Measuring Technological Innovation over the Long Run
We use textual analysis of high-dimensional data from patent documents to create new indicators of technological innovation. We identify significant patents based on textual similarity of a given patent to previous and subsequent work: these patents are distinct from previous work but are related to subsequent innovations. Our measure of patent significance is predictive of future citations and correlates strongly with measures of market value. We identify breakthrough innovations as the most significant patents – those in the right tail of our measure – to construct indices of technological change at the aggregate, sectoral, and firm level. Our technology indices span two centuries (1840-2010) and cover innovation by private and public firms, as well as non-profit organizations and the US government. These indices capture the evolution of technological waves over a long time span and are strong predictors of productivity at the aggregate and sectoral level.
We thank Pierre Azoulay, Nicholas Bloom, Diego Comin, Carola Frydman, Kyle Jensen, and seminar participants at AQR, Harvard, and the NBER Summer Institute for valuable comments and discussions. We are grateful to Kinbert Chou, Inyoung Choi, Jinpu Yang and Jiaheng Yu for excellent research assistance and to Enrico Berkes and Cagri Akkoyun for sharing their data. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
I have received consulting income from AQR Capital Management exceeding $10,000 over the past three years. AQR Capital Management is a global investment management firm, which may or may not apply similar investment techniques or methods of analysis as described herein. The views expressed here are those of the authors and not necessarily those of AQR.