Catching-up and regional disparities: a resource-allocation approach

Capello, Roberta; Cerisola, Silvia

doi:10.1080/09654313.2020.1823323

Cited by 9 publications

(4 citation statements)

References 50 publications

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“…The 6 countries contain 21 predominantly urban regions (URB), 84 intermediate regions (INT), and 80 predominantly rural regions (RUR) (see Table 1 and Appendix). As we have mentioned, the economic growth of the first-tier-level capitals of the 6 countries considerably differs from the other regions (Capello and Cerisola, 2021;Lengyel & Kotosz, 2018), therefore, we classified the 6 capitals into a separate category. Based on the above, we identified 4 types, thus 15 regions remained in the category of predominantly urban regions, which we consider second-tier urban areas.…”

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confidence: 99%

Convergence and Catch-Up of the Region Types in the Central and Eastern European Countries

Egri,

Lengyel

2023

Appl. Spatial Analysis

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Our study investigates the economic growth and catch-up of the NUTS3 regions of 6 Central and Eastern European (CEE) member states of the European Union (EU), 4 countries acceding in 2004 (Czechia, Poland, Hungary, and Slovakia) and further two admitted in 2007 (Bulgaria and Romania), compared to the average of 14 older members of the EU between 2000 and 2019. We based our analysis on the urban–rural region types of the EU in the case of 185 regions, identifying predominantly urban, intermediate, and predominantly rural types. We apply Theil Index to examine the development of disparities and test the phenomena with unconditional β-convergence hypothesis. The analysis indicates that the growth of all CEE countries and their regions is faster than the EU14 average; the capitals considerably exceed it, the catch-up of other urban regions is also relatively fast, while it is very slow in the case of other regions. The convergence between the 185 regions is weak, based on the EU region typology it was initially strong between the capitals, moderate in the case of intermediate and rural types, while divergence can be observed in the urban types. The catch-up of less developed regions is very slow despite EU cohesion funding, even though 80% of the population live here. The stagnation of regional disparities and slow catch-up of less developed regions indicate the poor efficiency of the EU cohesion policy.

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confidence: 99%

Convergence and Catch-Up of the Region Types in the Central and Eastern European Countries

Egri,

Lengyel

2023

Appl. Spatial Analysis

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“…H2. Increasing regional disparities are influenced by differential technological progress and access to technology through speculative labour (Agasisti & Bertoletti, 2022;Bailey & De Propris, 2019;Barrios et al, 2019;Capello & Cerisola, 2021;Cartone et al, 2021;Ehrlich & Overman, 2020;Gardiner et al, 2020;Iammarino et al, 2019;Mykhnenko & Wolff, 2019;Panzera & Postiglione, 2022). H3.…”

Section: Methodsmentioning

confidence: 99%

“…An interesting research starts from the need to find direct quantitative validation between the allocation of regional assets and regional inequalities (Capello & Cerisola, 2021). The analysis takes into account the relative weight of each economic resource in the process of decreasing regional disparities.…”

Section: Regional Disparities and Trends Of Technological Progressmentioning

confidence: 99%

The Implementation of the Regional Development Index in the New Geo-Political Context

Ionescu,

Zlati,

Antohi

et al. 2023

Technological and Economic Development of Economy

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The opportunity of this research topic lies in the current extremely challenging geopolitical context that has led to the outbreak of multiple crises: energy, economic, social, financial, food, etc. The European Union has thus positioned itself in the face of these phenomena by creating several instruments to manage crises and increase resilience: civil protection mechanism, integrated mechanism for political response to crises, cross-border cooperation and solidarity mechanisms, food security crisis plans, resilience measures against physical and digital risks.The paper aims to analyse the necessity of changing the regional approach across the EU under the impact of the pandemic. Specific regional socio-economic indicators for EU NUTS2 regions are analysed during 2010–2021. The main objective of this research is to quantify the regional socioeconomic and to realise comparative analysis related to three moments in time: the latest EU enlargement, the economic crisis and the pandemic. The novelty of the study was the building of a new dynamic multi-criteria model assessing the strategic perspective, which was built to offer pertinent solutions for the regional decision makers in order to ensure cohesion and sustainable development on short and medium term.

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“…With the inclusion of country dummy variables in the econometric specifications (columns 2a-c), no spatial autocorrelation can be detected. There is empirical evidence that the inclusion of country dummies largely reduces the importance of spatial correlation [56,57]. Country dummies, which account for unspecified country-specific attributes that may affect regional resilience, are jointly significant at the 1% level.…”

Section: (1a) Ols (1b) Sem (1c) Slm (2a) Ols (2b) Sem (2c) Slm (3) Hlmmentioning

confidence: 99%

Spatial Connectivity and Regional Economic Resilience in Turbulent Times

Giannakis

Papadas

2021

Sustainability

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The increasing number of economic shocks and disruptions and their highly heterogeneous territorial impacts reopened the debate on the ability of regions to withstand and recover from exogenous shocks. This paper focuses on the recessionary impact of the 2008 global financial economic crisis. It empirically explores the relationship between pre-crisis spatial connectivity and economic resilience across European Union (EU) regions over the 2008–2015 period. The empirical analysis is performed on a sample of 1312 NUTS-3 regions in 25 countries. Standard, spatial and multilevel hierarchical regression models are applied to investigate the effect of spatial connectivity and other pre-crisis determinants on regional economic resilience across three geographical scales: national, NUTS-2 regional and NUTS-3 regional. The results show that accessibility is an important factor for EU NUTS-3 regions to build resilience capabilities to exogenous shocks. Our findings demonstrate that higher accessibility is associated with greater regional economic resilience. The model results indicate a positive effect of migration and a negative effect of the ageing population on regional reaction to the crisis. Our analysis highlights the importance of country effects and spatial spillover effects on the ability of regions to shape resilience capabilities.

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Catching-up and regional disparities: a resource-allocation approach

Cited by 9 publications

References 50 publications

Convergence and Catch-Up of the Region Types in the Central and Eastern European Countries

Convergence and Catch-Up of the Region Types in the Central and Eastern European Countries

The Implementation of the Regional Development Index in the New Geo-Political Context

Spatial Connectivity and Regional Economic Resilience in Turbulent Times

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