The Regional Competitiveness Index (RCI) has been measuring the major factors of competitiveness over the past ten years for all the NUTS-2 level regions across the European Union. The Index measures with more than 70 comparable indicators the ability of a region to offer an attractive and sustainable environment for firms and residents to live and work.

The current edition of the RCI, while maintaining the structure of the previous editions, uses an updated methodological framework, to facilitate comparison over time. To highlight the change in methodology, this new edition is referred to as RCI 2.0. In addition, starting from the original data used in 2016 and 2019, the scores have been re-calculated using the new methodology, and labelled as RCI 2.0, 2016 edition, and RCI 2.0, 2019 edition, and are available for download on this website. The resulting rankings do not replace the RCI rankings published in 2016 and 2019, produced with the old methodology. This is why the scores for RCI 2.0 2019 and RCI 2.0 2016 are published without rankings.

The 2022 edition of RCI 2.0 shows large differences in regional competitiveness in the EU. In line with previous editions, it show a polycentric pattern, with a strong performance of regions hosting large urban areas in the EU. The gap between the capital city region and the rest, however, varies among EU Member States, with more competitive countries tending to have a smaller gap between their capital city region and its other regions, as well as lower internal variation. Between 2016 and 2022, regional competitiveness has improved in the less developed regions, while the performance of transition regions has been more mixed. More developed regions continued to have the highest scores, but they have converged on the EU average.

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This lookup table provides lists of the NUTS3 regions that are classed by DG REGIO with specific geographic characteristics as follows:
- Mountain regions: NUTS 3 regions having at least 50% of population and/or at least 50% of area in mountain areas;
- Island regions: NUTS 3 regions entirely composed of islands, or being part of islands;
- Sparsely populated regions: NUTS 3 regions having a population density below 12.5 inhabitants/km²;
- RUPS or Outermost territories - specifically recognised in Article 349 of the Treaty on the Functioning of the European Union.

1. PURPOSE: This data set provides, in a single source, regionalised (NUTS-2) annual EU expenditure data (in current prices) for specific EU funds - ERDF, Cohesion Fund, EAFRD/EAGGF and ESF. This data can be used, among other purposes, to facilitate analysis on the effects of the EU funds.

In terms of the regionalisation of EU payments made, this work expands and replaces data sets previous published on the ERDF and Cohesion fund with additional years and additional EU funds.
The following studies on ERDF and Cohesion fund expenditure are to a large extent now superseded:
- "Geography of expenditure" Work Package 13 of the ERDF and CF ex-post evaluation 2007-2013 - http://ec.europa.eu/regional_policy/en/policy/evaluations/ec/2007-2013/#13
- "Final Report - ERDF and CF Regional Expenditure" (2000-2006) - SWECo 2008 - http://ec.europa.eu/regional_policy/sources/docgener/evaluation/pdf/expost2006/expenditure_final.pdf
- "The Territorial Effects of the Structural Funds" (1994-1999) - ESPON 2005, study 2.2.1 - http://www.espon.eu/main/Menu_Projects/Menu_ESPON2006Projects/Menu_PolicyImpactProjects/structuralfundsimpact.html

The dataset does not contain any identification of the thematic nature of the investments made. This is because there was no harmonised system or information available across funds and across programme periods to estimate the thematic composition of the investments. For the ERDF and Cohesion Fund the studies cited above provided some thematic information for specific programme periods.

2. SCOPE

2.1 REGIONALISATION: This dataset provides the most complete historic picture available to date on the annual EU payments made - in EUR in current prices of the year in question (not adjusted or expressed as “in 2015 prices”) - under different shared management funds mapped to or estimated by NUTS-2 regions;
=> presented in the column "EU_payment_annual".

The work undertaken to regionalise the payment data is predominantly based on the NUTS-2013 version as described in the final report of the 2016 WIIW study (link below). In case the EU funded programmes covered more than one NUTS-2 region the regionalisation of the payments was carried out using either (1) regionalised data provided by the managing authorities or (2) the application of certain apportioning rules to the payments in order to estimate the share of the payment by NUTS-2 region.

In regionalising the data, information on the specific national or regional funding programme, which were the source of the payments, was not retained in the processing of the dataset.

2.2 MODELLING OF “REAL” EXPENDITURE: The yearly breakdown of the dataset follows the cycle of the European Commission payments to the Member States and not the date on which real expenditures took place on the ground. This characteristic may negatively affect any subsequent analytic work to carry out policy assessments.

In order to develop a more realistic estimate of the annual profile of real expenditure, the Commission tasked BERGEN to undertake work to develop a modelling of the “real” annual expenditure on the ground, and to test the robustness and sensitivity of assumptions used (see link to final report below).
=> the modelled annual expenditure is presented in the column "Modelled_annual_expenditure" and it represents the mean of 100 000 simulations on the annual EU payments to estimate real expenditure.
=> The column "Standard_Deviation_of modelled_annual_expenditure" provides the standard deviation of the “modelled_annual_expenditure" which is the root mean square of the set of deviations between each estimated annual payment in 100 000 simulations and the mean of the estimations, presented in the column "Modelled_annual_expenditure”.
=> The column "Standard_Error_of_modelled_annual_expenditure" provides the Standard Error of the "Modelled_annual_expenditure", which is a measure of how far this mean is likely to be from its expected value; that is, its scatter in repeated ex

The source data used in this analysis are :
1. Based on EDGAR, the global GHG emissions database from the European Commission's Joint Research Centre. The reported data include emissions from energy, industry, buildings, transport, agriculture, and waste but they exclude emissions from shipping and aviation. These latter emissions cannot be assigned to individual regions. GHG include carbon dioxide, methane, nitrous oxide and fluorinated gasses. https://edgar.jrc.ec.europa.eu/
2. GDP and population data: https://knowledge4policy.ec.europa.eu/territorial/ardeco-database_en

The source data used in this analysis are :
1. Based on EDGAR, the global GHG emissions database from the European Commission's Joint Research Centre. The reported data include emissions from energy, industry, buildings, transport, agriculture, and waste but they exclude emissions from shipping and aviation. These latter emissions cannot be assigned to individual regions. GHG include carbon dioxide, methane, nitrous oxide and fluorinated gasses. https://edgar.jrc.ec.europa.eu/
2. GDP and population data: https://knowledge4policy.ec.europa.eu/territorial/ardeco-database_en

This data story explains the gender equality indices of advantage and disadvantage: