252

102,370

2017

80,42257,517

98,69088,55475,052

100,890

Source: Author calculations using weighted National Science Foundation data. Salary data were adjusted for inflation using
consumer price index. Thirteen years after PhD corresponds the median post-PhD experience in the sample.

32

D. The number of R&D tasks, span of control, and depth of the hierarchy at large firms
In this section we repeat the estimations presented in Table 1 but for the sample limited to workers at
the largest firms. SDR surveys contain responses to the question about broadly defined size categoriesof the employers, measured by the responders’ estimates of how many people worked for the
principal employer across all locations. There are eight categories, ranging from 10 or fewer
employees to 25,000+ employees. For the purpose of this analysis, we define firms in the two largest
categories, 5,000-24,999 employees and 25,000+ employees, as “large firms.” In columns (1), (3),and (5), we present estimation results from eight surveys in which the question about employer age
was asked, so the sample is limited to employees at large firms that we know are not startups. In
columns (2), (4), and (6), we employ data from all 11 available surveys under the assumption thatfirms with 5,000 or more employees are highly unlikely to be startups. Results from all surveys andfrom the eight surveys where we know firm age are very similar.
Comparing the estimation results in Table A4 to those in Table 1 pertaining to workers at all
established firms, we notice that the coefficient on the time trend variable in the number of R&D tasks
is smaller among workers at large firms compared with all workers and is about half the magnitude of
the corresponding coefficient for founders in column (1) of Table 1. Thus, workers at large firms facean increase in the burden of knowledge at about half the pace of founders. Perhaps even moreinterestingly, the coefficient on the time variable in the regression where the dependent variable is thedepth of the hierarchy is twice the magnitude of the coefficient in the same regression for the sample
of all workers at established firms in Table 1. It appears that to offset the increasing burden ofknowledge, large firms create deeper hierarchies over time, and this allows them to implement agreater division of labor.
Table A4.
Panel A. Factors affecting the number of R&D tasks for employees at large firms.

Time
Experience (years after PhD)
Constant
Other controls: gender, ethnicity,
occupation, U.S. state of employment
Observations
R-squared

DV: IHS-transformed number of R&D tasks(1)(2)
Workers at large established
Workers at all large firms
firms
0.0057***0.0045***(0.001)(0.000)-0.0048***-0.0051***(0.000)(0.000)1.0095***1.0372***(0.073)(0.063)
Included

Included

36,1150.226

47,9870.222

33

Panel B. Factors affecting the span of control and depth of the hierarchy

Time
Experience (years after PhD)
Constant
Other controls: gender, ethnicity,
occupation, U.S. state of employment
Observations
R-squared

DV: span of control (IHStransformed # of individuals directlysupervised)(3)(4)
Workers at large
Workers at all
establishedlarge firms
firms-0.0036***-0.0028***(0.001)(0.001)0.0077***0.0063***(0.001)(0.001)0.6277***0.6230***(0.130)(0.113)

DV: depth of hierarchy (IHStransformed # of individuals
indirectly supervised)(5)(6)
Workers at large
Workers at all
establishedlarge firms
firms
0.0065***0.0065***(0.001)(0.001)0.0117***0.0099***(0.001)(0.001)-0.1227-0.0877(0.158)(0.131)

Included

Included

Included

Included

36,1150.210

47,9870.222

36,1150.489

47,9870.502

Estimation method: OLS. ***, **, and * denote statistical significance at the 1 percent, 5 percent, and 10 percent levels,
respectively. Since there are observations with zero number of R&D-related work activities as well as zero span of control anddepth of hierarchy, we use the inverse hyperbolic sine (IHS) transformations, defined as 𝑦 = 𝑙𝑛%𝑥 + √𝑥 ! + 1*.

