AI 中文总结
该研究开发基于不可知网络的框架，利用领英数据定义三种技能复杂性指标，发现接近多样性前沿的劳动者适应力更强，为衡量劳动力适应力提供数据驱动方法。
AI 中文摘要
随着人工智能变革劳动力市场，理解劳动者具备适应性的原因愈发重要。现有方法通常用职业、教育背景或预定义技能分类来刻画人力资本，却难以深入洞察劳动者技能组合的结构如何塑造其对技术变革的适应力。我们开发了一种基于不可知网络的框架，可直接从技能共现的观测模式中重构技能的层级结构与多样性。利用领英（LinkedIn）提供的240万名美国劳动者及16753种不同技能的纵向数据，我们引入了三个互补的技能复杂性衡量指标：专业化（衡量生产深度）、多样性（衡量适应广度）以及多样性前沿（衡量在劳动者专业化水平下可达到的最高多样性）。研究表明，这些维度可预测不同的职业结果：专业化与进入更高薪资职业的关联最为紧密，而多样性则与更广泛的技能积累及职业流动相关。与具有相似专业化水平但技能组合较窄的劳动者相比，接近多样性前沿的劳动者显著更有可能掌握新技能、获得晋升，且转入自动化暴露程度更低的职业。这些发现区分了生产性资本与适应性资本，证明劳动者的适应能力不仅取决于是否拥有专业化专长或广泛能力，更取决于两者的结合。更广泛而言，我们的框架提供了一种数据驱动的方法，用于衡量劳动力适应力并确定再培训路径，为理解快速变革劳动力市场中的人力资本提供了新工具。
英文摘要
As artificial intelligence transforms labor markets, understanding what makes workers adaptable has become increasingly important. Existing approaches typically characterize human capital using occupations, educational credentials, or predefined skill taxonomies, providing limited insight into how the structure of workers' skill portfolios shapes resilience to technological change. We develop an agnostic network based framework that reconstructs the hierarchy and diversity of skills directly from observed patterns of skill co occurrence. Using longitudinal data on 2.4 million United States workers and 16,753 distinct skills from LinkedIn, we introduce three complementary measures of skill complexity: specialisation, capturing productive depth; diversity, capturing adaptive breadth; and the diversity frontier, measuring the highest attainable diversity conditional on a worker's level of specialisation. We show that these dimensions predict distinct career outcomes. Specialisation is most strongly associated with sorting into higher wage occupations, whereas diversity is associated with broader skill accumulation and occupational mobility. Workers closest to the diversity frontier are significantly more likely to acquire new skills, receive promotions, transition into occupations with lower exposure to automation than workers with comparable levels of specialisation but narrower skill portfolios. These findings distinguish productive from adaptive capital and demonstrate that workers' adaptive capacity depends not simply on possessing specialised expertise or broad capabilities, but on combining both. More broadly, our framework provides a data driven approach for measuring workforce resilience and identifying reskilling pathways, offering new tools for understanding human capital in rapidly changing labor markets.
