Artificial Intelligence, Education, and EntrepreneurshipOctober 26, 2020
Abstract
The scarcity of the human capital needed for R&D in Artificial Intelligence (AI) created an unprecedented brain drain of AI professors from universities in 2004-2018. We exploit this brain drain as a source of variation in students’ domain-specific knowl- edge and provide causal evidence that domain-specific knowledge is important for startup formation, seed funding, round A funding, and funding growth rate. The effect is the largest for top universities, PhD students, and startups in the area of deep learn- ing. These results contribute to the entrepreneurial finance literature and challenge the current view that entrepreneurs are jacks-of-all-trades, master of none (Lazear 2004).
Keywords: Artificial intelligence, startup financing, entrepreneurship, human capital, innovation, education, deep learning
∗We are grateful to Ron Kaniel, Adrien Matray, Cade Metz, Michael Roach, Scott Stern, Zvi Wiener, participants at 2019 Stanford HAI-AI Index Roundtable Workshop, and seminar participants at the Hebrew University and the University of Alberta for their helpful comments and discussions. Michael Gofman acknowledges the Alfred P. Sloan Foundation for financial support (Grant number: G-2020-14005). Zhao Jin acknowledges the Ewing Marion Kauffman Foundation for financial support (Grant number: G-201806-4434) and Crunchbase for access to its database. All errors are our own.
1Michael.Gofman@simon.rochester.edu, University of Rochester. 2Jinzhao@ckgsb.edu.cn, Cheung Kong Graduate School of Business.
1 Introduction
Startups and entrepreneurship are important for innovation, employment, and economic growth (e.g., Schumpeter 1942; King and Levine 1993). It is important to understand what are the main determinants of entrepreneurship. In this paper, we study the role of founders’ domain-specific knowledge on the formation and funding of startups. Ex-ante, the role of domain-specific knowl- edge for startup success is not clear. Some founders of the tech startups that turned into the most valuable companies in the world are college dropouts (e.g., Bill Gates, Steve Jobs, and Mark Zuckerberg), while others took advanced computer science classes and excelled in their academic studies (e.g., Jeff Bezos, Larry Page, and Sergey Brin). The existing literature emphasizes the role of general education because entrepreneurs need to be jacks-of-all-trades, master of none (Lazear 2004).1 The main contribution of this paper is to provide a causal evidence that formal education that develops specialized knowledge helps students to establish startups and to attract funding from VCs.
Our analysis is based on a novel sample of 177 AI startups founded by 363 AI entrepreneurs. It is especially important for AI startups to understand factors that increase entrepreneurship and help to attract VC funding because of their exponential growth and high potential for creative destruc- tion. According to Perrault et al. (2019), investments in global AI startups reached a record of $40B in 2018, representing a nearly thirty-fold growth from 2010 to 2018 with an average annual growth rate of over 48%. AI becomes one of the most promising and disruptive General Purpose Technol- ogy (GPT) with a potential to change every aspect of our lives and spur economic growth (Aghion et al. 2017; Trajtenberg 2018; Acemoglu and Restrepo 2018; Cockburn et al. 2018). The largest companies and countries in the world fight for leadership in AI. Sundar Pichai, CEO of Google, said in 2016, “In the next 10 years, we will shift to a world that is AI-first.” In February 2019, the White House issued an executive order titled, “Maintaining American Leadership in AI”.2 Over a short period of time, AI has become “a key driver of the Fourth Industrial Revolution”.3 We study startups that are at the forefront of this revolution.
To identify a causal effect of domain-specific knowledge on the probability of starting a com- pany and attracting funding is quite challenging, mainly due to the difficulty in measuring the variation in the domain-specific knowledge that students receive during their studies at the univer-
1Education could also be purely a costly signaling device without any intrinsic value (Spence 1973). 2https://www.whitehouse.gov/ai 3World Economic Forum https://www.weforum.org/projects/reimagining-regulation-for-the-age-of-ai
1
sity. To overcome this challenge, we use an unprecedented brain drain of AI faculty from academia to industry as a source of variation in students’ AI-specific knowledge. The significant pay gap be- tween industry and academia makes it virtually impossible for universities to retain their best AI professors. Top AI researchers, such as Geoffrey Hinton and Yann LeCun who won the 2018 Tur- ing Award, were hired by Google and Facebook respectively to lead their AI projects. Geoffrey Hinton’s student, Ilya Sutskever, got paid more than $1.9 million by OpenAI in 2016 (Metz 2018a). In addition to the unmatchable compensation package, industry jobs attract AI professors due to superior computational resources, big data, and the ability to deploy their intellectual products at scale (Etzioni 2019).
We find that during 2004-2018, there are 221 AI faculty departures. 131 AI professors com- pletely left universities to pursue an industry career or to establish their startups, and 90 AI pro- fessors worked for a private company or opened his/her own startup while still retaining affiliation with the university. We also identify 160 AI faculty who moved to another North American uni- versity during the same period. As a comparison, Azoulay et al. (2017) document that for every life scientist who left for an industry job from 1977 to 2006, 90 moved to another department in academia. The high ratio of AI faculty who left for the industry job to AI faculty who moved to another university is one of the signs that North American universities have been experiencing an unprecedented AI brain drain to the industry.
We use the vast AI brain drain as a shock to study the effect of founders’ AI knowledge on startup formation and funding.4 The idea for identification is that faculty departures to the indus- try reduce the knowledge transfer to the students. If domain-specific education is an important factor in the creation and the funding of startups, we should expect that the reduction in student’s AI knowledge due to faculty departures to the industry would result in less startup formation by students and these startups would attract less capital.5
We find that domain-specific knowledge is important for AI entrepreneurship both on the ex- tensive and intensive margins. On the extensive margin, we find that AI faculty departures have a negative effect on the AI startups of students who graduated from the professors’ former uni- versities. In particular, we find that the departures by tenured professors’ during the time window
4Usually the term “brain drain” is used in the context of immigration of highly skilled works to another country (Kwok and Leland 1982), in this paper we use this term to describe the exodus of AI professors to the industry.
5In a recent Bloomberg op-ed, Ariel Procaccia, a computer science professor at CMU, says “If industry keeps hiring the cutting-edge scholars, who will train the next generation of innovators in artificial intelligence?” (Procaccia 2019).
2
[t− 6, t− 4] have the largest effect on students graduating in year t. Specifically, for a given uni- versity, departure of one tenured AI professor during the time window [t − 6, t − 4] on average reduces the number of future AI entrepreneurs who graduate in year t by about 11%. Untenured faculty departures do not show a significant effect. Moreover, the effect is most pronounced for entrepreneurs, who graduate from the top 10 universities, who hold PhD degrees, who establish startups in the field of deep learning,6 and who start new firms within the first two years of their graduation. We also find that when tenured AI professors leave universities and they are replaced by faculty from lower-ranked schools or by untenured AI professors, the negative effect of the faculty departures on AI startups intensifies.
The extensive margin results suggest that gaining specialized knowledge from professors is important for establishing a startup. First, we see the effect mainly for departures prior to students’ enrollment. Second, tenured faculty, especially at the top 10 computer science departments, are more likely to be star professors whose knowledge students can utilize in their startups, while untenured professors can leave because they have not received tenure and they are also less likely to supervise PhD students. Third, deep learning is a novel field in machine learning that drives many recent innovations. The fact that faculty departures disproportionately affect deep learning startups suggests that the source of knowledge in this advanced area of AI is coming from AI professors. Any non-specialized skill transfer from professors to students, such as leadership skills, should be unrelated to the type of technology used by startups. Fourth, our finding that PhD students are the most affected category suggests that advanced knowledge of AI is important for startup success. PhD students make the largest investment in specialized knowledge and also depend more on interaction and learning from professors than undergraduate or master students. Lastly, the fact that the negative effect of tenured professors’ departures are intensified when they are replaced by untenured professors or professors from lower-ranked schools suggests the importance of high quality specialized knowledge for startup formation.
On the intensive margin, we find that tenured AI faculty departures at time [t− 6, t− 4] have a negative effect on the early-stage funding of AI startups with founders who graduated in year t from the affected universities.7 Specifically, the departure of one tenured AI professor in time window [t−6, t−4] decreases, on average, first-round and series-A round funding by, respectively,
6Deep learning is a machine-learning technique that uses neural networks with a very large number of layers (LeCun et al. 2015).
7Our focus is on early-stage startups because, as of the year 2018, 85% of the AI startups in our sample are still in the early stage (i.e., no later than series A round).
