Gender Bias in Rumors among Professionals: An Identity-Based Interpretation

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Authors Alice H. Wu
Journal/Conference Name Review of Economics and Statistics
Paper Category
Paper Abstract This paper measures gender bias in discussions about women versus men in an online professional forum. I study the content of posts that refer to each gender, and the transitions in the topics between consecutive posts once attention turns to one gender or the other. Discussions about women tend to emphasize their personal characteristics instead of professional accomplishments. Posts about women are also more likely to lead to deviations from professional topics than posts about men. I interpret these findings through a model that highlights posters' incentives to boost their own identities relative to the underrepresented out-group in a profession.
Date of publication 2019
Code Programming Language Python
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