Licensing and Innovation Regimes in Pharmaceutical R&D
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摘要
This study develops a theoretical framework for pharmaceutical licensing that distinguishes incremental from novel innovations, linking information precision to market outcomes. Using a product-level dataset and Double Machine Learning (DML) with a causal DML-IV instrument (Phase III failures), the authors show that licensing raises predicted success probabilities overall, but the impact on returns varies by innovation regime: incremental projects exhibit return equalization (higher licensing success with lower post-licensing returns), while novel frontier projects often lack this offset, implying lemons-type frictions. The empirical results are decomposed across risk profiles, illustrating that competitive equilibrium holds for incremental projects but is weaker at the innovation frontier, where information frictions persist. The paper concludes with policy implications on transparency and contract design to better price frontier innovations [page::0][page::1]