3
$1.68 million and $6.6 million. Also, the funding growth rate of a representative AI startup in our sample is reduced from 630% to 246% by departure of one tenured AI professor in time window window [t− 6, t− 4]. More recent departures (i.e., one to three years prior to the entrepreneur’s graduation) or untenured faculty departures do not have a significant effect.
Our intensive margin results suggest that access to funding and the funding growth rate depend on the specialized knowledge of entrepreneurs. On average, AI entrepreneurs in our sample start new AI startups over two years after graduation, receive seed funding three years after graduation, and receive Series A funding four and a half years after graduation. It means that a significant gap exists between the time professors depart and the time when a startup receives funding. This long-term effect of AI brain drain on funding can be explained by the lack of knowledge transfer from professors to students and by the importance of specialized knowledge for VC funding. An alternative explanation is that professors who leave for the industry might have VC connections, especially in the same city, that benefit students. When professors leave, students lose these con- nections. However, we do not find a larger effect of AI brain drain on startups in the same location as the university or on startups that do not have MBA co-founders. Another alternative is that VCs who took a class from the same professor as the founders might have a higher propensity to invest in the startup. But, this alternative would not explain our results for deep learning startups or why the negative effect is stronger when professors are replaced by professors from lower-ranked schools or by untenured professors.
We also study alternative explanations for our extensive margin results. One explanation is that professors who leave for the industry take their best students with them. Inconsistent with this explanation, we see a strong negative effect for departures that take place 4-6 years prior to students’ graduation (before students’ enrollment) and no effect for departures 1-3 years prior to graduation. Students selection is another important concern. Maybe the best students enroll in a different university if they see AI professors leaving for industry from their target school. In this case, the negative effect should be stronger for departing professors who do not keep university affiliation. We find the opposite. The effect is driven by faculty who take industry jobs or establish own startups but keep university affiliation. These are likely to be the most accomplished pro- fessors because universities agree to forego an option to replace them by letting them keep their affiliation.
There could be unobservables not captured by university fixed effects that cause a university to lose its AI faculty and to produce fewer AI entrepreneurs in the future. These unobservables
4
should also be affecting the amount of funding AI startups receive a decade after faculty depar- tures. These are unlikely to be university-specific shocks because we do not find a negative effect on non-AI startups. We also rule out city-level shocks by showing that there is no effect of AI faculty departures in one city on local AI startups established by entrepreneurs who graduated from universities located in other cities. To address residual endogeneity concerns, we instrument AI faculty departures 6-4 years prior to student’s graduation with AI professors’ average number of citation received 8-6 years before student’s graduation.8 On the one hand, companies are more likely to hire star faculty with a high number of (recent) citations (Zucker et al. 2002). On the other hand, citations are unlikely to directly affect the creation of startups by the students who graduate six to eight years later, and are highly unlikely to directly affect the financing of those startups a decade later.9 The results from the 2SLS regression further strengthen our causal interpretation of the negative effect of AI brain drain on the number of startups by students and the amount of funding.10
Related literature. Previous studies have found that financing (Hall and Lerner 2010; Kaplan and Strömberg 2003; Kerr et al. 2011; Corradin and Popov 2015; Bernstein et al. 2017; Ersahin et al. 2020), working experience in tech firms (Gompers et al. 2005; Elfenbein et al. 2010), peer effects (Nanda and Sørensen 2010; Lerner and Malmendier 2013), and age (Azoulay et al. 2020) are important determinants for startup’s creation. Our main contribution is show the importance of university-acquired domain-specific knowledge both for startup creation and early-stage financing.
Kaplan et al. (2009) study 50 VC-backed companies and emphasize the importance of non- human assets by showing that business lines are stable from inception to IPO, while management turnover is substantial. On the other hand, Ewens and Marx (2018) show that VCs add value through replacing the founders. Bernstein et al. (2017) study the importance of the founding team in startups’ early-stage funding. They design a randomized field experiment for 21 early-stage startups and show that the average investor pays more attention to the founding team information rather than the information about business ideas and current investors. In a recent study, Bernstein et al. (2019) find that when local opportunities arise, the firm formation is almost entirely driven by
8We also use two alternative instrumental variables, the cumulative number of industry coauthors for all the AI professors at a given university up to t− 6 and the average number of citations received in [t− 8, t− 6] based on the papers coauthored with people working in industry.
9Extensive analysis of the exclusion restriction appears in Section 5.2.2. 10All our regressions include university fixed-effects and a dynamic ranking of computer science departments. Even
if citations provide an additional signal about the quality of the department and its students, we would expect to see an increase in the number of AI startups following an increase in citations, not a decline.
5
skilled entrepreneurs. To the best of our knowledge, our paper is the first to show that highly spe- cialized human capital acquired at universities is a major determinant of entrepreneurship activity both on the extensive and on the intensive margins.
Lazear (2005) uses data from Stanford MBA alumni and find that those who end up being entrepreneurs study a more varied curriculum when they are in the program than do those who end up working for others, suggesting a university’s generalist, rather than specialist, training is a more important determinant of its graduates’ entrepreneurship. Wagner (2003) uses data from Germany and finds a positive correlation between the diverse experience across several domains and a probability to establish a new firm. Our paper challenges the jack-of-all-trades, master of none characterization of entrepreneurs (Lazear 2004). It is the first to provide causal evidence that domain-specific knowledge is important for startup formation and funding.
Our paper also contributes to the debate about the effect of tech giants on entrepreneurship. On the one side, Babina and Howell (2018) document a knowledge spillover effect showing that corporate R&D leads to more startups by the employees. Jin (2020) uses Amazon’s HQ2 finalists decision to show that the prospect of getting funded or acquired by tech giants have a positive effects on entrepreneurship. On the other side, Kamepalli et al. (2020) argue that big tech platforms create a “kill zone” around their business. They show that investments by VCs in startups drop after Facebook and Google make major acquisitions in the area in which these startups operate.11 Our paper shows that big tech firms can also have a negative effects on entrepreneurship through their poaching of university professors and reducing the diffusion of AI knowledge.
More broadly, the paper contributes to a new literature that studies AIâs impact on the economy and society. In a recent paper, Acemoglu and Restrepo (2018) develop a framework in which AI can have both a negative and a positive effect on demand for labor. The negative effect is due to the displacement risk, and the positive effect is due to improved productivity and higher capital accumulation. Korinek and Stiglitz (2017) and Guerreiro et al. (2017) discuss the channels through which AI can increase inequality and the best way to address this concern. Aghion et al. (2017) study the implications of AI on economic growth. To the best of our knowledge, this is the first finance paper about AI.12 Our first contribution to this literature is to show that scarcity of AI
11Related, Cunningham et al. (2020) find that big pharma firms make acquisitions to discontinue target’s innovation process in order to preserve its current business.
12There is a growing literature that uses deep learning and machine learning as a tool for finance applications (Heaton et al. 2017; Chen et al. 2019; Aubry et al. 2019; Erel et al. 2018; Gu et al. 2020) but AI itself is not the focus of this literature.
6
human capital is important for innovation by AI startups. This scarcity can also reduce labor- augmenting technical progress, increase inequality, and negatively impact opportunities for AI- driven economic growth. Our second contribution to this literature is to analyze the obstacles of AI startup formation and funding. Given the importance of innovation to the economy (Romer 1990), these obstacles should not be overlooked.
2 Data Description and Summary Statistics
2.1 AI Entrepreneurs and AI Startups
Entrepreneurs’ and startups’ information is based on a sample from the CrunchBase database.13
According to the Kauffman Foundation, CrunchBase is "the premier data asset on the tech/startup world."14 One of the advantages of CrunchBase over other commercial databases is the broad cov- erage due to crowdsourcing and data coverage from TechCrunch. As a result, CrunchBase sample includes startups that are not financed by venture capitals, compared to the data from VentureX- pert. Dalle et al. (2017) compare the coverage of CrunchBase with that of OECD Entrepreneurship Financing Database and find that starting from 2010, the start year of our sample, CrunchBase has broader coverage in both the US and other countries.
More importantly, relative to other databases, CrunchBase provides two unique features that are necessary for our paper. First, CrunchBase provides technology-specific or product-specific keywords that allow us to clearly identify AI startups. To classify AI entrepreneurs/startups, we rely on the business categories CrunchBase provides. AI entrepreneurs/startups have at least one of the following categories: artificial intelligence, machine learning, deep learning, neural net- works, robotics, face recognition, image processing, computer vision, speech recognition, natural language processing, autonomous driving, autonomous vehicle, and the semantic web. Second, given that AI is still an emerging technology and AI brain drain does not become prevalent at most universities until 2012, it is important to use a database that provides the most recent data available. CrunchBase allows us to track newly established AI startups until 2018.
