A new mapping of technological interdependence
This research addresses the problem of understanding innovation dynamics for policymakers and economists, though it is incremental in applying existing methods to new data.
The study examined how technological interdependence influences innovation by analyzing neighbor innovativeness and network structure using text mining and network analysis on 6.5 million USPTO patents from 1976 to 2021, finding that network linkages are as important as neighbor innovativeness in the long run, with short-run effects differing in timing and duration.
How does technological interdependence affect innovation? We address this question by examining the influence of neighbors' innovativeness and the structure of the innovators' network on a sector's capacity to develop new technologies. We study these two dimensions of technological interdependence by applying novel methods of text mining and network analysis to the documents of 6.5 million patents granted by the United States Patent and Trademark Office (USPTO) between 1976 and 2021. We find that, in the long run, the influence of network linkages is as important as that of neighbor innovativeness. In the short run, however, positive shocks to neighbor innovativeness yield relatively rapid effects, while the impact of shocks strengthening network linkages manifests with delay, even though lasts longer. Our analysis also highlights that patent text contains a wealth of information often not captured by traditional innovation metrics, such as patent citations.