The Socioeconomic Diversity of European Regions. CES Working Paper, no. 131, 2006 (original) (raw)

The Socioeconomic Diversity of European Regions

It is well-known that there are significant differences among the European Union regions, which have been heightened due to the most recent enlargement in 2004. This paper aims to analyze this diversity and propose a classification of European Regions (EU) that is adjusted to the different axes of socioeconomic development and, simultaneously, is useful for European regional policy purposes. The data used in this paper were published by the European Union Statistical Office (Eurostat) and correspond to the main statistical indicators of NUTS2 (Nomenclature of Territorial Units for Statistics) regions in the EU. Multivariate statistical techniques allowed the identification of clusters of socioeconomic similarity, which are contrasted with the classes considered in the financial proposal of the European Commission (EC) for the period 2007-2013. It was found that each of the two main groups of the EC classification -convergence regions and competitiveness and employment regionscomprises at least two significantly different groups of regions, which differ not only in their average income but also in other indicators associated with their particular weaknesses. Also, it has been revealed that two other groups-phasing-in regions and phasing-out regions -, beyond their inexpressive denomination, lack homogeneity, being spread throughout different clusters.

The European regional policy and the socio-economic diversity of European regions: A multivariate analysis

European Journal of Operational Research, 2008

There are significant differences among the European Union regions, which have been heightened due to the most recent enlargement in 2004. This paper aims to analyse this diversity and to propose a classification of European regions that is adjusted to the different axes of socio-economic development and, simultaneously, is useful for European regional policy purposes. Multivariate statistical techniques allow the identification of clusters of socio-economic similarity, which are contrasted with the classes considered in the financial proposal of the European Commission (EC) for the period 2007-2013.

Multidimensional Clustering of EU Regions: A Contribution to Orient Public Policies in Reducing Regional Disparities

Social Indicators Research

This paper applies multidimensional clustering of EU-28 regions with regard to their specialisation strategies and socioeconomic characteristics. It builds on an original dataset. Several academic studies discuss the relevant issues to be addressed by innovation and regional development policies, but so far no systematic analysis has linked the different aspects of EU regions research and innovation strategies (RIS3) and their socioeconomic characteristics. This paper intends to fill this gap, with the aim to provide clues for more effective regional and innovation policies. In the data set analysed in this paper, the socioeconomic and demographic classification associates each region to one categorical variable (with 19 categories), while the classification of the RIS3 priorities clustering was performed separately on "descriptions" (21 Boolean categories) and "codes" (11 Boolean Categories) of regions' RIS3. The cluster analysis, implemented on the results of the correspondence analysis on the three sets of categories, returns 9 groups of regions that are similar in terms of priorities and socioeconomic characteristics. Each group has different characteristics that revolve mainly around the concepts of selectivity (group's ability to represent a category) and homogeneity (similarity in the group with respect to one category) with respect to the different classifications on which the analysis is based. Policy implications showed in this paper are discussed as a contribution to the current debate on post-2020 European Cohesion Policy, which aims at orienting public policies toward the reduction of regional disparities and to the enhance complementarities and synergies within macro-regions.

Regional disparities in Europe

The European Labour Market, 2006

Economy has been widely characterised by regional disparities. This paper aims to evaluate if different regional economic structures, such as productive mix and labour market composition, contribute to this disparities and to what extent they prevent the convergence and/or favour divergent clusters of regions. To this purpose we shall apply a multivariate analysis method, named STATIS, to a set of regional characteristic indicators that will allow us to estimate some latent factors which are able to measure the regional differences and their dynamic.

Regional disparities and convergences in the European Union

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2013

This paper analyses the disparities and convergences between 97 regions of the European Union in the period 2000 to 2008. The methodology is based on the Gini coeffi cient, Disparity Range Coeffi cient, Average Disparity Range Coeffi cient, and and -convergence. The study tests the hypothesis that the EU regions are converging economically. The subject is relevant as the welfare disparities among the EU regions and their possible convergence represents an economically and politically important issue for the EU. The EU is aiming at decreasing regional welfare disparities through the cohesion policy. The study analyses the convergence within the time span where there was substantial EU enlargement with a disparity eff ect on the whole EU. The study concludes that the level of disparities among the EU regions is relatively low. The convergence analysis provided mixed results, depending on the methodology used. The tested hypothesis was not confi rmed fully.

A multivariate methodology to uncover regional disparities: A contribution to improve European Union and governmental decisions

European Journal of Operational Research, 2003

The aim of this paper is to present a new methodology to classify the levels of socio-economic development of a countryÕs territory, in order to support regional development policy. This classification is obtained through the use of multivariate statistical methods -factor and cluster analysis, and is based on a wide number of demographic, economic, health, education, employment and culture indicators. The Portuguese continental territory is used as the working example. Results lead to the identification of nine axes of socio-economic characterisation, and the division of the Portuguese territory into four regions with differing degrees of development, reflecting the well-known asymmetry between coastal and inland zones. The Ôsocio-economic regionsÕ uncovered with this methodology allow a much more useful characterisation of the Portuguese territory, for policy making, than does the NUTS-2 classification scheme used by the European Union.