The sample we obtain from CrunchBase includes 363 AI entrepreneurs and 985 Non-AI (IT) entrepreneurs who graduated from 69 North American universities during 2010 - 2018. An AI
13CrunchBase would be a less than ideal database for studying entrepreneurship in non-tech industries, but for our study, the database’s tech industry focus works to our advantage.
14Source: https://www.kauffman.org/microsites/state-of-the-field/topics/finance/equity/venture-capital
7
entrepreneur is identified if he or she starts an AI startup after received the highest degree in our sample. The 69 universities are selected so that at least one AI entrepreneur graduated from each of those universities before 2010. In total, they founded or co-founded 177 AI startups and 591 Non- AI (IT) startups. These AI startups innovate in the areas of cybersecurity, retail, early detection of breast cancer, solutions for the oil and gas industry, augmented reality, sleep therapy, supply chain planning, and more.
One of the contributions of the paper is to compare AI startups and AI entrepreneurs and to non-AI startups and non-AI entrepreneurs in other fields of the information technology (IT) sector. In Table 2, we compare characteristics of 363 AI entrepreneurs from 177 AI startups with charac- teristics of 985 entrepreneurs from 591 IT startups that are not AI.15 We find that the distribution of the educational levels for AI founders is skewed towards higher degrees. Specifically, we find that AI entrepreneurs, on average, hold higher academic degrees than non-AI entrepreneurs, with as many as 26% holding a PhD degree, as opposed to 10% for non-AI entrepreneurs. And 24% of AI startups have at least one founder with a PhD degree, while only 7% of other startups in the IT field have at least one PhD-holding founder. In terms of early-stage funding, compared to other IT startups, AI startups raised $1.01 million more in the first round and $2.9 million more in the series A round. We also find that AI entrepreneurs are less likely to be the sole founder relative to non-AI entrepreneurs. They have a larger time gap between the graduation year for their highest degree and the year when they established their startup. All these facts suggest that AI startups require a higher level of expertise and knowledge than startups in non-AI fields.
We document that top North American universities produced the most alumni who established AI startups after receiving their highest degree (see Figure OA1 in the Online Appendix). In our sample, 78 MIT graduates and 72 Stanford graduates established AI startups. Carnegie Mellon University, ranked third according to this metric, produced 39 AI entrepreneurs. The Canadian university with the most AI entrepreneur alumni is the University of Waterloo with 22 AI en- trepreneurs.
2.2 AI Professors
For AI professors leaving for an industry job, we exploit a hand-collected sample from LinkedIn. To obtain AI professors from LinkedIn, we employ two searching processes. The first involves
15Our sample includes only founders who graduated on or after 2010 because we need a lag between professors’ departure and students’ graduation.
8
directly searching in Google with inputs such as: site:linkedin.com/in/ “Professor” and “Artificial Intelligence”16, which allows us to retrieve all the LinkedIn profiles returned by Google.
Our second method is to search in LinkedIn using reviewers’ and program committee mem- bers’ names of AI related conferences. We picked three of the most prestigious conferences in the field of machine learning and artificial intelligence, which are International Conference on Ma- chine Learning (ICML), Neural Information Processing Systems (NeurIPS), and the Association for the Advancement of Artificial Intelligence (AAAI), and extracted the names of reviewers and committee members from 2008 to 2018. Because there are also reviewers and committee members from the industry, this procedure includes faculty who left academia for the industry. We manually check each person’s LinkedIn page and sometimes their academic webpage to verify the results.
As we only consider tenured or tenure-track faculty, we exclude the sample people with titles like “Adjunct Professor”, “Clinical Professor ”, and “Research Professor”. We classify assistant professors as tenure-track faculty and associate or full professors as tenured faculty.
The final sample includes 221 tenure-track and tenured AI faculty in North American univer- sities who started to work for a private company or established their own startup between 2004 and 2018.17 This sample includes 131 faculty who left their academic positions completely and 90 faculty who retained their university affiliations. Figure 1 shows the aggregate time trend for the combined sample of complete and partial departures (i.e., still retain the university affiliations). We also identify 160 tenure-track or tenured faculty who moved within North American academia. In our empirical analysis, we use both the faculty who leave for industry and for another university.
We also download data of faculty size at the top 100 universities’ computer science depart- ments from CSRankings.org, which provides the number of full-time, tenure-track and tenured computer science (CS) faculty for each year based on data from DBLP.18 DBLP provides open bibliographic information on major computer science journals and proceedings. Its website states that “DBLP indexes over 4.4 million publications, published by more than 2.2 million authors. To this end, DBLP indexes about 40,000 journal volumes, more than 39,000 conference and workshop proceedings, and more than 80,000 monographs.”19
16Another example of a search input is: site:linkedin.com/in/ “Professor” and “Deep Learning” . In general, we search for professors who mention one of the subfields of AI, such as natural language processing or autonomous driving, on their LinkedIn profiles.
17Our sample can underreport the actual number of AI faculty departures to industry because some professors might not have LinkedIn accounts or leave their LinkedIn profiles out-of-date.
18See http://csrankings.org/faq for detailed information about CSRankings.org. 19Source: https://dblp.org/faq.
9
Last, we downloaded citation data of AI faculty from Google Scholar, which are used as a proxy for the quality of their research.
2.3 AI Brain Drain
Although we use AI brain drain to generate the variation in student-specific knowledge, AI brain drain is an interesting phenomena on its own.20 One of the contributions of the paper is to use a hand-collected dataset of AI faculty departures to document the brain drain of AI professors from universities into the industry. Despite lots of public interest in the brain drain of AI professors to the industry (Lowensohn 2015; Economist 2016; Sample 2017; Spice 2018; Procaccia 2019), there is no study that systematically investigates this issue.
We identify 221 tenured and tenure-track AI faculty who accepted an industry position or started their own startups between 2004 and 2018. Figure 1 shows an exponential trend in the number of faculty departures. In our sample, there were no AI professors leaving academia in 2004 whereas 41 AI professors left completely or partially in 2018. Figure 1 also shows the citation ratio of AI faculty who left for an industry job to all AI faculty. For each year and each university, the citation ratio is calculated using the sum of Google Scholar citations from faculty who left for an industry job (stock at the time of departure) divided by the sum of citations from all AI faculty in this university; we then average across universities to get a proportion of citations for a given year. Only 4% of total AI citations from a university are from professors who left for the industry in 2010, on average, whereas AI professors who left academia for an industry job in 2018 account for about 20% of a university’s total AI citations.
Panel A and B of Figure 2 show the top 18 North American universities in terms of the number of faculty who took industry positions or established own startups from 2004 to 2018. The three universities that lost the most AI faculty are Carnegie Mellon University (CMU), the University of Washington, and UC Berkeley. CMU lost 16 tenured faculty members and no untenured faculty, and the University of Washington lost 7 tenured and 4 assistant professors. For Canadian univer- sities in our sample, the University of Toronto lost the most AI professors, 6 tenured faculty and 3 assistant professors.
20Previous studies of brain drain from universities focused on outcomes within academia. Waldinger (2010) inves- tigates the effect of the expulsion of mathematics professors in Nazi Germany on PhD student outcomes. Borjas and Doran (2012) study the effect of immigration of Soviet Union mathematicians to U.S. on the productivity of American mathematicians. Our paper focuses on the link between AI brain drain and economic and financial outcomes, such as firm formation and funding.
10
Panel A of Figure 2 further considers whether there is any difference between the departures of tenured faculty and untenured faculty for an industry job. We see that the two groups of faculty members exhibit similar trends. Panel B further looks into whether AI professors still keep their university affiliation after they accept an industry position or start their own startups. We identify departed AI professors with university affiliation in two steps. First, we select professors who accept an industry position with an open-end date (i.e., present) for their university positions on their Linkedin profiles. An open-end date indicates that the professors are still employed by the university. However, to ensure that an open-end date on Linkedin profile is up-to-date, we manually check the professors with an open-end date on their universities’ websites. If they still appear on the universities’ website with active teaching or supervising record, we consider them departures with university affiliation. Panel B of Figure 2 depicts the AI brain drain for the group with and without university affiliation. Both groups exhibit similar growing trend from 2004 to 2018.