E. Type of entrepreneurial opportunity
In the main text, we limited our sample of founders to owners of new incorporated businesses. As
mentioned, this was motivated by our desire to focus on the most innovative and entrepreneurial
portion of the sample of business owners and the self-employed. Past research (see Braguinsky,
Klepper, and Ohyama, 2012; Ohyama, 2015) has employed the relation between the job and thehighest degree as an alternative proxy for entrepreneurs in these data. SDR surveys contain answers to
the question about this relationship, with possible answers being “closely related,” “somewhat
related,” and “not related at all.” Following the methodology in those studies, we constructed adummy that equals one if the answer was “closely related” and zero otherwise, and we call suchbusiness owners/self-employed in new businesses “education-job related” (EJR) founders, regardlessof whether their businesses were incorporated. About half the self-employed and business owners andalso founders in the sample fall into this category.
In Table A5 we repeated the estimations of equation (1) on the sample of EJR founders,
whether their firm was incorporated or not. The number of R&D tasks exhibits a positive time trendthat is very similar in magnitude to the sample of incorporated startup founders in Table 1, Column(1). EJR founders have to cope with a number of R&D tasks that are, once again, increasing over timeat twice the rate experienced by workers at large, established firms (Table A4). However, we see thatin this case, the time trend is offset by experience (years after PhD), meaning that more experienced
EJR founders tend to perform fewer R&D tasks over time. Like the founders in the main sample, EJR
founders on average do not have recourse to deeper hierarchies, and while the coefficient on timetrend in the regression with the span of control as the outcome variable is estimated to be positive, it is
only marginally statistically significant.
34

Table A5. The number of R&D tasks, the span of control, and the depth of the hierarchy for EJR
founders
DV:
Time
Experience (years after PhD)
Constant
Other controls: gender, ethnicity,
occupation, U.S. state of employment
Observations
R-squared

IHS-transformed
number of R&D
tasks
0.0103***(0.002)-0.0050***(0.002)
1.1084***(0.216)

IHS-transformed
span of control

IHS-transformed
depth of hierarchy

0.0061*(0.003)-0.0046*(0.002)
0.7942*(0.446)-0.0015(0.003)0.0012(0.003)0.6389(0.393)

Included

Included

Included

2,093
0.478

2,093
0.257

2,093
0.317

Estimation method: OLS. ***, **, and * denote statistical significance at the 1 percent, 5 percent, and 10 percent levels,
respectively.

Table A6. Earnings of EJR founders
DV:
Time
Experience (years after PhD)
Experience squared
Experience x time
IHS-transformed number of R&D tasks
Span of control (IHS-transformed # of individuals directly supervised)
Depth of hierarchy (IHS-transformed # of individuals indirectly supervised)
Constant
Other controls: gender, ethnicity, occupation, U.S. state of employment
Observations
R-squared

Log (real earnings)-0.0119***(0.004)0.0169**(0.007)-0.0005**(0.000)0.0008***(0.000)-0.0499(0.035)0.0827***(0.024)0.0651***(0.021)11.0642***(0.255)
Included
2,093
0.236

Estimation method: OLS. ***, **, and * denote statistical significance at the 1 percent, 5 percent, and 10 percent levels,
respectively.

In Table A6 we report the results of the earnings regression on EJR founders. Overall, theestimations look similar to those presented for incorporated founders in Table 2. There is a declining
time trend for earnings for EJR founders of about 1.2 percent per year at the baseline. This decline isoffset by the interaction between the time trend and work experience. Given the mean years after PhD
of 14.2 years in this sample, the negative time trend is almost but not completely offset by experienceat the mean number of years after PhD. As in Table 2, it can be seen that EJR founders are notcompensated for performing more R&D tasks over time. The point estimate is even negative in this35

sample. The span of control and the depth of the hierarchy are both positively associated withearnings, just as in the sample of incorporated founders. Overall, results presented in the main text canbe seen to be robust to use of this alternative definition of Schumpeterian firms.
F. R&D-focused founders
Table A7 presents the dynamics of the share of R&D-focused founders, owners of established
incorporated businesses, and workers at established firms in the total number of individuals in eachcategory, as well as the share of R&D-focused owners of all incorporated businesses and of R&Dfocused workers at all large firms, using all available surveys. Founders and owners in charge of theirown businesses on average are less likely to be R&D-focused than workers, but there is an increasing
trend over time that is even more pronounced among all owners than it is among founders. Using themost comprehensive data from all surveys, column (3) reports that the share of R&D-focused ownersof incorporated businesses almost doubles from the mid-1990s to the mid-2010s. The share of R&Dfocused workers at established firms, in contrast, remains stable and even appears to decline slightly
in the sample of all workers at large firms.
Table A7. Shares of R&D-focused founders and workers(1)Founders
1995
1997
1999
2001
2003
2006
2008
2010
2013
2015
2017

