Research
Research
Machine Versus Muscle, Bot Versus Brain: Effects of Artificial Intelligence on Heterogeneous Skill Groups (Job Market Paper)
Abstract: This paper studies effects of artificial intelligence (AI) on employment and wages for heterogeneous skill groups in the U.S. by introducing and analyzing a task-based framework. I first categorize labor into four skill groups based on skill specializations: (1) abstract and AI-intensive; (2) abstract-intensive but not yet AI-related; (3) routine-intensive; and (4) manual-intensive. The demand for AI skills is then measured by matching phrases for AI-developing skills to descriptions of online job postings. I document a consistent upward trend in the share of AI postings for the high-skilled AI-complement group during my sampling period, 2012-21. There is a strong growth in both employment and wages for abstract and AI-intensive occupations associated with an increasing demand for AI skills, while abstract but not-yet-AI occupations have much smaller growth. Middle-skilled occupations experience wage declines associated with an increase in the standard deviation of the intensity that AI-developing skills are required for job tasks. Employment and wage gaps between abstract and AI-intensive occupations and other skill groups widen as the labor market favors workers with AI skills, consistent with my theoretical model's implications. I also discuss whether AI is possibly a general-purpose technology.
Awards and Honors: Winner, Poster Competition, MSU Future of Work Conference, 2024 (see more on MSUToday, the daily news of Michigan State University)
Presentation:
2024: Society of Labor Economists (SOLE) Annual Meeting, MSU Future of Work Conference (poster session), UM-MSU-UWO Labor Day, Grand River Workshop (Michigan State University), Southern Economic Association (SEA) Annual Meeting
2023: Association for Public Policy Analysis & Management (APPAM) Fall Research Conference
College Major Choices under the Rapid Growth of General-Purpose Technology: A Study on AI
Abstract: This paper studies how the rise in AI shapes college major choice. I propose a new method to measure how well a major prepares students to work with AI by matching phrases for AI subfields with college major descriptions. I then define AI skill-related majors as those that provide AI-related skill training. Those majors that are most complementary to AI have systematically high growth rates of bachelor’s degree conferrals from 1990 to 2019. In contrast, I find evidence suggesting that majors that are most exposed to AI-driven substitution grow relatively slowly, especially at elite universities.
Presentation:
2023: Society of Labor Economists (SOLE) Annual Meeting (poster session), U.S. Census Bureau ML/AI Affinity Group, Southern Economic Association (SEA) Annual Meeting
2022: Red Cedar Conference (Michigan State University)
The Effect of Vaccine Mandates on Disease Spread: Evidence from College COVID-19 Mandates (with Riley Acton, Emily Cook, Scott Imberman, and Michael Lovenheim). Forthcoming, Journal of Human Resources.
Understanding the Role of Transparency in the Job Matching Process for Travel Nurses (with Hye Jin Rho, and Christine Riordan)
AI Adoption and Gender Wage Gaps