Decoding Gender Bias: The Role of Personal Interaction

Abstract

Subjective performance evaluation is an important part of hiring and pro- motion decisions. We combine experiments with administrative data to under- stand what drives gender bias in such evaluations in the technology industry. Our results highlight the role of personal interaction. Leveraging 60,000 mock video interviews on a platform for software engineers, we find that average rat- ings for code quality and problem solving are 12 percent of a standard deviation lower for women. We use two field experiments to study what drives these gaps. Our first experiment shows that providing evaluators with automated performance measures does not reduce gender gaps. Our second experiment compares blind to non-blind evaluations without video interaction: There is no gender gap in either case. These results rule out traditional models of dis- crimination. Instead, we show that gender gaps widen with extended personal interaction, and are larger for evaluators from regions where implicit associa- tion test scores are higher. This dependence on personal interaction provides a potential reason why audit studies often fail to detect gender bias.