0.261
0.288
0.316
0.359

0.309
0.308
0.373
0.316

(2)
(3)
(4)
Owners of established
Owners of all
Workers atincorporated businesses incorporated businesses established firms
0.178
0.118
0.167
0.509
0.150
0.189
0.520
0.157
0.202
0.508
0.297
0.312
0.512
0.309
0.288
0.279
0.285
0.524
0.285
0.289
0.503
0.287
0.303
0.512
0.328
0.325
0.517

(5)
Workers at alllarge firms
0.600
0.586
0.598
0.578
0.555
0.545
0.566
0.561
0.531
0.553
0.547

Source: Author calculations using weighted National Science Foundation data.

In Table A8 we present the results of earnings regressions on the sample of R&D-focusedfounders and R&D-focused workers at established firms. As discussed in the main text, the trend
toward declining earnings is much stronger in this subsample of founders, although the offsetting
impact of the interaction term between experience is also stronger, implying that at the mean of years
after PhD, the baseline negative time trend is more or less offset. As for R&D-focused workers, somecoefficients are somewhat smaller in magnitude than for the full sample but, overall, the results look
similar.

36

Table A8. Earnings of R&D-focused founders and workers at established firms
DV: Log (real earnings)

Time
Experience (years after PhD)
Experience squared
Experience x time
IHS-transformed number of R&D tasks
Span of control (IHS-transformed # of individuals directlysupervised)
Depth of hierarchy (IHS-transformed # of individuals
indirectly supervised)
Constant
Other controls: gender, ethnicity, occupation, U.S. state of
employment
Observations
R-squared(1)
Founders(2)
Workers at
established firms-0.0445***(0.012)-0.0161(0.013)-0.0003(0.000)0.0026***(0.001)-0.1521(0.123)0.1109**(0.054)0.0549(0.044)11.9358***(0.477)
Included690
0.234

0.0027***(0.001)0.0293***(0.001)-0.0005***(0.000)0.0001***(0.000)0.0326***(0.009)0.0372***(0.003)0.0364***(0.004)10.7582***(0.048)
Included
33,7100.179

Estimation method: OLS. Robust standard errors in parentheses. ***, **, and * denote statistical significance at the 1 percent,
5 percent, and 10 percent levels, respectively. Since there are observations with zero numbers of R&D-related work activities
as well as zero span of control and depth of hierarchy, we use the inverse hyperbolic sine (IHS) transformations, defined as
𝑦 = 𝑙𝑛%𝑥 + √𝑥 ! + 1*.

Table A9 presents summary statistics on annual real earnings of R&D-focused founders andworkers at established firms, as in Table A3. Less experienced founders’ earnings suffer the biggest
decline: these decrease by about 40 percent in the surveys conducted in 2010s compared to the first
four surveys. More experienced founders, as well as workers at established firms (regardless ofexperience), by contrast, saw a modest upward time trend in their earnings.
Table A9. Annual real earnings of founders and workers at established firms: R&D-focused (in US $)Founders
Survey YearAll

≤13 years after
PhD

1997
1999
2001
2003
2010
2013
2015
2017

61,60690,15589,662
95,88084,476
86,56878,85578,56560,568101,111
116,805
112,174
62,871
66,953
57,66253,605Workers at established firms>13 years after
PhD

All

≤13 years afterPhD>13 years after
PhD

62,11684,90370,07583,704
101,204
97,002
91,37597,25874,012
74,77278,47379,89583,497
82,53685,262
83,61964,877
66,47069,676
71,083
71,94670,02372,856
72,51085,41785,97689,87392,675
97,154
94,876
97,517
95,924

Source: Author calculations using weighted National Science Foundation data. Salary data were adjusted for inflation using
consumer price index. Thirteen years after PhD corresponds the median post-PhD experience in the sample.