Figure 3 presents firms that hired most of the AI professors. In our sample, Google, Amazon, and Microsoft poached the most AI professors from North American universities. From 2004 to 2018, Google and its subsidiary, DeepMind, together hired 23 tenure-track and tenured AI professors from North American universities. Amazon and Microsoft respectively hired 17 and 12 AI professors. Apart from technology firms, we also see that large firms from the finance industry poach AI professors, such as Morgan Stanley, American Express, and JP Morgan. It is worth noting that that these publicly traded firms hired about 45% of the 221 professors in our sample.
To induce the best AI professors to leave their tenure-track or tenured positions, the industry offers them millions of dollars in compensation (e.g., Metz 2017, 2018a). The corporate poaching of AI faculty has raised public concerns about its negative impact on universities (e.g., Economist 2016; Sample 2017). “That raises significant issues for universities and governments. They also need A.I. expertise, both to teach the next generation of researchers and to put these technologies into practice in everything from the military to drug discovery. But they could never match the salaries being paid in the private sector.” said Yoshua Bengio, one of the 2018 Turing Award winners and a professor at the University of Montreal (Metz 2017).
Although the compensation data that AI professors receive from industry positions are not publicly available, anecdotal evidence may provide a sense of how large the pay gap is. Ilya Sutskever, Geoffrey Hinton’s student, went to work for OpenAI in 2016, a non-profit organization that aims to democratize AI, where he was paid more than $1.9 million dollars that year. “I turned down offers for multiple times the dollar amount I accepted at OpenAI. Others did the same.” Mr.
11
Sutskever said (Metz 2018a).21 As a comparison, the highest-paid AI professor at UC Berkeley received $448,742 in 2018.22 The large pay gap and better computational resources and access to the commercial datasets offered by industry positions make it nearly impossible for universities to retain their best AI professors. In this paper, we use AI brain drain as a source of variation in students’ AI-specific knowledge to provide a causal evidence about the effect of specialized education on entrepreneurship.
3 Empirical Analysis
This section presents empirical evidence of how domain-specific knowledge affects the creation and funding of AI startups by exploiting the variation of AI faculty’s departures to industry. We have two concerns about using AI brain drain as a shock to students’ domain-specific knowledge. First one is that AI brain drain may generate variation in other factors, such as connection to VC, that could also affect formation and financing of startup by university graduates. We address this concern in Section 4. The second concern is that the variation generated by AI brain drain may not be completely exogenous. We address this concern in Section 5.
3.1 Empirical Design
For the extensive margin analyses, we conduct university-level analyses. Specifically, we test whether AI professors’ departures from a university for the industry affect the number of AI star- tups established by students who graduated from this university. Given that AI faculty departures could repeatedly occur at a given university, instead of a difference-in-differences specification, we have a simple panel OLS specification as follows23
ln(1+AI Entrepreneur j,t) = αt +θ j +βAI Brain Drain j,[t−6,t−1]+φXj,t + ε j,t (1)
, where αt are the graduation year fixed effects, θ j are university fixed effects, AI Entrepreneurr jt
is defined as the number of graduates who graduate from university j in year t and then start AI
21“Well-known names in the A.I. field have received compensation in salary and shares in a company’s stock that total single- or double-digit millions over a four- or five-year period.” (Metz 2017). See also Metz (2018b).
22The wage data is from https://ucannualwage.ucop.edu/wage/ 23Because the dependent variable, AI Entrepreneur jt , is count data, in addition to OLS, we also use both Poisson
and Negative Binomial model for the same regressors as a robustness in Section 6.
12
startups, β captures the effect of AI Brain Drain on the students’ ability to start AI firms after they graduate, and other regressors are defined in Table 1. Because of the lag between graduation and startup inception (the average is about two years), graduates in 2018 mechanically have fewer AI entrepreneurs than graduates in 2010. We control for this by adding time fixed effects. In addition, we add university fixed effects to control for the fact that some universities produce more AI entrepreneurs because of their location near areas like Silicon Valley or other factors.
To measure the AI faculty departures, we first look at total AI faculty departure during the time window, [t−1, t−6], where t indicates the student’s graduation year.24 The corresponding variable, AI Brain Drain[t−6,t−1], is calculated as the sum of each year’s AI faculty departures scaled by each year’s AI faculty size for past six years at each university.
The time window [t−1, t−6] does not allow us to disentangle the effects between those pro- fessors who have more opportunity to interact with students who graduate in year t and those with less opportunity or no opportunity to interact with students who graduate in year t. To address this issue, we measure AI faculty departure separately at two time windows [t− 3, t− 1] and [t− 6, t−4] for the departures, where t is the graduation year. Effectively, we break down the variable AI Brain Drain[t−6,t−1], into two variables, AI Brain Drain[t−6,t−4] and AI Brain Drain[t−3,t−1]. If a professor departs four to six years prior to student’s graduation year, this professor has probably no interaction with the student because undergraduate programs last four years, master programs are usually two years long and PhD programs are five years long, with most of the classes taken in the first couple of years.
t−6 t−5 Departures Period 1
t−4 t−3 t−2 Departures Period 2
t−1 t
Startups by Graduates
This figure plots the timeline for our empirical design. For departures, we focus on tenure-track and tenured AI professors who left academia for an industry job in two time windows, [t − 6, t − 4] and [t − 3, t − 1]. For student entrepreneurs, we look at people who graduate in year t and subsequently establish an AI company.
24We use a window instead of putting a specific year because it is difficult to determine precisely the year when a professor actually stopped perform its teaching and supervisory duties at the university. Professor can take a leave of absence before they officially resign.
13
Moreover, we look at the effects of tenured professors’ departures and untenured departures separately because of the potential differences in their impact on students. In addition, untenured professors could be leaving academia because they were denied tenure. Therefore, departures by tenured professors are likely to have a larger effect on students. So we further break down the vari- able AI Brain Drain[t−6,t−4] into Tenured Brain Drain[t−6,t−4] and Untenured Brain Drain[t−6,t−4]. For the period [t-3,t-1], we break down the variable AI Brain Drain[t−3,t−1] into Tenured Brain Drain[t−3,t−1]
and Untenured Brain Drain[t−3,t−1].
3.2 The Creation of AI Startups (Extensive Margin)
The regression sample includes 69 universities North American universities that produced at least one AI entrepreneur who received his/her highest degree before 2010. 59 of these universities are in the US and 10 universities are in Canada. Table 3 presents summary statistics for these 69 universities from 2010 to 2018. From this table we learn that an average department has around 7.8 AI professors during our sample period. For our comparative statics we will use departure of one AI faculty member, which would be 12.8% of the average AI group.
The OLS results of the estimation of equation (1) appear in Table 4. In column (1), the coeffi- cient on the variable AI Brain Drain[t−6,t−1] is negative and statistically significant at 5% level.25
Once we break down AI Brain Drain[t−6,t−1] into AI Brain Drain[t−6,t−4] and AI Brain Drain[t−3,t−1]
in column (2), only the coefficient on AI Brain Drain[t−6,t−4] is negative and statistically significant at 5% level.
In column (3), when we examine the impacts of tenured and untenured faculty separately, Untenured faculty leaving for an industry job has no effect, but departures by tenured profes- sors have a statistically significant effect. In terms of economic significance, the coefficient on Tenured Brain Drain[t−6,t−4] is -0.863 in column (3), which implies that the departure of one tenured AI professor during the time window [t−6, t−4] on average reduces the number of future AI entrepreneurs who graduate in year t by about 11%.26
In addition, in column (4), we show that AI faculty departures have no effect on Non-AI en- trepreneurship (i.e. students who start firms after their graduation in the area of information tech-
25Robust standard errors are clustered at university city level because we cannot assume that two observations in the same city are independent.
26One departure represents 12.8% of the sample average AI faculty size. 11% is calculated as the coefficient, -0.863, on Tenured Brain Drain[t−6,t−4] multiplied by 12.8%.
14
nology but not in AI). This result implies that the negative effect is unlikely to be driven by would- be AI entrepreneurs switching to other IT fields. It also suggests that that the effect is driven by an unobservable university-level shock because such shock would affect non-AI entrepreneurs as well. Overall, the results in Table 4 show that faculty departures to the industry have a long-lasting negative effect on startup formation.
3.3 Examining Heterogeneous Effects
In this subsection, we investigate various heterogeneous effects for the results of the extensive mar- gin. Table 5 examines whether the negative effects shown above are heterogeneous with respect to the CS department’s ranking. We also study whether Canadian universities are affected differently from US universities. As shown in columns (1) through (3), the effects are much stronger for the Top 10 CS departments, as ranked by CSRanking.org based on the number of publications and ad- justed for faculty size. In addition, in column (4), we do not see the impacts of faculty departures on Canadian universities being significantly different from those on American universities.
