Innovation Networks and Economic Growth

National Bureau Of Economic Research Inc, Cambridge MA

Investigators

Abstract

This project seeks to establish a method for understanding how innovation networks affect broad economic outcomes. We know that innovation happens in a network environment and has network effects; that is, a firm, scientist, or individual developing a new product or process is affected by the decisions and findings of other innovators, and in turn has that same effect on others. For example, an innovation in one specific area may spur a large number of related innovations. The project will use data from patent applications to develop measures of the network links between broad classes of patents. The team will then seek to incorporate these measures into a model of overall economic growth to consider questions about the implications of new technologies for overall productivity growth and employment. They also want to develop predictions about the ways in which public and private R&D affect the macroeconomy in different ways, and tools that may help to determine how industrial policy can affect the competitiveness of the US macroeconomy. The team uses data from the US Patent and Trademark Office (USPTO) for the years from 1975 to 2009. They are modeling the network structure of innovations across types of innovation, and are including measures of innovation quality. The goal is to determine how a patent in one area increases (or decreases) the probability of innovation in other areas. The network description will be used to measure the size of spillovers (including first-order and second-order effects). Variation generated by the network will be used to investigate implications of new technologies for employment/output/wages across cities. The innovation network will also be integrated into a structural model of resource allocation in the aggregate economy. The PIs will determine where university and public research lab patents sit in the innovation network; they will use the result to consider whether there are systematic differences between the network effects of publicly funded R&D vs. privately funded R&D.