Working Papers

Networks feature prominently in venture capital markets. This paper focuses on alumni networks and exploits a new partner with new alumni networks joining a venture capital company as a plausibly exogenous change to the VC’s alumni networks. New alumni ties lead to an 8.21% increase in investments in startups with alumni founders, while induce a 22.93% increase in failure rates and a 17.53% decline in acquisition rates. Supplementary tests suggest that although venture capitalists benefit from networks through better information, preference for alumni startups offsets the benefits and induces capital misallocation.


with Adrien d'Avernas, Andrea Eisfeldt, Richard Stanton, and Nancy Wallace

The deposit business differs at large versus small banks.  We provide a parsimonious model and extensive empirical evidence supporting the idea that much of the variation in deposit-pricing  behavior between large and small banks reflects differences in ``preferences and technologies.''  Large banks offer superior liquidity services but lower deposit rates, and locate where customers value their services.  In addition to receiving a lower level of deposit rates on average, customers of large banks exhibit lower demand elasticities with respect to deposit rate spreads.  As a result, despite the fact that the locations of large-bank branches have demographics typically associated with greater financial sophistication, large-bank customers earn lower average deposit rates.  Our explanation for deposit pricing behavior challenges the idea that deposit pricing is mainly driven by pricing power derived from the large observed degree of concentration in the banking industry.


with Yuchen Chen and Xuelai Li

This paper examines how the technology literacy of venture capital (VC) firms influences investment in deep-tech startups. Using novel matched data from PitchBook and Revelio Labs, we show that tech-literate VCs, proxied by the share of Ph.D.-trained partners, are scarce, geographically concentrated, and more likely to fund deep-tech ventures. Startups backed by these VCs experience lower failure rates and higher IPO probabilities. Based on these findings, we develop and calibrate a dynamic matching model with hiring frictions for Ph.D.-trained partners. The model reveals that lowering the cost for Ph.D.s to become VC partners does not necessarily raise their equilibrium share, providing a potential explanation for the persistently low and recently declining presence of Ph.D. partners in the industry.