The Economics of Advice: Evidence from Startup Mentoring

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Management Science (2025)

ABSTRACT: This paper examines the role of advice in early firm development and growth, drawing on detailed data from a global program where angel investors and venture capitalists (VCs) mentored founders over several months. Leveraging variation in mentors' availability to support start-ups because of personal scheduling conflicts, I find that advice significantly improves start-ups' future market performance. To explore how advice shapes early firm development, I develop a novel typology of start-up activities, finding that a defining element of mentors' advice is to do less and learn more. Although angels and VCs are consistent in this message, they differ significantly in when they choose to advise start-ups in achieving their business objectives. Angels are more likely than VCs to help founders design and execute product market experiments, whereas VCs provide more mentoring support on business analysis and planning tasks. I find evidence consistent with the hypothesis that experimentation is a skill developed via learning-by-doing, and angels have a skill advantage in that domain because of having more operational experience.

Information Frictions and Employee Sorting Between Startups

w/ Kevin Bryan and Mitchell Hoffman

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American Economic Journal (forthcoming)

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.

Price Theory of Silicon Valley

w/ John Horton

(preparing manuscript for resubmission to journal)

ABSTRACT: We model an entrepreneurial cluster as three interconnected kinds of markets: the market for venture capital, the labor market for high-skilled individuals, or “engineers,” and the product markets startups hope to serve. Engineers choose to either found a startup as an entrepreneur—pursuing a single business idea and obtaining seed funding from VCs in ex- change for equity—or join an established startup as an employee. The model predicts the equilibrium number of startups formed and their success probability, as well as the wages of engineers, the share of equity retained by founders, the fraction of engineers pursuing entrepreneurship, and the profits of successful startups. The equilibrium is affected by the cost of founding a startup, the extent of product markets, the supply of engineers, and the “supply” of startup ideas. The model predicts the total output of the entrepreneurial system and the social value of the marginal startup..

How does Industry Affiliation of Academic Scientists Affect the Rate and Direction of Research?

(manuscript ready for conference presentation)

ABSTRACT: The implications of academic collaborations with industry have long been the nexus of contentious debate. The prevailing concern is that industry causes research to lose its fundamental depth and become commercially driven. This paper presents evidence that these concerns are misplaced for areas of research that have commercial value--that is, the specific domains for which these concerns are raised. The empirical analysis uses large-scale, manually improved bibliometric data from artificial intelligence research. For identification, I use the unexpected and significant success of the neural network techniques revealed at the ImageNet 2012 benchmark competition, which sharply increased the industry's demand for AI scientists, but more so for scientists with higher expertise in the breakthrough field. While industry affiliation significantly increases the usefulness of research, it does not diminish its novelty. In addition, industry affiliation raises both the publication and the quality of science produced by academic scientists. Results are consistent with the explanation that, at least in the medium term, academics in short supply can negotiate higher academic freedom while utilizing commercial resources for their research.

Media Mentions: The Economist - Million Dollar Babies | The Economist - Battle of the Brains

Activity Sequencing in Startups

w/ Jorge Guzman

(preparing manuscript for conference presentations)

ABSTRACT: In this paper, we investigate the sequence of startup activities over time to understand the mechanisms underlying the prioritization of activities in startups. We leverage a novel typology of startup activities using a database early-stage, science-based startups. We show that entrepreneurs, particularly first-time founders, under-prioritize learning. Using Latent Markov Models, we 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.