Marlene Koffi

« Innovative Ideas and gender inequality »

Abstract:

This paper analyzes the recognition of women’s innovative ideas in comparison to the recognition received by men. Bibliometric data from research in economics are used to investigate whether there are gender biases in citation patterns. Based on machine learning techniques, I establish the similarities between papers, then build links between articles, identifying the papers citing a given paper, those cited by it, and those that should be cited. I uncover striking heterogeneity in terms of authorship. Omitted papers from references are 15 to 30 percent more likely to be female-authored than male-authored. The most likely to be omitted are papers written by women working at mid-tier institutions, publishing in non-top journals. This gender omission bias is also more prevalent when there are only males in the citing paper. Finally, I present suggestive evidence that being omitted with respect to past publications may affect an author’s future productivity.