37

G. Human capital
SDR surveys have a longitudinal aspect, as each new survey between 1995 and 2013 returns toprevious survey respondents, and new PhD recipients are added to every survey. The attrition rate
between adjacent surveys is about 10-15 percent. In 2015, however, the survey was sent to an almost
entirely new sample of respondents (the attrition rate from the 2013 to the 2015 survey is about 90
percent). Hence, we use two waves of panel data available for founders: the 1997-2013 wave and the2015-2017 wave.
To estimate changes in the human capital of founders we use the longitudinal aspect of thedata and look at all those employed at a private-for-profit business in a survey. We create a dummythat equals one if the individual is reported to be a startup founder in the next survey and zero if he or
she remained employed at a private-sector for-profit business in the next survey as well. We thencompare these “pre-choice” salaries to obtain an estimate of the human capital differential between
future founders and those who remained in paid work for other firms, based on how much they earned
while both types of individuals were employed for pay.
Because the demographics are different across future founders and those who remained in
paid work for other firms (in particular, the former tend to have more experience than the latter), in
Figure A8 we present predicted logged pre-choice salaries from an earnings regression estimatedseparately on the data from each relevant pre-choice survey (1995, 1997, 1999, 2001, 2008, 2010, and2015), in which the dependent variable is the pre-choice logged salary, and the explanatory variables
are work experience and its squared term, tenure on the current (pre-choice) job, gender and ethnicity
dummies, as well as 75 occupation dummies, U.S. state of employment (Massachusetts, New York,
California, etc.) dummies, and eight employer size-category dummies. Figure A8 plots these predicted
logged pre-choice salaries and the 95 percent confidence intervals.
Figure A8. Predicted logged pre-choice salaries for those becoming startup founders in the next
survey and those remaining as wage workers

Source: Author estimates based on National Science Foundation data. The bars represent 95 percent confidence intervals.

38

As seen in Figure A8, the average predicted earnings of those who become founders in thenext survey are lower than the average predicted earnings of those who remain at private sector forprofit businesses in the 1990s but converge in 2001 and exceed those of workers who continue towork for other firms in 2008-2010. Because of the small sample size of founders in these longitudinaldata, the 95 percent confidence intervals for the most part overlap. The only time when there is astatistically significant difference at the 5 percent level between predicted pre-choice salaries of thesetwo categories of individuals, in 2010, the pre-choice predicted salaries of future founders are actuallyhigher than those who continue to work for other firms. This suggests that PhDs who left paid
employment to start their own incorporated businesses between 2010 and 2013, that is, right after theGreat Recession, had significantly higher human capital than those who remained in paid work forothers. In any case, the evidence in Figure A8 does not support a decline in the human capital offounders.
For the sake of comparison, Figure A9 presents the same pre-choice salaries but in the raw
data. The picture is qualitatively the same, except that the pre-choice salaries of future founders withmore experience are not lower than those who remained in paid work for others even in the 1990s.
Figure A9. Actual pre-choice salaries for those becoming startup founders in the next survey
and those remaining wage workers

Source: Author calculations using weighted NSF data. The bars represent 95 percent confidence intervals.

In Figure A10 we look at an alternative measure of human capital––the share of individuals
who graduated from top-ranked PhD programs. SDR data contain Integrated Postsecondary

Education codes for the institutions from which the respondents graduated and their field ofspecialization in the graduate program. We matched these codes with the National Research Council(NRC) PhD program rankings published in 1993 and constructed a dummy that equals one if theindividual graduated from a PhD-granting institution in the top 15 in the NRC rankings and zerootherwise. We also added to these top-ranked programs graduates whose specific PhD area wasamong the top 15 in the NRC’s area rankings (the areas were mathematics and physics, engineering,39

social sciences, and biological sciences). Figure A10 is consistent with Figure A8 in showing thatfounders lag behind in terms of the share graduating from top-ranked programs in the 1990s but catchup to a large degree after 2003. Notably, the catchup trend is even more pronounced among founders
with below the median years after PhD, despite the evidence presented that they suffer an increasing
earnings penalty over time.
Figure A10. Share of graduates from top-ranked programs

Source: Author calculations using weighted National Science Foundation data.

