Author
Abstract
Despite the seemingly fast development and wide diffusion of technologies in recent decades, concerns have been raised as to whether invention is slowing down. A question has also arisen as to whether the vast accumulation of technical knowledge, instead of speeding up the productivity of subsequent knowledge creation, has, on the contrary, become a “burden of knowledge” that makes it harder to find new ideas. We engage with these concerns by examining nearly 7 million utility patents granted by the U.S. Patent Office and characterizing the growth process of patenting from 1976 to 2018. Although the rate of patenting has steadily increased, patenting productivity as measured as patents per distinct inventor has continuously declined in utility patents in general and for technological frontier fields of biotechnology, climate change mitigation and adaptation, and artificial intelligence. The rapid growth rate of new patents can be credited to an increase in the number of individuals engaged in inventive activity rather than improved productivity. In the U.S., the proportion of the population engaging in patenting has grown significantly. Nevertheless, the growth of the inventive labor force and new patents relies more heavily on experienced inventors than new inventors. As the size of patenting teams keeps growing, the typical inventor participates in a growing number of patents while representing a declining proportion of the inventive labor responsible for patented inventions. We find evidence that as the stock of accumulated patented inventions grows, patenting productivity declines, suggesting that past invention makes it harder for inventors to find new knowledge. In the language of economics, invention (as tracked by patenting) has experienced extensive growth driven by the increase of the inventive labor force with declining productivity and a growing division of labor.
Suggested Citation
Wang, Jieshu & Lobo, José, 2024.
"Extensive growth of inventions: Evidence from U.S. patenting,"
Technological Forecasting and Social Change, Elsevier, vol. 207(C).
Handle:
RePEc:eee:tefoso:v:207:y:2024:i:c:s0040162524003822
DOI: 10.1016/j.techfore.2024.123586
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