Giuseppe Arbia

The role of spatial effects in the empirical analysis of regional concentration

Abstract. Economists have recently devoted an increasing attention to the issue of spatial concentration of economic activities. However, surprisingly enough, most of the empirical work is still based on the computation of very basic statistical measures in which the geographical characteristics of data play no role. By making use of a series of empirical examples we show that spatial concentration consists of two different features that are rarely kept as separate in the statistical analysis: an a-spatial concept of variability which is invariant to permutations, and the concept of polarization that refers to the geographical position of observations.

Error propagation modelling in raster GIS: overlay operations

Performing data manipulations on maps that possess error as a result of the process of data collection leads to error propagation. The errors that are present in maps are modified by such operations in ways that may undermine the purposeofanalysisand lead to increased uncertainty in thevalidity ofthe conclusions that are drawn. This paper analyses how source map error propagates as a result of overlay operations. Geman and Gemans corruption model for individual source map error is used for the analysis which allows for attribute measurement error and location error that can then interact with the (true) source map geography. This paper reports theoretical results on the univariate overlay problem and then extends these results through simulation. Throughout a set of source maps and error processes are used with specified properties in order to examine in detail the interactions that can take place between the different elements of the source map structure and the error process. The paper uses ANOVA metho...

Convergence in Per-capita GDP across European Regions using Panel Data Models Extended to Spatial Autocorrelation Effects

Most of the empirical works in regional convergence are based on either cross-sectional or “a-spatial” panel data models. In this paper, we propose the use of panel data econometrics models that incorporate an explicit consideration of spatial dependence effects (Anselin, 1988; Elhorst, 2001; 2003). This allows us to extend the traditional convergence models to include a rigorous treatment of regional spillovers and to obtain more reliable estimates of the parameters. We consider two models respectively based on the introduction of a spatial lag among the explanatory variables (the “spatial lag model”) and imposing a spatial autoregressive structure to the stochastic component (the “spatial error model”). We apply such a modelling framework to the long-run convergence of per-capita GDP of 125 EU-NUTS2 regions observed yearly in the period 1977-2002. A comparison of the results obtained using the two spatial panel data specifications with the main evidence available in the literature is also provided.

Modelling the geography of economic activities on a continuous space

Abstract. In the present article we propose a spatial micro econometric approach for studying the geographical concentration of economic activities. We analyse the incentives to use this approach rather than the traditional one based on regional aggregates. As an example, we present our prototypical theoretic model – to be seen as a continuous space version of Krugmans concentration model – that includes birth, survival and growth components. We present a numerical estimation of the birth model for a set of data referring to the concentration of the manufacturing industries in the San Marino Republic.

Using Spatial Panel Data in Modelling Regional Growth and Convergence

In this paper we use spatial dependence panel data models to analyse regional growth behaviour in Italy. Controlling for fixed-effects allows us to disentangle the effect of spatial dependence (or spatial interaction) from that of spatial heterogeneity and of omitted variables and, thus, to properly investigate the regional convergence process within the country.

Error Propagation Through Map Operations

In image processing and geographic information systems, a new map is constructed by carrying out a sequence of operations on a set of source maps. These operations typically include the adding, ratioing, and overlaying (or buffering) of two or more maps. But each source map may contain error. There may be error associated with measuring attribute values and in specifying the location (or position) of attribute values. This article investigates through theory and simulation the effects of different types of source-map error on both the size and pattern of errors in the resulting map and examines how these effects also depend on the spatial structure of the true source maps.

Does Evidence on Regional Economic Convergence Depend on the Estimation Strategy? Outcomes from Analysis of a Set of NUTS2 EU Regions

Abstract The regional economic convergence/divergence issue has been discussed extensively recently, but results obtained are not always interpretable unequivocally as a consequence of the different estimation strategies used. As it is widely recognized, the most common theoretical framework applied to measure the speed of economic convergence among countries or regions remains the β-convergence approach, linked to the neoclassical Solow model. There have been many attempts to consider variations of the basic cross-sectional specification ranging from panel data models to Bayesian spatial econometric techniques. The application of spatial econometric methodologies is an essential tool for proper statistical inference on regional data. In this context, the aim of this paper is to connect the different results obtained in the literature. More specifically, we address whether or not evidence on convergence depends upon the estimation strategy, by taking the same set of data and systematically comparing the results obtained from different estimation strategies. The results from a set of NUTS2 EU regions conclude that both the model implied by the cross-sectional analysis and the one referring to the space-time dynamics incorporated in the panel specification point to convergence. The concept of convergence implied is, however, quite different, as demonstrated throughout the paper.

Detecting negative spatial autocorrelation in georeferenced random variables

Negative spatial autocorrelation refers to a geographic distribution of values, or a map pattern, in which the neighbors of locations with large values have small values, the neighbors of locations with intermediate values have intermediate values, and the neighbors of locations with small values have large values. Little is known about negative spatial autocorrelation and its consequences in statistical inference in general, and regression-based inference in particular, with spatial researchers to date concentrating mostly on understanding the much more frequently encountered case of positive spatial autocorrelation. What are the spatial contexts within which negative spatial autocorrelation should be readily found? What are its inferential consequences for regression models? This paper presents selected empirical examples of negative spatial autocorrelation, adding to the slowly growing literature about this phenomenon.

Spatial Econometric Modeling of Regional Convergence in Continuous Time

In this article, the authors use a continuous-time framework to model the potential convergence dynamics in a group of regions. They propose a model based on the classical Lotka-Volterra predator-prey system of two equations—a model originally proposed by Samuelson in 1971 to perform dynamic economic analysis—and extend the model to the case of more than two regions by introducing dependence on neighboring regions. The authors state the conditions under which the system of regions moves toward a mathematically stable point of convergence and show that the model can be seen as more general than the popular β-convergence model. Finally, they also consider statistical inference and introduce a discrete approximate solution based on simultaneous dynamic least squares to estimate the parameters of the model.

The use of GIS in spatial surveys

The paper is divided into three parts. The first part reviews GIS technologies as essential background to the remainder of the paper. The second part of the paper aims to show the impact and potential of employing GIS technologies in survey processing and, in particular, in survey design. We show how, by employing a GIS-assisted computer-intensive sampling strategy, it is possible to substantially reduce the costs of surveys based on area sampling whilst maintaining the same level of accuracy. The third part of the paper aims at increasing awareness among GIS users of distortion effects induced on statistical analysis by the error propagation which occurs when GIS operations are based on two or more maps that individually contain errors. The paper considers the error properties of output maps in such circumstances.