Analysis ERDF 2014-2020 cycle in Italy

Author

Luisa M. Mimmi

Published

November 1, 2022

ERDF spending in Italy

This is a quick exploration of some EU structural funds aimed at verifying how much we have been able to disburse (given amounts planned and deemed eligible) for the 2014-2020 funding cycle.
Specifically data are taken from the ESIF 2014-2020 Finance Implementation Details (99js-gm52) dataset, then I drill down to the Italian subset of programs under the ERDF (European Regional Development Fund).

The most important financial variables observed in euro (over time) are:

total_amount = Total decided amount (EU + National allocation), also referred to as “Total Cost”
total_eligible_cost = Total amount (EU + National) allocated to the projects (operations) selected, also referred to as “total eligible cost” reported by the national and regional programmes to the Commission.
total_eligible_expenditure = Total expenditure eligible for reimbursement, as reported by the beneficiary projects to the programmes. Also referred to as “total eligible expenditure”, it is reported by the national and regional programmes to the Commission.

Other qualifying variables are:

cci = “Unique identifier” of each operational programme.
to_short = Short name for the “thematic objective”. For example: “Research and Innovation” stands for “Strengthening research, technological development and innovation.
priority = Priority Axis: the investment pillars in the programme. Unfortunately, since there is no harmonised numbering or naming of Priority Axes, this variable is useless for comparison.
category_of_region = The three main categories of region are: “less developed”, “transition”, and “more developed”.

ERDF by region category

Note

Actually, the data are up to date (October 2022) and last reference date shown is 31-12-2021, but I am not sure whether programs are now closed or still been implemented… (deadlines were extended due to Covid).

ERDF by theme category

Note

In some cases (e.g. Figure 2, in section Research and Innovation) the amount decided (total_eligible_cost) exceeds the total_amount programmed. Why? This happens generally in the last years of a programme period. They do this in order to avoid the risks 1) that some of the decided / selected projects fail to materialise or 2) that irregularities occur over the programme life time which lead to the withdrawal of support to those projects.

ERDF: percent amount spent by theme & by program

Collapsible table made following this example

Tip

Note that Id of Programs can be used to learn more information about them at this web link, inserting the cci code in the “Keyword” search box

TBC…

Reference

Using some tools for the first time, I followed this tutorials/examples:

Quarto (see presentation here)
R package reactable (see examples here)

The code written (in R) to retrieve and clean the data can be found at this Github repo

--- title: "Analysis ERDF 2014-2020 cycle in Italy" author: "Luisa M. Mimmi" date: "`r format(Sys.time(), '%B %Y')`" toc: true format: html: html-math-method: katex code-tools: true self-contained: true execute: warning: false ---     ```{r} #| echo: false # pkg_list <- c( # "tidyverse", "gt", "gtExtras", "reactable", "ggiraph", "here", "quarto", # "rmarkdown", "gtsummary", "palmerpenguins", "fs", "skimr" # ) # install.packages(pkg_list) ``` ## ERDF spending in Italy This is a quick exploration of some EU structural funds aimed at verifying how much we have been able to disburse (given amounts planned and deemed eligible) for the 2014-2020 funding cycle. Specifically data are taken from the [**ESIF 2014-2020 Finance Implementation Details (99js-gm52)**](https://dev.socrata.com/foundry/cohesiondata.ec.europa.eu/99js-gm52) dataset, then I drill down to the Italian subset of programs under the **ERDF (European Regional Development Fund)**. The most important financial variables observed in euro (over time) are:  - `total_amount` = Total decided amount (EU + National allocation), also referred to as *"Total Cost"* - `total_eligible_cost` = Total amount (EU + National) allocated to the projects (operations) selected, also referred to as *"total eligible cost"* reported by the national and regional programmes to the Commission. - `total_eligible_expenditure` = Total expenditure eligible for reimbursement, as reported by the beneficiary projects to the programmes. Also referred to as *"total eligible expenditure"*, it is reported by the national and regional programmes to the Commission. Other qualifying variables are: - `cci` = *"Unique identifier"* of each operational programme. - `to_short` = Short name for the *"thematic objective"*. For example: "Research and Innovation" stands for "Strengthening research, technological development and innovation. - `priority` = Priority Axis: the investment pillars in the programme. Unfortunately, since there is no harmonised numbering or naming of Priority Axes, this variable is useless for comparison. - `category_of_region` = The three main categories of region are: *"less developed"*, *"transition"*, and *"more developed"*. ## ERDF by region category ![ERDF by region category](fig/ERDF_eligVspent_reg_time_facet.png){#fig-plot1} :::{.callout-note} Actually, the data are up to date (October 2022) and last reference date shown is **31-12-2021**, but I am not sure whether programs are now closed or still been implemented... (deadlines were extended due to Covid). ::: ## ERDF by theme category ```{r} #| echo: false #| label: fig-plot2 #| fig-cap: "ERDF by theme category" #| code-fold: true knitr::include_graphics(here::here("fig", "ERDF_eligVspent_theme_time_facet.png")) ```  ::: callout-note In some cases (e.g. @fig-plot2, in section **Research and Innovation**) the amount decided (`total_eligible_cost)` exceeds the `total_amount` programmed. Why? This happens generally in the last years of a programme period. They do this in order to avoid the risks 1) that some of the decided / selected projects fail to materialise or 2) that irregularities occur over the programme life time which lead to the withdrawal of support to those projects. :::  ```{r} #| echo: false #| eval: false #| label: fig-plot3 #| fig-cap: "ERDF by theme category INTERACTIVE" #| code-fold: true library(ggiraph) plot1_int <- readRDS(file = "fig/p2_int.Rds") girafe( ggobj = plot1_int#, # width_svg = 6, # height_svg = 3 ) # ?? for some reasons it messe up the theme ``` ## ERDF: percent amount spent by theme & by program Collapsible table made following [this example](https://glin.github.io/reactable/articles/building-twitter-followers.html) :::{.callout-tip} Note that Id of Programs can be used to learn more information about them at this web [link](https://ec.europa.eu/regional_policy/en/atlas/programmes/), inserting the _**cci**_ code in the "Keyword" search box ::: ```{r} #| echo: false #| code-fold: true #| label: tbl-table1 #| tbl-cap: "ERDF by theme category INTERACTIVE" library(reactable) t_react2 <- readRDS(file = "fig/t_react2.Rds") t_react2 ```  # TBC... # Reference Using some tools for the first time, I followed this tutorials/examples: + `Quarto` (see presentation [here](https://rstudio-conf-2022.github.io/get-started-quarto/materials/07-plots-tables.html#/block-layout-arbitrary-content)) + R package `reactable` (see examples [here](https://glin.github.io/reactable/articles/building-twitter-followers.html#finishing-touches)) The code written (in R) to retrieve and clean the data can be found at this [Github repo](https://github.com/Lulliter/plan2implem)