27

Spatial Analysis of Economic

Convergence

Sergio J. Rey and Julie Le Gallo

Abstract

This chapter reviews some of the major econometric models, approaches and issues related to the
spatial dimensions of economic convergence and inequality. Key themes concern the implications
of spatial dependence (autocorrelation) and heterogeneity for the specification, estimation, and
interpretation of convergence models on the one hand and, on the other, the treatment of these
spatial effects in the analysis of distributional dynamics and the application of related exploratory
data analysis methods. We draw linkages between recent contributions in the mainstream econo-
metric literature and developments in spatial econometrics and regional science We identify a
number of areas where cross-fertilization between these fields would be beneficial.

27.1 Introduction 1252
27.2 Space and econometric modeling of convergence 1253
27.2.1 Models of economic growth 1253
27.2.2 The cross-sectional approach to growth and convergence 1255
27.2.3 Dealing with heterogeneity 1258
27.2.4 Theoretical foundations of spatial effects 1262
27.3 Exploratory spatial data analysis of convergence 1264
27.3.1 Exploratory spatial data analysis 1265
27.3.1.1 Spatialσ-convergence 1265
27.3.1.2 Markov chain models 1266
27.3.1.3 Spatial Markov 1267
27.3.1.4 Spatial rank mobility 1269
27.3.2 Exploratory space-time data analysis 1272
27.3.2.1 Spatial transitions 1272
27.3.2.2 Space-time paths 1276
27.3.3 Stochastic kernels 1277
27.3.3.1 Estimation 1277
27.3.3.2 Regional conditioning and spatial filtering 1278
27.3.4 Space-time kernels 1280
27.4 Conclusion 1282

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