Entrepreneurship in times of changing technology and labor markets
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Authors
McLemore, Trevor David
Issue Date
2025
Type
Dissertation
Language
en_US
Keywords
AI startups , artificial intelligence , entrepreneurship , innovation , machine learning , opportunity
Alternative Title
Abstract
This dissertation investigates research questions in entrepreneurship, with a particular focus on the effects of recent and emerging technologies. Chapter One explores how entrepreneurship and market structure affect emerging technologies, while Chapters Two and Three examine the impact of these technologies on entrepreneurship. I incorporate Artificial Intelligence (AI) both as a methodological tool (Chapter One) and as a subject of inquiry (Chapter Three). The first chapter draws on data from the Google Play Store and applies Natural Language Processing techniques to measure the similarity of app descriptions, analyzing the relationship between market concentration and product differentiation. The second chapter uses data from the American Community Survey and the timing of Uber’s entry into metropolitan areas to assess the impact of rideshare on taxi drivers’ wages and labor supply. The third chapter reviews the emerging literature on AI and entrepreneurship. The first chapter, “Do Apps Play Follow the Leader? Testing the Relationship between Market Power and Product Similarity with Language Models”, coauthored with Kym Pram, examines the relationship between product similarity and market concentration. We employ Bidirectional Encoder Representations from Transformers (BERT) to embed product descriptions and use the Herfindahl-Hirschman Index (HHI) to capture market concentration. Our analysis reveals a robust U-shaped relationship that flattens for recently updated apps and apps where users interact. These findings suggest that the incentive for acquisition-driven market entry is the dominant mechanism only in markets characterized by high concentration. The second chapter, “Revisiting the Uber Effect”, is a solo-authored paper that replicates and extends a study by Berger et al. (2018) on whether Uber drivers displace conventional taxi drivers. The analysis leverages the timing of Uber’s market entry as exogenous variation in a difference-in-differences approach that analyzes taxi driver wage, salary, and labor supply. I find an 8.5-9.8% decrease in hourly earnings among wage employed drivers, no significant effect on salary, and a 7.7-12.3% decrease in labor supply of wage employed drivers. I also identify data irregularities in the Berger et al. (2018) paper and find that the larger and more statistically significant results for wage and salary that they found were not robust. The third chapter, “Artificial Intelligence and Entrepreneurship”, coauthored with Frank M. Fossen and Alina Sorgner, reviews the literature on impacts of AI on entrepreneurship. It begins by clarifying definitions of AI to eliminate ambiguity and provide context to how various studies use the term. The chapter discusses theoretical frameworks and empirical evidence related to the adoption of AI technologies and how AI technologies affect entrepreneurial opportunities, decision making under uncertainty, entry barriers, and business performance. An original empirical analysis from the German Socio-economic Panel is introduced, showing that entrepreneurs demonstrate greater awareness and use of AI technologies than paid employees. We review indirect impacts of AI on entrepreneurship through the labor market, finding evidence suggesting that automation results in higher levels of necessity entrepreneurship while transformative technologies, those that do not necessarily displace workers, lead to higher levels of opportunity entrepreneurship. The entrepreneurial ecosystem literature suggests AI reshapes the importance and configuration of existing ecosystem elements and processes and may reduce the role of geography in entrepreneurial activity. We conclude with a discussion on regulation of AI, with a focus on developments within the European Union.