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.

We develop a theory of career paths and earnings where agents organize in production hierarchies. Agents climb these hierarchies as they learn stochastically from others. Earnings grow as agents acquire knowledge and occupy positions with more subordinates. We contrast these and other implications with US census data for the period 1990 to 2010, matching the Lorenz curve of earnings and the observed mean experience-earnings profiles. We show the increase in wage inequality over this period can be rationalized with a shift in the level of the complexity and profitability of technologies relative to the distribution of knowledge in the population. (JEL D83, E24, J24, J31)

We develop a theory of career paths and earnings in an economy in which agents organize in production hierarchies. Agents climb these organizational hierarchies as they learn stochastically from other individuals. Earnings grow over time as agents acquire knowledge and occupy positions with larger numbers of subordinates. We contrast these and other implications of the theory with U.S. census data for the period 1990 to 2010. The model matches well the Lorenz curve of earnings as well as the observed mean experience-earnings profiles. We show that the increase in wage inequality over this period can be rationalized with a shift in the distribution of the complexity and profitability of technologies relative to the distribution of knowledge in the population. Santiago Caicedo Department of Economics University of ChicagoEsteban Rossi-Hansberg Princeton UniversityApril 6, 2016 AbstractWe develop a theory of career paths and earnings in an economy in which agents organize in production hierarchies. Agents climb these organizational hierarchies as they learn stochastically from other individuals. Earnings grow over time as agents acquire knowledge and occupy positions with larger numbers of subordinates. We contrast these and other implications of the theory with U.S. census data for the period 1990to 2010. The model matches well the Lorenz curve of earnings as well as the observed mean experience-earnings pro…les. We show that the increase in wage inequality over this period can be rationalized with a shift in the distribution of the complexity and pro…tability of technologies relative to the distribution of knowledge in the population.

We study firm responses to a large‐scale change in apprenticeship regulation in Colombia. The reform requires firms to train, setting apprentice quotas that vary discontinuously in firm size. We document strong heterogeneity in responses across sectors, where firms in sectors with high skill requirements tend to avoid training apprentices, while firms in low‐skill sectors seek apprentices. Guided by these reduced‐form findings, we structurally estimate firms' training costs. Especially in high‐skill sectors, many firms face large training costs, limiting their willingness to train apprentices. Yet, we find substantial overall benefits of expanding apprenticeship training, in particular when the supply of trained workers increases in general equilibrium. Finally, we show that counterfactual policies taking into account heterogeneity across sectors can deliver similar benefits from training while inducing less distortions in the firm‐size distribution and in the allocation of resources across sectors.