Dancing with the Stars: Innovation Through Interactions
| Autor: | Ernest Miguelez, Valerio Sterzi, Stefanie Stantcheva, Ufuk Akcigit, Santiago Caicedo |
|---|---|
| Přispěvatelé: | horain, anne, Groupe de Recherche en Economie Théorique et Appliquée (GREThA), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), University of Chicago, Massachusetts Institute of Technology (MIT), Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Endogenous growth theory
Knowledge management [QFIN]Quantitative Finance [q-fin] 9. Industry and infrastructure business.industry 05 social sciences Innovation process Human capital [QFIN] Quantitative Finance [q-fin] 0502 economics and business Key (cryptography) 050207 economics business Productivity ComputingMilieux_MISCELLANEOUS 050205 econometrics |
| Zdroj: | NBER Working Paper NBER Working Paper, 2018 |
| Popis: | An inventor's own knowledge is a key input in the innovation process. This knowledge can be built by interacting with and learning from others. This paper uses a new large-scale panel dataset on European inventors matched to their employers and patents. We document key empirical facts on inventors' productivity over the life cycle, inventors' research teams, and interactions with other inventors. Among others, most patents are the result of collaborative work. Interactions with better inventors are very strongly correlated with higher subsequent productivity. These facts motivate the main ingredients of our new innovation-led endogenous growth model, in which innovations are produced by heterogeneous research teams of inventors using inventor knowledge. The evolution of an inventor's knowledge is explained through the lens of a diffusion model in which inventors can learn in two ways: By interacting with others at an endogenously chosen rate; and from an external, age-dependent source that captures alternative learning channels, such as learning-by-doing. Thus, our knowledge diffusion model nests inside the innovation-based endogenous growth model. We estimate the model, which fits the data very closely, and use it to perform several policy exercises, such as quantifying the large importance of interactions for growth, studying the effects of reducing interaction costs (e.g., through IT or infrastructure), and comparing the learning and innovation processes of different countries. |
| Databáze: | OpenAIRE |
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