Exposure to generative artificial intelligence and employment: an application to the French socio-occupational classification
LIFT Working Paper 2024-01 — January 2024
This paper analyzes the exposure of French occupations to generative artificial intelligence through the socio-occupational classification. Drawing on O*NET data and a GPT-4 assessment protocol, it proposes two indices: substitutability and complementarity. The results reveal strong heterogeneity across occupations, with higher exposure for the most skilled occupations. This approach helps to better identify automation risks and complementarity opportunities, and underscores the need to account for adoption costs and endogenous transformations to anticipate impacts on employment.
Exposure, AI and employment, France
This paper analyzes the exposure of French occupations to generative artificial intelligence through the socio-occupational classification. Drawing on O*NET data and a GPT-4 assessment protocol, it proposes two indices: substitutability and complementarity. The results reveal strong heterogeneity across occupations, with higher exposure for the most skilled occupations. This approach helps to better identify automation risks and complementarity opportunities, and underscores the need to account for adoption costs and endogenous transformations to anticipate impacts on employment.
PDF in French