RCI 2010: Some in-depth analysis

The European Commission has recently published the first edition of the Regional Competitiveness Index (RCI) (Annoni and Kozovska, 2010). The index provides a tool to improve the understanding of competitiveness at the regional level by showing the strengths and weaknesses of each of the European regions at the NUTS2 level in a number of dimensions related to competitiveness. The analysis offered by the first edition of the RCI is a snapshot of regional competitiveness as it is in 2010 and is based upon data mostly spanning between 2007 and 2009. The present document takes a step further and offers a two-fold analysis based on the RCI indices: an exploratory spatial data analysis and an analysis of possible relationships between exogenous indicators and the RCI index and sub-indices.JRC.DG.G.3-Econometrics and applied statistic

EU Regional Competitiveness Index (RCI) 2010

The joint project between DG Joint Research Centre and DG Regional Policy on the construction of a EU Regional Competitiveness Index (RCI) aims at producing a composite indicator which measures the competitiveness of European regions at the NUTS 2 level for all EU Member States. It is a two year project, running from November 2008 to November 2010. The concept of ¿competitiveness¿ has been largely discussed over the last decades. A broad notion of competitiveness refers to the inclination and skills to compete, to win and retain position in the market, increasing market share and profitability, thus, being commercially successful (Filó, 2007). The concept of regional competitiveness which has gained more and more attention in recent years, mostly due to the increased attention given to regions as key in the organization and governance of economic growth and the creation of wealth. An important example is the special issue of Regional Studies 38(9), published in 2004, fully devoted to the concept of competitiveness of regions. Regional competitiveness is not only an issue of academic interest but of increasing policy deliberation and action. This is reflected in the interest devoted in the recent years by the European Commission to define and evaluate competitiveness of European regions, an objective closely related to the realization of the Lisbon Strategy on Growth and Jobs. Why measuring regional competitiveness is so important? Because ¿if you can not measure it, you can not improve it¿ (Lord Kelvin). A quantitative score of competitiveness will help Member States in identifying possible regional weaknesses together with factors mainly driving these weaknesses. This in turn will assist regions in the catching up process. Given the multidimensional nature of the competitiveness concept, the structure of RCI is made of eleven pillars which describe the concept, taking into account its ¿regional¿ dimension, with particular focus on the region¿s potential. The long-term perspective is in fact essential for European policy and people¿s skills are understood to play a key role for EU future, as also underlined by the president of the Lisbon Council in his recent policy brief (Hofheinz, 2009). For this reason the RCI includes aspects related to short and long-term capabilities of regions, with a special focus on innovation, higher education, lifelong learning and technological availability and use, both at the individual and at the enterprise level. As the framework of RCI aims at addressing all elements relevant to competitiveness, from inputs to outputs, the following figure shows how the different pillars relate to these dimensions. A number of indicators have been selected to describe these dimensions with criteria based on coverage and comparability as well as within pillar statistical coherence. Most indicators come from Eurostat but where data was not available, alternative source were considered. A detailed univariate and multivariate statistical analyses have been carried out on the set of candidate indicators for the setting-up and refinement of the composite. Each choice with a certain degree of uncertainty has been submitted to a full robustness analysis to evaluate the level of variability of regions final score and ranking.JRC.DG.G.9-Econometrics and applied statistic

Towards a Benchmark on the Contribution of Education and Training to Employability: In-depth Analysis of Key Issues

The present report has been commissioned by DG Education and Culture to the Centre for Research on Lifelong Learning (CRELL) in the context of the Council Conclusions on a Strategic Framework for European cooperation in Education and Training for the next decade (¿ET 2020¿), May 2009. Given the importance of enhancing employability through education and training in order to meet current and future labour market challenges, the Council invited the European Commission to submit a proposal for a possible European benchmark in this area by the end of 2010. After the discussion note (EUR 24147 EN 2010), this report constitutes the second step towards the selection of an indicator to be proposed as benchmark. The six alternative options retained by the Expert Group on the Education for Employability Benchmark are reviewed one by one in more details.JRC.DG.G.9-Econometrics and applied statistic

Education and Long-Term Unemployment

This paper investigates the relationship between education and long-term unemployment when considering regional economic differences and other relevant variables at the individual and at the local level, using data from the 2004-2006 EU-SILC (11 countries). The analysis has been run using both a binary logit model and a binary scobit model. Our results suggest that the probability of an individual to be in long-term unemployment decreases with her educational level. There is a decrease in returns to education after the age of 40, which confirms the assumption of an obsolescence of skills defended in the human capital literature. With regard to the regional settings, younger workers (20-30) and older workers (50-65) tend to benefit more from the dynamics offered by highly competitive regions.JRC.DG.G.9-Econometrics and applied statistic

Education and Long-Term Unemployment

This paper investigates the relationship between education and long-term unemployment when considering regional economic differences and other relevant variables at the individual and at the local level, using data from the 2004-2006 EU-SILC (11 countries). The analysis has been run using both a binary logit model and a binary scobit model. Our results suggest that the probability of an individual to be in long-term unemployment decreases with her educational level. There is a decrease in returns to education after the age of 40, which confirms the assumption of an obsolescence of skills defended in the human capital literature. With regard to the regional settings, younger workers (20-30) and older workers (50-65) tend to benefit more from the dynamics offered by highly competitive regions

The role of regional clusters and firm size for firm efficiency

Clusters have increasingly gained attention in policy discourses at all levels ¿ regional, national, international. Clusters and cluster-based economic development have slowly become a sort of a ¿mantra¿ in policy areas related to anything that touches upon regional development, competitiveness, innovation, entrepreneurship, small and medium-size enteprises¿ (SMEs) development for policymakers and economic development professionals. To understand better the different agendas in which clusters play a role, one should keep in mind the different levels of discussion ¿ firm-level business strategy, regional and local development policies and national competitiveness discourses. Each of these levels has its characteristics and specific features of which clusters are an important part. The most common view on the benefit from clusters is the micro-level approach which underlines factors related to increased firm-level efficiency and productivity due to the availability of more specialized assets and suppliers with shorter reaction times (e.g. Becattini, 1990), higher propensity for innovation due to the possibility for collaboration with local educational institutions and absorption of local knowledge spillovers, and creating more intense relationships with customers (e.g. Cooke, 2001; Asheim, 1994). On the regional level, clusters are expected to play a crucial role for higher productivity and competitiveness of the region/locality and for effectively participating in synergies and productive competition with the surrounding environment (e.g. European Commission, 2008; OECD, 2005; 2007; Cumbers and MacKinnon, 2004). On the national level, clusters are deemed as an important driver of a country¿s competitiveness and as an essential driver of innovation (e.g. Porter, 1990; 1998).JRC.DG.G.9-Econometrics and applied statistic

Are Firms in Knowledge and Technology-Intensive Sectors Located within Regional Clusters more Efficient? Some Empirical Evidence from Eastern Europe

Clusters have increasingly gained attention in policy discourses at all levels ¿ regional, national, and at the level of the European Union (EU). In a 2008 declaration, the ex-EU Commissioner for Enterprise and Industry, Günter Verheugen, underlined the role of clusters for the EU economy: 'We need more world-class clusters in the EU. Clusters play a vital role in the much needed innovation of our businesses. They are powerhouses of job creation. Therefore we suggest that cluster policy efforts at all levels should aim to raise excellence and openness for cooperation, while respecting the competitive market-driven nature of clusters'. This is one of many recent signs of the growing attention to the subject on behalf of the European Commission which has also included the creation of the European Cluster Observatory, a repository of data on cluster locations, cluster policies and initiatives, at the end of 2007, and the European Cluster Memorandum, a document providing policy agenda for the promotion of European innovation through clusters, in 2008. The paper tests for the impact on firm performance of location within knowledge and technology-intensive (KTI) regional clusters in two Eastern European countries ¿Poland and Romania. Using newly available data on cluster location from the European Cluster Observatory and matching it with firm-level financial data from the Amadeus database, published by Bureau Van Dijk, the paper investigates for the presence of a cluster effect, perceived as higher efficiency of firms located within regional clusters in comparison with firms of the same sectors located outside. The choice of Eastern European countries is driven by two reasons. Clusters were nonexistent as the centrally-planned system decided on firm location from above and thus, any freedom of firms deciding on their locational choice was largely nonexistent. Furthermore, cluster policies have been a new approach to regional and local economic development with no precedents, and have been introduced mostly parallel to the integration into the EU. The choice of knowledge and technology-intensive sectors, which have a high tendency to cluster, for this study is due to the fact that they are sectors where a great part of innovative activities take place.JRC.I.1-Modelling, Indicators and Impact Evaluatio

