Three Essays on the Interplay between Entrepreneurship, iInnovation and Socioeconomic Phenomena

My dissertation is composed of three chapters that explore the relationships between entrepreneurship, innovation, and the broader economic and social dynamics that are shaping the modern world. In the area of entrepreneurship and innovation, one aspect that is often examined is that of the relationship between individuals and firms. In my work, I examine the role that social and economic factors play in shaping the environment within which entrepreneurs and innovators work and grow. The first chapter of the dissertation focuses on the impact of immigration on entrepreneurship and explores the consequences of start-up location on the number of immigrant-founded start-ups and their performance. I find that immigration has a positive effect on immigrant entrepreneurship only in non-enclave areas. Additional analyses uncovering the mechanism suggest that discrimination faced by immigrants in non-enclave areas might be the main driver for the increased entrepreneurship. In a second chapter on entrepreneurship, jointly authored with John Voorheis, we explore how entrepreneurship influences income inequality and social mobility in the United States. Shedding light on who gains from entrepreneurship is crucial to understanding whether investments in incubating potentially innovative start-up firms will produce socially beneficial outcomes. We find that entrepreneurship increases income inequality. Further, we find that this increase in income inequality arises because almost all the individual gains associated with increased entrepreneurship accrue to the top section of the income distribution. In the third chapter, joint with Michela Giorcelli and Nico Lacetera, we study the interplay between scientific progress and culture through text analysis on a corpus of about eight million books, with the use of machine learning techniques. We focus on a specific scientific breakthrough: the theory of evolution by Charles Darwin. Besides examining the diffusion of certain concepts that characterized this theory, we document their semantic changes over time. Our findings thus show a complex relationship between two key factors of long-term economic growth: science and culture. We argue that considering the evolution of these two factors jointly can offer new insights to the study of the determinants of economic development, and machine learning is a promising tool to explore these relationships.Ph.D