Working Paper: CEPR ID: DP18495
Authors: Alessandra Bonfiglioli; Rosario Crin; Gino Gancia; Ioannis Papadakis
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 and for those in STEM occupations. 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
JEL Codes: J23; J24; O33
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| AI exposure (C45) | employment (J68) |
| AI adoption (C45) | job automation (L23) |
| AI adoption (C45) | widening inequality (D31) |
| AI exposure (C45) | negative employment effects for low-skill and production workers (F66) |
| AI exposure (C45) | positive employment effects for high-wage workers (J68) |