Abstract
This paper, relying on a still relatively unexplored long-term dataset on U.S. patenting activity, provides empirical evidence on the history of labor-saving innovations back to early nineteenth century. The identification of mechanization/automation heuristics, retrieved via textual content analysis on current robotic technologies by Montobbio et al. (Robots and the origin of their labour-saving impact, LEM Working Paper Series 2020/03), allows to focus on a limited set of CPC codes where mechanization and automation technologies are more prevalent. We track their time evolution, clustering, eventual emergence of wavy behavior, and their comovements with long-term GDP growth. Our results challenge both the general-purpose technology approach and the strict 50-year Kondratiev cycle, while they provide evidence of the emergence of erratic constellations of heterogeneous technological artefacts, in line with the development-block approach enabled by autocatalytic systems.
Highlights
The existence of labor-saving heuristics driving the rate and direction of technological change is a documented pattern, since the inception of the First Industrial Revolution
The role played by time-saving heuristics in shaping the direction of mechanization has been emphasized by von Tunzelmann (1995) with reference to the cotton industry in the British Industrial Revolution: the massive increase in labor productivity resulted from the use of innovation and discovery through which a spinner was able to produce in a day as much yarn as previously required by a full year of work, without mechanization
The labor-saving heuristics identified by Montobbio et al (2020) via textual analysis on current robotic technologies allow to focus on a coherent set of technological CPC classes, the historical evolution of which is analyzed in terms of timing, clustering, periodic behavior, and comovements with GDP growth
Summary
The existence of labor-saving (hereafter, LS) heuristics driving the rate and direction of technological change is a documented pattern, since the inception of the First Industrial Revolution. More established notions of process vs product innovations are explored in Van Reenen (1997); Lachenmaier and Rottmann (2011); Harrison et al (2014) Both the novelty of the empirical analysis, by fully exploiting the long-run historical dimension of the USPTO dataset, still relatively unexplored, the use of wavelet analysis to study patent data, and enriches our understanding of the long run history of the constellations of artefacts behind current LS robotic technologies. LS patents and general robotic patents (see Montobbio et al 2020, footnote 14): For empirical evidence investigating current Industry 4.0 trends in the automotive industries, see Moro et al (2019); Cirillo et al (2021). 4 Codes which belong to CPC ‘raccord’ class Y are left full digit
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
