This report is part of a series of studies that examines Europeans' awareness of and attitudes towards EU Regional Policy. It is based on 4 previous surveys (2017, 2015, 2013, 2010).

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.

WHAT IS EUSF? The European Union Solidarity Fund (EUSF) was set up to respond to major natural disasters and express European solidarity to disaster-stricken regions within Europe. The Fund was created as a reaction to the severe floods in Central Europe in the summer of 2002. Since then, it has been used for disasters covering a range of different catastrophic events including floods, forest fires, earthquakes, storms and drought.

The dataset presents financial and other information on the applications submitted for EUSF support between 002 and 2020.

For more information: https://ec.europa.eu/regional_policy/en/funding/solidarity-fund/

Share of green employment by year and by NUTS2 regions. the dataset also contains averages by level of development

This dataset presents the anonymous user replies to surveys conducted in mid-2017, mid-2019 and in February 2021. Check out the replies received here: https://cohesiondata.ec.europa.eu/d/meu6-rykt

This dataset presents the anonymous user replies to surveys conducted in mid-2017, mid-2019 and in February 2021. Check out the replies received here: https://cohesiondata.ec.europa.eu/d/meu6-rykt

This data story explains the regional gender indices of female advantage and female disadvantage:
https://cohesiondata.ec.europa.eu/stories/s/Regional-gender-equality-in-the-EU/vtrw-h5vm

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.

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