Labor Space: A Unifying Representation of the Labor Market via Large Language Models

Seongwoon Kim, Yong-Yeol Ahn, Jaehyuk Park

Keywords: Labor Market, Word Embedding, Skill, Job, Industry, Firm, Large Language Model

TL;DR: We introduce Labor Space, an unifying representation of heterogeneous labor market entities, which offers unprecedented analytical capabilities through the exploration of complex inter-unit relations.

Abstract:

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 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.

Track: Social Networks, Social Media, and Society

Submission Guidelines Scope: Yes

Submission Guidelines Blind: Yes

Submission Guidelines Format: Yes

Submission Guidelines Limit: Yes

Submission Guidelines Authorship: Yes

Student Author: Yes

Submission Number: 882