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In this note we explore the main demographic differentials in the spread and impact of COVID-19 paying special attention to the combined effect of age and gender, and to the differences at territorial level where population density plays a large role in the diffusion and outcome of the disease in terms of morbidity and mortality. The information is important for designing an exit strategy from COVID-19 and anticipating the rebound for certain segments of the population with differential medical needs, particularly those living in high-density locations., JRC.E.6-Demography, Migration and Governance

Gender equality is one of the fundamental values of the European Union and the European Pillar of Social Rights establishes it as one of its key principles. While there are several measures of gender equality at country level there is none that capture regional differences in Europe. This new regional gender equality monitor consists of two composite indices that address two specific and complementary aspects of this multifaceted phenomenon. The first index assesses the female disadvantage by measuring regional differences when females are doing worse than males. The second index measures the female level of achievement compared to the best regional performance. The indices are called the Female Disadvantage Index (FemDI) and the Female Achievement Index (FemAI). Viewing together the two indices facilitates the understanding of where women are at disadvantage and where they are performing well across the different regions and between the Member States., JRC.I.1-Monitoring, Indicators & Impact Evaluation

This report studies population dynamics in Europe. Its purpose is threefold. First, the report offers a literature review of the main drivers of population growth. Second, an empirical analysis is carried out in order to unveil the determinants of population growth in EU sub-regions (NUTS3 level) over the period 2000-2010. Spatial econometrics is employed to account for spatial dependence among neighbouring regions. Third, the existing evidence on the long-run relationship between economic and population growth is discussed, followed by an empirical assessment of the relationship between these two aggregates in Europe over the period 1960-2010. Time-series econometric tools are used for this analysis. The main findings of both the literature reviews and empirical analyses are discussed, along with their implications and future extensions.

etadatos de publicación El Séptimo Informe sobre la Cohesión proporciona los datos y la información necesarios para verificar el grado de cohesión (o división) de Europa desde el punto de vista económico, social y territorial; de ese modo, nos permite comprobar con mayor claridad y objetividad lo que se ha conseguido y qué medidas es necesario adoptar en el período financiero posterior a 2020. El informe sienta las bases, por tanto, para diseñar la política de cohesión del futuro.

Territorial cohesion or territorial disparities? Two alternative scenarios for regional growth. Demographic and economic projections are essential tools in many domains of policy making. They allow policymakers anticipating future trends and explore alternative scenarios, and provide the basis to assess impacts of policies. This report regionalizes recently published EU reference demographic and economic projections from country to NUTS3 level. Two alternative scenarios have been used to carry out the regionalization: ‘trend’ and ‘convergence’. In the trend scenario recent observed growth rates continue, whereas in the convergence scenario less developed regions grow faster than more developed ones. While the trend scenario accentuates current territorial disparities generating more concentration of production and population, the convergence scenario promotes a more balanced growth and therefore more territorial cohesion., JRC.H.8-Sustainability Assessment

The aim of this report is to present the within-country variability in the EU citizens’ perceptions of the generalised and institutional trust, quality of public service and local governance based on their experiences and opinions expressed in three surveys. By within-country variability we understand differences in citizens’ perceptions between cities or between (1) cities and (2) towns, suburbs and rural areas. We deal with the citizens’ opinions expressed in the surveys we used. The within-country variability in EU citizens’ perceptions of the trust, corruption, local governance and quality of public service and governance are investigated using several composites presenting the differences in citizens’ perceptions from three different perspectives and using three different data sets. First, with the European quality of life survey, we explore the level of (1) general trust, (2) institutional trust and (3) quality of public service in different with respect to degree of urbanisation areas in the EU countries. Second, with the Social Diagnosis survey, we examine the level of general trust and attitude towards free riding in 27 of the largest Polish cities. Finally, using data from the World Justice Project we investigate perceptions of law enforcement, generalised and institutional trust, corruption, bribing and performance of the local government in 58 of the largest EU cities. Our results showed that in general, there are differences in measured phenomena between EU countries, and especially within EU countries in relation to the degree of urbanisation and at city level., JRC.DDG.01-Econometrics and applied statistics

