Browsing by Subject "Agglomeration"

This dissertation explores the relationship between external shocks local labor markets and entrepreneurship. The first and main essay investigates the effects of a large firm's geographical expansion (anchor firm) on local worker transitions into startup employment through wage effects in industries economically proximate to the anchor firm. Using hand collected data on large firms' site searches matched to administrative Census microdata, I exploit lists of anchor firms' site selection process to employ a difference-in-differences approach to compare workers and employers in winning counties to those in counterfactual counties. Counties are balanced along a number of socio-economic characteristics as well as ex ante industry distribution, firm size distribution, and firm age distribution. The arrival of an anchor firm induces entrepreneurship in industries linked through input-output channels by a magnitude of 120 new establishments that account for over 2,300 jobs. Relative to young firms in counterfactual counties, these new firms grow 12% faster in five-year employment growth and have a 7% lower failure probability. These effects are strongest in the most specialized and knowledge-intensive industries. Attracting an anchor firm to account appears to have limited spillover effects in employment that are mainly driven by reorganization of incumbent firms in input-output industries with occupational similarity of the anchor firm that face rising labor costs.

The second essay provides a blueprint for understanding the dynamics surrounding mass layoffs and business closures. This essay creates a novel data set linking geocoded Business Registration data to public layoff notifications data. This data can be used to understand how local entrepreneurship can reduce unemployment spells and earnings penalties for low wage displaced workers. Workers eventually employed by startups experience faster post-displacement wage growth than those eventually employed by mature firms. In final essay, I provide motivation for research investigating the spatially heterogeneous effects the advancement of certain industries inhibit entrepreneurship in others. I decompose a Bartik employment measure of demand for a region's labor. The decomposition shows that the recovery from the Great Recession was led by capital-intensive industries (e.g., transportation manufacturing and machinery manufacturing) that are typically inversely associated with local entrepreneurship. Interestingly, the inverse association of these industries and entrepreneurship appears to spillover into other industries. These industries include transportation equipment manufacturing and machinery manufacturing. This set of observations motivates this dissertation's research agenda to understand the cross-industry relationships that drive an area's level of entrepreneurship and labor market dynamism.

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.