Citation Gender Gaps in Top Economics Journals

• Authors: Miguel Díaz Salazar, Manu García, J. Ignacio Conde-Ruiz and Juan-José Ganuza.
• BSE Working Paper: 1529 | May 25
• Keywords: gender gaps , machine learning , structural topic model , research fields , gendered language
• JEL codes: I20, J16, Z13

This paper investigates the existence and drivers of gender citation gaps in the five leading journals in economics. Using a comprehensive dataset of 7,244 articles published between 1999 and 2023, we examine whether female-authored papers are cited more frequently than male-authored ones, and whether this pattern persists after controlling for differences in research topics. We apply Structural Topic Modeling (STM) to abstracts to estimate latent research themes and complement this approach with field classifications based on JEL codes. Our results show that female-authored papers initially display a citation premium—receiving up to 16 log points more citations—but this advantage becomes statistically insignificant once we control for research field composition using either STM topics or JEL codes. These findings suggest that horizontal gender differences in thematic specialization, rather than bias in citation behavior, account for most of the observed citation gap. Our analysis highlights the importance of accounting for field heterogeneity when assessing academic recognition and contributes to ongoing discussions about fairness and diversity in economics publishing.