Discrimination in the Venture Capital Industry: Evidence from Field Experiments

TL;DR

This study uses field experiments to reveal how venture capitalists' discrimination based on gender and race varies with startup context, highlighting implicit biases, statistical discrimination, and temporary effects during COVID-19.

Contribution

It provides new empirical evidence on context-dependent discrimination and introduces a consistent estimator to measure polarization and sources of investor biases.

Findings

Discrimination varies by startup context and is influenced by implicit biases.

Statistical and implicit discrimination are key drivers of anti-minority behaviors.

Temporary increase in discrimination against Asian founders during COVID-19.

Abstract

This paper examines discrimination by early-stage investors based on startup founders' gender and race using two complementary field experiments with real U.S. venture capitalists. Results show the following. (i) Discrimination varies depending on the context. Investors implicitly discriminate against female and Asian founders when evaluating attractive startups, but they favor female and Asian founders when evaluating struggling startups. This helps to reconcile the contradictory results in the extant literature and confirms the theoretical predictions of "discrimination reversion" and "pro-cyclical discrimination" phenomena. (ii) Among multiple coexisting sources of discrimination identified, statistical discrimination and implicit discrimination are important reasons for investors' "anti-minority" behaviors. A consistent estimator is developed to measure the polarization of investors' discrimination behaviors and their separate driving forces. (iii) Homophily exists when investors provide anonymous encouragement to startups in a non-investment setting. (iv) There was temporary, stronger discrimination against Asian founders during the COVID-19 outbreak.