Table 6 investigates heterogeneous effects with respect to whether the AI entrepreneurs’ highest degrees are a bachelor or PhD, whether the startup utilizes or develops the deep learning technol- ogy, and whether the gap between graduation and inception is less than two years. In column (1), the dependent variable only includes AI entrepreneurs whose highest degrees are bachelor and is calculated as the natural log of one plus the number of AI entrepreneurs with only bachelor degrees. The coefficient on our main faculty departure measure, Tenured Brain Drain[t−6,t−4], is statistically significant at 5% level, and the magnitude is similar to that in the main results shown in Table 4. In column (2), we only focus on AI entrepreneurs who hold PhD degrees. The coefficient on the variable, Tenured Brain Drain[t−6,t−4], is statistically significant at 5% level. Compared to the undergraduate results in column (1), the magnitude for PhD entrepreneurs is about 30% higher. The results from columns (1) and (2) indicate that the negative effects apply to both undergraduate and PhD students.
Recent advancements in deep learning algorithms is a major factor in the renewed interest in AI. Figure OA4 in the Online Appendix shows the exponential growth in Google search index for the term “deep learning” and in the total number of Google citations for the three most influential deep-learning papers published before 2010 by the three 2018 Turing Prize winners.
Deep learning could be the Invention of the Method of Invention (IMI), a term coined by
15
Griliches (1957) in his seminal work on hybrid corn. Cockburn et al. (2018) write “If it [deep learning] is also a general-purpose IMI, we would expect it to have an even larger impact on economy-wide innovation, growth, and productivity as dynamics play out—and to trigger even more severe short-run disruptions of labor markets and the internal structure of organizations.” They continue, “It is possible that deep learning will change the nature of scientific and technical advance itself.”27
Given the importance of GPT technologies for the economy (David 1989; Autor et al. 1998; Bresnahan and Trajtenberg 1995; Bresnahan et al. 2002; Rosenberg and Trajtenberg 2004), in columns (3) and (4) of Table 6 we look specifically at AI startups in the area of deep learning. There are 106 startups in our sample that have a keyword “deep learning” in their description. Indeed, these startups operate in very different fields (cybersecurity, retail, breast cancer early detection, solutions for the oil and gas industry, augmented reality, sleep therapy, supply chain planning, and more), consistent with the possibility that deep learning is a general-purpose technology that can be applied across many industries.
In column (3) of Table 6, the dependent variable only includes AI entrepreneurs who establish startups that use deep learning technology. The coefficient on Tenured Brain Drain[t−6,t−4] is statistically significant at 5% level, and the magnitude is very similar to that in the main results shown in Table 4. In column (4), we focus on AI entrepreneurs who establish startups in other sub- fields in AI. The coefficient on Tenured Brain Drain[t−6,t−4] is statistically insignificant. These results (i.e., the negative effects are the strongest on deep-learning startups) suggest that founders’ ability to acquire deep learning knowledge from professors is particularly important for AI startup formation. Among all the subfields in AI, establishing a deep-learning startup probably relies mostly upon domain-specific knowledge. This result is consistent with our knowledge transfer channel, for which we have an extensive discussion in Section 4.
In columns (5) and (6), we compare the impacts of faculty departures on graduates who start new firms within two years after their graduation and those who start new ventures two years after they graduate. This comparison is important because if students have been out of school for several years then there are many other factors that may drive entrepreneurial activity such as exposure to ideas through employment, personal wealth, opportunity costs, etc. Therefore, we expect the
27See also Gil et al. (2014) who write “The challenge presented by advances in AI is that they appear to be research tools that not only have the potential to change the method of innovation itself but also have implications across an extraordinarily wide range of fields.”
16
impacts of AI faculty departure to be greatest on students who are right out of school. This is indeed what we observe from columns (4) and (5). In column (4), we only focus on graduates who start new firms within two years, and the coefficient on Tenured Brain Drain[t−6,t−4] is statistically significant at 5% level, while in column (5), there are no effects on graduates who established startups two years after they graduate.
3.4 Early-Stage Entrepreneurial Financing (Intensive Margin)
Early-stage startups are particularly hard to finance, given the lack of tangible assets and the severe asymmetric information (Hall and Lerner 2010). As a result, the information about the human capital of a founding team becomes important in terms of attracting early-stage funding (Bernstein et al. 2017). However, through which mechanism (e.g., knowledge, networking, and signaling) the human capital affects early-stage financing still remains a question. Moreover, as we discussed earlier, AI startups are much more likely to have PhD founders than other non-AI startups in the IT sector. Thus, in the field of AI, knowledge transfer from university professors to AI entrepreneurs may have a significant impact on their human capital. In this section and Section 5, we provide causal evidence that it is the knowledge, rather than signaling or networking, through which human capital affects funding.
As shown in Panel B of Table 2, as of the year 2018, 85% of the AI startups in our sample are still in the early stage (i.e., no later than series A round). Therefore, our focus is on the first and series A rounds. Table 2 also shows that compared to other IT startups, AI startups raised $1.01 million more in the first round and $2.9 million more in the series-A round.
Our extensive-margin results show that the negative impacts are mainly due to tenured faculty departures in time window [t − 6, t − 4]. Based on this result, for intensive margin, we again examine the impacts of faculty departures for tenured vs. untenured and time window [t−3, t−1] vs. [t−6, t−4]. As a result, we use the following OLS model to test the effects of faculty departures on students’ ability to attract startup funding.
Funding Amounti, j,k,t,τ = ατ +θ j +δk + γ1Untenured Brain Drain j,[t−1,t−3]+ γ2Untenured Brain Drain j,[t−4,t−6]
+ γ3Tenured Brain Drain j,[t−1,t−3]+ γ4Tenured Brain Drain j,[t−4,t−6]+φXi,j,k,t,τ + εi, j,k,t,τ
(2)
, where the dependent variable, Funding Amounti, j,k,t,τ , is the total funding amount from a financ- ing round (either first-round or series-A in our case) in year τ for a startup founded (co-founded)
17
in state k by entrepreneur i who graduated from university j in year t, ατ is the financing year fixed effect, and θ j is the university fixed effect, δk is the state fixed effect.28 The financing year fixed effects can control for the cyclicality of venture financing, univeristy fixed effects can control for alumni’s VC connection, and startup’s state fixed effects factor in venture capital industry develop- ment in different states in the US. Entrepreneur-level control variables include the university rank, the number of years between graduation and startup inception, and prior exit (acquisition or IPO) experience. Firm-level control variables are the number of founders and the number of investors at each financing round (either first-round or series-A round in our case).
Table 7 shows the effects of AI faculty departures on AI startups’ early-stage funding. The OLS results in columns (1) the effect of AI faculty departures on AI startups’ first-round funding. Only the coefficient on Tenured Brain Drain[t−6,t−4] is negative and statistically significant at the 1% level. The coefficients on Tenured Brain Drain[t−3,t−1], Untenured Brain Drain[t−3,t−1], and Untenured Brain Drain[t−6,t−4] are all insignificant. Together, these results indicate that only the tenured AI faculty departures during [t−6, t−4] have a negative impact on the first-round funding of AI startups by entrepreneurs who graduate in year t. Untenured AI departures or early tenured AI departures (i.e., during [t−3, t−1]) have no impacts on entrepreneurs who graduate in year t. For the economic magnitude, the departure of one tenured AI professor in [t−6, t−4] results in about a $1.68 million decrease in first-round funding.29 These results are consistent with the knowledge transfer channel because tenured professors are, on average, more knowledgeable. And students are likely to take classes and do research under the supervision of the professors who depart during students’ studies. Therefore, the negative effect should be larger for tenured faculty departures and for the departures before students’ arrival on campus.
In column (2) and (3), we show the effects of AI faculty departures on AI startups’ funding growth from the first to the second round and funding of Series-A round, respectively. The results are consistent with those in column (1) in the sense that only tenured AI faculty departures during [t− 6, t− 4] have a negative impact, and untenured AI departures or early tenured AI departures (i.e., during [t − 3, t − 1]) have no impacts. As for economic magnitude, the departure of one tenured AI professor in [t − 6, t − 4] could decrease the funding growth rate of a representative AI startup in our sample from 630% to 246%. And the departure of one tenured AI professor in
28First-found is the first funding round we can observe for a given firm in the CrunchBase database. 29One departure represents 12.8% of the sample average AI faculty size. The $1.68M reduction in funding is
calculated as the coefficient, -13.1, on Tenured Brain Drain[t−6,t−4] in column (1) multiplied by 12.8%.
