A new mapping of technological interdependence

TL;DR

This paper investigates how technological interdependence influences innovation by analyzing patent data through novel text mining and network analysis methods, revealing the long-term importance of network structure and the immediate effects of neighbor innovativeness.

Contribution

It introduces new methods combining text mining and network analysis to study technological interdependence using a large patent dataset, highlighting the temporal dynamics of influence.

Findings

Network linkages are as influential as neighbor innovativeness in the long run.

Positive shocks to neighbor innovativeness have rapid short-term effects.

Strengthening network linkages impacts innovation with a delayed but longer-lasting effect.

Abstract

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.