H. Science-based PhD fields
In this part we focus on areas that are especially dependent on prior cumulative knowledge. In our
main analysis we control for 72 dummies for various occupation, but many founders list theiroccupation as “managers,” which precludes further analysis. To obtain a proxy for those whose
startups are likely to be especially dependent on cumulative knowledge, we use the SDR data on thefield of the PhD. Specifically, we define as “science based” the following four broad fields of studies(variables MRMENG/NMRMENG in SDR surveys): biological sciences, environmental life sciences,chemical engineering, and electrical and computer engineering (dealing with hardware). Based on
more detailed classifications, we also add the following narrower fields (variables
MRMED/NMRMED in SDR surveys): bioengineering and biomedical engineering, petroleum
engineering, biochemistry and biophysics, cell and molecular biology, and microbiological sciences
and immunology. About a third of founders and a similar share of workers at established firms in our
sample have degrees in these fields. We then repeat major parts of our analysis for science-based
PhDs. (We also tried another definition that defined founders/workers who worked in occupations
corresponding to the PhD fields above to be science based, adding managers with PhDs in sciencebased fields. The findings were similar to those presented here. Details are available upon request. We
also tried a more inclusive definition that would combine the two. Again, the results were similar.)40

In Figure A11 we present the dynamics of the share of founders and workers employed atstartups similar to Figure 1 for those with science-based PhDs. The picture is similar to Figure 1, but
the decline in the share of startups is more precipitous (a decline of about 50 percent from 1997 to2017). The decline in the share employed at startups is approximately the same as in Figure 1,
suggesting that startup team sizes might be increasing, especially in this category.
Figure A11. Dynamics of the share of science-based PhDs employed at or founders of startups

Source: Author calculations using weighted National Science Foundation data.

Table A10. Probability among science-based PhDs of being a founder
DV: probability of an incorporated business owner being a startup owner
Time
Time squared
Experience (years after PhD)
Dummy set at one if a science-based PhD, zero otherwise
Interaction term between time and the dummy set at one if a science-based PhD, zero otherwise
Constant
Other controls: gender, ethnicity, U.S. state of employment, occupations
Observations
R-squared-0.0172***(0.004)0.0006***(0.000)-0.0079***(0.000)0.0867***(0.029)-0.0048***(0.002)0.576***(0.073)
Included
8,911
0.086

Estimation method: OLS, linear probability model. Robust standard errors in parentheses. ***, **, and * denote statisticalsignificance at the 1 percent, 5 percent, and 10 percent levels, respectively.

In Table A10 we put this observation to a more formal test. The dependent variable is theprobability of being a founder, with the sample consisting of all incorporated business owners, and thevariables of interest are the time trend, work experience after PhD, and a dummy set at one if theindividual had received a PhD in one of science-based PhD fields and zero otherwise, as well as its
interaction with the time trend. The estimations are carried out by a linear probability model, althoughprobit and logit specifications lead to very similar results.
41

The results in Table A10 indicate that over the past 20 years, the probability of being afounder has declined by about 20 percentage points (-0.0172*20 + 0.0006*400) overall. This closely
corresponds to what we saw in the raw data in Figure 1. Individuals with science-based PhDs were 8.7
percentage points more likely to be founders in 1997—again, very similar to the raw data. However,among individuals with science-based PhDs, the probability of being a founder declines by anadditional 0.5 percentage points each year, so that by 2017 they are indistinguishable from other PhD
recipients in science and engineering. Overall, we see evidence here that the decline in startup entry
rates is even more severe in science-based fields than in other fields.
Is this related to the burden of knowledge? To probe this, we once again look at the dynamics
of R&D tasks as well as other tasks for science-based founders and compare them to their peers at
established firms, shown in Figure A12, Panels A and B, following the same format as Figure 3.
Figure A12
Panel A. The number of all, R&D, and management work activities among science-based founders

Panel B. The number of all, R&D, and management work activities among science-based workers at
established firms

Source: Author calculations using weighted National Science Foundation data.

