International Regional Science Review

Elhorst cung cấp các quy trình Matlab để ước lượng các mô hình bảng không gian tại trang web của ông ấy. Bài báo này mở rộng các quy trình đó để bao gồm quy trình hiệu chỉnh độ thiên lệch được đề xuất bởi Lee và Yu nếu mô hình bảng không gian chứa các hiệu ứng cố định không gian và/hoặc thời gian, các ước lượng hiệu ứng trực tiếp và gián tiếp của các biến giải thích được đề xuất bởi LeSage và Pace, và một khung lựa chọn để xác định mô hình bảng không gian nào mô tả dữ liệu tốt nhất. Để minh họa các quy trình này trong môi trường thực nghiệm, một mô hình cầu nhu cầu cho thuốc lá được ước lượng dựa trên dữ liệu bảng từ bốn mươi sáu tiểu bang của Hoa Kỳ trong giai đoạn 1963–1992.

Urban ecological systems are characterized by complex interactions among social, economic, institutional, and environmental variables. These interactions generate complex human-dominated landscapes, which significantly influence the functioning of local and global earth ecosystems and the services they provide to humans and other life on earth. Urban development fragments, isolates, and degrades natural habitats; simplifies and homogenizes species composition; disrupts hydrological systems; and modifies energy flow and nutrient cycling. Urban areas also appropriate a large share of earth’s carrying capacity from other regions in terms of resource input and waste sinks. Change in ecological conditions that result from human actions in urban areas ultimately affect human health and well-being. In this article, the author reviews the empirical evidence on the effects that patterns of urban development have on ecosystem function. Urban development affects the spatial heterogeneity of the landscape (i.e., pattern of variation in land cover) and spread of disturbance (i.e., invasive species). The author proposes that alternative urban patterns generate differential ecological effects. The review reveals that the interactions between urban development patterns and ecosystem dynamics are still poorly understood. The author draws on an empirical study of the Puget Sound metropolitan region currently developed at the University of Washington to propose directions for future empirical research that can inform strategies to minimize urban impacts on ecosystems.

This article reviews a growing literature investigating how “immigrant” diversity relates to urban economic performance. As distinct from the labor-supply focus of much of the economics of immigration, this article reviews work that examines how growing heterogeneity in the composition of the workforce may beneficially or harmfully affect the production of goods, services, and ideas, especially in regional economies. Taking stock of existing research, the article argues that the low-hanging fruit in this field has now been picked and lays out a set of open issues that need to be taken up in future studies in order to fulfill the promise of this work.

This article pays special attention to the spatial dimension of public capital in regional economic growth. We assume that the effect of infrastructure on productivity depends on the type of public infrastructure in question: that is, local infrastructures enhance economic activity in the area where they are located, whereas transport and communication infrastructure may produce both benefits in the area where they are located and positive or negative spillovers to other regions. We also obtain conclusions on whether the link between growth and public capital depends on the amount of existing public capital stocks. Results for the Spanish provinces during a long time period suggest that the returns to local infrastructure are much greater than the returns to transportation, although in both cases, we observe strong decreasing returns to their accumulation. Finally, negative spillovers across regions in transport capital investments are obtained.

This article appraises recent advances in the spatial econometric literature. It serves as the introduction to a collection of new papers on spatial econometric data analysis brought together in this special issue, dealing specifically with new extensions to the spatial econometric modeling perspective. Although the initial development of the field of spatial econometrics has been rather slow, the Dixit-Stiglitz revolution and the emergence of the New Economy Geography have been instrumental in uplifting the significance and the use of spatial data analysis techniques. Concurrent developments in other social sciences parallel this situation in economics. The upsurge in spatial econometrics is, among other things, driven by the recognition that traditional spatial econometric models are insufficient to capture modern theoretical developments. Therefore, this issue brings together a collection of articles on space-time and discrete choice modeling, spatial nonstationarity, and the methodology and empirics of regional economic growth models.

This paper derives Lagrangian multiplier (LM) tests to jointly test for functional form and spatial error correlation. In particular, this paper tests for linear and log-linear models with no spatial error dependence against a more general Box-Cox model with spatial error correlation. Conditional LM tests and modified Rao score tests that guard against local misspecification are also derived. These tests are easy to implement and are illustrated using Anselin’s crime data. The performance of these tests is also compared using Monte Carlo experiments.

This essay assesses the evolution of the way in which spatial data analytical methods have been incorporated into software tools over the past two decades. It is part retrospective and prospective, going beyond a historical review to outline some ideas about important factors that drove the software development, such as methodological advances, the open source movement and the advent of the Internet and cyberinfrastructure. The review highlights activities carried out by the author and his collaborators and uses SpaceStat, GeoDa, PySAL, and recent spatial analytical web services developed at the ASU GeoDa Center as illustrative examples. It outlines a vision for a spatial econometrics workbench as an example of the incorporation of spatial analytical functionality in a cyberGIS.

This article surveys empirical evidence from U.S. regions concerning measurement of productivity levels and patterns of change in those levels over time. Literature reviewed includes both econometric studies and more recent work that has heavily emphasized sources-of-growth accounting. Key articles are discussed and evaluated in detail, particularly from a methodological perspective. Further research topics are suggested. They focus on the changing role over time in the relation between agglomeration economies and urban productivity, on whether regional productivity levels are converging over time, and on the reconciliation of divergent regional estimates of total factor productivity to account, for example, for industry mix effects.

In this applied study, we use US county data to examine patterns in fine particulates (also called fine particulate matter or PM_2.5) ambient concentrations as a measure of air pollution within the framework of the environmental Kuznets’ curve (EKC). We pay particular attention to the role of social capital and notions of ruralness. Consistent with expectations, we find that peak of the EKC ranges between US$24,000 and US$25,500 for PM_2.5 concentrations depending on the estimator used. Also consistent with expectations, higher levels of social capital places downward pressure on PM_2.5 concentrations, but that effect is weaker in more rural areas. The implication is that the promotion of economic growth may harm the environment at lower levels of income but will improve the environment as income continues to grow.