Discrimination in the Venture Capital Industry: Evidence from Two Randomized Controlled Trials

Zhang, Ye

This paper examines discrimination based on startup founders' gender, race, and age by early-stage investors, using two randomized controlled trials with real venture capitalists. The first experiment invites U.S. investors to evaluate multiple randomly generated startup profiles, which they know to be hypothetical, in order to be matched with real, high-quality startups from collaborating incubators. Investors can also donate money to randomly displayed startup teams to show their anonymous support during the COVID-19 pandemic. The second experiment sends hypothetical pitch emails with randomized startups' information to global venture capitalists and compares their email responses by utilizing a new email technology that tracks investors' detailed information acquisition behaviors. I find three main results: (i) Investors are biased towards female, Asian, and older founders in"lower contact interest"situations; while biased against female, Asian, and older founders in"higher contact interest"situations. (ii) These two experiments identify multiple coexisting sources of bias. Specifically, statistical discrimination is an important reason for"anti-minority"investors' contact and investment decisions, which was proved by a newly developed consistent decision-based heterogeneous effect estimator. (iii) There was a temporary, stronger bias against Asian founders during the COVID-19 outbreak, which started to fade in April 2020.