Poster #85 - Who Hires AI Talent? Impacts of AI on Employer Hiring Behavior and Labor Market Dynamics

• In Event: Friday Poster Reception

Abstract

The rapid development of Artificial Intelligence (AI) has reshaped employers’ demand for skills and workers in recent years. In response to these changes, it is critical for policymakers, educators, and the future workforce to understand how new technologies influence employers’ hiring preferences, and in turn, affect local labor market dynamics, including job creations, job separations, unemployment spells, and earnings. Moreover, new technologies may exacerbate or narrow labor market inequality for workers with different characteristics, such as gender and education. Identifying early signals of these shifts is essential for designing workforce and education policies that promote equitable outcomes.

In this paper, we adopt a unique approach, the Industries of Ideas (IofI) framework, to estimate the causal impacts of AI adoption on local labor markets. We identify AI talent as researchers involved in AI-related research (including faculty, postdoctoral researchers, graduate students, and research staff) and students who have completed AI-related courses. We then track the employers that hire these AI talents and define them as AI talent hiring (AI-TH) employers. We construct a measure of employer AI intensity based on the number, the fields, and the composition of AI talent hires. For example, employers are more likely to hire AI researchers for AI development and AI-trained students for AI applications. We then use a difference-in-differences (DiD) design to examine how employer AI intensity affects employment, earnings distributions, unemployment, and the demographic composition of workers hired by the employers (e.g., gender and education levels).

Our analysis combines multiple rich administrative datasets and focuses on Ohio as a large representative state. To identify AI talent, we utilize the UMETRICS data, which provide detailed university administrative records on research funding and research team members from academic years 2009-2025, along with flags for authors of AI-related publications and their collaborators. We also use data from Ohio Higher Education Information (HEI) system, which include over 25 years of course enrollment and degree records from public colleges and universities, to identify students who have completed AI-related coursework. We then match these individuals to Ohio workforce data to identify AI-TH employers and use the full workforce data to construct labor market outcome measures. Specifically, the Unemployment Insurance (UI) wage records include quarterly employment and earnings for each employee at each employer, and the Quarterly Census of Employment and Wages (QCEW) data provides additional employer-level details, such as industry and location.  These linked data allow us to examine how employment, earnings, and unemployment evolve in the Ohio labor market following the hiring of AI talent.

Authors

• Tian Lou, The Ohio State University
  Presenting Author

• Bruce A Weinberg, The Ohio State University
  Non-Presenting Co-Author

• Jiong Wu, Ohio State University
  Non-Presenting Co-Author