Digitalization, AI Capabilities, Tasks: Occupational AI Exposure and Wage Inequality across Italian Provinces

Gianluca Risi

EconStor Preprints from ZBW - Leibniz Information Centre for Economics

Abstract: This paper investigates the impact of digitalization and AI on wage inequality both between and within task-based groups of workers across Italian provinces. We contribute along two aspects: building a novel vertical categorization of occupational task dimensions designed to capture the distinct labor market implications of traditional digitalization and AI, and constructing the AI Occupational Catch-Up Index (AI-OCUI), the first empirical operationalization of the OECD AI Capability Indicators framework. Using a panel regression model for Italian NUTS3 regions over 2015-2018, we find that neither technology affects between-group inequality, while traditional digitalization reduces wage dispersion within the cognitive group and AI exposure compresses inequality within the non-routine group - a differentiation consistent with the distinct task profiles targeted by each technological wave. Both effects are attenuated in cities, where agglomeration dynamics moderates the equalizing potential of digital technologies. These findings contribute to the growing literature on the wage implications of digital transformation, with relevant implications for policy.

Keywords: Digitalization; Artificial Intelligence; AI Capability Indicators; Task-based framework; Wage inequality; Italian provinces (search for similar items in EconPapers)
JEL-codes: J24 O33 R11 R23 (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:esprep:340911

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