The geography of economic activity refers to the distribution of population, production, and consumption of goods and services in geographic space. The geography of growth and development refers to the local growth and decline of economic activity and the overall distribution of these local changes within and across countries. The pattern of growth in space can vary substantially across regions, countries, and industries. Ultimately, these patterns can help explain the role that spatial frictions (like transport and migration costs) can play in the overall development of the world economy. The interaction of agglomeration and congestion forces determines the density of economic activity in particular locations. Agglomeration forces refer to forces that bring together agents and firms by conveying benefits from locating close to each other, or for locating in a particular area. Examples include local technology and institutions, natural resources and local amenities, infrastructure, as well as knowledge spillovers. Congestion forces refer to the disadvantages of locating close to each other. They include traffic, high land prices, as well as crime and other urban dis-amenities. The balance of these forces is mediated by the ability of individuals, firms, good and services, as well as ideas and technology, to move across space: namely, migration, relocation, transport, commuting and communication costs. These spatial frictions together with the varying strength of congestion and agglomeration forces determines the distribution of economic activity. Changes in these forces and frictions—some purposefully made by agents given the economic environment they face and some exogenous—determine the geography of growth and development. The main evolution of the forces that influence the geography of growth and development have been changes in transport technology, the diffusion of general-purpose technologies, and the structural transformation of economies from agriculture, to manufacturing, to service-oriented economies. There are many challenges in modeling and quantifying these forces and their effects. Nevertheless, doing so is essential to evaluate the impact of a variety of phenomena, from climate change to the effects of globalization and advances in information technology.
Elisa Tosetti, Rita Santos, Francesco Moscone, and Giuseppe Arbia
The spatial dimension of supply and demand factors is a very important feature of healthcare systems. Differences in health and behavior across individuals are due not only to personal characteristics but also to external forces, such as contextual factors, social interaction processes, and global health shocks. These factors are responsible for various forms of spatial patterns and correlation often observed in the data, which are desirable to include in health econometrics models. This article describes a set of exploratory techniques and econometric methods to visualize, summarize, test, and model spatial patterns of health economics phenomena, showing their scientific and policy power when addressing health economics issues characterized by a strong spatial dimension. Exploring and modeling the spatial dimension of the two-sided healthcare provision may help reduce inequalities in access to healthcare services and support policymakers in the design of financially sustainable healthcare systems.
Land is everywhere: the substratum of our existence. In addition, land is intimately linked to the dual concept of location in human activity. Together, land and location are essential ingredients for the lives of individuals as well as for national economies. In the early 21st century, there exist two different approaches to incorporating land and location into a general equilibrium theory. Dating from the classic work of von Thünen (1826), a rich variety of land-location density models have been developed. In a density model, a continuum of agents is distributed over a continuous location space. Given that simple calculus can be used in the analysis, these density models continue to be the “workhorse” of urban economics and location theory. However, the behavioral meaning of each agent occupying an infinitesimal “density of land” has long been in question. Given this situation, a radically new approach, called the σ -field approach, was developed in the mid-1980s for modeling land in a general equilibrium framework. In this approach: (1) the totality of land, L , is specified as a subset of ℝ 2 , (2) all possible land parcels in L are given by the σ -field of Lebesgue measurable subsets of L , and (3) each of a finite number of agents is postulated to choose one such parcel. Starting with Berliant (1985), increasingly more sophisticated σ -field models of land have been developed. Given these two different approaches to modeling land within a general equilibrium framework, several attempts have thus far been proposed for bridging the gap between them. But while a systematic study of the relationship between density models and σ -field models remains to be completed, the clarification of this relationship could open a new horizon toward a general equilibrium theory of land.
Economic and social activities in different locations interact through systematic connections, which can be modeled as network structures. For example, production processes combine various inputs, tasks, and intermediate products that are spread over space; laborers transmit knowledge and skills along networks of work relations; products are delivered through transportation networks; and local public goods have external effects that spill over into the network of neighborhoods. Such networks bring benefits to connected nodes in the form of externalities. Approaches adopted for modeling networks of locations or that are applicable to spatial economics can be placed into two major categories. First, networks can be formed endogenously when nodes choose links strategically. Thus, networks are outcomes that emerge from strategic equilibria. This approach anylyzes the patterns of networks that are in equilibrium and patterns that are efficient. Second, networks can be a background structure with fixed existing links. In this approach, centrality measures are designed to indicate the importance of a node in the network. In many contexts, these measures determine the equilibrium and efficient behavior of nodes. Networks can be applied to broad issues in urban, regional, and location economics, such as neighborhood interactions, transportation, local public goods, trade, industrial sites, business operations. The strategic connection approach models the network as a strategic game. Both cooperative and noncooperative equilibrium concepts have been adopted in the literature. A link may form cooperatively when both nodes are better off, or one node may force a link noncooperatively onto another. The structure of intracity and intercity networks can be investigated using this framework: In a city, neighborhoods are networks of blocks, which are connected by streets and sidewalks; external benefits spill over into connected blocks; locally integrated neighborhoods emerge in equilibrium; and cities are connected by intercity transportation networks. In such models, the core–periphery patterns of cities are found to emerge in the equilibrium. The structural approach treats network structures as exogenously fixed, and links are not subject to change. In such settings, centrality measures, which indicate how centrally connected the position of a node is in the network, determine the behaviors of nodes. For example, when there are widespread externalities so that payoffs of nodes are determined by efforts of all connected nodes, the equilibrium effort of a node is proportional to its Bonacich centrality measure. Centrality measures determine equilibrium and efficient outcomes in other network settings as well. Examples of such are how conformity in peer networks affects criminal behaviors, how nodes choose security investments against the spread of infection in the network, how intercity transportation networks determine the distribution of city size, and how community residents choose the number of visits to an urban center. Futher findings include, for example, in an economy-wide trade network of intermediate inputs, local economic shocks can cause aggregate production fluctuations; in a network of neighboring jurisdictions, voluntary contributions to local public goods are neutral to income transfers; in a geographical trade network, a firm that already exports to a location will have a higher probability of exporting to a second location if the two locations have a larger volume of trade; and firms spread adverse impacts from a local economic shock through their internal networks across regions.