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| Natura: | Preprint |
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2021
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| Accesso online: | https://arxiv.org/abs/2101.11505 |
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| _version_ | 1866916884368064512 |
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| author | Tong, Di Wu, Lingfei Evans, James Allen |
| author_facet | Tong, Di Wu, Lingfei Evans, James Allen |
| contents | Substantial scholarship has estimated the susceptibility of jobs to automation, but little has examined how job contents evolve in the information age as new technologies substitute for tasks, shifting required skills rather than eliminating entire jobs. Here we explore patterns of occupational skill change and characterize occupations and workers subject to the greatest reskilling requirements. Recent work found that changing skill requirements are greatest for STEM occupations in the 2010s. Nevertheless, analyzing 167 million online job posts covering 727 occupations, we find that skill change is greatest for low-skilled occupations when accounting for distance between skills. We further investigate the differences in skill change across employer and market size, as well as social demographic groups. We find that jobs from small employers and markets experienced larger skill upgrades to catch up with the skill demands of their large employers and markets. Female and minority workers are disproportionately employed in low-skilled jobs and face the most significant skill adjustments. While these varied skill changes could create uneven reskilling pressures across workers, they may also lead to a narrowing of gaps in job quality and prospects. We conclude by showcasing our model's potential to chart job evolution directions using skill embedding spaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2101_11505 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Low-skilled Occupations Face the Highest Upskilling Pressure Tong, Di Wu, Lingfei Evans, James Allen Computers and Society Machine Learning Substantial scholarship has estimated the susceptibility of jobs to automation, but little has examined how job contents evolve in the information age as new technologies substitute for tasks, shifting required skills rather than eliminating entire jobs. Here we explore patterns of occupational skill change and characterize occupations and workers subject to the greatest reskilling requirements. Recent work found that changing skill requirements are greatest for STEM occupations in the 2010s. Nevertheless, analyzing 167 million online job posts covering 727 occupations, we find that skill change is greatest for low-skilled occupations when accounting for distance between skills. We further investigate the differences in skill change across employer and market size, as well as social demographic groups. We find that jobs from small employers and markets experienced larger skill upgrades to catch up with the skill demands of their large employers and markets. Female and minority workers are disproportionately employed in low-skilled jobs and face the most significant skill adjustments. While these varied skill changes could create uneven reskilling pressures across workers, they may also lead to a narrowing of gaps in job quality and prospects. We conclude by showcasing our model's potential to chart job evolution directions using skill embedding spaces. |
| title | Low-skilled Occupations Face the Highest Upskilling Pressure |
| topic | Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2101.11505 |