Date of Award

2023-08-31

Degree Name

PhD Leadership Studies

Dissertation Committee

Fred J. Galloway, EdD, Co-Chair; Marcus Lam, PhD, Co-Chair; Kevin D. Ward, PhD, Member

Keywords

Artificial Intelligence, Labor Markets

Abstract

As more evidence builds that artificial intelligence (AI) is a new general-purpose technology driving a fourth industrial revolution, scholars have begun to consider its potential impact on labor markets. The current debate among researchers is centered on whether AI will ultimately produce net new job gains or losses and what type of workers will benefit or be displaced. While no consensus has developed yet within the literature on AI’s predicted net employment impact, a majority of studies are forecasting that a skill-biased technological change will occur.

This exploratory study contributes to the current literature by operationalizing Webb’s objective patent-based AI Exposure Index at a local labor market level. The study leveraged longitudinal data analysis to measure the effect of AI exposure on changes to employment at an occupational level from 2010-2019 in San Diego County, California. By applying this exploratory methodology, the study yielded several noteworthy findings. First, the analysis showed an overall positive association between employment totals and AI exposure across all levels of Webb's AI Exposure Index. Second, preliminary evidence of potential skill-bias change was noted with non-high-skill occupations exhibiting slower employment growth compared to high-skill occupations at similar levels of AI exposure. Lastly, specific occupational groups and occupations displayed potential early indications of employment loss attributable to AI exposure. For example, the occupation titled “Pickers and Packagers, Hand” within the material movers and transportation occupational group demonstrated both high levels of AI exposure and reductions in employment totals during the period analyzed. However, it is critical to emphasize that large standard errors limit the precision of model estimates.

This study has implications for local labor market leaders by providing insights into AI exposure and employment trends. This exploratory methodological approach has potential for application to other local labor markets and offers opportunities for further scholarly research. Finally, this study makes a novel contribution to the labor literature with its localized focus, objective methodology and preliminary occupational-level employment change findings.

Document Type

Dissertation: Open Access

Department

Leadership Studies

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