Labor markets and inequality
Ceyhun Elgin · Routledge eBooks · 20 Oct 2026
Where this sits in the debate
- Will AI destroy more jobs than it creates? · its finding: it depends (why studies differ)
- Will AI widen inequality? · its finding: it depends (why studies differ)
Direction of the finding as read from the abstract by an AI model (AI-written).
Key takeaway AI-written
A book chapter on how AI changes labour markets and income distribution, moving from skill- and routine-biased technical change to a task- and workflow-based view of generative AI. It argues exposure is not unemployment and that distributional outcomes depend on adoption costs, complementary investment, institutions, bargaining power and how productivity rents are shared.
Abstract
This chapter examines how artificial intelligence (AI) changes labor markets and the distribution of income. It begins by distinguishing skill-biased, routine-biased, and task-biased technological change, then explains why generative AI requires a further shift in emphasis from occupations to tasks, workflows, and organizational control. The chapter argues that AI affects inequality through several channels at once: direct task displacement, productivity gains, the creation of new tasks, changes in market power, and differences in worker bargaining power. A central claim is that exposure to AI is not the same as technological unemployment. The distributional consequences depend on adoption costs, complementary investments, institutions, and the allocation of productivity rents inside firms and across markets. This chapter, therefore, treats AI neither as an automatic engine of mass unemployment nor as a neutral productivity tool, but as a general-purpose technology whose labor market effects are mediated by economic structure and policy.
About this publication
Academic journal. Published in an academic journal — typically peer-reviewed before publication, though indexing alone doesn't guarantee review depth.
- Published
- 2026 (recently)
- Citations
- 0 (OpenAlex)
0 citations recorded by OpenAlex — too few 2026 papers in this collection for a fair comparison.
Related by theme, not necessarily by method or conclusion.
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