The Rise of Generative AI: Modelling Exposure, Substitution, and Inequality Effects on the US Labour Market

Raphael Auer, David Koepfer and Josef Sveda

No 19874, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: How exposed is the labour market to ever-advancing AI capabilities, to what extent does this substitute human labour, and how will it affect inequality? We address these questions in a simulation of 711 US occupations classified by the importance and level of cognitive skills. We base our simulations on the notion that AI can only perform skills that are within its capabilities and involve computer interaction. At low AI capabilities, 7% of skills are exposed to AI uniformly across the wage spectrum. At moderate and high AI capabilities, 17% and 36% of skills are exposed on average, and up to 45% in the highest wage quartile. Examining complementary versus substitution, we model the impact on side versus core occupational skills. For example, AI capable of bookkeeping helps doctors with administrative work, freeing up time for medical examinations, but risks the jobs of bookkeepers. We find that low AI capabilities complement all workers, as side skills are simpler than core skills. However, as AI capabilities advance, core skills in lower-wage jobs become exposed, threatening substitution and increased inequality. In contrast to the intuitive notion that the rise of AI may harm white-collar workers, we find that those remain safe longer as their core skills are hard to automate.

Keywords: Labour markets; Artificial intelligence; Employment; Inequality; Automation; ChatGPT; Gpt; Large Language Models; Wages (search for similar items in EconPapers)
JEL-codes: E24 E51 G21 G28 J23 J24 M48 O30 O33 (search for similar items in EconPapers)
Date: 2025-01
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