18
[t−6, t−4] results in about a $6.6 million decrease in series-A round funding.30
3.5 Faculty Replacement and Partial Departures
In this subsection, we first test whether professors leaving for other universities, rather than indus- try, could pose similar negative effects on entrepreneurship by university graduates. Then we show whether the negative effects are similar between professors who took a full-time industry job and professors who also have university affiliations.
3.5.1 Faculty Replacement
Besides the 221 departures from academia to the industry, there are also 160 departures to a dif- ferent North American university.31 In columns (1) of Table 8, we study whether the effect of the AI faculty departures to other universities on AI startups is the same as the effect of faculty departures to the industry while controlling for the industry departures. Unlike the findings for AI faculty who accepted an industry job, the results in column (2) of Table 8 show that tenured or tenure-track professors who move to other North American universities do not have a negative effect on AI startups. This could be because AI faculty who accept industry jobs might be different from those who choose to stay in academia in terms of the quality.
In columns (2) through (5) of Table 8, we examine whether new AI tenured/untenured profes- sors, who come to the universities that previously experienced AI faculty departures, can remedy the negative effects due to the tenured faculty departures. In column (2), the coefficient on the interaction term, Tenured Brain Drain[t−6,t−4]×Untenured Move In[t−3,t−1], is negative and statis- tically significant. This result implies that the negative effect, due to tenured faculty departure in time window [t − 6, t − 4], is amplified by untenured faculty who come in later in time win- dow [t − 3, t − 1]. In column (3), the coefficient on the variable, Tenured Brain Drain[t−6,t−4]× Tenured Move In[t−3,t−1], is statistically insignificant, suggesting that new tenured professors are unable, if not worsen, to offset the negative effects on student entrepreneurship.
In columns (4) and (5) of Table 8, we examine the replacement effect of substitute professors who come from the higher-ranked universities and those who come from lower-ranked institutions separately. We consider a university to be ranked lower (higher) than another one if it is ranked at
30The calculation procedure for economic magnitude in column (2) and (3) is similar to that of column (1) 31We extract this information from the faculty’s LinkedIn profiles.
19
least five spots lower (higher), based on the CS department ranking. In column (4), the coefficient on the interaction term between Tenured Brain Drain[t−6,t−4] and Move In From Low[t−3,t−1] (i.e., the number of professors who come from lower-ranked universities during [t− 3, t− 1] scaled by the AI faculty size) is negative (significant at the 1% level), suggesting that the negative effects are worsened by faculty who come from lower-ranked universities. Furthermore, even for professors who come from higher-ranked universities, the negative effect is not offset, as suggested by the results from column (5). The sample size is small for this category because tenured professors rarely move to lower-ranked departments.
The findings from columns (2) through (5) in Table 8 suggest that AI professors who leave for industries are probably the top researchers in the field of AI and are hard to replace them with someone of similar quality, especially during the times when all departments are experiencing some level of brain drain. As a result, students continue to be negatively affected by AI faculty who joined the industry four to six years prior to their graduation even if new AI faculty join the department.
3.5.2 Partial Departure (University Affiliation)
In Table 9, we examine the effects separately for AI faculty who work for companies while keeping university affiliations (referred to affiliated brain drain) and AI professors who took a full-time industry job (referred to non-affiliated brain drain). The details of how we classify a departure as affiliated brain drain are presented in Section 2.3. Column (1) of Table 9 examine the effects of affiliated and non-affiliated AI brain drain separately in the departure time window [t− 6, t− 1]. The coefficient on the affiliated brain drain is negative and statistically significant while the coefficient on non-affiliated brain drain is insignificant. In Table OA11 of the Online Appendix we check whether the effect of non-affiliated brain drain and affiliated brain drain depends on the new faculty who join the department. For the affiliated brain drain, the type of replacement does not matter. This is not surprising because the new faculty are not replacing the existing faculty who dedicate part of their time to the institution and another part to the outside job. However, for non-affiliated brain drain we find a striking result. When professors who leave completely are replaced by professors from lower ranked departments, there is a strong and significant negative effect on students’ entrepreneurship. When these professors are replace by professors from higher ranked departments the effect is positive and statistically significant.
These results suggest that affiliated departures have a negative effect unconditionally, while
20
non-affiliated departures have a negative effect only if the departed professors are replaced by professors from lower ranked schools. This finding is consistent with knowledge-transfer channel because AI faculty who have dual positions (i.e. industry and academia) do not allow the university to hire someone instead. As a result, the net effect of the departure is only negative because these professors start to teach less and allocate less time to mentoring students.32 Professors who leave universities completely free up a position. If this position can be replaced by the same quality or higher quality professor, we should not see a negative effect according to the knowledge transfer story. If anything, the effect can be positive if the university can attract someone better. However, if the replacement is of lower quality, then the net effect of the departure on students’ knowledge is negative.
In addition, although university fixed effects rule out the school signalling effect (Spence 1973), the fact that the negative effect is coming from affiliated brain drain allows us to further rule out the signaling effect coming from departed professors. For example, maybe venture capitalists know professors and think that professors participate in admissions of students. When a star professor leaves, the perceived quality of students is reduced. However, the perceived quality should drop more for complete departures (non-affiliated brain drain) and for professors who depart after en- rollment of students to the university. We find the opposite, suggesting that the results are not driven by dynamic signaling.
We then turn our focus on the group of affiliated AI departures. In column (2) of Table 9, we examine the effects separately for departure time windows [t − 3, t − 1] and [t − 6, t − 4]. In column (3), we show the effects separately for tenured professors who partially left university and untenured partial departures. The results in column (2) and (3) are consistent with our main results in the sense that for affiliated AI departures, the negative effects concentrate in the departure window [t−6, t−4] and in the group of tenured AI professors.
32Professors who keep their positions are more likely to be influential and highly cited researchers making their partial departure even more important for students.
21
4.1 AI Brain Drain and Students’ AI knowledge
AI startups require a highly specialized human capital that constitutes a high barrier to entry for many potential entrepreneurs. Table 2 shows that in our sample, 26% of AI entrepreneurs hold a PhD degree, whereas it is only 10% for non-AI entrepreneurs. Moreover, 29% of AI entrepreneurs’ highest degrees is a Master’s degree, while 21% of non-AI entrepreneurs’ highest degree is a Master’s degree. Moreover, Medium (Stanford 2018) writes, “In a report, Paysa found that 35 percent of AI positions require a Ph.D. and 26 percent require a master’s degree. Why? Because AI is a rapidly growing field and when you study at the Ph.D. level and participate in academic projects, they tend to be innovative if not bleeding edge, and that gives the student the experience they need for the work environment.”
The knowledge transfer channel attributes the negative effect of AI faculty departures on star- tups to the reduced quality and quantity of AI knowledge acquired by students. If students do not have access to the best AI professors then they are less likely to become AI entrepreneurs after graduation. We find a strong empirical support for this channel.
First, we show that the effect is the strongest for students in the top universities, startups in the area of deep learning, departures by tenured professors, and departures by professors four to six years prior to students’ graduation. These results are consistent with knowledge transfer channel because the quality of knowledge to be transferred to students is probably higher for tenured profes- sors in top universities, deep learning requires high training and the scarcity of knowledge makes the knowledge transfer particularly important, and departures that take place prior to students’ en- rollment to the program reduce students’ ability to acquire knowledge more than departures that happen after students enroll and take some classes from the departing professors.
Second, the IV regression results, shown in the Section 5.2, indicate that professors with the highest levels of knowledge, proxied by the number of their citations, are more likely to leave academia. The negative effect on entrepreneurship is driven by departures that are predicted by citations, suggesting that the results come from departures of the more knowledgable professors and not the less knowledgable. In fact, we find that when the departing professors are replaced by professors from a lower ranked computer science departments, the negative effect of the departures to the industry is even stronger.
However, it is possible that the brain drain of AI professors into the industry can affect the
22
number as well as the financing of AI startups established by students at the affected universities via other channels. In this section, we discuss possible economic channels and argue that the most likely channel is the disruption in the transfer of critical knowledge from professors to students.
4.2 Alternative Explanations
Professors’ VC connections. It could be that an AI professor does not contribute any knowledge to students, but rather helps them find investors given the importance of connection in VC’s invest- ment decision (Gompers et al. 2020). When faculty departs for the industry, future students lose the industry connections that these professors have.
