EU Regional Competitiveness Index RCI 2013

To measure the different dimensions of competitiveness at the regional level, the European Commission has developed the Regional Competitiveness Index – RCI. The RCI was published in 2010 and this is the 2013 edition, which includes most recent data and implements improvements and refinements. RCI 2013 reveals a strong regional dimension of competitiveness, which national level indicators cannot capture, and a polycentric pattern with strong capital and metropolitan regions in many parts of Europe. Some capital regions are surrounded by similarly competitive regions, but in many countries, particularly in the less developed Member States in Central and Eastern Europe, regions neighboring the capital are less competitive.JRC.G.3-Econometrics and applied statistic

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

Tree-based approaches for understanding growth patterns in the European regions

The paper describes an empirical analysis to understand the main drivers of economic growth in the European Union (EU) regions in the past decade. The analysis maintains the traditional factors of growth used in the literature on regional growth - stage of development, population agglomeration, transport infrastructure, human capital, labour market and research and innovation - and incorporates the institutional quality and two variables which aim to reflect the macroeconomic conditions in which the regions operate. Given the scarcity of reliable and comparable regional data at the EU level, large part of the analysis has been devoted to build reliable and consistent panel data on potential factors of growth. Two non-parametric, decision-tree techniques, randomized Classication and Regression Tree and Multivariate Adaptive Regression Splines, are employed for their ability to address data complexities such as non-linearities and interaction eects, which are generally a challenge for more traditional statistical procedures such as linear regression. Results show that the dependence of growth rates on the factors included in the analysis is clearly non-linear with important factor interactions. This means that growth is determined by the simultaneous presence of multiple stimulus factors rather than the presence of a single area of excellence. Results also conrm the critical importance of the macroeconomic framework together with human capital as major drivers of economic growth of countries and regions. This is overall in line with most of the economic literature, which has persistently underlined the major role of these factors on economic growth but with the novelty that the macroeconomic conditions are here incorporated. Human capital also has an important role, with low-skilled workforce having a higher detrimental eect on growth than high-skilled. Not surprisingly, other important factors are the quality of governance and, in line with the neoclassical growth theory, the stage of development, with less developed economies growing at a faster pace than the others. The evidence given by the model about the impact of other factors on economic growth such as those on the quality of infrastructure or the level of innovation seems to be more limited and inconclusive. The analysis conclusions support the reinforcement of the EU economic governance and the conditionality mechanisms set in the new architecture of the EU regional funds 2014-2020 whose rationale is that the eectiveness of the expenditure is conditional to good institutional quality and sound economic policies

A Robust Model to Measure Governance in African Countries

Levels of performance of any government do matter in determining the quality of the civil society. The Ibrahim Index of African Governance developed by the Harvard Kennedy School shows how governance can be measured. The Index assesses governance issues over time (2000, 2002, 2005, 2006) and for 48 African countries south of the Sahara, according to a five-pillar conceptual structure: (a) Safety and Security, (b) Rule of Law, Transparency, and Corruption, (c) Participation and Human Rights, (d) Sustainable Economic Opportunity, and (e) Human Development. This report aims at validating and critically assessing the methodological approach undertaken to build the 2006 Index of African Governance, by raising two key questions: o Is the Index of African Governance internally sound and consistent from a statistical and conceptual point of view? o What scenarios could have been used to build the Index and how do the results from these scenarios compare to the original results? The overall assessment of the 2006 Index by means of multivariate analyses, uncertainty and sensitivity analyses reveals no particular shortcomings in the conceptual structure. Data-driven narratives on governance issues in Africa are also offered in this report with a view to show directions of discussions and messages that stem from an index-based analysis of governance. Overall, the Index of African Governance can be reliably used to identify weaknesses, propose remedial actions, allow for easy spatial and temporal comparisons (benchmarking), to prioritize countries in Africa of relatively low governance content, monitor and evaluate policies effectiveness and ultimately to funnel resources to countries through, for example, multilateral and bilateral agreements between African countries.JRC.G.9-Econometrics and applied statistic

