Title

Gender Stereotype in Academia: Evidence from Economics Job Market Rumors Forum

Author(s)

Alice Wu Alice Wu (Princeton University)

Abstract

This paper examines whether people in academia portray and judge women and men differently in everyday "conversations" that take place online. I combine methods from text mining, machine learning and econometrics to study the existence and extent of gender stereotyping on the Economics Job Market Rumors forum. I first design a propensity score model to infer the gender a post mainly refers to from text, and simultaneously identify the individual words with the strongest association with gender. The words selected provide a direct look into the gender stereotyped language on this forum. Through a topic analysis of the posts, I find that when women are under discussion, the discourse tends to become significantly less academic or professionally oriented, and more about personal information and physical appearance. Moreover, a panel data analysis reveals the state dependence between the content of posts within a thread. In particular, once women are mentioned in a thread, the topic is likely to shift from academic to personal. Finally, I restrict the analysis to discussions about specific economists, and find that high-profile female economists tend to receive more attention on EJMR than their male counterparts.

Creation Date

2017-09

Section URL ID

Paper Number

2017-09

URL

https://drive.google.com/file/d/0BwjFN4HbBrDBbnFqZzdLWThDb0U/view

File Function

Jel

J16, J23, M51, J71, I23

Keyword(s)

Suppress

false

Series

9