Business Clusters in Eastern Europe: Policy Analysis and Cluster Performance

Clusters have increasingly become an essential part of policy discourses at all levels, EU, national, regional, dealing with regional development, competitiveness, innovation, entrepreneurship, SMEs. These impressive efforts in promoting the concept of clusters on the policy-making arena have been accompanied by much less academic and scientific research work investigating the actual economic performance of firms in clusters, the design and execution of cluster policies and going beyond singular case studies to a more methodologically integrated and comparative approach to the study of clusters and their real-world impact. The theoretical background is far from being consolidated and there is a variety of methodologies and approaches for studying and interpreting this phenomenon while at the same time little comparability among studies on actual cluster performances. The conceptual framework of clustering suggests that they affect performance but theory makes little prediction as to the ultimate distribution of the value being created by clusters. This thesis takes the case of Eastern European countries for two reasons. One is that clusters, as coopetitive environments, are a new phenomenon as the previous centrally-based system did not allow for such types of firm organizations. The other is that, as new EU member states, they have been subject to the increased popularization of the cluster policy approach by the European Commission, especially in the framework of the National Reform Programmes related to the Lisbon objectives. The originality of the work lays in the fact that starting from an overview of theoretical contributions on clustering, it offers a comparative empirical study of clusters in transition countries. There have been very few examples in the literature that attempt to examine cluster performance in a comparative cross-country perspective. It adds to this an analysis of cluster policies and their implementation or lack of such as a way to analyse the way the cluster concept has been introduced to transition economies. Our findings show that the implementation of cluster policies does vary across countries with some countries which have embraced it more than others. The specific modes of implementation, however, are very similar, based mostly on soft measures such as funding for cluster initiatives, usually directed towards the creation of cluster management structures or cluster facilitators. They are essentially founded on a common assumption that the added values of clusters is in the creation of linkages among firms, human capital, skills and knowledge at the local level, most often perceived as the regional level. Often times geographical proximity is not a necessary element in the application process and cluster application are very similar to network membership. Cluster mapping is rarely a factor in the selection of cluster initiatives for funding and the relative question about critical mass and expected outcomes is not considered. In fact, monitoring and evaluation are not elements of the cluster policy cycle which have received a lot of attention. Bulgaria and the Czech Republic are the countries which have implemented cluster policies most decisively, Hungary and Poland have made significant efforts, while Slovakia and Romania have only sporadically and not systematically used cluster initiatives. When examining whether, in fact, firms located within regional clusters perform better and are more efficient than similar firms outside clusters, we do find positive results across countries and across sectors. The only country with negative impact from being located in a cluster is the Czech Republic

Mutual productivity spillovers and regional clusters in Eastern Europe: some empirical evidence

In recent years FDI inflows towards transition countries have progressively increased, further stimulated by the entrance of some countries in the European Union. Traditional theoretical studies predict that foreign-owned companies serve as a very important source of technology transfer and productivity spillovers for the host countries. At the same time, the current applied literature finds mixed results with regards to the actual spillover effects from foreign-owned to local companies. This strand of literature on FDI is based on the fact that the MNEs' motivations for investing abroad are characterized mostly by the possibility of exploiting their pre-existing ownership advantages. However, a new approach towards MNEs as asset-seeking entities has been recently growing. This paper tests for the presence of traditional direct horizontal spillovers from foreign to domestic firm as well as of reverse horizontal spillovers from domestic to foreign firm in the context of two Eastern European countries (Poland and Romania). We have further introduced the concept of regional clusters as industrial environments theoretically more prone to induce mutual spillovers between foreign and domestic firms. In this respect, this paper examines two hypotheses: firstly, whether the overall effect of direct spillover is greater for firms in clusters compared to non-clustered firms, and secondly whether the reverse spillover effect actually takes place and if clusters have any role. The econometric analysis is based on a sample of more than 7000 manufacturing firms in the two Eastern European countries. By comparing two estimation methodologies, OLS first difference (after having first estimated the coefficients of the production function with the Levinsohn and Petrin estimator), and a dynamic system GMM, we test the presence of direct and/or reverse spillovers between foreign and domestic firms considering the role of regional clusters. The evidence found with reference to direct spillovers in clusters is in some cases positive confirming the hypothesis of a presence of a cluster effect. Reverse spillovers effects have been found in some cases, both in cluster and outside clusters, and even in low-tech sectors. This suggests that the presence of clusters could be a determinant in FDI localization decisions as there is a possibility of reverse spillovers, even if the host country does not possess higher technological capacity. Furthermore, clusters do seem to be industrial environments where the possibility of direct spillovers is considerable.JRC.I.1-Modelling, Indicators and Impact Evaluatio

Circling the Education Data Globe

This chapter explores some of the best practices in the world in the collection, management and governance of education data with the aim of providing food for thought on the challenges of conceiving, explaining the need for and implementing a "Holy Grail" of education data - i.e. a "robust longitudinal data system." It includes snapshots of the current situations in Italy, England and Korea.JRC.G.9-Econometrics and applied statistic