Job-Market Paper
Learning through Sequential Interactions in the Market for Venture Capital
Abstract: This paper develops a theory of dynamic learning through repeated search in the market between venture capitalists and entrepreneurs with startups of unknown potential value. In the model, entrepreneurs repeatedly meet with venture capitalists, and engage in R&D to develop their projects independently. Both of these activities are informative for the entrepreneur, as venture capitalists draw private signals about the unknown type of the project, and R&D outcomes also depend on the underlying potential value. Using administrative data on the universe of U.S employers, I create a novel dataset of businesses in the market for venture capital. I use these data to estimate the model and then to quantitatively value each source of information in the model. I find that the private assessments of venture capitalists and entrepreneurs are more valuable as sources of information than what entrepreneurs learn from attempts at scaling-up their projects through R&D, with venture capitalists' assessments being more informative than entrepreneurs'. Reducing the venture capitalists' assessment expertise to that of entrepreneurs lowers the value generated by this market by 7%. Moreover, through this information production venture capitalists exert a positive externality in the market. In a counterfactual in which entrepreneurs subsidize 1.5% of venture capitalists entry costs, entrepreneur welfare is greater by 2.2%. In an alternate model in which venture capitalists have no assessment expertise, in a counterfactual with the same transfer scheme, entrepreneur welfare is lower by 2.1%.
Works in Progress
Managerial Experience and Entrepreneurial Human Capital (With Daniel Jaar and Ruben Piazzesi) (Short presentation slides)