According to the connection channel, the negative effect of faculty departure should be stronger for local startups because professors are more likely to know VCs in their city. Table OA3 in the Online Appendix shows that the negative effect of AI faculty departures is not driven by students who establish their startups in the same city as their universities’ city. To further test the VC connection channel, Table OA4 in the Online Appendix shows that, conditional on being AI en- trepreneurs, AI founders who are affected by AI faculty departures are not more likely to have co-founders who hold an MBA degree. If the founders have difficulties to get VC connections due to faculty departures, they should be more likely to partner with MBAs who can find such connections via their b-school’s alumni network. We do not find a support for this explanation. This channel also does not explain why the effect of AI faculty departures is stronger when these professors are replaced by faculty from lower ranked schools.
VC’s educational background. Venture capitalists may be more likely to invest in startups whose founders share the same educational background with them. In our case, it is possible that venture capitalists are more like to invest in entrepreneurs who took the AI courses from the same AI professors as they did. If this was the case, then it might explain why the departures during [t − 6, t − 4] exhibit a negative effect but the departures during [t − 3, t − 1] do not. Moreover, this alternative explanation cannot explain why startups in deep learning are disproportionately affected. It also cannot explain why the results are stronger for startups established within two years from students’ graduation.
Professors’ general skills. It is possible that professors who leave for industry or establish their own startups have better general skills, such as leadership skills. Therefore, instead of AI- specific knowledge, students may just learn the managerial skills from the departing professors.
23
However, any non-specialized skill transfer from professors to students should be unrelated to the type of technology used by startups. The fact that faculty departures disproportionately affect deep learning startups is inconsistent with this explanation.
Following professors. A professor’s departure for an industry job may increase the probability that the professor’s previously supervised students will join his or her current firm. When profes- sors help students to get lucrative positions at their companies, it could increase the opportunity costs of establishing a startup.
It is plausible that poached students by departed professors are more knowledgeable in the area of AI. If that is the case, then this explanation is consistent with our argument that specialized knowledge is needed for AI startups. The concern is that those professors hire students not based on their AI knowledge but based on some other traits (e.g., good communication skills). In this case, when we see a drop in AI startups, we cannot attribute it to the lack of knowledge or conclude that knowledge is important.
The fact that in Table 6, we find deep learning startups to be most affected is critical here, because it is advanced technology that requires specialized knowledge but does not require more communication skills or soft skills than other technologies. Also, soft skills are much harder to assess without direct interaction. If those soft skills are the explanation for the negative effect of faculty departures on the number of startups by students, we would expect to see this negative effect especially pronounced for faculty departures that take place one to three years prior to stu- dents’ graduation, rather than for departures that take place four to six years prior to graduation. Our empirical results show that the negative effect exists for departures that take place four to six years prior to graduation and there is no effect for the more recent departures. Moreover, this ex- planation cannot explain why the negative effect on the startups is larger when the departing faculty is replaced by faculty from lower ranked schools. We conclude that the reduction in the number of startups is unlikely to be explained by students following professors to join their companies and to forego the opportunity to establish their own startup.
Substitution to other fields. Departures of AI faculty could cause would-be AI entrepreneurs to switch to other fields. For example, if a student is interested in AI but majors in a different field or switches a thesis topic because of the lack of faculty to teach AI classes or supervise AI research. In columns (5) of Table 4 we test whether AI professor departures affect the number of graduates from the same university who later establish startups in other fields of information technology. We do not find an empirical support for the substitution effect because AI faculty departures do not
24
positively affect the number of non-AI startups in other fields of information technology. Moreover, in Table 12, we do find that the AI faculty departures do not have any impact on the early-stage funding of non-AI (IT) startups established by affected university graduates.
School selection. We would expect fewer students who are interested in AI enroll to a univer- sity that has experienced a lot of AI faculty departures. As a result, faculty departures could affect AI entrepreneurship by a decline in enrollment of future AI entrepreneurs, who instead enroll at other universities that are less affected by AI faculty departures. If we are worried that students might select another university after professors join tech firms, we should be looking on this prob- lem from the perspective of the students. Specifically, what is the information set available to them? Do they search each professor on the university’s websites and do they search professors on LinkedIn? It is a plausible assumption that undergraduate students and master students who are not expected to work on research would not do that. We also have four tests to rule out this hypothesis.
In Table 9, we show that AI faculty who left partially (i.e., working for companies while keep- ing university affiliations) show quantitatively similar negative effects to our main results shown in Table 4. This result is inconsistent with school selection explanation. Because the degree of the outside activity is not necessarily observable for students in general and for undergraduate stu- dents in particular. According to the selection story, the negative effect should be stronger for full departures than for partial departures. We find the opposite.
To further investigate the selection channel, we study the number of AI startups in other univer- sities that are similar to the focal university in terms of ranking or geography. If students switch to another similarly ranked university or university in the same locations as a result of faculty depar- tures in a given university, then we should see an increase in AI startups founded by graduates of the other universities. In columns (1) and (2) of Table 10, we examine whether prospective students within the universities that have similar CS department rankings will switch to universities that are less affected by AI faculty departure. Specifically, we divide the 69 universities in our sample into five groups, based on the variable, Rank, that is defined in Table 1. We then test whether AI faculty departures during the time window [t−6, t−4] at one university can predict the sum of AI entrepreneurs who graduate in year t from other universities within the same ranking group. We do not find any evidence supporting the redistribution of students within the universities that have similar CS department ranking.
In columns (3) and (4), we examine the redistribution of students within a region. We select
25
36 universities in our sample based on 12 regions, the details of which are shown in Table 10. The dependent variable is the sum of AI entrepreneurs who graduated in year t from a university in the same region but not the university in question. For example, we would regress the number of AI startups by Harvard alumni on AI faculty departures at MIT. Consistent with the results from testing the redistribution of students within the same ranking group, the results from columns (5) and (6) do not support the redistribution of students within a region.
Besides the tests of student redistribution, in Table OA5 in the Online Appendix, we test whether AI faculty departures affect universities’ ability to attract talented students. To measure the quality of incoming students, we use the number of recipients of the NSF Graduate Research Fellowship Program (GRFP) in the AI field as a proxy for student quality. If faculty departures neg- atively affect enrollment by high quality students, who later become AI entrepreneurs, we would expect to see a drop in the GRFP recipients following faculty departures. We do not find such an effect of tenure departures neither in the [t − 3, t − 1] nor in the [t − 6, t − 4] window on the number of the fellowship recipients in year t at a given university. Together, these findings suggest that the school selection channel is unlikely to explain the drop in the number of AI entrepreneurs following AI faculty departures to the industry.
5 Unobservable Factors
There is a potential concern that our results are affected by some unobservable factors that are not controlled by fixed effects and controls in the benchmark specification. A deteriorating local economy is unlikely to be such an unobservable factor because if our results are driven by the local economy, we should also observe the impacts of faculty departures during [t−3, t−1] on AI entrepreneurs who graduate in year t. Nevertheless, it is possible that some local shocks affect both the AI faculty departures during [t−6, t−4] and the AI startups by entrepreneurs who graduate in year t, while they have no impacts on the AI faculty departures during [t−3, t−1]. We address this omitted-variable concern by conducting a placebo test at city-level and employing an instrumental variable approach.
26
5.1 Location-Specific Factors
We conduct a test to address the concern that universities’ unobservable location-specific factors may confound our results. Our test only focuses on the entrepreneurs who found AI startups in a city where they did not attend a university. If our results were driven by some unobservable local shock, this shock would hinder startup activities overall, regardless of whether or not entrepreneurs are alumni of local universities.
Table 11 provides a city-level analysis with the dependent variable being the number of en- trepreneurs who found AI startups in city j during year t but are not alumni of city j’s universities. For independent variables, we aggregate the university-level measures in previous tests by taking the average for each city and year. Instead of a negative correlation between faculty departures and non-alumni entrepreneurs’ local startup activities because both are driven by negative local shocks, Table 11 presents a positive correlation. This positive correlation makes it implausible that negative local shocks explain our result that AI faculty departures reduce the number of AI startups founded by alumni.
5.2 Instrumental Variable Approach
t−8 t−7
First-Round Funding
Series-A Funding
This figure plots the timeline for our instrumental variable approach. For citations, we use AI faculty’s average citations received during [t−8, t−6] at a given university. For AI entrepreneurs in our sample, they start new AI startups, on average, in year t + 2.2. (i.e., over two years after graduation), receive their first-round funding, on average, in year t+3 (i.e., three years after the AI entrepreneurs have graduated), and receive their Series-A-round funding, on average, in year t +4.5 (i.e., four and a half years after the AI entrepreneurs have graduated).
