制药研发中的许可与创新机制
Licensing and Innovation Regimes in Pharmaceutical R&D
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中文总结 AI 辅助
研究制药研发中许可对创新分配的影响，构建含质量与创新机制差异的模型，用数据和双重机器学习方法测试预测，发现许可总体提高成功概率，回报均等化主要适用于渐进式项目，揭示市场表现取决于创新类型。
中文摘要 AI 辅助
我们研究了许可如何影响制药研发中的创新分配。我们构建了一个模型，其中项目在质量和创新机制上存在差异，区分了渐进式创新和新颖式创新。渐进式项目的信息精度较高，新颖式项目的信息精度较低，这在技术市场中产生了不同的均衡动态。该模型预测，许可对渐进式创新维持正向选择和竞争性回报均等化，而新颖式项目可能表现出较弱的筛选，与柠檬型摩擦一致。我们使用产品层面的数据和双重机器学习方法，对这些预测在成功概率和货币回报方面进行了测试。我们发现，许可总体上提高了成功概率，但回报均等化主要适用于渐进式项目。对于新颖式创新，许可没有表现出相同的均衡调整，这表明存在残余的市场缺陷。使用外生的产品线冲击对许可进行工具变量分析因果性地证实了这一模式：对于渐进式的“仓促”许可，竞争性风险回报权衡得以保留，但对于新颖式许可则不然。我们的结果调和了技术市场中关于竞争效率和信息摩擦的证据，表明市场表现系统地取决于所交易的创新类型。
英文摘要
We study how licensing affects the allocation of innovation in pharmaceutical R&D. We develop a model in which projects differ in both quality and innovation regime, distinguishing between incremental and novel innovations. Information precision is higher for incremental projects and lower for novel ones, generating different equilibrium dynamics in the market for technology. The model predicts that licensing sustains positive selection and competitive return equalization for incremental innovation, while novel projects may exhibit weaker screening consistent with lemons-type frictions. Using product-level data and Double Machine Learning methods, we test these predictions across success probabilities and monetary returns. We find that licensing increases success probability overall, but return equalization holds primarily for incremental projects. For novel innovation, licensing does not exhibit the same equilibrium adjustment, suggesting residual market imperfections. Instrumenting for licensing using exogenous pipeline shocks confirms this pattern causally: the competitive risk-return trade-off is preserved for incremental 'rushed' licenses, but it breaks down for novel ones. Our results reconcile evidence on both competitive efficiency and information frictions in markets for technologies, showing that market performance depends systematically on the type of innovation being transacted.
