Seniority-Biased Technological Change (SBTC) / 연공편향 기술변화 · 2020s–present

Seniority-Biased Technological Change

연공편향 기술변화

A labor economics concept describing a pattern of technological change in which generative AI adoption disproportionately reduces demand for junior workers while maintaining or increasing demand for senior workers. Extending the classic framework of skill-biased technological change (SBTC), which explained labor market polarization by education and skill, seniority-biased technological change focuses on how AI's substitution and complementarity effects diverge by career stage even within the same occupation. The core mechanism: routinized knowledge tasks typically assigned to junior hires (document drafting, data entry, basic analysis) are readily automated by AI, while the tacit knowledge, organizational management, and contextual judgment of senior workers remain difficult to automate, making AI a complement rather than a substitute for experienced labor.

Marginal Productivity Theory

Sources

  1. bok.or.kr Bank of Korea BOK Issue Note No. 2025-30 (2025.10.30): 'AI 확산과 청년고용 위축: 연공편향(seniority-biased) 기술변화를 중심으로' — the foundational Korean-language empirical study documenting that 98.6% of youth job losses (211,000) over three years occurred in AI high-exposure industries, while 50+ employment grew by 209,000, with 69.9% of gains in those same high-exposure industries. Coined and defined the Korean term 연공편향 기술변화.
  2. papers.ssrn.com Hosseini, Seyed M. and Guy Lichtinger (2025), 'Generative AI as Seniority-Biased Technological Change: Evidence from U.S. Résumé and Job Posting Data,' Harvard University working paper. The academic origin paper: using LinkedIn résumé data covering 62 million workers across 285,000 firms (2015–2025), finds that following GenAI adoption, junior employment declines sharply (~10% after six quarters) in adopting firms relative to non-adopters, while senior employment remains largely unchanged. The decline is concentrated in GenAI-exposed occupations and driven by slower hiring rather than increased separations.
  3. digitaleconomy.stanford.edu Stanford Digital Economy Lab seminar (2025.09.22): Hosseini and Lichtinger presented their working paper establishing the seniority-biased technological change framework at the DEL Seminar Series.
  4. bok.or.kr Bank of Korea blog post (2025.11.06): 'AI 확산 초기, 청년고용은 왜 감소하는가?' — popular exposition of the BOK findings, explaining the mechanism: junior workers perform codified, book-learning knowledge tasks automatable by AI, while seniors possess tacit knowledge and social skills that make AI complementary rather than substitutive.
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