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Stochastic dominance tests are used to measure whether distributions are directionally‐distinct. Using stochastic dominance measures, an income distribution can be measured to be more favorable for its members at all income levels than another distribution. This contrasts with conventional σ and β‐convergence approaches that compare distributions at a set of predetermined income levels. Given this comprehensiveness, stochastic dominance measures can yield novel insight into the relationship between income distributions, and thus to the relative economic welfare of inhabitants of different societies. However, stochastic dominance measures used in the analysis of regional income convergence have ignored the complications of geography. In this paper we examine the properties of common tests for stochastic dominance using various descriptive models of geographically‐realistic income growth processes. We find that the false positive rate of stochastic dominance tests increases significantly when comparing spatially‐informed income distributions growing over time. Further, this over‐eager rejection rate requires only that one distribution be spatially‐dependent, unlike some other bivariate statistics. We demonstrate that both the parametric and non‐parametric spatial filtering procedures are effective at resolving these issues in restricted circumstances for higher‐order dominance tests. Overall, while providing a novel and useful perspective on income distribution dynamics, stochastic dominance measures are demonstrated to have undesirable properties when used in geographically‐realistic scenarios and they resist one common method to address these issues. Solutions to the concerns expressed here remain open. [ABSTRACT FROM AUTHOR]

Segregated areas may occur around an attractive park or a waste incinerator, but the magnitude and group membership of the people in closest proximity will likely be difierent. We therefore introduce a local segregation measure that can be applied to any location within a metropolitan area, and that can identify the group that is relatively more concentrated around that reference location. We further introduce an inference approach to identify the statistical significance of a particular segregation value. In an exploratory setting the index can be used to generate a map of hot spots, and seed the question: 'why is this group significantly concentrated around that location?' [ABSTRACT FROM AUTHOR]

The article aims to explain regional economic development and economic convergence under the theme of spatial econometrics. The major idea behind the economic convergence is decreasing returns to capital accumulation. The economic convergence can be achieved by variety of theoretical and statistical models. There are so many advanced studies came out. They will facilitate convergence model specification and statistical testing for better efficiency.

This paper explores the implications that spatial effects can hold for the application of measures of σ-convergence. The bias of a common indicator of σ-convergence is examined for a family of spatial process models including: [a] spatial lag, [b] spatial error, and [c] spatial moving average. We show that the measure of σ-convergence is sensitive to a number of distinct influences including global dispersion, spatial dependence, and a variety of forms of spatial heterogeneity. We suggest a decomposition of the convergence indicator into two components: one reflecting global dispersion, and one reflecting the influence of spatial effects. We then illustrate this approach with a case study of the U.S. states over the 1929–2000 period. [ABSTRACT FROM AUTHOR]

Abstract. Recent research on integrated econometric+input-output modeling for regional economies is reviewed. The motivations for and the alternative methodological approaches to this type of analysis are examined. Particular attention is given to the issues arising from multiregional linkages and spatial effects in the implementation of these frameworks at the sub-national scale. The linkages between integrated modeling and spatial econometrics are outlined. Directions for future research on integrated econometric and input-output modeling are identified. [ABSTRACT FROM AUTHOR]