Artificial Intelligence and Jobs: Evidence from US Commuting Zones

Alessandra Bonfiglioli (), Rosario Crinò, Gino Gancia and Ioannis Papadakis

No 10685, CESifo Working Paper Series from CESifo

Abstract: We study the effect of Artificial Intelligence (AI) on employment across US commuting zones over the period 2000-2020. A simple model shows that AI can automate jobs or complement workers, and illustrates how to estimate its effect by exploiting variation in a novel measure of local exposure to AI: job growth in AI-related professions built from detailed occupational data. Using a shift-share instrument that combines industry-level AI adoption with local industry employment, we estimate robust negative effects of AI exposure on employment across commuting zones and time. We find that AI’s impact is different from other capital and technologies, and that it works through services more than manufacturing. Moreover, the employment effect is especially negative for low-skill and production workers, while it turns positive for workers at the top of the wage distribution. These results are consistent with the view that AI has contributed to the automation of jobs and to widen inequality.

Keywords: artificial intelligence; automation; displacement; labor (search for similar items in EconPapers)
JEL-codes: J23 J24 O33 (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-ain, nep-lma, nep-tid and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: Artificial Intelligence and Jobs: Evidence from US Commuting Zones (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_10685

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