This dissertation consists of three essays on spatially differentiated markets. Generally, I explore how firms and consumers behave in such markets using structural modeling, causal inference and machine learning techniques, as well as high-resolution spatial data. In the first chapter, I study the efficiency of firm location configurations in the food service industry. I start by collecting a comprehensive industry dataset and estimating a structural model of consumer demand and firm entry. Next, I computationally approximate the welfare gains available through reconfiguration of firms in the market space. In the second chapter, my coauthor and I investigate the benefits professional sports arenas provide for the local economy. Specifically, we use daily foot traffic data and causal inference tools to estimate the spillover effect of the stadium visits on the visits to nearby businesses. In the third chapter, I again study the food service industry and provide a descriptive analysis of firm characteristics associated with business survival. While using both econometric and machine learning techniques, I demonstrate how the two approaches can complement each other in building a robust set of descriptive statistics.

Creator

• Sedov, Dmitry

DOI

• https://doi.org/10.21985/n2-cw1c-hp55

Subject

• Economics

Language

• en

Alternate Identifier

• http://dissertations.umi.com/northwestern:15552
• etdadmin_upload_817827

Keyword

• spatial competition
• stadiums
• applied econometrics
• industrial organization
• spatial spillovers
• food service

Date created

• 2021-01-01

Resource type

• Dissertation

Rights statement

• In Copyright