42

Comparing Panel A of Figure A12 with the corresponding panel in Figure 3, we see that, first,
perhaps not surprisingly, founders with science-based PhDs overall perform more R&D tasks thanother founders. They also experience a bigger absolute increase in the number of such tasks (from 1.3
to 2.0 between 1997 and 2017) compared to all PhDs, although percentage-wise the rate of increase issimilar, about 55 percent in both cases. As with all PhDs, their management tasks do not exhibit atime trend, leading to an increase in the overall number of tasks handled by founders from 4.8 to 5.6
in this sample (4.5 to 5.2 in the full sample). Thus, while time trends are similar between founders
with science-based PhDs and other founders, the absolute burden of knowledge is significantly higher
for the former than for the latter
The picture presented in Panel B, by contrast, does not reveal large differences between
workers with science-based PhDs and workers with other PhDs at established firms, neither in thechange in tasks nor in absolute levels. In particular, workers at established firms with science-based
PhDs performed 1.94 R&D tasks in 1997, and that number increased to 2.08 in 2017 (with a peak of
2.17 in 2010). the corresponding numbers for all PhDs were 1.86, 2.08, and 2.10 at the peak in 2015.
The number of management-related tasks and other tasks is also very similar across these two samples
for workers at established firms.
Another measure of the burden of knowledge suggested by Jones (2009) is age (workexperience). In Table A11 we present the results of a regression analysis in which the dependent
variable is the (IHS-transformed) number of years after PhD, and the variables of interest are the timetrend, a dummy set at one if the individual has received a PhD in one of the science-based PhD fields
and zero otherwise, as well as its interaction with the time trend.
Table A11. Dynamics of founders’ work experienceTime
Dummy set at one if a science-based PhD, zero otherwise
Interaction term between time and the dummy set at one if ascience-based PhD, zero otherwise
Constant
Other controls: gender, ethnicity, U.S. state of employment,
occupations
Observations
R-squared

DV: IHS-transformed years after PhD
0.0070***0.0070***0.0090***(0.003)(0.003)(0.003)0.04890.1251(0.046)(0.083)-0.0061(0.005)2.4124***2.4017***2.3769***(0.350)(0.354)(0.359)Included

Included

Included

2,101
0.164

2,101
0.165

2,101
0.166

Estimation method: OLS. Robust standard errors in parentheses. ***, **, and * denote statistical significance at the 1
percent, 5 percent, and 10 percent levels, respectively.

The average number of years after PhD for founders increases by about 0.7 percent with eachcalendar year, so founders are estimated to have about 14 percent more work experience after their
PhD, controlling for demographics, geographic location, and occupation in 2017 than in 1997.
43

Interestingly, this does not translate into a much older age for founders (see Section 5.6 in the maintext). If years after PhD is replaced by age in the regression in Table A11, the coefficient on the timetrend drops to 0.0013 (statistically marginally significant), indicating that founders were, if at all, just
2.6 percent older in 2017 than they were in 1997.
Consistent with Jones (2009), we also see that founders with science-based PhDs tend to havemore work experience after PhD than other founders, although the standard errors are high and thecoefficients are not statistically significant at conventional levels. The issue appears to be theoccupation dummies that offset the effect of science-based occupations in the estimations in Table
A11. If we omit occupational controls, the coefficient on the dummy to capture science-based PhDs
(not shown) doubles in magnitude and becomes statistically significant at the 5 percent level,
indicating that founders with science-based PhDs tend to have about 10 percent more work experience
than other founders. However, there is no pronounced trend toward founders with science-based PhDs
becoming more experienced over time, suggesting that requirements for more post-doctoral work
experience prior to becoming a founder increase more or less uniformly for all founders. Also
consistent with the burden of knowledge theory, workers with science-based PhDs at establishedfirms tend to have a larger span of control and more hierarchical depth than workers at other firms,
but, once again, there is no trend toward either span of control or depth of hierarchy increasing overtime for this category of workers compared to other workers. Details are available upon request.
I. Supply of PhDs
Figure A13 shows that the number of PhDs awarded in science and engineering fields increased
approximately 45 percent, from 28,486 in 1997 to 41,294 in 2007.
Figure A13. PhDs awarded in science and engineering by U.S. colleges and universities, 1997–2007

