For research attempting to investigate why economic activities are distributed unevenly across geographic space, new economic geography (NEG) provides a general equilibrium-based and microfounded approach to modeling a spatial economy characterized by a large variety of economic agglomerations. NEG emphasizes how agglomeration (centripetal) and dispersion (centrifugal) forces interact to generate observed spatial configurations and uneven distributions of economic activity. However, numerous economic geographers prefer to refer to the term new economic geographies as vigorous and diversified academic outputs that are inspired by the institutional-cultural turn of economic geography. Accordingly, the term geographical economics has been suggested as an alternative to NEG.
Approaches for modeling a spatial economy through the use of a general equilibrium framework have not only rendered existing concepts amenable to empirical scrutiny and policy analysis but also drawn economic geography and location theories from the periphery to the center of mainstream economic theory. Reduced-form empirical studies have attempted to test certain implications of NEG. However, due to NEG’s simplified geographic settings, the developed NEG models cannot be easily applied to observed data. The recent development of quantitative spatial models based on the mechanisms formalized by previous NEG theories has been a breakthrough in building an empirically relevant framework for implementing counterfactual policy exercises. If quantitative spatial models can connect with observed data in an empirically meaningful manner, they can enable the decomposition of key theoretical mechanisms and afford specificity in the evaluation of the general equilibrium effects of policy interventions in particular settings.
Several decades since its proposal, NEG has been criticized for its parsimonious assumptions about the economy across space and time. Therefore, existing challenges still require theoretical and quantitative models on new microfoundations pertaining to the interactions between economic agents across geographical space and the relationship between geography and economic development.
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Article
New Economic Geography
Ching-mu Chen and Shin-Kun Peng
Article
Search and Matching, and Price Formation in Real Estate Markets
David Genesove
Search models rationalize housing market dynamics where perfect asset models fail to, by incorporating the time and cost of identifying attractive opportunities to trade. They have been used to consider housing market responses to external shocks, such as widespread foreclosure, transaction taxes, seasonal mobility, improved information on listed homes, and endogenous cyclicality. They provide a framework for time to trade, list prices, and trading volume, which have no role in standard frictionless models. Empirically, time to trade offers an intensive margin as an alternative to studying the extensive margin of trade.
Search models are composed of a number of common components: typically, a meeting function, specifying how many buyers visit a seller as a function of the number of buyers and sellers in the market; a match quality distribution; a sale mechanism that determines when trade occurs and, if so, at what price; and a specification of the number of households entering the market as buyers, sellers, or both. The precise form of each of these components will vary according to the research focus, with those less central to the issue at hand being simplified if necessary.
Usually, the researcher has more information on seller search than buyer search, as the attributes of the home are listed and those of the buyer household are not, because coding property attributes is much easier than coding household attributes. However, the internet now gives researchers access to digital traces left by searching buyers. Common empirical difficulties include incomplete information on home quality and missing information on visits that do not end in trade and rejected offers. Difficulties in separating out state-dependence from seller heterogeneity, whether from impatience, search cost, or some other factor, impede the empirical analysis of stigmatization of long-searching sellers, stock-flow matching, finite horizons, and learning.
Article
Spatial Models in Econometric Research
Luc Anselin
Since the late 1990s, spatial models have become a growing addition to econometric research. They are characterized by attention paid to the location of observations (i.e., ordered spatial locations) and the interaction among them. Specifically, spatial models formally express spatial interaction by including variables observed at other locations into the regression specification. This can take different forms, mostly based on an averaging of values at neighboring locations through a so-called spatially lagged variable, or spatial lag. The spatial lag can be applied to the dependent variable, to explanatory variables, and/or to the error terms. This yields a range of specifications for cross-sectional dependence, as well as for static and dynamic spatial panels.
A critical element in the spatially lagged variable is the definition of neighbor relations in a so-called spatial weights matrix. Historically, the spatial weights matrix has been taken to be given and exogenous, but this has evolved into research focused on estimating the weights from the data and on accounting for potential endogeneity in the weights.
Due to the uneven spacing of observations and the complex way in which asymptotic properties are obtained, results from time series analysis are not applicable, and specialized laws of large numbers and central limit theorems need to be developed. This requirement has yielded an active body of research into the asymptotics of spatial models.
