Date of Award

Spring 5-1-2026

Document Type

Thesis

Degree Name

Bachelor of Arts (BA)

Department

Economics

First Advisor

Monica Das

Abstract

Artificial intelligence(AI) is reshaping labor markets either through replacement or reinstatement effect. This paper examines the relationship between AI exposure and occupation-level wages in the United States from 2019 to 2024. Building on the AI Occupational Exposure index developed by Felten, Raj, and Seamans (2021), I combine O*NET task and skill data with Bureau of Labor Statistics Occupational Employment and Wage Statistics data to analyze whether occupations with higher AI exposure are associated with higher or lower wages. I also included control variables such as task characteristics, especially routine task intensity, software skills, and social skills. The results show that AI exposure is positively associated with occupational wages in all five regression model that I built. Another finding of this paper is that the interaction between AI exposure and routine task intensity is negative, suggesting that the AI wage premium is smaller for routine-intensive occupations and larger for non-routine cognitive occupations. The comparison between high-AI and low-AI occupations also suggests that high-AI occupations is enjoying a larger absolute wage gains from 2019 to 2024, which means there is an increasing inequality of labor market with the introduction of AI, even though low-AI occupations had higher percentage wage growth.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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