Productivity, Technical Change, and Public Policies
Saturday, Jan. 5, 2019 8:00 AM - 10:00 AM
- Chair: Stefanie Stantcheva, Harvard University
Dancing with the Stars: Innovation Through Interactions
AbstractAn 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.
Robots, Trade, and Luddism: A Sufficient Statistic Approach to Optimal Technology Regulation
AbstractTechnological change, from the advent of robots to expanded trade opportunities, tends to create winners and losers. How should government policy respond? And how should the overall welfare impact of technological change on society be valued? We provide a general theory of optimal technology regulation in a second best world, with rich heterogeneity across households, linear taxes on the subset of firms affected by technological change, and a nonlinear tax on labor income. Our first results consist of three optimal tax formulas, with minimal structural assumptions, involving sufficient statistics that can be implemented using evidence on the distributional impact of new technologies, such as robots and trade. Our second result is a comparative static exercise illustrating that while distributional concerns create a rationale for non-zero taxes on robots and trade, the magnitude of these taxes may decrease as the process of automation and globalization deepens and inequality increases. Our final result shows that, despite limited tax instruments, technological progress is always welcome and valued in the same way as in a first best world.
From Imitation to Innovation: Where Is All That Chinese R&D Going?
AbstractWe construct a dynamic model where firms are heterogeneous in productivity and are subject to distortions. The productivity distribution evolves endogenously as the result of the decisions of individual firms that seek to upgrade over time their productivity. Firms can adopt two strategy to improve their productivity: imitation and innovation. The theory bears predictions about the behavior of firms and the aggregate equilibrium. We perform the structural estimation of the stationary state of the dynamic model using a Simulated Method of Moments approach which targets moments of the empirical distribution of R&D and productivity growth. We estimate the model using data from Taiwan and mainland China. The estimation highlights some interesting findings. The model predictions align well with the data, and the estimated model also yields a
good quantitative fit to the data. There are some important differences between Taiwan and China. R&D investments are significantly less productive in China. The evidence is consistent with a significant
- O3 - Innovation; Research and Development; Technological Change; Intellectual Property Rights
- O4 - Economic Growth and Aggregate Productivity