A Divided Europe? Regional Convergence and Neighbourhood Spillover Effects

Carrington, Anca

doi:10.1046/j.0023-5962.2003.00226.x

Cited by 38 publications

(23 citation statements)

References 14 publications

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“…This is also strongly supported by recent empirical findings Carrington (2003), and Ertur and Koch (2006). Failing to control for spatial dependencies if present would result in OLS producing biased and inconsistent estimates (see Florax and de Graaff (2004) for a discussion).…”

supporting

confidence: 73%

Effects of the EU-Enlargement on Income Convergence in the Eastern Border Regions

Wilhelmsson

2009

SSRN Journal

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[Abstract] This paper analyses the effect of the EU enlargement process on income convergence among regions in the EU and in the Eastern neighbourhood of the EU. The data used is NUTS II regions in the EU and Oblasts' of Russia over the period 1996-2004. The estimation techniques used take into account both regional and spatial heterogeneity. The main findings are that the regional income differences are reduced within EU15. The income convergence within the EU is mainly driven by reductions in the differences across countries rather than by a reduction in regional differences within countries. When differences in initial conditions in the regions are controlled for by fixed regional effects there are strong evidences of convergence among regions in all studied country groups.

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supporting

confidence: 73%

Effects of the EU-Enlargement on Income Convergence in the Eastern Border Regions

Wilhelmsson

2009

SSRN Journal

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“…The same spatial model with spatially lagged growth is also estimated by Carrington (2002) for 110 EU regions for the more recent period 1989-98, where she finds that convergence is reduced in the spatial specification dropping from 3.6 per cent to 1.8 per cent. On the member state level, a thorough spatial convergence analysis for German regions is provided by Niebuhr (2001).…”

Section: Introductionmentioning

confidence: 99%

Regional Convergence in the European Union, 1985- 1999: A Spatial Dynamic Panel Analysis

Badinger¹,

Müller²,

Tondl³

2004

Regional Studies

187

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[1985][1986][1987][1988][1989][1990][1991][1992][1993][1994][1995][1996][1997][1998][1999]. So far there is no direct estimator available for dynamic panels with strong spatial dependencies. We propose a two-step procedure, which involves first spatial filtering of the variables to remove the spatial correlation, and application of standard GMM estimators for dynamic panels in a second step. Our results show that ignorance of the spatial correlation leads to potentially misleading results. Applying a system GMM estimator on the filtered variables, we obtain a speed of convergence of 6.9 per cent and a capital elasticity of 0.43. JEL No.: C23, O00, R11

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“…In addition, we use control variables. Previous studies on the determinants of regional economic growth reveal that initial GDP per capita, degree of urbanisation, and spatial effects are important determinants of regional growth of GDP per capita and/or labour productivity (see, among others, Armstrong 1995;López-Bazo et al 2004;Badinger et al 2004;Carrington 2003;Ertur et al 2006;Ciccone 2002;Fingleton 2001). The empirical growth model is extended by the inclusion of spatial effects.…”

Section: Empirical Modelmentioning

confidence: 99%