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 and consequences of changes in occupational skill and characterize occupations and workers subject to the greatest re-skilling pressure. Recent work found that changing skill requirements are greatest for STEM occupations. Nevertheless, analyzing 167 million online job posts covering 727 occupations over the last decade, we find that re-skilling pressure 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, and find that these differences tend to widen the economic divide. Jobs from large employers and markets experienced less change relative to small employers and markets, and non-white workers in low-skilled jobs are most demographically vulnerable. We conclude by showcasing our model's potential to precisely chart job evolution towards machine-interface integration using skill embedding spaces.

翻译：大量学术研究估算了工作被自动化取代的可能性，但鲜有研究探讨信息时代中，随着新技术替代任务、工作内容如何演变——这改变了所需技能而非直接消除整个工作岗位。本文探究职业技能变化的模式与后果，并对面临最大再技能化压力的职业和工作者进行特征刻画。近期研究发现，STEM职业对技能变化的要求最高。然而，通过分析过去十年覆盖727个职业的1.67亿条在线招聘信息，我们发现：在考虑技能间距离时，低技能职业承受的再技能化压力最大。我们进一步探究不同雇主规模、市场规模以及社会人口群体间的技能变化差异，发现这些差异往往加剧经济鸿沟。相较于小型雇主和市场，大型雇主和市场的工作变化更小；而低技能岗位中的非白人工作者在人口统计学上最为脆弱。最后，我们展示了模型通过技能嵌入空间精确描绘职业向人机界面整合演变的潜力。