In this paper we postulate, measure, and evaluate the importance of cost-impacts from spatial and industrial spillovers for analysis of economic performance. To accomplish this, we incorporate measures of "activity levels" of related states and industries in a cost function model, and estimate their associated thick market and agglomeration effects in terms of shadow values and elasticities. We focus on the food processing sector, the proximity of own-industry activity in neighboring states, and the supply- and demand- side "drivers", associated with urbanization and localization economies (represented by the GSP and agricultural intensity in the own and neighboring states). We find significant cost-savings benefits to a states' food processing sector of being close to other food manufacturing centers (high levels of food processing activity in neighboring states). We also find it beneficial to be in a state with high purchasing power (demand), and to have neighboring states that are agriculture-based (supply). However, it also seems costly to actually be located in a heavily agricultural or rural state, possibly due to diseconomies from "thin markets" associated with infrastructure support and labor markets.

Economic analysis of production processes and performanace typically neglects consideration of spatial and industry inter-dependencies that may affect economic performance, although there is increasing theoretical recognition that such linkages may be both substantive and expanding. In particular, thick market or agglomeration effects may arise due to knowledge or other types of spillovers associated with own-industry (horizontal), and supply-side or demand-driven(vertical), externalities. In this paper we provide a conceptual and empirical framework for measuring and evaluating such spillovers, which allows us both to quantify their cost-effects, and to evaluate their contribution to location decisions. We focus on the U.S. food manufacturing sector, and the spillovers that may occur across states within the sector and from agricultural production (supply) and consumer buying power (demand). And we find substantive total and marginal cost-impacts in both spatial and industry dimensions, which appear to be motivating forces for regional concentration patterns of the U.S. food manufacturing industries.

In recent years, many hubs in the highly interdependent U.S. air transport network have become congested, leading to delays for business travelers and freight shipments. Recent events in this industry may have temporarily reduced this congestion, but contributed to other types of disruptions. Since delays and disruptions at one node of the network exacerbate problems throughout the system, airport infrastructure expansion to enhance traffic flows and security in large hubs may confer substantive spillover benefits in the form of travel-time savings and reliability. This may in turn translate into increased worker productivity and shipping efficiency, and thus lower costs, for manufacturing firms. In this paper we evaluate the impacts of such spillovers, by applying spatial econometrics techniques to a cost function framework, using state-level data on airport and highway infrastructure, and manufacturing production. We find that increasing own-state airport infrastructure tends to generate cost-saving benefits for the state's manufacturing industry, primarily due to non-production labor- and materials-savings. However, airport expansion in connected hubs has an even greater impact, implying an important externality component of such investment. Also, unless airport expansion is accompanied by highway infrastructure investment, congestion seems to counteract the associated benefits, especially in large-hub states with less than 5 percent of the nation's passenger enplanements.

The size and significance of public infrastructure investment impacts on costs and productivity of private enterprise, and thus on economic health and growth, has proven nebulous to empirically substantiate. Various studies using alternative theoretical and econometric methodologies, and for different time periods, sectors, and countries, have tentatively established that such a productive impact exists and is statistically significant. It also seems smaller and more variable over time, space, and sector than was implied by initial studies on the "public capital hypothesis". One piece of the puzzle that has received little attention, however, is the role of spatial spillovers in driving infrastructure investment benefits. Such spillovers are not only conceptually important, but could also shed light on discrepancies between studies for different data, and particularly aggregation levels. In this study we apply a cost-based model to state-level U.S. manufacturing data, for capital, production and non-production labor, and materials inputs, and for the 1982-96 time period, in an attempt to untangle the private cost-saving contributions of inter- and intra-state public infrastructure investment. We carry out two kinds of spatial adaptations - a spatial autocorrelation adjustment and a spatial spillover theoretical modification - to the estimating system consisting of a Generalized Leontief cost function and input demand equations, to address this issue. We find that intra-state public infrastructure benefits appear larger in magnitude when inter-state spillovers are directly recognized, as well as being invariably statistically significant. Inter-state spillovers are also directly beneficial to manufacturing firms, although their contribution appears smaller in size when temporal serial correlation is recognized in addition to spatial correlation.