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DOES SPATIAL HETEROGENEITY MATTER IN THE EU REGIONS’ CONVERGENCE INCOME PROCESS?

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Furková A.
Ekonomický časopis (Journal of Economics)
|
2020
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vol. 68
|
issue 7
677 – 698
EN
This paper explores the role of spatial heterogeneity in the EU regions’ convergence income process. For this purpose we tested income convergence hypothesis of the 245 NUTS 2 European Union regions during the period 2003 – 2014. We used spatial econometric approach which allowed an explicit modelling of both spatial effects: spatial heterogeneity and spatial autocorrelation. Our results showed an appropriate consideration of the role of spatial effects. First, we found that the rate of economic growth in the region is not only affected by the exogenous characteristics of the region, but also potentially by the rate of economic growth in neighbouring regions. Secondly, estimated spatial regime model identified two spatial regimes – convergence clubs. The regions under the first spatial convergence regime included economically „weaker” regions (mainly regions of post-socialist countries, regions of Portugal, Spain, Greece and southern regions of Italy) and these regions are converging separately from the rest of regions of the EU.
2

SPATIAL ECONOMETRIC MODELLING OF REGIONAL CLUB CONVERGENCE IN THE EUROPEAN UNION

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Furková A., Chocholatá M.
Ekonomický časopis (Journal of Economics)
|
2016
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vol. 64
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issue 4
367 – 386
EN
This paper focuses on the testing of income convergence of the EU regions using both non-spatial and spatial approaches. The main motivation for this analysis was the fact that the classical income convergence models suffer from a misspecification due to omitted spatial dependence among regions. Our empirical results provide support for the absolute beta-convergence modelling from spatial econometric perspective in our sample of 252 NUTS 2 regions over the period 2000 – 2011. Another serious finding is that the assumption of a single steady-state for all regions often mismatches with the reality. We found the club spatial beta-convergence models to be more appropriate for analysed data.
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