Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)

Graduate Group

Applied Economics

First Advisor

Gilles Duranton


This dissertation studies how the spatial distributions of firms and consumers shape their interactions in local credit markets. For firms, proximity provides information about the preferences and credit quality of local consumers. For consumers, it facilitates gathering information about product availability and the prices at local firms. I explore these dynamics by developing stylized models that illustrate the key dynamics of interest and motivate empirical estimations that I take to data. In particular, this dissertation uses many novel big data assets that provide new insights into the functioning of local credit markets.

In the first chapter, I study whether online retail is a complement or substitute to local offline economies by studying how consumers reorganize their trips to grocery stores and coffee shops after they become online grocery shoppers. To do so, I use new, detailed data on the daily online and offline transactions of millions of anonymized customers. My results show that consumer behaviors can create positive complementarities between online retail and some brick-and-mortar stores, creating both winning and losing stores and consumers to online retail. In the second chapter, I study the impact of branch presence on mortgage credit outcomes in the surrounding neighborhood using the density of nearby bank branch networks to instrument for actual branch presence. I find that lenders with branches lend more mortgages to borrowers in the surrounding neighborhood and that those operated by local lenders have the most positive impact for low socioeconomic-status borrowers. However, I show that branches disadvantage competing lenders by lowering the credit-quality of the competing lenders' applicant pool. This adverse selection causes an aggregate negative effect of branch presence on neighborhood mortgage outcomes. In the third chapter, co-authored with Benjamin J. Keys and Jane K. Dokko, we construct a novel county-level dataset to analyze the relationship between rising house prices and non-traditional features of mortgage contracts. We apply a break-point methodology and find that, in many markets, rising use of non-traditional mortgages predates the start of the housing boom and continues to rise thereafter.

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Economics Commons