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Exposure of occupations to technologies of the fourth industrial revolution

arXiv 2110.13317 ↗First posted 25 Oct 2021cs.CY cs.CL econ.GNcited by 4

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Abstract (arXiv)

The fourth industrial revolution (4IR) is likely to have a substantial impact on the economy. Companies need to build up capabilities to implement new technologies, and automation may make some occupations obsolete. However, where, when, and how the change will happen remain to be determined. Robust empirical indicators of technological progress linked to occupations can help to illuminate this change. With this aim, we provide such an indicator based on patent data. Using natural language processing, we calculate patent exposure scores for more than 900 occupations, which represent the technological progress related to them. To provide a lens on the impact of the 4IR, we differentiate between traditional and 4IR patent exposure. Our method differs from previous approaches in that it both accounts for the diversity of task-level patent exposures within an occupation and reflects work activities more accurately. We find that exposure to 4IR patents differs from traditional patent exposure. Manual tasks, and accordingly occupations such as construction and production, are exposed mainly to traditional (non-4IR) patents but have low exposure to 4IR patents. The analysis suggests that 4IR technologies may have a negative impact on job growth; this impact appears 10 to 20 years after patent filing. Further, we compared the 4IR exposure to other automation and AI exposure scores. Whereas many measures refer to theoretical automation potential, our patent-based indicator reflects actual technology diffusion. Our work not only allows analyses of the impact of 4IR technologies as a whole, but also provides exposure scores for more than 300 technology fields, such as AI and smart office technologies. Finally, the work provides a general mapping of patents to tasks and occupations, which enables future researchers to construct individual exposure measures.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 1 of its 43 references that are in the map. First posted on arXiv 25 Oct 2021.

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1 of 43 references.

References · 1

  1. 2017Advances in Pre-Training Distributed Word Representationscited by 207

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4 papers.