分析埃及1万份岗位数据，发现仅四成高风险岗位人员可转型。
Graph-Based Analysis of AI-Driven Labor Market Transitions: Evidence from 10,000 Egyptian Jobs and Policy Implications
- 构建技能图谱，识别岗位间可转移技能
- 仅24.4%高危岗位人员具备可行转型路径
- 过程型技能是关键突破口，适合政策制定者
我们基于9,978份埃及岗位招聘数据、19,766项技能活动和84,346个岗位-技能关联关系（误差率0.74%），回答自动化导致的失业者有多少能转入安全岗位。尽管20.9%的岗位面临高自动化风险，但仅有24.4%的受影响工人具备可行转型路径——即至少有3项共享技能且技能迁移率超过50%。剩余75.6%面临结构性流动障碍，需全面再培训而非渐进式技能提升。在4,534条可行转型路径中，过程导向型技能占比最高，达15.6%。研究挑战了劳动力可无缝适应的乐观叙事，表明新兴经济体需要主动设计转型路径，而非被动匹配技能。
原文摘要 · Abstract (English)
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.
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