Innovative Ideas and Gender Inequality

This event is part of our Social and Decision Analytics Seminar Series. 

Speaker: Dr. Marlène KOFFI, Assistant Professor in the Department of Economics at the University of Toronto

Abstract: This paper analyzes the recognition of women's innovative ideas in comparison to men, using bibliometric data from economics research. Based on machine learning, Dr. Koffi establishes the similarities between papers and links them by identifying the paper citing, those cited by it, and those that should be cited. She uncovers striking heterogeneity in terms of authorship. On average, papers omitted from references are 15% more likely to be female-authored than male-authored. Variations in gender perception based on given names reveal that articles written by women with female-sounding names are more likely to be omitted than articles by women with male-sounding names.

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