Breaking New Ground, Reinforcing Old Gaps: Gender Disparities in Access to Emerging Research Frontiers
Carolina Biliotti, Luca Verginer, and Massimo Riccaboni. Available at arXiv:2404.04707 and EUI ECO Working Paper Series
Breaking New Ground, Reinforcing Old Gaps: Gender Disparities in Access to Emerging Research Frontiers
Carolina Biliotti, Luca Verginer, and Massimo Riccaboni. Available at arXiv:2404.04707 and EUI ECO Working Paper Series
Research Novelty and the Gendered Hierarchy of Recognition
Carolina Biliotti, Massimo Riccaboni, Jeffrey W. Lockhart and James A. Evans. Draft available soon.
Abstract: We explore the relationship between gender differences in the kinds of innovation pursued and scientific rewards accrued. Combining multiple data sources, we find that papers by women are more likely to innovate by connecting previously disconnected scientific literature, indicating a higher propensity for interdisciplinary contributions. When women innovate in this way, their work is more disruptive than when men do so, but their science is more harshly penalized as it becomes more innovative, and as it comes to be more widely adopted after publication. In comparison, men’s papers are more likely to innovate by combining previously disconnected scientific terms, and they are more likely to benefit from doing so. For equally innovative science, men’s articles are usually placed in journals of higher prestige, although gender gaps evaporate for the most innovative science. Men’s works also receive more recognition for engaging in this activity, with credit less likely to accrue for those who build upon them. Our findings highlight asymmetric returns to different kinds of innovation in papers across gender.
Phase 2 Product Success in Early-Stage Biopharma Licensing Deals: Insights from Contract Text and High-Dimensional Modeling
Carolina Biliotti, Filippo Chiarello, Giacomo Marzi, and Massimo Riccaboni. Draft available soon.
Abstract: We study how textual information in biopharma licensing agreements relates to early-stage product success, focusing on Phase 2 clinical trials where uncertainty is highest. Using proprietary data on biopharma licensing deals and publicly sourced licensing contracts, we combine deal-level features with textual representations, including large language model embeddings and topic-model features. Textual information provides modest complementary insight, primarily capturing latent project characteristics not directly observable from deal attributes. Building on predictive modeling, we then use Double/Debiased Machine Learning to examine associations between contractual payment clauses and development outcomes while adjusting for high-dimensional deal and project characteristics. Our results illustrate both the promise and the limitations of integrating textual analysis with predictive modeling for evaluating early-stage licensing deals.