Quality of Life at the sub-national level: an operational example for the EU

This study is the outcome of the European Commission joint project DG JRC / DG REGIO on the measure of quality of life of European regions. European Union cohesion policy supports the economic and social development of regions, especially lagging regions, throughout an integrated approach with the ultimate goal of improving citizens' wellbeing. In this setting, measuring quality of life at the sub-national level is the first step for assessing which regions can assure or have the potential to assure good quality of life and which cannot. The project simultaneously features three innovative points. First the attempt to measure QoL for the European Union regions (NUTS1/NUTS2). Second, the adoption of a type of aggregation, at the lowest level of QoL dimensions, which penalizes inequality across indicators, for mitigating compensability. Third, the inclusion of housing costs in the computation of individual's.JRC.G.3-Econometrics and applied statistic

Cambio y continuidad en la calidad de gobierno: tendencias en la calidad de gobierno subnacional en los estados miembros de la UE

Despite massive investments, studies suggest that anticorruption efforts often times fail and that countries and regions with historically deficient quality of government tend to be stuck in a vicious cycle of high levels of corruption and inadequate public service delivery. However, this study suggests that despite the stickiness of subnational quality of government, regional quality of government does shift over time. Using the 2021 European Quality of Government Index (EQI), and comparing the results to previous rounds of this survey, we show that there has indeed been noticeable shifts in the regional level of Quality of Government both within countries and across time. Overall, we find a slight increase in the perceived quality of government of European regions compared with 2017. However, some regions have evaded the positive trend, most notably in Poland and Hungary, whose response to the pandemic – probably not coincidentally – has involved important infringements of democratic rights and institutions. These changes in Quality of government call for a close mapping of the trends within countries and across regions and a focus on their determinants. To this end, the paper also serves as an introduction to the use of 2021 European Quality of Government (EQI) index, which is the most comprehensive survey to date to measure perceptions of subnational quality of government with a total of 129,000 respondents in 208 NUTS 1 and NUTS 2 regions and all EU 27-member state countries.A pesar de masivas inversiones económicas, los estudios sugieren que los esfuerzos anticorrupción a menudo fracasan y que los países y regiones con una calidad de gobierno históricamente deficiente tienden a quedar atrapados en un círculo vicioso de altos niveles de corrupción y una prestación inadecuada de los servicios públicos. Sin embargo, este artículo sugiere que, a pesar de cierta continuidad en la calidad de gobierno a nivel subnacional, hay cambios sustanciales a lo largo del tiempo. Usando el Índice Europeo de Calidad de Gobierno (EQI) de 2021, y comparando los resultados con oleadas anteriores de esta encuesta, mostramos que, de hecho, ha habido modificaciones notables en el nivel regional de Calidad de Gobierno tanto dentro de los países como a través del tiempo. En general, encontramos un ligero aumento en la percepción ciudadana de la calidad de gobierno de las regiones europeas en comparación con 2017. No obstante, algunas regiones han evadido esta tendencia positiva, sobre todo en Polonia y Hungría, países cuya respuesta a la pandemia, probablemente no por casualidad, ha implicado importantes violaciones de los derechos ciudadanos y libertades democráticas. Estos cambios en la calidad de gobierno exigen un análisis más detallado de las tendencias dentro de cada país, así como de sus causas. Con este objetivo, este artículo también sirve como introducción al uso de la edición de 2021 del Índice Europeo de Calidad de Gobierno (EQI), que es la encuesta más completa hasta la fecha para medir las percepciones ciudadanas de la calidad de gobierno subnacional con un total de 129,000 encuestados en 208 regiones NUTS 1 y NUTS 2, y de los 27 estados miembros de la UE

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

Chronic infection with non-tuberculous mycobacteria in patients with non-CF bronchiectasis: Comparison with other pathogens

