Licensing and Innovation Regimes in Pharmaceutical R&D

2026-07-22

AI Summary

This study investigates how licensing regimes shape the allocation of resources and market efficiency between incremental and novel innovation in pharmaceutical R&D. By developing a theoretical model that distinguishes between these two innovation types, integrating product-level data with double machine learning, and employing exogenous pipeline shocks as instrumental variables for causal identification, the paper provides the first systematic evidence of licensing’s differential screening effects across innovation categories. It demonstrates that while licensing generally enhances project success rates, it achieves an optimal risk–reward balance only for incremental innovations. In contrast, novel innovations are hindered by adverse selection and information frictions akin to a “lemons” market, which disrupts their risk–reward trade-off. These findings reconcile conflicting empirical evidence in technology markets regarding competitive efficiency and informational asymmetries.

Abstract

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.

Problem

• licensing
• pharmaceutical R&D
• incremental innovation
• novel innovation
• market for technology

Innovation

• licensing
• incremental innovation
• novel innovation
• information frictions
• Double Machine Learning