Exploring the economic effects of artificial intelligence on the labour market and entrepreneurship
Abstract:
The rapid advances of artificial intelligence (AI) have the potential to significantly influence economic growth, the future of work, as well as the nature and scope of entrepreneurial activity. Therefore, both academia and public press have great interests in AI's effect on occupations and firms. However, there is a lack of systematic empirical evidence on AI's positive effects on occupations and AI entrepreneurship.
In this dissertation, I include two studies to explore the granular impact of AI on our society in different contexts. The first study relates to the impact of AI on occupations in the labour market. Specifically, Study 1 attempts to improve the methodologies for quantifying AI benefits and risks of occupations. With the rapid advances of AI, increasingly more jobs may benefit from or be replaced by AI. Nevertheless, we know little about the extent to which AI may positively or negatively affect a variety of occupations. This is because most of the existing literature has only focused on the displacement effects of AI at the occupation level rather than both benefits and risks at the work activities (WA) level. Therefore, we theorize three mechanisms that AI may benefit employees' careers: productivity-enhanced AI jobs, intelligence-augmented AI jobs, and AI-enabling jobs. The unique mechanism proposed in this study is the productivity-enhanced AI jobs, in which AI can automate supplemental WAs but not core value-added WAs. By merging Occupation Information Network (O*NET) data with a large LinkedIn data set, we conduct individual-level analyses regarding how skills accumulated from prior work experiences, educational background, and demographics may correlate with the displacement risk and three types of AI benefits. Our results suggest that these critical factors have differential effects on three mechanisms of AI benefits.
Study 2 investigates whether and which type of AI academic research in universities can create regional knowledge spillover effects on promoting the quantity and quality of start-ups. Specifically, we examine which kind of AI knowledge has spillover effects on regional AI entrepreneurship by focusing on the interdisciplinarity of AI research, heterogeneous effects from different AI subfields, the theoretical nature of AI research, and the research impact of AI research. Using data of start-ups from Crunchbase.com and AI conference publications from CSRankings.com, we find that knowledge spillovers from university AI research indeed contribute to the creation and enhancement of VC financing performance of local start-ups at the MSA level in the United States. Moreover, our results suggest that high interdisciplinary AI knowledge spillovers are more likely to contribute to the creation of start-ups. We also find significant heterogeneous effects of knowledge spillovers in different AI subfields. In addition, the knowledge spillovers from theoretical AI research and impactful AI research have stronger effects on the creation of start-ups.
Overall, this thesis develops a better understanding of the effects of AI on the labour market and regional entrepreneurship. Our findings offer novel and essential implications for the policy makers on the future of work and the practice of promoting AI entrepreneurship