Does the location and the institutional background matter in convergence modelling of the EU regions?

Chocholatá, Michaela; Furková, Andrea

doi:10.1007/s10100-016-0447-6

Cited by 11 publications

(6 citation statements)

References 19 publications

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“…[3,17]). To investigate the different convergence characteristics for a subgroup of analysed regions, the dummy variable i D in a multiplicative form should be included [6]. This variable indicates whether the region i belongs to a tested subgroup of regions (…”

Section: Methodsmentioning

confidence: 99%

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Income disparities and convergence across regions of Central Europe

Chocholatá

Furková

2016

CRORR

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This paper deals with the analysis of regional income disparities of the net disposable income of households (in Euro per inhabitant) across the regions ) during the period 2000-2013. The analysis deals with the 82 NUTS 2 (Nomenclature of Territorial Units for Statistics) regions and is based on the concept of sigma-convergence, betaconvergence and growth-volatility relationship. Preliminary analysis concentrating on mapping of the analysed indicators is followed by consideration of the region's location supported by the results of spatial autocorrelation testing. The sigma-convergence analysis reveals the persistence of disparities in the net disposable income of households in the period 2000-2013 both at the national and subnational level. Although the results of spatial analysis have proved the existence of spatial dependence, following the classical approach, the beta-convergence concept is tested with the use of both non-spatial and spatial models. The potentially different convergence characteristics of Visegrad 4 countries' (Czech Republic, Slovakia, Poland, Hungary) regions and regions of Austria and Germany as well as the examination of the possible relationship between the regional growth and volatility are also taken into account in the econometric convergence modelling.

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Section: Methodsmentioning

confidence: 99%

“…Studies dealing with regional growth mention the spatial aspect in convergence analysis e.g. [6,10,11,13,17,18]. To assess the connections among analysed regions, the spatial matrix W is used.…”

Section: Introductionmentioning

confidence: 99%

Income disparities and convergence across regions of Central Europe

Chocholatá

Furková

2016

CRORR

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“…by Monastiriotis (2007), one has to keep in mind that the spatial spill-overs concerning the employment do not need to link to similar processes based on other indicators, e.g. income (Chocholatá & Furková, 2017), tertiary educational attainment (Chocholatá, 2018) or patent applications at the European Patent Office (Furková, 2016). Although the dissimilarity in the spatial patterns across different indicators can present a limitation for policy makers to face complex problems through a properly chosen policy instruments, the results of the ESDA analysis for individual indicators (e.g.…”

Section: Discussionmentioning

confidence: 99%

The analysis of employment rates in the context of spatial connectivity of the EU regions

Chocholatá

Furková

2018

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Research background: The main objective of this paper is to analyse the employment rates in the context of spatial connectivity of the EU regions. Employment rate is declared as one of the important indicators of the strategic document Europe 2020. The achievement of high levels of employment in individual regions plays therefore an important role. Purpose of the article: The aim of the paper is to verify the possible spill-over effects within the EU regions and their territorial interconnection in the context of employment rates. Methods: Analysis is based on tools of the Exploratory Spatial Data Analysis (ESDA) to consider spatial connectivity of the EU regions. Findings & Value added:The results show that the statistically significant clusters of regions with high employment rates are situated mainly in the central, northern and northwestern part of the EU while the clusters with low values are located mainly in Greece,

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“…The ignorance of spatial correlation and the fact that regions are perceived as "islands" in the economic space lead to possible biased results and consequently misleading conclusions from empirical studies. The spatial aspects have been already incorporated, e.g., in many studies dealing with regional income convergence (Qin et al, 2017;Chocholatá and Furková, 2017;Lolayekar and Mukhopadhyay, 2019) at which it is assumed that spatial spillover effects will have a significant impact on income convergence of regions. Also, we can find several studies that handle issue of spatial heterogeneity and their results indicate that economic behaviour is unstable in space, and income convergence is characterized by multiple local equilibrium states -convergence clubs (Qin et al, 2017;Papalia and Betarelli, 2012;Pan et al, 2015;Furková, 2020).…”

Section: Introductionmentioning

confidence: 99%

Spatial Heterogeneity and Spillovers of Employment in the EU Regions

Chocholatá,

Furková

2023

Zagreb International Review of Economics and Business

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This paper focuses on the employment problem in the context of EU regions. Two main hypotheses were verified. The first hypothesis was related to the spatial heterogeneity problem, i.e., we hypothesised that relationship between the employment rate and the explanatory variables (GDP per inhabitant, educational attainment level and compensation of employees) may vary spatially. The second hypothesis dealt with the spatial autocorrelation, i.e., we assumed that the regional employment process is not isolated and that the neighbourhood of the regions also plays a significant role. As the main methodological tool the spatial regime models were applied. Spatial analysis of employment rate data indicated two spatial regimes. The results revealed the spatial instability of estimated parameters across the two regimes. Also, the spatial regional interconnections within both regimes were confirmed. Statistical significance of spillover effects of considered employment factors outlines the high importance of spatial spillovers.

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Does the location and the institutional background matter in convergence modelling of the EU regions?

Cited by 11 publications

References 19 publications

Income disparities and convergence across regions of Central Europe

Income disparities and convergence across regions of Central Europe

The analysis of employment rates in the context of spatial connectivity of the EU regions

Spatial Heterogeneity and Spillovers of Employment in the EU Regions

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