Abstract Introduction The aim of this study is to compare characteristics of non-cystic fibrosis bronchiectasis (NCFBE) patients with chronic infections with non-tuberculous mycobacteria (NTM) versus those with Pseudomonas aeruginosa or other colonizations. Methods This was an observational, perspective study of consecutive NCFBE adult patients attending the outpatient bronchiectasis clinic at the San Gerardo Hospital in Monza, Italy, during 2012 and 2013. Patients with a chronic infection were included in the study and divided into three groups: those with NTM (Group A); those with P. aeruginosa (Group B); and those with other pathogens (Group C). Patients with both NTM and another pathogen were included in Group A. Comparison among the three study groups was performed using X 2 or Fisher exact test for categorical variables or Kruskal–Wallis or Mann–Whitney test for continuous variables. Results A total of 146 patients (median age 67 years, 40% males) were enrolled: 19 belonged to Group A, 34 to Group B and 93 to Group C. Within group A, 6 patients had only NTM isolation, 7 patients had NTM and P. aeruginosa co-infection and 6 patients had NTM plus another pathogen. The most common isolated pathogens among NTM was Mycobacterium avium complex (15 patients, 79%). A total of 4 patients (21%) with NTM were on active treatment. Patients affected by NTM pulmonary infection had a significantly less severe clinical, functional and radiological involvement compared with patients colonized by P. aeruginosa , see Table. Group A (NTM) n = 19 Group B ( P. aeruginosa ) n = 34 Group C (Others) n = 93 p Value ∗ p Value # p Value + Age (years), median (IQR) 70 (64–75) 74 (67–79) 66 (53–72) 0.001 0.172 0.050 Male, n (%) 8 (42) 15 (44) 36 (33) 0.660 – – BMI, median (IQR) 22 (19–26) 24 (21–25) 24 (21–27) 0.352 – – BSI, median (IQR) 5 (4–9) 12 (8.5–16) 5 (3–7) 0.001 0.001 0.090 Bhalla score, median (IQR) 21 (15–34) 36 (30.5–40.5) 16 (10.5–21.5) 0.001 0.016 0.076 Idiopathic etiology, n (%) 8 (42) 11 (32) 37 (40) 0.721 – – Post-infective etiology, n (%) 8 (42) 16 (47) 29 (31) 0.244 – – Exacerbations/y, median (IQR) 1 (0–2) 2 (1.5–3.5) 2 (1–2) 0.040 0.024 0.132 FEV1%, median (IQR) 85 (59.75–109.5) 58.5 (48.25–74) 84 (62–102) 0.002 0.010 0.857 FVC%, median (IQR) 94.5 (70–109.75) 65 (56–81.5) 88 (69.5–101.5) 0.003 0.003 0.270 ∗ Among the three groups: # Group A vs. Group B; + Group A vs. Group C; BMI: Body mass index; BSI: bronchiectasis severity index; y: year. Conclusions Colonization with P. aeruginosa seems to have the highest impact on the clinical, functional and radiological status of patients with NCFBE. No specific characteristics may help to identify NTM versus other pathogen colonizations. Thus, diagnostics for atypical mycobacteria should be performed on all patients with NCFBE, as suggested by recent international guidelines

Analysis of the use of models by the European Commission in its Impact Assessments for the period 2009 - 2014

Impact Assessments (IA)are a key element in the development of policy proposals by the European Commission (EC). They provide evidence for political decision makers on the advantages and disadvantages of possible policy options by assessing their potential impacts. This evidence should be quantified whenever possible, and hence it is of interest to examine to what extent models have been used in IAs. The purpose of this report is to understand how the EC is positioned with respect to external providers as regards modelling contributions to IAs, as well to provide an input into potential future development of the Commission's model portfolio. The results of a statistical analysis for the period 2009-2014 shows that 16 % of the published 512 IAs used models or were predominantly model-based. In terms of absolute numbers, Directorate Generals (DGs) CLIMA, ENER, ENV and MOVE account for more than half (51) of the 91 model-based IAs . Within the model-based IAs, 52% used results exclusively or partially provided by external contractors, while 48% used models run in-house by DG JRC. The Commission uses a wide range of models (91 for the IAs during 2009-2014), roughly 60% of which were used only once. The 24 most frequently-used models represent 70% of all cases in which modelling were used for an IA. Notably 11 of them account for roughly 48%, almost all being run by contractors, mostly in tandem for energy-transport-climate policy scenarios. The most frequently-used model is PRIMES, used in 28 out of 263 cases (11% of the cases), followed by 10 other models which are used exclusively or predominantly with PRIMES in the context of the energy-climate scenarios (GEM-E3, TREMOVE, CAPRI, POLES, G4M, GAINS, GLOBIOM, LUISA, PROMETHEUS and TRANSTOOLS). Frequently-used models for the economic and monetary union are the in-house models QUEST and SYMBOL. The most frequently-used models of DG JRC number about 10 which have been used in 46 cases; these are energy models (GEM-E3 and POLES), followed by environmental models (the LUISA modelling platform and LISFLOOD), the micro-economic model for financial markets SYMBOL, the transport model TRANSTOOLS and the agricultural models belonging to the iMAP platform - CAPRI and AGLINK-COSIMO.JRC.A.2-Work Programm