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

Kim, Seongwoon / Ahn, Yong-Yeol / Park, Jaehyuk

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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.

Issue Date
2023-12
Publisher
KDI School of Public Policy and Management
Keywords
Labor Market; Word Embedding; Skill; Job; Industry; Firm; Large Language Model
Pages
12
Series Title
KDIS Working Paper 23-11
URI
https://archives.kdischool.ac.kr/handle/11125/53435
URL
https://ssrn.com/abstract=4703367
DOI
10.2139/ssrn.4703367
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