Source: Doctorate Recipients from U.S. Universities: 2018, National Center for Science and Engineering Statistics, National
Science Foundation, available at https://ncses.nsf.gov/pubs/nsf20301/.

44

J. Computer/IT-related occupations and tasks
The first year in which survey responses about new businesses become available is 1997, so wecannot verify directly whether the dot com boom of the late 1990s temporarily increased thepropensity to found/be employed in startups above the long-term trend. We note, however, that otherstudies found that the trend toward declining business dynamism is long-term and while it may havebeen marginally influenced by the dot com boom of the late 1990s, the effect appears not that large(see, e.g., Figure 7 in Akcigit and Ates, 2019a). Furthermore, while computers and IT were no doubt
becoming more used in the jobs that doctorate degree holders are likely to perform, most of thespecialization of the doctorate recipients in our data are not in computer or IT-related areas.
To make sure that the basic trends toward declining rate of startups (and employment instartups) are not driven by the trends in computer/IT-related occupations or tasks, we havereconstructed Figure 1 while excluding all such occupations (computer and information scientists,system analysts, database and network administrators, computer software and hardware engineers,
computer programmers, etc.) and also excluding those who reported that computer-related work
activities (computer applications, programming, system development) occupied at least 10 percent of
their time during a typical work week. The results are presented in Figures A14 and A15, respectively.
Figure A14. Dynamics of the share of PhDs employed in or founders of startups, excluding
computer/IT-related occupations

Source: Author calculations using weighted National Science Foundation data.

Both figures look almost exactly the same as Figure 1 in the main text. The rate of startups incomputer-related occupations themselves (not shown) also fell over time, by about one half from thelate 1990s to the late 2010s.

45

Figure A15. Dynamics of the share of PhDs employed in or founders of startups, excluding
computer/IT-related tasks

Source: Author calculations using weighted National Science Foundation data.

While the trend in startup founding rates and employment in startups is similar between
computer-related and computer-unrelated occupations, the share of doctorate recipients who areemployed in computer-related occupations in our data as a whole is steady among incorporated
business owners and is actually even slightly increasing over time among those employed in theindustry (Table A12). More precisely, there appears to be two periods where the latter fraction isincreasing, from 1997-2001 and then once again, from 2008-2017. Significantly, there is no relation
between these trends and the trend toward declining startups. The same is true of the dynamics of theshare of PhDs performing computer-related tasks on their jobs.
Table A12. Share of PhDs in computer-related occupations and conducting computer-related tasks.

1995

Share of PhDs in computer-related
occupations in total
Industry
Incorporated
employmentbusiness owners
0.099
0.051

Share of PhDs with computer-related tasks
in total
Industry
Incorporated
employmentbusiness owners
0.418
0.314

1997

0.128

0.076

0.401

0.299

1999

0.150

0.070

0.427

0.317

2001

0.156

0.058

0.412

0.291

2003

0.138

0.070

0.375

0.295

2006

0.132

0.068

0.314

0.238

2008

0.125

0.067

0.324

0.230

2010

0.140

0.049

0.348

0.236

2013

0.148

0.062

0.355

0.233

2015

0.156

0.064

0.364

0.239

2017

0.167

0.064

0.392

0.258

Note: PhDs with “computer-related tasks” are those who reported that computer-related work activities occupied at least 10
percent of their time during a typical work week.

46