We measure area-specific poverty in the European Union (EU) at the second level of the nomenclature of territorial units for statistics (NUTS 2). We construct the regional human poverty index (RHPI), which comprises four dimensions: social exclusion, knowledge, a decent standard of living, and a long and healthy life. The RHPI provides information regarding the relative standing of a given country with respect to the level of poverty but also shows the variability of poverty within a country with respect to NUTS 2. The approach we propose has four properties. (1) Because the RHPI comprises only six indicators it is relatively simple to be replicated in subsequent years. (2) The RHPI provides information about the absolute magnitude of poverty experienced by Europeans in a given country and provides information about the relative standing of the country. (3) The RHPI shows the variability of poverty within a country with respect to NUTS 2. (4) The RHPI shows satisfactory statistical coherence confirmed by the results of correlation analysis and principal component analysis. As confirmed by uncertainty analysis, the RHPI also shows satisfactory robustness to the normative assumptions made during the construction process. The RHPI also has some limitations. First, the conceptual model of the RHPI relies mostly on the conceptualisation of the poverty index proposed by the United Nations (UN) and data availability. Second, although research on poverty has developed rapidly in recent years, it has failed to establish the relative importance of poverty dimensions and thus guide us in establishing aggregation weights. This failure has resulted in a necessity to formulate certain a priori assumptions. Third, all indicators we proposed are of objective nature, which may influence the results and final conclusions. However, this is intentional and reflects the best approach that is achievable (due to lack of disaggregated at the NUTS 2 level subjective indicators) in order to measure poverty in the NUTS 2 regions. Fourth, in our computations percentage of population below the income poverty line in NUTS 2 regions are calculated using national poverty lines and without taking into account social transfers in kind. Regarding the social transfer in kind, without this type of adjustment household income is generally underestimated in countries with extensive public services, like in the Nordic member states, and overestimated in those where households have to pay for most of these services (Annoni et al., 2012; EC, 2010). However, disaggregated at the NUTS 2 level data with this respect are not available, therefore our approach is the best achievable. Then, the national, instead of the regional, poverty lines are applied to highlight the differences between regions within the same country, as suggested by Betti at al. (2012). The RHPI is computed for all NUTS 2 regions in 28 EU countries. Our results show that the scale of poverty differs considerably within the EU countries, with RHPI scores ranging between 9.23 for Prague and more than 65 for Bulgarian Yugoiztochen and Severozapaden. We also find that substantial differences in levels of poverty between regions are present in all of the EU countries. The only exceptions to this finding are small EU countries where neither NUTS 1 nor NUTS 2 regions exist. Our results also show that, in general, in NUTS 2 regions comprising a capital, the poverty level is lower than the country average. The only exceptions are Vienna, Brussels, and Berlin, where poverty measured by the RHPI is higher than the country average. By contrast, Bucharest, Sofia, Bratislava, Prague, Budapest, and Madrid exhibit decisively lower levels of poverty than their country averages, JRC.DDG.01-Econometrics and applied statistics

This report follows from a project entitled “Regional Human Development” on request of the Directorate-General Regional and Urban Policy (DG REGIO) of the European Commission. The objective of the project was to develop indicators that are capable of measuring patterns and trends in human development across the regions of the EU member states. The main contribution of this report lies in a proposal for conceptualizing and measuring human development at the European regional level across multiple years using. The results of the EU-RHDI show a clear north-west/south-east divide across EU regions when it comes to the overall index. Within countries differences exist as to regional performance in human development. In general, capital city regions seem to outperform non-capital city regions within countries. This is readily seen across regions in eastern EU member states where the large intra-country differences in scores are largely driven by the capital city outperforming other regions by a length. Zooming in on the results of the individual dimensions, we find in general that the EU is especially characterized by a west/east divide. In health, southern regions are often outperforming northern regions. However, southern regions’ relative good performance in health contrasts sharply with their underperformance in income and especially knowledge., JRC.DDG.01-Econometrics and applied statistics

In this study, we measure the area-specific poverty in the European Union (EU). To this end, we measure poverty at the sub-national level in two ways: (i) using the EU nomenclature of territorial units (NUTS 1 mostly); (ii) using different with respect to the degree of urbanisation areas within countries. The measurement of poverty is based on the Multidimensional Poverty Index (UN MPI) by Alkire and Santos (2010, 2013). With the data from the European Union Survey on Income and Living Conditions (EU SILC), we formulate the Index of Multidimensional Poverty at the regional level, namely the Multidimensional Poverty Index (MPI-reg). The MPI-reg framework comprises three dimensions — health, education, and standard of living — quantified by three sub-indexes: Multidimensional Poverty in Health Index (MPI–H), Poverty in Education Index (MPI–E) and Multidimensional Poverty in Living Standards Index (MPI–L), respectively. The MPI-reg was computed for 23 EU countries in 2010, 24 EU countries in 2007 and 2011, and 25 countries in 2008 and 2009. Our results show that the level of poverty in the EU ranges from 2–3 % to 15–25 %, with Denmark and Sweden being unequivocally the least poor countries and Latvia, Bulgaria and Romania, the poorest countries. We also indicate that there is a positive relationship between the stratification level and all adjusted headcount ratios, headcount ratios and intensity of poverty scores. This positive relationship implies that there are countries where there is no stratification with respect to poverty (e.g. Sweden, Denmark, the Czech Republic, and Finland) and countries, usually poor ones, such as Romania, Bulgaria and Lithuania, but also Belgium and Italy, where considerable stratification with respect to poverty occurs. In general, in poor and moderately poor countries, the worst situation with respect to poverty is observed in sparsely populated areas, and the best situation occurs in densely populated areas. On the other hand, in the best scoring countries, poverty is relatively higher in the densely populated areas compared to the less well-populated areas. Additionally, our analysis showed that between 2005–07 and 2009–11, changes in inequality with respect to poverty occurred. We demonstrated that a decrease in inequality most often occurred in Poland and Spain, whereas Belgium and Italy we most often spotted as countries with growing regional differences. The results indicated that the European Union regions are strongly diversified with respect to poverty. This implies that regardless of the spatial location of the region and the definition of the region, considerable within-country differences are indicated if only sub-national levels are available. Therefore, relying only on countrywide estimates may be misleading when properly assessing the relative standing of a region with respect to poverty., JRC.DDG.01-Econometrics and applied statistics

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 statistics

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 statistics