Essays on the Dynamic Decisions of Homeowners and Retailers

Urban problems have several features that make them inherently dynamic. Large transaction costs all but guarantee that homeowners will do their best to consider how a neighborhood might change before buying a house. Similarly, stores face large sunk costs when opening, and want to be sure that their investment will pay off in the long run. In line with those concerns, different areas of Economics have made recent advances in modeling those questions within a dynamic framework. This dissertation contributes to those efforts.

Chapter 2 discusses how to model an agent’s location decision when the agent must learn about an exogenous amenity that may be changing over time. The model is applied to estimating the marginal willingness to pay to avoid crime, in which agents are learning about the crime rate in a neighborhood, and the crime rate can change in predictable (Markovian) ways.

Chapters 3 and 4 concentrate on location decision problems when there are externalities between decision makers. Chapter 3 focuses on the decision of business owners to open a store, when its demand is a function of other nearby stores, either through competition, or through spillovers on foot traffic. It uses a dynamic model in continuous time to model agents’ decisions. A particular challenge is isolating the contribution of spillovers from the contribution of other unobserved neighborhood attributes that could also lead to agglomeration. A key contribution of this chapter is showing how we can use information on storefront ownership to help separately identify spillovers.

Finally, chapter 4 focuses on a class of models in which families prefer to live

close to similar neighbors. This chapter provides the first simulation of such a model in which agents are forward looking, and shows that this leads to more segregation than it would have been observed with myopic agents, which is the standard in this literature. The chapter also discusses several extensions of the model that can be used to investigate relevant questions such as the arrival of a large contingent high skilled tech workers in San Francisco, the immigration of hispanic families to several southern American cities, large changes in local amenities, such as the construction of magnet schools or metro stations, and the flight of wealthy residents from cities in the Rust belt, such as Detroit.

Brands, Close Relationships, and Consumer Well-Being

Consumers have relationships with other people, and they have relationships with brands similar to the ones they have with other people. Yet, very little is known about how brand and interpersonal relationships relate to one another. Even less is known about how they jointly affect consumer well-being. The goal of this research, therefore, is to examine how brand and interpersonal relationships influence and are influenced by consumer well-being. Essay 1 uses both empirical methods and surveys from individuals and couples to investigate how consumer preferences in romantic couples, namely brand compatibility, influences life satisfaction. Using traditional statistical techniques and multilevel modeling, I find that the effect of brand compatibility, or the extent to which individuals have similar brand preferences, on life satisfaction depends upon power in the relationship. For high power partners, brand compatibility has no effect on life satisfaction. On the other hand, for low power partners, low brand compatibility is associated with decreased life satisfaction. I find that conflict mediates the link between brand compatibility and power on life satisfaction. In Essay 2 I again use empirical methods and surveys to investigate how resources, which can be considered a form of consumer well-being, influence brand and interpersonal relations. Although social connections have long been considered a fundamental human motivation and deemed necessary for well-being (Baumeister and Leary 1995), recent research has demonstrated that having greater resources is associated with weaker social connections. In the current research I posit that individuals with greater resources still have a need to connect and are using other sources for connection, namely brands. Across several studies I test and find support for my theory that resource level shifts the preference of social connection from people to brands. Specifically, I find that individuals with greater resources have stronger brand relationships, as measured by self-brand connection, brand satisfaction, purchase intentions and willingness to pay with both existing brand relationships and with new brands. This suggests that individuals with greater resources place more emphasis on these relationships. Furthermore, I find that resource level influences the stated importance of brand and interpersonal relationships, and that having or perceiving greater resources is associated with an increased preference to engage with brands over people. This research demonstrates that there are times when people prefer and seek out connections with brands over other people, and highlights the ways in which our brand and interpersonal relationships influence one another.

A Framework to Support the Sharing and Reuse of Computable Phenotype Definitions Across Health Care Delivery and Clinical Research Applications.

INTRODUCTION: The ability to reproducibly identify clinically equivalent patient populations is critical to the vision of learning health care systems that implement and evaluate evidence-based treatments. The use of common or semantically equivalent phenotype definitions across research and health care use cases will support this aim. Currently, there is no single consolidated repository for computable phenotype definitions, making it difficult to find all definitions that already exist, and also hindering the sharing of definitions between user groups. METHOD: Drawing from our experience in an academic medical center that supports a number of multisite research projects and quality improvement studies, we articulate a framework that will support the sharing of phenotype definitions across research and health care use cases, and highlight gaps and areas that need attention and collaborative solutions. FRAMEWORK: An infrastructure for re-using computable phenotype definitions and sharing experience across health care delivery and clinical research applications includes: access to a collection of existing phenotype definitions, information to evaluate their appropriateness for particular applications, a knowledge base of implementation guidance, supporting tools that are user-friendly and intuitive, and a willingness to use them. NEXT STEPS: We encourage prospective researchers and health administrators to re-use existing EHR-based condition definitions where appropriate and share their results with others to support a national culture of learning health care. There are a number of federally funded resources to support these activities, and research sponsors should encourage their use.