Spatial regimes in regional European growth: an iterated spatially weighted regression approach

Andreano, M. Simona; Benedetti, Roberto; Postiglione, Paolo

doi:10.1007/s11135-016-0415-1

Cited by 23 publications

(11 citation statements)

References 43 publications

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“…This aspect may be of extreme importance to policy makers as it leads to an additional component to take into account to reduce disparities between regions. In fact, our analyses verify the global presence of convergence as for previous analyses involving the European Union (see, among others, [52,55]). However, positive local spillovers may not have any suitable effect when low growth levels are present in the neighbours.…”

Section: Discussionsupporting

confidence: 87%

Economic Convergence in EU NUTS 3 Regions: A Spatial Econometric Perspective

2020

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The topic of economic convergence is still crucial in the European Union (EU) as promoting regional growth and the reduction of disparities remains a key objective. In this paper we investigate development and economic growth across EU regions. Particular attention is given to σ-convergence and β-convergence. These analyses are carried out for regional units corresponding to the third level of the NUTS (Nomenclature of Territorial Units for Statistics) classification. Focusing on a refined geographical scale could offer a detailed picture of the regional growth dynamics within the EU. Additionally, we use a spatial augmented version of the conditional β-convergence model to take into account the spatial interdependencies among regions. Findings shed light on the impact of spatial interaction effects and on the need of territorial policies to achieve convergence in the EU. This aspect highlights how coordinating the regional development policies between regions is pivotal to achieve economic, as well as social and political stability within the EU.

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Section: Discussionsupporting

confidence: 87%

Economic Convergence in EU NUTS 3 Regions: A Spatial Econometric Perspective

2020

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“…In Italy, to only test for spatial dependence would be somewhat misleading (Andreano, Benedetti, & Postiglione, 2017). To consider spatial instabilities we explore spatial heterogeneity.…”

Section: Methodsmentioning

confidence: 99%

A Spatio‐temporal analysis of COVID‐19 outbreak in Italy

Ghosh

Cartone

2020

Regional Science Policy & Practice

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Within two weeks from the first detection of the SARS-CoV-2 positive patient on 21st February, from Lombardy the disease has spread over every region in Italy. The main objective of this study is to identify spatial effects and spatiotemporal patterns of the outbreak of COVID-19 in different regions of Italy. Spatial indicators for different periods, as Moran's I, local Moran, LISA clusters, Getis and Ord G, and scatterplots are used for this purpose. Results confirm the great presence of spatial effects as well as changes in spatial regimes between the quarantine and the easing phase. The evidence could be of help for policymakers to a proper assessments of health strategies aware of local characteristics.

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“…Most recently the direct relationship of spatial dependency (Anselin 2002) and technical efficiency of farms is also demonstrated in . A different approach that accounts for both spatial dependence and spatial heterogeneity is presented in the recent works of Andreano et al (2017), Billé et al (2017) and Billé et al (2018). In the area of non-parametric efficiency analysis, the recent work by has proposed a framework that is possible to consider the concept of spatial dependence into nonparametric efficiency models that is accounting the spatial proximity of peers rather than the relationship between inputs, outputs and the set of contextual exogenous factors with direct impact to production capacity.…”

Section: Discussion: Conclusionmentioning

confidence: 99%

Accounting for rainfall and the length of growing season in technical efficiency analysis

Gadanakis

2018

Oper Res Int J

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The physical environment of farming systems is rarely considered when conducting farm level efficiency analysis, which is likely to lead to bias of performance measurements based on benchmarking methods such as Data Envelopment Analysis (DEA). We incorporate variations of the physical environment (rainfall and length of growing season) through the specifications of the linear programming in DEA to investigate performance measurement bias. The derived technical efficiency estimates are obtained using a sub-vector DEA which ensures farms are compared in a homogenous environment (i.e. accounting for differences in rainfall levels amongst distinct farm units). We use the Farm Business Survey to analyse a representative sample of 245 cereal farms in the East Anglia region between 2009 and 2010. Efficiency rankings obtained from a standard DEA model and a non-discretionary DEA model that incorporates the variations in the physical environment. We show that incorporating rainfall and the length of the growing season as non-discretionary inputs into the production function had significantly altered the farm efficiency ranking between the two models. Hence, to improve extension services to farmers and to reduce biased estimates of farm technical efficiency, variations in environmental conditions need to be integral to the analysis of efficiency.

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Spatial regimes in regional European growth: an iterated spatially weighted regression approach

Cited by 23 publications

References 43 publications

Economic Convergence in EU NUTS 3 Regions: A Spatial Econometric Perspective

Economic Convergence in EU NUTS 3 Regions: A Spatial Econometric Perspective

A Spatio‐temporal analysis of COVID‐19 outbreak in Italy

Accounting for rainfall and the length of growing season in technical efficiency analysis

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