  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.
