Land subsidence is the gradual settling or sudden sinking of the earth's surface. According to the United States Geological Survey more than 80% of identified subsidence in the United States is a result of groundwater removal. Due to the hydrologic structure and reliance on the Potomac Aquifer, the Southern Chesapeake Bay region of Virginia has suffered from land subsidence since the 1940s. In coastal regions, land subsidence can increase the risk of flooding. This paper presents a mathematical simulation that predicts land subsidence from groundwater pumping. This simulation is used to see how the location of groundwater pumping, as well as the amount of amount of groundwater pumped would differ from two different groundwater pumping policies. The first policy is aimed at limiting land subsidence in the region, while the second policy aims at limiting the damages from land subsidence. These two policies are used to show that a spatially heterogeneous groundwater pumping policy is necessary to minimize the damages from groundwater pumping when land subsidence is present. / Master of Science

This dissertation consists of three empirical essays of which contributions consist, first, in developing spatial weight matrices based on more than just pure geographical proximity for the modeling of interregional externalities. Second, my essays propose different approaches to discover spatial heterogeneity in the data generating processes, including the interregional externalities, under investigation. This dissertation provides Economic Geographers and Regional Scientists interested in the modeling and measurement of spatial externalities a set of practical examples based on new datasets and state-of-the-art spatial econometric techniques to consider for their own work. I hope my dissertation will provide them with some guidance on how various aspects of spatial externalities can be incorporated in traditional spatial weight matrices and of how much the impact of externalities can be spatially heterogeneous. The results of the dissertation should help spatial and regional policy makers to understand better various aspects of interregional dependence in regional economic systems and to devise locally effective and place-tailored spatial and regional policies. The first essay investigates the negative spatial externalities of irrigation on corn production. The spatial externalities of irrigation water are well known but have never been examined in a spatial econometric framework so far. We investigate their role in a theoretical model of profit-maximizing farming and verify our predictions empirically in a crop production function measured across US Corn Belt counties. The interregional groundwater and surface water externalities are modeled based on actual aquifer and river stream network characteristics. The second essay examines the positive spatial externalities of academic and private R&D spending in the frame of a regional knowledge production function measured across US counties. It distinguishes the role of local knowledge spillovers that are determined by geographical proximity from distant spillovers that we choose to capture through a matrix of patent creation-citation flows. The advantage of the latter matrix is its capacity to capture the technological proximity between counties as well as the direction of knowledge spillovers. These two elements have been missed in the literature so far. The last essay highlights and measures the presence of spatial heterogeneity in the marginal effect of the innovation inputs, more especially of the interregional knowledge spillovers. The literature of knowledge production function has adopted geographically aggregated units and controlled for region-specific conditions to highlight the presence of spatial heterogeneity in regional knowledge creation. However, most empirical studies have relied on a global modeling approach that measures spatially homogenous marginal effects of knowledge inputs. This essay explains the source of the heterogeneity in innovation and then measures the spatial heterogeneity in the marginal effects of knowledge spillovers as well as of other knowledge input factors across US counties. For this purpose, the nonparametric local modeling approaches of Geographically Weighted Regression (GWR) and Mixed GWR are used.