An understanding of sector-specific intraurban industrial location lies at the core of urban spatial structure theories and provides the knowledge-base that informs a range of important policy issues. In terms of theoretical approaches, there is still active research extending the bid-rent framework of neo-classical economics, there is widespread interest in the role of Marshallian industrial districts and agglomeration economies, and, there is, within geography, increasing interest in the role of place-dependence (especially for institutions) in shaping the evolution of urban economic space. Despite the importance of understanding firm location choices at the intraurban scale, empirical modeling has seriously lagged behind current theorizing. A significant obstacle to developing relevant models has been the accessibility of firm location data at scales suitable for intraurban analysis. Both theoretical and policy formation are starved for new empirical work that would jointly increase industry and spatial resolution and incorporate important institutional features of the real urban landscape. This research will develop and evaluate a modeling approach, based on hierarchical spatial Cox regression, that admits covariates consistent with each of the three major theoretical approaches mentioned above. Industry-specific location data will come from address geo-coded and longitudinally matched establishment records from the U.S. Bureau of Labor Statistics. Institutional influences, in the form of zoning, will be incorporated directly into the estimator using a sampling function to account for the disjoint nature of urban economic space. Models will be fitted and evaluated for several U.S. metropolitan areas that are selected to provide a diverse set of institutional contexts. Rather than adhering to a single theoretical perspective, the overarching goal of the research is to develop and validate a modeling approach that will provide a common ground allowing for a broader range of theory testing that bridges economics and geography.
Several urban policy and public investment decisions are directly informed by theories of intraurban industry location. An important subset of these would include: (1) transportation planning - siting decisions for new construction and program designs for mass transit, (2) welfare-to-work programs which attempt to alleviate spatial- and skills- mismatch issues, (3) community development through industrial targeting (either local tax policy or incentives), (4) environmental justice issues related to the location of dirty industries, and (5) smart growth policies concerned with mitigating effects stemming from the suburbanization of both industry and population. This research will develop and test a statistical modeling approach that will admit more realistic specifications of intraurban industrial location decision-making. The results of the models will provide a stronger basis for urban policy analysis and will help to refine theories of urban industrial structure and intraurban employment dynamics.