Article
Spatial Pattern and City Size Distribution
Tomoya Mori
Many large cities are found at locations with certain geographic and historical advantages, or the first nature advantages. Yet those exogenous locational features may not be the most potent forces governing the spatial pattern and the size variation of cities. In particular, population size, spacing, and industrial composition of cities exhibit simple, persistent, and monotonic relationships that are often approximated by power laws. The extant theories of economic agglomeration explain some aspects of this regularity as a consequence of interactions between endogenous agglomeration and dispersion forces, or the second nature advantages.
To obtain results about explicit spatial patterns of cities, a model needs to depart from the most popular two-region and systems-of-cities frameworks in urban and regional economics in which the variation in interregional distance is assumed away in order to secure analytical tractability of the models. This is one of the major reasons that only few formal models have been proposed in this literature. To draw implications about the spatial patterns and sizes of cities from the extant theories, the behavior of the many-region extension of the existing two-region models is discussed in depth.
The mechanisms that link the spatial pattern of cities and the diversity in size as well as the diversity in industrial composition among cities are also discussed in detail, thought the relevant theories are much less available. For each aspect of the interdependence among spatial patterns, size distribution and industrial composition of cities, the concrete facts are drawn from Japanese data to guide the discussion.
Article
Urbanization and Emerging Cities: Infrastructure and Housing
Gilles Duranton and Anthony J. Venables
Urbanization is a central challenge of our times. At its core, it is an urban development challenge that requires addressing transportation and housing in cities. Transport improvements can reduce travel times and improve the spatial reach of urban dwellers. But these improvements may be crowded out by latent demand for travel and may lead to worse congestion, pollution, and other negative externalities associated with urban traffic. To evaluate the effects of transport improvements, direct travel effects must be measured. Then, an improvement in traffic conditions somewhere may spill over to other areas. Firms and residents may also relocate, so economic growth close to a transport improvement may just result from a displacement of economic activity from other areas. Conversely, better accessibility is expected to foster agglomeration effects and increase productivity. Valuing these changes is difficult, as it requires being able to quantify many externalities such as congestion delays, scheduling gains, and greater job accessibility. Housing policies present different challenges. More fundamental policies seek to enable housing construction by offering more secure property rights, up-to-date land registries, and competent land-use planning—all complex endeavors and all necessary. Other housing policies rely on heavy government interventions to provide housing directly to large segments of the urban population. These policies often flop because governments fail to link housing provision with job accessibility and appropriate land-use planning. Housing is also an expensive asset that requires significant initial funding, while credit constraints abound in the urbanizing world. Policymakers also need to choose between small improvements to extremely low-quality informal housing, retrofitting modern housing in already-built urban areas, or urban expansion. All these options involve sharp trade-offs, subtle induced effects, and complex interactions with transport. All these effects are difficult to measure and challenging to value.
Article
Urban Sprawl and the Control of Land Use
Alex Anas
Urban sprawl in popular sources is vaguely defined and largely misunderstood, having acquired a pejorative meaning. Economists should ask whether particular patterns of urban land use are an outcome of an efficient allocation of resources. Theoretical economic modeling has been used to show that more not less, sprawl often improves economic efficiency. More sprawl can cause a reduction in traffic congestion. Job suburbanization can generally increase sprawl but improves economic efficiency. Limiting sprawl in some cities by direct control of the land use can increase sprawl in other cities, and aggregate sprawl in all cities combined can increase. That urban population growth causes more urban sprawl is verified by empirically implemented general equilibrium models, but—contrary to common belief—the increase in travel times that accompanies such sprawl are very modest. Urban growth boundaries to limit urban sprawl cause large deadweight losses by raising land prices and should be seen to be socially intolerable but often are not. It is good policy to use corrective taxation for negative externalities such as traffic congestion and to implement property tax reforms to reduce or eliminate distortive taxation. Under various circumstances such fiscal measures improve welfare by increasing urban sprawl. The flight of the rich from American central cities, large lot zoning in the suburbs, and the financing of schools by property tax revenues are seen as causes of sprawl. There is also evidence that more heterogeneity among consumers and more unequal income distributions cause more urban sprawl. The connections between agglomeration economies and urban sprawl are less clear. The emerging technology of autonomous vehicles can have major implications for the future of urban spatial structure and is likely to add to sprawl.
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