Gender Bias and Learning in Social Networks

Abstract

Our experiment identifies biases in information updating by men and women in exogenously-formed networks. We include circle and star network structures to see how the structure of networks and level of information available interact with gender. We find that both men and women in star networks exhibit homophily. In contrast, circle network participants do not exhibit any gender-based bias. Rather, including gender information in circle networks increases men’s willingness to update their own information. These differences result in opposing aggregate behavior; gender information increases the rate of consensus in circle networks but decreases the rate of consensus in star networks.

(joint with Sam Stelnicki and Xiaomin Bian)

Mir Adnan Mahmood

Economist, Bates White Economic Consulting

I am an Economist at Bates White Economic Consulting. I received my PhD in Economics from The Ohio State University.