PERSISTENCE OF REGIONAL UNEMPLOYMENT: APPLICATION OF A SPATIAL FILTERING APPROACH TO LOCAL LABOR MARKETS IN GERMANY*

ABSTRACT The geographical distribution and persistence of regional/local unemployment rates in heterogeneous economies (such as Germany) have been, in recent years, the subject of various theoretical and empirical studies. Several researchers have shown an interest in analyzing the dynamic adjustment processes of unemployment and the average degree of dependence of the current unemployment rates or gross domestic product from the ones observed in the past. In this paper, we present a new econometric approach to the study of regional unemployment persistence, in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment. First, we propose an econometric procedure suggesting the use of spatial filtering techniques as a substitute for fixed effects in a panel estimation framework. The spatial filter computed here is a proxy for spatially distributed region‐specific information (e.g., the endowment of natural resources, or the size of the “home market”) that is usually incorporated in the fixed effects coefficients. The advantages of our proposed procedure are that the spatial filter, by incorporating region‐specific information that generates spatial autocorrelation, frees up degrees of freedom, simultaneously corrects for time‐stable spatial autocorrelation in the residuals, and provides insights about the spatial patterns in regional adjustment processes. We present several experiments in order to investigate the spatial pattern of the heterogeneous autoregressive coefficients estimated for unemployment data for German NUTS‐3 regions. We find widely heterogeneous but generally high persistence in regional unemployment rates.     

Influence of Diaphragm Flexibility on Seismic Response of Unreinforced Masonry Buildings

The effects of diaphragm flexibility on the seismic response of low-rise unreinforced masonry buildings are examined using one-way stiffness- and strength-eccentric single-story systems subjected to unidirectional ground excitation. A wide range of diaphragm stiffnesses are considered. Results show that diaphragm flexibility can induce different effects depending on the configuration of the system and the level of diaphragm flexibility. When diaphragm is relatively stiff, amplified displacement demands can be imposed on the flexible side of the structure. When diaphragm is relatively flexible, peak displacements of in-plane loaded walls generally reduce. A diaphragm classification is developed to capture these salient effects.     

Toward a Theory of Spatial Statistics: Another Step Forward

An update is presented to an earlier piece outlining some steps that needed to be taken toward the establishment of a theory of spatial statistics. Findings that have appeared since this first paper are summarized and interpreted, and extensions and suggestions are offered for the further establishment of a basis for a theory of spatial statistics. Topics include boundary considerations, the role of latent spatial dependencies, and small-sample-size issues. These topics embrace the problems of data transformations, edge effect bias, reference sampling distributions, multivariate autocorrelation models, conditional expectations, and higher-order autoregressive structures. In part, a course is charted for the next step to be taken.     

From Spatial Analysis to Geospatial Science

When Spatial Analysis was published in 1968, it drew together the fruits of the first decade of geography's quantitative revolution. In the decades that have followed, quantitative geography has both diffused and concentrated, abandoned some themes, made major progress on others, and in the contemporary form of geospatial analysis has become an innovative multidisciplinary enterprise. In this article, we sketch the broad outlines of this history, lay out the main threads along which technical capabilities have developed, and describe what appear to us to be the leading questions at the research frontier. Even as many geographers disavow social science, geospatial science has emerged as a lusty arena marked by intellectual vigor, conceptual growth, and enhanced analytic abilities. What now is taking shape is a spatially integrated social–environmental science that is transcending older disciplinary attachments, boundaries, and constraints.     

Heterogeneity of Attribute Sampling Error in Spatial Data Sets

This paper considers the standard error of the estimate of the mean of a spatially correlated variable in the case where data are obtained by a process of random sampling. Two distinct mean estimation problems are identified: estimating the area mean and estimating the population mean. Methods are described for obtaining standard error estimates in the two cases and, within the limits of publicly available information, the methods are implemented on average household income data at the census tract scale for Syracuse, New York. The purpose of the paper is to draw attention to issues of data precision in relation to sampled geographic information on averages and in particular to consider the problems of estimating standard errors using such data. The paper also examines the extent to which standard errors vary between census tracts.