Identifying Clusters of European Regions Based on Their Economic and Social Characteristics

2011

Nowadays, globalization, technological innovation, migration and population ageing, make it increasingly difficult to predict the future of regions. Identifying the key problems that regions face and considering how these findings could be effectively used as a basis for planning region’s improvement, are essential in order to improve the conditions in the European Union regions. Measuring the development of a region

Geospatial dataset for analyzing socio-economic regional divergence of European regions

This data article presents macroeconomic data that can be used for comparative territorial studies. The data cover a sample of 413 regions (national administrative-territorial units corresponding to second level of a common classification of territorial units for statistics of the European Commission – NUTS 2 level region of the European Union, and comparable administrative-territorial units outside the EU) of 48 European countries, including Cyprus, Turkey , the European part of Russia, and two partially recognized states – the Republic of Kosovo and the Pridnestrovian Moldavian Republic. The statistical database covers a five-year period of 2010–2014. This dataset is created to enhance our understanding of the contemporary coastalization dynamics in Europe. Despite the fact that coastal regions of European countries exhibit an extensive level of development and remain attractive to human settlement, industry localization, and investment flows their contribution to the socioeconomic development of Europe is unclear. The reported data cover a series of macroeconomic data on key indicators traditionally used in comparative analysis of regional development: average annual population, gross regional product (GRP) in purchasing power parity (PPP), labor productivity, population density and GRP (PPP) values per sq.km. Accounting for differences in geoeconomic position of the European regions enables to distinguish four subtypes of regions with a particular emphasis on the coastal area: coastal border, coastal other, coastal hinterland, and inland other. An additional focus is made on differentiating the performance indicators of regions depending on their border geo-economic position: border regions with a state

Comparing European Regions

The European Journal of Comparative Economics, 2007

The European continent, in the last two decades, has become an excellent case of comparative study, thanks to the possibility of contrasting "Old European" countries, based on old-established (more or less well functioning) market economies, and the "New" countries of the European Union and, more generally, the Central and Eastern European countries, that have undergone a deep, complex and (partly) diversified process of transition. Development in Europe has been uneven: over time, between countries and within them. Among all possible imbalances, we have decided to focus here on the regional dimension. The existence of regional disparities in economic structure and performance poses relevant questions not only to the scholar, but also to the policymaker, both on equity grounds and with reference to economic efficiency. This special issue of the Journal, focusing on European Regions, includes seven papers, differing in methodologies, the dimensions of time and space, each dealing with specific economic variables and-naturally-obtaining different results, although some interesting elements of convergence across the different papers will be highlighted. All of them, however, have two common features, since they (i) analyse European countries and (ii) focus on regional (sub-national) characteristics. In particular, two papers investigate "old and new" EU-25 regions (at two different levels of disaggregation); two others study EU-15 countries and regions, highlighting policy assessments or implications; one paper reviews the empirical literature on transition economies, stressing especially the common features of all EU-10 new members and Russia; and, lastly, two papers analyse the regional experiences of individual countries, Serbia and Great Britain. The approach of all papers is mainly empirical, but some also make use of interesting theoretical models. Most analyses concentrate on general economic development issues, on the growth of GDP or productivity; however, three papers investigate specifically labour markets. Since a structural approach is generally followed, the economic structures of the regions-especially the sectoral mix of production and/or employment-are at the heart of many studies. Spatial variables, such as accessibility or distance, are in some case explicitly taken into account. The specific objectives of each paper are clearly illustrated, although the structure of some papers is quite complex 1. All of them present interesting 106

Differentiation of internal regions in the EU countries

Insights into Regional Development

The aim of the article is to study safety and sustainability of differentiation of performance of internal regions (NUTS 3) in the EU countries measured by the Sub-national Human Development Index (SHDI). The authors examine differentiation of the SHDI of internal regions in the EU countries by means of correspondence of distribution of this indicator [SHDI] of regional performance to Gauss curve, as well as by analyzing the SHDI of internal regions in the EU countries with the help of the coefficient of variation. As follows from the research, the authors proved that differentiation of regional performance in the EU over the last three decades were not chaotic but they were subjected to certain regularities: the distribution of performance of internal regions is normal, with metropolitan areas almost always being leaders of regional performance; regional differences in the area that is now the EU were increasing during the collapse of the Eastern European Socialist Bloc in the early 1990s, and they were declining later, as the regions adapted to the new conditions. So, identified regularities in performance of internal regions (NUTS 3) in the EU countriesnormal distribution and spatial convergencehave been considered by the authors as safe and sustainable for further development of the whole EU and its countries.