The labor market is a complex ecosystem comprising diverse, interconnected entities, such as industries, occupations, skills, and firms. Due to the lack of a systematic method to map these heterogeneous entities together, each entity has been analyzed in isolation or only through pairwise relationships, inhibiting comprehensive understanding of the whole ecosystem. Here, we introduce $\textit{Labor Space}$, a vector-space embedding of heterogeneous labor market entities, derived through applying a large language model with fine-tuning. Labor Space exposes the complex relational fabric of various labor market constituents, facilitating coherent integrative analysis of industries, occupations, skills, and firms, while retaining type-specific clustering. We demonstrate its unprecedented analytical capacities, including positioning heterogeneous entities on an economic axes, such as `Manufacturing--Healthcare'. Furthermore, by allowing vector arithmetic of these entities, Labor Space enables the exploration of complex inter-unit relations, and subsequently the estimation of the ramifications of economic shocks on individual units and their ripple effect across the labor market. We posit that Labor Space provides policymakers and business leaders with a comprehensive unifying framework for labor market analysis and simulation, fostering more nuanced and effective strategic decision-making.

翻译：劳动力市场是一个复杂的生态系统，由产业、职业、技能和公司等多样化且相互关联的实体构成。由于缺乏将这些异质性实体系统映射的方法，每个实体往往被孤立分析或仅通过成对关系进行研究，从而阻碍了对整个生态系统的全面理解。在此，我们引入《劳动空间》（Labor Space）——一种通过应用大型语言模型并进行微调而获得的劳动力市场异质性实体的向量空间嵌入。劳动空间揭示了劳动力市场各组成部分之间复杂的关系网络，促进了产业、职业、技能和公司的连贯整合分析，同时保留了类型特定的聚类特征。我们展示了其前所未有的分析能力，包括将异质性实体定位于经济轴线上（例如"制造业—医疗保健"）。此外，通过允许对这些实体进行向量算术运算，劳动空间能够探索复杂的单元间关系，进而估计经济冲击对单个单元的影响及其在劳动力市场中的连锁效应。我们认为，劳动空间为政策制定者和商业领袖提供了一个全面的统一框架，用于劳动力市场分析与模拟，从而促进更细致且有效的战略决策。