Research Projects
Are Angel Investors More Likely than Venture Capitalists to Drive Entrepreneurial Experimentation?
Files:[Main Paper][Appendices][Supplemental Material]
ABSTRACT: Although angel investors and venture capitalists (VCs) both participate in the supply side of the same market, providing capital and advice to startup firms, they are distinct in several ways. The differences in when they deploy capital are well studied. The differences in when they provide advice are not. Using a sample of 7,914 mentoring decisions by seed-stage investors from which I construct a novel typology of startup activities, I report among the first empirical findings on systematic differences in angel advice versus VC advice. Angels are more likely than VCs to provide advice on the design and execution of experiments, whereas VCs are more likely than angels to provide advice on analysis. While analysis is a skill that can be learned from studying, hypothesis testing is a skill developed via learning-by-doing. I report evidence consistent with the hypothesis that angels are more likely to provide experimentation advice because they have a skill advantage in that domain due to operational experience. I also provide evidence that is inconsistent with alternative explanations, including financial incentives and selection.
Information Frictions and Employee Sorting Between Startups
w/ Kevin Bryan and Mitchell Hoffman
Additional Files: [Main Paper][AEA RCT Registry][Appendices][RCT Screenshots][Supplemental Material]
ABSTRACT: Would workers apply to better firms if they were more informed about firm quality? Collaborating with 26 science-based startups, we create a custom job board and invite business school alumni to apply. The job board randomizes across applicants to show coarse expert ratings of all startups' science and/or business model quality. Making this information visible strongly reallocates applications toward better firms. This reallocation holds even when restricting to high-quality workers. The treatments operate in part by shifting worker beliefs about firms' right-tail outcomes. Despite these benefits, workers make post-treatment bets indicating highly overoptimistic beliefs about startup success, suggesting a problem of broader informational deficits.
Does Mentorship Drive Startup Performance? Yes, But Only for High Learners
w/ Ajay Agrawal and Avi Goldfarb
(manuscript ready for conference presentation)
ABSTRACT: Mentorship is a staple component of private sector accelerators designed to maximize equity value and also of public sector initiatives created to support economic development. This paper examines whether, how, and when mentorship enhances startup performance. We show that mentorship drives startup performance. To address endogeneity concerns due to mentor selection, we exploit randomness in the availability of mentors to spend time with startups due to personal scheduling conflicts. We then show that one channel through which mentorship operates is founders learning how to set priorities for their companies. We conduct this empirical study using a novel panel of 289 high-technology startups participating in a global eight-month program for seed-stage companies.
How does Industry Affiliation of Academic Scientists Affect the Rate and Direction of Research?
(manuscript ready for conference presentation)
ABSTRACT: The potential implications of academic scientists' collaboration with industry have long been the nexus of contentious debate. The prevailing concern is that academic scientists' industry affiliation nudges their research towards questions of commercial value at the expense of reducing its fundamental depth. This paper presents evidence that these concerns are misplaced in domains with high commercial potential. Results show that academic scientists in artificial intelligence who affiliate with a firm produce more and higher quality research while publishing work that is not significantly different from those of their pure academic colleagues. The increase in the rate and quality of industry-affiliated scientists is more salient in `hot' sub-fields. Evidence suggests that more sought-after academics may be particularly fit to benefit from industry affiliation due to their ability to acquire more resources and freedom for pursuing their existing research interests. For identification, I exploit the fact that in the 2012 ImageNet competition, the computational innovations used by a team revealed strong signals about the commercial potential of artificial neural networks, leading to a sudden increase in the industry's demand for AI scientists. For policy, I discuss the possibility that the heightened commercial interest in hot fields may negatively affect the rate and direction of research by starving other potentially impactful lines of inquiry of funding and doctoral research resources.
Media Mentions: The Economist (Million Dollar Babies and Battle of the Brains)
Learning vs. Doing: The Effect of Business Uncertainty on Entrepreneurial Activities
(preparing manuscript for conference presentations)
ABSTRACT: Over the past decade, public and private startup mentorship programs have proliferated. Yet the empirical investigation of this phenomenon is scant. I examine advice in the context of change in startup activities. Resource-constrained entrepreneurs trade off prioritizing between learning about and evaluating their options versus implementing them. In a setting where mentorship advice regulates this trade-off, I show that, relative to mentors, entrepreneurs under-prioritize simple search and planning activities—a form of entrepreneurial learning that is broadly termed “analysis.” Mentors’ call for more learning through analysis is precisely at the expense of de-emphasizing the implementation of ideas in the short term. I show that this result is driven by mentors’ perceived uncertainty of the startup’s quality, where perceived uncertainty is proxied from mentors’ expectation error dispersion and sentiment variation.
Activity Sequencing in Startups
(preparing manuscript for conference presentations)
ABSTRACT: In this paper, I investigate the sequence of startup activities over time to understand the mechanisms underlying the prioritization of activities in startups. I develop a novel typology of startup activities using a database of 371 early-stage, science-based startups. I show that entrepreneurs, particularly first-time founders, under-prioritize learning. Using Latent Markov Models I show that the sequence of activities in early-stage startups from learning to implementation of ideas and acquisition of resources increases startups’ success in accessing capital.
Other Work in Progress
The Effect of Noisy Learning on Startup Performance, with Joshua Gans, Erin Scott, Scott Stern (data collection and empirical analysis)
Database Development
Database, Methodological Tools, and Research Opportunities: Creative Destruction Lab and Early-Stage Technology Ventures
Perhaps the most significant obstacle to conducting entrepreneurship research is the lack of high-quality systematic data on startups that allow us to test critical hypotheses about how startups form, grow, and succeed. To address this challenge, I have hand-collected detailed data from several hundred high-technology startups participating in Creative Destruction Lab (CDL), a global entrepreneurship program. This database is, to my knowledge, the largest of its kind, offering granular longitudinal visibility on the process of firm development in many high-growth startups across various technological domains, ranging from quantum computing to space. The relational database developed consists of nearly 20 tables that cover information ranging from the background of founders and their mentors to details about changes in the product and target market of each startup, as well as the type and amount of financial and non-financial resources they received along the way.
To help accelerate entrepreneurship research, these data are made available to 20 scholars across eight institutions. As of 2023, 15 research projects using these data are underway, of which the following two have been published:
2021: Per Davidsson, Denis Gregoire, Maike Lex; Venture Idea Assessment (VIA): Development of a needed concept, measure, and research agenda; Journal of Business Venturing
2022: Álvaro Parra, Ralph A. Winter; Early-stage venture financing; Journal of Corporate Finance
Creative Destruction Lab is an entrepreneurship program for early-stage science-based start-ups founded in 2012 by Professor Ajay Agrawal at the Rotman School of Management. My work to compile its data has been generously supported by CDL, the Federal Government of Canada (Strategic Innovation Fund), and many faculty and staff of the University of Toronto’s Rotman School of Management. As of June 2020, eighteen scholars at the University of Toronto, Harvard University, the University of Chicago, HEC Montreal, HEC Paris, Dalhousie University, the University of British Columbia, and the University of Calgary have been approved and given access to use these data for fifteen research projects.
The Rate and Direction of Academic Research in Artificial Intelligence
Ajay Agrawal and I collected data on a decade of conference proceedings of major AI conferences to understand how the state of the labor market for AI scientists and the distribution of scientific productivity has changed since the 2012 ImageNet Competition.
Findings from these data were presented to world leaders from government, industry, and the scientific community at the 2015 Future of Life Institute conference. For an overview on this conference see this Washington Post article.