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, I establish the similarities between papers and link them by identifying the paper citing, those cited by it, and those that should be cited. I uncover 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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https://uottawa-ca.zoom.us/j/91999003281?pwd=VXdYS3U0djEvYWJWWnA4V3VRb3ZMUT09