2021-2027 financial details planned broken down by categorisation dimensions and by specific objectives

This dataset presents information on EU payments to the ESI Funds programmes as well as to FEAD, IPA-E and ENI supported programmes.
Annual snapshots are provided in the data and therefore the data must be filtered by year to have an accurate view of the total EU planned amount and payments made.
Data is reported on cumulative basis, i.e. the amounts shown in 2017 represent the cumulative payments made from the beginning of the programming period (2014-2017 up to date).
The dataset is updated on a daily basis.

This dataset provides information on expected achievements (targets) set for ERDF and Cohesion Fund programmes **specific indicators** defined by national and regional programmes, the decided (forecasts from selected projects) and implemented values.

The reference date for the annual reporting of progress by the programmes achievements is the end of each calendar year.
This dataset contains:
- Time series of the cumulative amounts reported annually for decided and implemented values (i.e. 2016 values include the 2015 values);
- To avoid double counting, the cumulative values from different years **should NOT be aggregated**;
- Allows comparison of the targets year on year (targets change with reallocation of financial resources);
- Allows comparison of decided (forecast) and implemented values with the targets set (allowing users to calculate the % rate of progress);

For more information on the common indicators' definitions used by each fund please go to the "Related Information" sections at the bottom of the fund specific pages and the data on the **common indicators** is available in this separate dataset: https://cohesiondata.ec.europa.eu/2014-2020/ESIF-2014-2020-Achievement-Details/aesb-873i

This dataset provides a list of the different programmes funded by the ESI Funds (2014-2020) by ESI Fund with the unique CCI numbers. It is a reference / lookup list.
The data is taken from the ESIF 2014-2020 Finances planned dataset (updated daily).

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

This file contains lists of the 2014-2020 categorisation dimensions and codes based on the linked dataset presenting the financial values planned and implemented.

The categorisation systems is defined in Implementing Regulation 215/2014 with 8 dimensions in total - http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32014R0215.
These dimension represent different ways of categorising the nature of the EU support (i.e. the intervention (activity) field, the form of finance, the territorial context, etc.).

In the programme documents only 4 dimensions were encoded for ERDF/ Cohesion Fund while 5 dimensions were encoded for ESF and can be found in the dataset. Data on 3 other dimensions is only provided in the implementation reports - Location, Economic Dimension, ERDF/CF Thematic Objective

This information note provides more detail on the categorisation system 2014-2020: http://ec.europa.eu/regional_policy/sources/docgener/informat/2014/guidance_categorisation_climate_en.pdf

This dataset provides information on planned (planned) financing under the different ESI Funds (2014-2020). The data is taken from the adopted financial tables (as available by February 2017) and is broken down by fund, programme, priority axis, thematic objective and category of region (more developed, less developed, etc. where available).
For ERDF, ESF and CF the "priority" columns relate to the priority axis (financing blocks) in the programmes; the "investment priority" related to the fixed lists of defined investment objectives set in the three specific fund Regulations.
For the European Agricultural fund for Rural Development (EAFRD) detail is also provided by EAFRD "Focus areas" and "Measures" (the EAFRD Focus Areas are detailed in the EAFRD fund Regulation - Article 5 and the EAFRD measures in Article 13-40 of the relevant Implementing Regulation ).
The data now covers 533 programmes available (adopted and draft) as available by February 2017 and includes the EU and national co-financing covered by the adoption decision. Financial allocations in the programme financial table may change over time (i.e. transfers between themes, between funds) and as a result the dataset will be updated regularly.