基于图的AI驱动劳动力市场转型分析：来自10,000个埃及职位的证据与政策含义
Graph-Based Analysis of AI-Driven Labor Market Transitions: Evidence from 10,000 Egyptian Jobs and Policy Implications
AI总结：
本文通过分析埃及10,000个职位的数据，发现高自动化风险工人中仅24.4%有可行过渡路径，需主动创建技能路径而非被动匹配。
摘要：
有多少被自动化取代的工人能够现实地过渡到更安全的工作？我们使用一个经过验证的知识图谱来回答这个问题，该图谱包含9,978个埃及职位发布、19,766种技能活动和84,346种职位-技能关系（0.74%的错误率）。尽管20.9%的职位面临高自动化风险，但我们发现只有24.4%的高风险工人有可行的过渡路径——定义为≥3个共享技能和≥50%的技能转移。剩下的75.6%面临结构性流动性障碍，需要全面重新培训，而不是渐进性提升。在4,534个可行的过渡中，过程导向的技能成为最高杠杆干预，出现在15.6%的路径中。这些发现挑战了对无缝劳动力适应的乐观叙事，表明新兴经济体需要主动路径创建，而不是被动技能匹配。
英文摘要：
How many workers displaced by automation can realistically transition to safer jobs? We answer this using a validated knowledge graph of 9,978 Egyptian job postings, 19,766 skill activities, and 84,346 job-skill relationships (0.74% error rate). While 20.9% of jobs face high automation risk, we find that only 24.4% of at-risk workers have viable transition pathways--defined by $\geq$3 shared skills and $\geq$50% skill transfer. The remaining 75.6% face a structural mobility barrier requiring comprehensive reskilling, not incremental upskilling. Among 4,534 feasible transitions, process-oriented skills emerge as the highest-leverage intervention, appearing in 15.6% of pathways. These findings challenge optimistic narratives of seamless workforce adaptation and demonstrate that emerging economies require active pathway creation, not passive skill matching.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
