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 Start-Ups

w/ Kevin Bryan and Mitchell Hoffman

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American Economic Journal: Applied Economics (2026)

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.

Ideas Don't Matter (At the Margin): Entry Congestion and Returns to Idea Quality in Startups

w/ John Horton

Files: Latest Draft

(preparing manuscript for resubmission to journal)

ABSTRACT: We develop a model in which startups are free to pursue any idea among a collection of ideas. This competition equalizes expected private returns across those ideas, despite differing in apparent ex ante quality. Workers can either found a startup or join a startup as an employee. Their choice equalizes expected returns across employment and entrepreneurship. We show that crowding is socially excessive on every idea except the “marginal idea” because entrepreneurs do not internalize business-stealing externalities. Because the business-stealing wedge grows with crowding, ideas that look unattractive ex ante have higher social returns at the margin. In fact, the marginal idea is the idea with the highest social return, and pushing the margin out—notably, through public R&D that expands the set of usable ideas—lessens congestion and improves efficiency. The model generates a sufficient statistic for social efficiency, which is the elasticity of startup success with respect to number of startup entrants. We compute this elasticity cross sectionally using data from 37,818 U.S. startups across 2,045 distinct ideas. We estimate a lower-bound of -0.18 for the elasticity of per-entrant success probability with respect to the number of entrants on an idea. This estimate grows to -0.86 in the sample of highly promising ideas, meaning that a 10% increase in entry on such ideas yields only a 1.4% increase in total successful exits.

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.