Data Science Skills as Structural Holes: Diffusion of Algorithmic Power in Contemporary Chinese Labor Market

• In Event: Asia and Asian American Section Roundtables Session
  In Refereed Roundtable (90 minute): Table 12: Asia and Asian America Section Roundtable - Work and Labor

Abstract

Data science-related skills, particularly algorithmic and coding expertise, are transforming Chinese labor market in the AI era. By conceptualizing the labor market as a dynamic skill network, we argue that data science skills act as structural holes, bridging previously disconnected domain-specific skill clusters across different occupations and consolidating algorithmic control. Drawing on a large-scale textual dataset of highly skilled job postings from liepin.com, we employ LLM-based data extraction, classification and network analysis to trace the diffusion of data science skills and their integration with two occupations pioneering the integration with data science: biomedicine and finance. Results demonstrate a marked increase in inter-occupational ties between data science and the other two occupations from 2015 to 2023, connected by a few key skills in data science. These patterns highlight the pivotal role of algorithmic control in reshaping the landscape of professional work. Making progress in using Retrieval-Augmented Generation (RAG) to solve the extreme multilabel text classification (XMTC) problem in large-scale, unstructured Chinese textual data at the job level, our analyses depict how data science is transforming human capital training, work dynamics and organizational transformation.

Authors

• Siqi Han, The Chinese University of Hong Kong

• Linfeng SHEN, The Chinese University of Hong Kong

• Wei Tang, The Chinese University of Hong Kong