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Evaluation and Learning in R&D Investment


This paper examines the role of spillover learning in shaping the value of exploratory versus incremental R&D. Using data from the pharmaceutical industry, we show that novel drug candidates generate more dynamic spillovers than incremental ones. That is, despite being more likely to fail in the development process, novel drugs are more likely to inspire the development of subsequent successful drugs. Motivated by this fact, we develop a model where firms are better able to evaluate the viability of incremental drugs, but where investing in novel drugs helps firms learn about future related projects. Our model provides an empirical diagnostic for assessing the relative value of evaluation versus learning, namely that if firms place greater value on learning, then they should set a lower revenue threshold for investing in novel relative to incremental drugs. We in fact find that firms place less value on learning: they are less likely to invest in novel drugs and in turn novel drugs have higher revenues on approval. Finally, we provide suggestive evidence that some of these patterns are driven by concerns about the appropriability of spillover knowledge.


Working Paper


Dimitris Papanikolaou, Danielle Li, Joshua Krieger, Alexander Frankel

Date Published



Papanikolaou, Dimitris, Danielle Li, Joshua Krieger, and Alexander Frankel. 2022. Evaluation and Learning in R&D Investment.


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