Regional economic resilience in the European Union: a numerical general equilibrium analysis

Pietro, Filippo Di; Lecca, Patrizio; Salotti, Simone

doi:10.1080/17421772.2020.1846768

Cited by 53 publications

(40 citation statements)

References 30 publications

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“…Research on urban economic resilience has received more attention [ 18 ]. Di Pietro et al measured the vulnerability, resistance and recoverability of regions to discuss the resilience and identify key regional features [ 19 ]. Islam et al found that severe adverse impact of the pandemic on global production, employment, and prices [ 20 ].…”

Section: Literature Reviewmentioning

confidence: 99%

An Empirical Perception of Economic Resilience Responded to the COVID-19 Epidemic Outbreak in Beijing–Tianjin–Hebei Urban Agglomeration, China: Characterization and Interaction

Zhang

Wang

et al. 2021

IJERPH

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The COVID-19 has caused a serious impact on the global economy, and all countries are in a predicament of fighting the epidemic and recovering their economies. Aiming to discuss the impact of the COVID-19 on the economic resilience of urban agglomerations, the economic data of each quarter from June 2019 to September 2020 of the Beijing–Tianjin–Hebei Urban Agglomeration are selected, and the economic development index (EDI) is calculated based on the entropy method. Combining the fundamental conditions of urban agglomerations and industrial policies during the COVID-19, urban economic resilience is discussed by the changing trend of the economic development index (EDI) and dividing into resistance and restoration. The results show that: (1) The economic development level of the urban agglomeration has been affected by the epidemic and has changed significantly. The change of endogenous power is the main cause of change.; (2) During the outbreak of the COVID-19, the economic resilience of the Beijing–Tianjin–Hebei urban agglomeration shows four different development types: high resistance and restoration, high resistance but low restoration, low resistance but high restoration, low resistance and restoration cities; (3) High resistance but low restoration, low resistance but high restoration, and low resistance and restoration cities influence each other, but the relationship between cities is mainly dependent ; (4) The economic restoration within the urban agglomeration forms a synergy, which promotes the economic recovery and development of the urban agglomeration during the recovery period of the COVID-19. Urban agglomerations should enhance the combined effect of resistance and increase the impact of high resistance and restoration cities on surrounding cities in the future.

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Section: Literature Reviewmentioning

confidence: 99%

An Empirical Perception of Economic Resilience Responded to the COVID-19 Epidemic Outbreak in Beijing–Tianjin–Hebei Urban Agglomeration, China: Characterization and Interaction

Zhang

Wang

et al. 2021

IJERPH

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“…They investigate various factors of economic resilience. For example, Pietro et al (2020), using an equilibrium model, demonstrate that the stability of economic systems depends on the nature of the external shock and characteristics of the economy itself, such as capital and labour intensity, openness, and specialization. The authors also highlight the importance of innovation, agglomeration effects, and education factors.…”

Section: Literature Reviewmentioning

confidence: 99%

How the 2020 pandemic affected tax revenues in Russian regions?

Малкина¹

2021

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Research background: The 2020 pandemic has proven to be stressful for regional tax systems. However, these systems reacted differently to lockdown and a decline in business activity, which was associated with both their structures and the specifics of their development. Purpose of the article: The aim of the article is to assess the impact of the 2020 pandemic on tax revenues of Russian regions, as well as to analyze the factors contributing to the resilience of regional tax systems to epidemiological crises. Methods: The study is based on monthly data from the Federal Tax Service of the Russian Federation on total tax revenues in 83 Russian regions for 2013?2020. For data up to March 2020, we construct stationary time series and plotted ARiMA regressions. Based on them, we forecast tax revenues for the period from April to December 2020, if there were no pandemic. The impact of the pandemic is calculated as the deviation of actual tax revenue from the forecast for the corresponding 9 months. Findings & value added: We find that the impact of the pandemic on tax revenues varies over time and space. The crisis hit the fiscal system most negatively in the first three full months of the lockdown (April?June 2020). Some mining regions of the Urals, Western and Eastern Siberia, specializing in the extraction of oil and gas, as well as non-ferrous metals, turned out to be the most vulnerable to the pandemic. The most resistant to it are the central and southern regions of the European part of Russia. Calculation of Pearson's correlations shows that the greatest drop in tax revenues occur in regions with a larger share of the mining industry in gross value added and MET in tax revenues, with a higher GRP per capita and an increased level of general economic instability. The smallest decline in tax revenue, or even its growth, is observed in regions with a larger share of personal income tax and property tax in tax revenues, a higher share of trade and processing industries, social sphere and public administration in gross value added, a higher degree of economic diversification and a larger share of small business in total turnover. The obtained results are applicable to manage the resilience of tax systems to epidemiological crises.

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“…There is a small CGE literature that complements case studies and econometric work in the analysis of regional resilience. Di Pietro et al (2020) use the RHOMOLO CGE model to identify the differential resilience of individual NUTS 2 (European Nomenclature of Territorial Units for Statistics at level two) regions to EU-wide temporary demand and supply shocks, focusing on the role of industrial and trade structure. On a smaller scale, Rose (2004) discusses CGE as 'a state of art tool' for the analysis of the behaviour of individual regions in response to adverse, focused supply-side shocks, such as natural disasters.…”

Section: Regional Resiliencementioning

confidence: 99%

“…There is a small CGE literature that complements case studies and econometric work in the analysis of regional resilience. Di Pietro et al. (2020) use the RHOMOLO CGE model to identify the differential resilience of individual NUTS 2 (European Nomenclature of Territorial Units for Statistics at level two) regions to EU-wide temporary demand and supply shocks, focusing on the role of industrial and trade structure.…”

Section: Regional Resiliencementioning

confidence: 99%

Resilience in a behavioural/Keynesian regional model

et al. 2020

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This paper constructs a regional dynamic macroeconomic model with an eclectic, broadly Keynesian and behavioural flavour. The model, which is parameterized on Scottish data, is used to identify the impact of expectations and business confidence on regional resilience. Simulations compare the evolution of the regional economy after a temporary negative export shock under a range of investment functions. The mainstream perfect foresight formulation generates a reduction in activity, which is small and is limited to the duration of the shock. The heuristic-based, imperfect-information investment models produce more negative, longer-lasting and unstable adjustment paths.

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Regional economic resilience in the European Union: a numerical general equilibrium analysis

Cited by 53 publications

References 30 publications

An Empirical Perception of Economic Resilience Responded to the COVID-19 Epidemic Outbreak in Beijing–Tianjin–Hebei Urban Agglomeration, China: Characterization and Interaction

An Empirical Perception of Economic Resilience Responded to the COVID-19 Epidemic Outbreak in Beijing–Tianjin–Hebei Urban Agglomeration, China: Characterization and Interaction

How the 2020 pandemic affected tax revenues in Russian regions?

Resilience in a behavioural/Keynesian regional model

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