Previous analysis shows that local or city-level unobservable factors are unlikely to be the driving force behind the negative impacts we documented earlier. To address the residual concern of omit- ted variables, such as university-level unobservable shocks, we rely on the instrumental variable approach.
27
5.2.1 Instrumental Variable: Average Number of Citations
We use the average number of AI-paper citations received during [t−8, t−6] at a given university as an instrumental variable to address the school-level unobservable factors.33 A high number of citations is likely to attract more industry attention and thus is more likely to generate an offer for the professor to leave academia (Zucker et al. 2002). Moreover, given the time period of our instrumental variable, it is unlikely that the number of citations received during [t− 8, t− 6] at a given university would be correlated with some school-level shocks that affect AI faculty departures during [t−6, t−4] and startup creations and financing by entrepreneurs who graduated in year t.
Since we use the average number of citations, it is possible that the professors who actually left are those with fewer citations. To verify whether the departing faculty members are actually above the average in terms of the citations, we conduct a Logit analysis to see whether the cumulative citations can predict the probability of a professor leaving for industry. In Table OA6, after con- trolling for the year fixed effects, the university fixed effects, and the number of years since first AI publication, we find that the cumulative citations are positively correlated with the probability of leaving for industry.
To establish the causal link between faculty departures and the outcome variables, we use the following 2SLS model:
First Stage: High Tenured Brain Drain j,[t−6,t−4] = αt +θ j +β1Average Citation j,[t−8,t−6]+ γ1X j,[t−6,t−4]+ ε jt (3)
Second Stage (Int. Margin): Funding Amounti, j,k,t,τ = αt +θ j +β2 High Tenured Brain Drain j,[t−6,t−4]+ γ2X j,[t−6,t−4]+ ε jt (4)
Second Stage (Ext. Margin): ln(1+AI Entrepreneur jt) = αt +θ j +β2 High Tenured Brain Drain j,[t−6,t−4]+ γ2X j,[t−6,t−4]+ ε jt (5)
To improve the predictive power of the first stage, instead of using the continuous variable to measure the AI faculty departures, we construct a dummy variable, High Tenured Brain Drain[t−4,t−6], that is equal to one if a university experiences above-median departures in a given year (i.e., Tenured Brain Drain[t−4,t−6] is above the median level in year t). It is easier to predict whether a university will experience more faculty departures for industry jobs relative to other universities than to predict what percentage of AI faculty members leave for the industry based on the univer-
33The results are qualitatively similar if we use average citations received during [t−6, t−4].
28
sity’s average number of citations. It is important to emphasize that there is a significant time gap between the departures that we instrument and the outcomes that we measure. First, the outcomes that we measure are for students who might not have been even enrolled at the university at the time of the departure. Second, with an average gap of two years between student’s graduation and establishing a startup and an average gap of one year between the startup’s inception and receiving funding for the first time, the startups are established and financed on average about ten years after the citations were received. These two features of the empirical design provide further validity to the instrument.
5.2.2 Exclusion Condition
For the exclusion condition to hold, our instrument, the average number of citations received dur- ing [t−8, t−6], needs to affect the creation and the funding of startups by affected graduates only via AI professors’ departures during [t− 6, t− 4]. Obviously, the Google citations and the future number of startups at a given university are both affected by the university-level characters. How- ever, this concern is largely mitigated by university fixed effect and time-variant computer science department rankings. Any story that claims that the exclusion restriction is violated should not be captured by the fixed effect and the rankings.
Moreover, given the large time gap between the citation and the outcome, an example of a violation of exclusion condition should also be able to explain why Google citations received in time window [t−8, t−6] directly affect: (i) the number of startups by students more than 6 years later (on average at time t + 2.2 ), (ii) the amount of funding that these startups attract in the first round (on average t +3), (iii) the amount of funding in round A (on average t +4.5), and (iv) the growth rate in the amount of funding between the first and the second rounds of funding.34 As a result, it is unlikely that the citations received during [t − 8, t − 6] would affect the number of startups and the funding that these startups received a decade later for reasons that are not related to faculty departures.
One major concern about the exclusion condition is that some university-level unobservable shock (e.g., a loss of major donors or a budget cut) may cause AI faculty with high citations to be more likely to leave because their outside options are better than those with low citations. In the meantime, those types of university-level shocks could also negatively impact students either
34For more information about firm-level statistics, see Table 2
29
because the affected universities are unable to attract high-quality students or because they cannot provide enough resources to students.
To address this potential violation of the exclusion condition, we conduct a placebo test with the formation and the early-stage funding of non-AI startups in the IT sectors as the outcome variables. Given that the impact of a university-level shock is hardly contained only on AI faculty and students who study AI, this placebo test can address the concern that a university-level unobservable shock could violate the exclusion condition for our instrumental variable. The results from Table OA1 are inconsistent with this concern.
5.2.3 The Results of 2SLS
Table 13 presents the 2SLS results for the extensive margin results. Consistent with the results from the OLS regression in Table 4, the second-stage results from column (2) show that the coefficient on the fitted value of High Tenured Brain Drain[t−4,t−6] obtained from the first stage is negative and statistically significant at 5%.
Column (3) in Panel A of Table 13 shows the first-stage results with a R2 of 0.53 and a Kleibergen-Paap F Statistic of 6.2. The results show that universities with AI faculty who have a high number of citations tend to have an above-median fraction of AI professors leaving for an industry job. The first-stage results are consistent with Zucker et al. (2002) who show that faculty quality is a major determinant of professors leaving for an industry job. Given that Kleibergen- Paap F Statistic is less than the conventional threshold, 10, for weak instrument (Stock and Yogo 2002), we employ Anderson-Rubin (AR) test to draw a weak-instrument robust inference. In just-identified case with single endogenous regressor, Moreira (2009) shows that AR test is op- timal because it is uniformly most powerful unbiased estimator. With 95% confidence level, the confidence set for the coefficient on High Tenured Brain Drain[t−4,t−6] is [−∞,−0.207]. Weak- instrument robust inference indicates that the coefficient is negative and statistically significant at 5%. Also, the 2SLS results are robust if we use citations received during [t-6, t-4]. Moreover, using two alternative instrumental variables, the citations based on papers coauthored with authors who work in industry and the number of industry coauthors, we get similar results for extensive margin. This complementary analysis appears in the section OA.2 of the online appendix.
Table 14 shows the effect of AI faculty departures on AI startups’ first-round funding. The OLS results in columns (1) and (2) show that the coefficients on both departure measures, Tenured Brain Drain[t−6,t−4] and High Tenured Brain Drain[t−6,t−4], are negative and statistically significant
30
at the 1% level. This result implies that AI startups with founders who are negatively affected by AI faculty departures, attract less funding in the first round.
Columns (3) and (4) show the 2SLS results with the average number of citations for AI pro- fessors as the instrumental variable. The coefficient on the fitted value of High Tenured Brain Drain[t−6,t−4] is negative and statistically significant at the 1% level. Because the coefficient on faculty departure during the time window [t−3, t−1] (Tenured Brain Drain[t−3,t−1]) is not statis- tically significant in the OLS regressions, we do not include it in the 2SLS. Moreover, the economic magnitude from the 2SLS regression is much larger than that from the OLS model in column (2). Since AI faculty from top schools are more likely to have high citations and get poached by firms from industry, the variation of fitted value of High Tenured Brain Drain[t−4,t−6] obtained from the first stage mainly comes from top schools. As a result, the larger magnitude from the 2SLS regression is consistent with our results from Table 5 that the negative effects due to AI faculty departures are stronger for top 10 CS departments. In addition, the Kleibergen-Paap F statistic from the first stage is 190, which is greater than the threshold, 10, for weak instruments (Stock and Yogo 2002), and the R2 from the first stage is 0.75.
Table 15 shows the effect of AI faculty departures on the series-A funding round of AI startups. The OLS regressions in columns (1) and (2) show that the coefficients on both departure measures, Tenured Brain Drain[t−6,t−4] and High Tenured Brain Drain[t−6,t−4], are negative and statistically significant at the 5% level, consistent with the findings from the first-round funding regressions. Columns (3) and column (4) in Table 15 report the first-stage and the second-stage results of 2SLS respectively. The coefficient on the fitted valu