Exposure of occupations to technologies of the fourth industrial revolution

Abstract

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. Researchers could validate our findings through further analyses with micro data, and our dataset can serve as a source for more complex labor market analyses. 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. We show that a combination of 4IR exposure with other automation measures may provide additional insights. For example, near-term automation might be driven by non-4IR technologies. 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.

Labour‐saving automation: A direct measure of occupational exposure

“…Table 1 presents a summary of the most relevant contributions discussed so far, with their methodologies and findings. With respect to the extant literature we advance along the following lines: first, we construct a direct similarity measure which is able to assign a specific value to the similarity across two dictionaries of words, respectively, covering the realm of technology (CPCs) and human functions (O*NET); second, rather than relying on patent titles and co-occurrences of specific verb-noun pairs (Webb, 2020), we extend a far more complete and accurate specification of technological content of patent titles to the entire dictionary of functions described in CPCs; third, by employing the CPCs classification rather than patent texts (Kogan et al, 2021;Meindl et al, 2021), we are able to create a matrix of similarity with every underlying technology, permitting the generalisation of our measure beyond specific robotic technology. In addition, we also avoid excluding the majority of textual content present in patent text which is clearly not consistent with the description of human functions.…”

“…The article documents the increasing entry of white-collar middle-paid occupations in the period 1940-1980, while since 1980 new jobs have been concentrated in services provided by both the highly educated and the less-educated. Another application of the Kogan et al (2021) measure was with reference to I4.0 patents in Meindl et al (2021), the authors matching in this case the patent text corpus with the "detailed work activities" (DWAs) section of the O*NET. According to their results, financial and professional occupations are more exposed to I4.0 patents compared to non-I4.0 patents.…”

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models

“…Prior research has employed various approaches to estimate the overlap between AI capabilities and the tasks and activities workers undertake in different occupations. These methods include mapping patent descriptions to worker task descriptions (Webb, 2020;Meindl et al, 2021), linking AI capabilities to occupational abilities documented in the O*NET database (Felten et al, 2018(Felten et al, , 2023, aligning AI task benchmark evaluations with worker tasks via cognitive abilities (Tolan et al, 2021), labeling automation potential for a subset of US occupations and using machine learning classifiers to estimate this potential for all other US occupations (Frey and Osborne, 2017), modeling task-level automation and aggregating the results to occupation-level insights (Arntz et al, 2017), collecting expert forecasts (Grace et al, 2018), and most relevantly to this paper, devising a new rubric to assess worker activities for their suitability for machine learning (Brynjolfsson et al, 2018(Brynjolfsson et al, , 2023. Some of these approaches have found exposure to AI technologies at the task-level tends to be diversified within occupation.…”

The Empirics of Technology, Employment and Occupations: Lessons Learned and Challenges Ahead

“…The paper documents the increasing entry of white-collar middle-paid occupations in the period 1940-1980; since 1980 new jobs have been concentrating in both high-educated and low-educated services. Another application of the Kogan et al (2021)'s measure has been used with reference to Industry 4.0 (I4.0) patents by Meindl et al (2021), matching in this case the patent text corpus with the "detailed work activities" (DWAs) section of the O*NET. According to their results, financial and professional occupations are more exposed to I4.0 patents compared to non I4.0 patents.…”