Research Similarity and Women in Academia

We investigate the extent to which research similarity between senior and junior researchers is related to promotion in academia and study implications for gender diversity among academic staff. Using data on the universe of job applications for tenure track assistant professor positions in economics in Italy, and applying NLP techniques (i.e., document embeddings) to the abstract of each publication of the scholars in our dataset, we propose a novel measure of research similarity that can capture the closeness in research topics, methodologies or policy relevance between candidates and members of selection committees. We show that the degree of similarity is strongly associated with the probability of winning. Moreover, while there are no gender differences in mean similarity, the maximum similarity with selection committee members is lower for female candidates. This gender gap disappears when similarity is calculated focusing only on female committee members. The results suggest that similarity bias in male-dominated environments may have implications for gender and research diversity.