0. Introduction
In this notebook we will build normalized database and after we will use them to calculate made-in indicators. We will reproduce figures and graphics of the working paper.
4 MRIO are used in this study (FIGARO Rel.2022, LR-WIOD Rel. 2022, WIOD Rel.2016, ICIO rel. 2021). All raw databases are available online. The first step is to normalize these data in the same format. Then it is possible to apply the same Avionic2 functions to these different databases.
Raw data links : - Figaro 2022 release, CSV flat format : link : Figaro 2022 link - LR-WIOD 2022 release : link : LR-WIOD 2022 link License and funding : Long-run WIOD is licensed under a Creative Commons Attribution 4.0 International License . The construction of the Long-run WIOD was supported by the Dutch Science Foundation (NWO) [grant number 453-14-012]. - WIOD 2016 release, RData format : link : WIOD 2016 link Article : Timmer, M. P., Dietzenbacher, E., Los, B., Stehrer, R. and de Vries, G. J. (2015), “An Illustrated User Guide to the World Input–Output Database: the Case of Global Automotive Production” , Review of International Economics., 23: 575–605 - ICIO 2021 release, CSV flat format : link : ICIO 2021 link Credits : OECD (2021), OECD Inter-Country Input-Output Database, ICIO informations
1. Build normalized databases from raw data.
During testing, we realized that depending on the power of the machine running Avionic2 codes, there could be limitations: some functions could not load large databases. In order to make these codes available to all users, whatever their hardware configuration, we decided to offer 2 options: 1/ Build a single database for each MRIO, which requires more power for loading/processing, but has the advantage of storing/managing a limited number of files. 2/ Build one database per year for each MRIO, which limits the memory required to load/process data, but multiplies the number of databases stored.
Some bases are initially denominated in dollars, so an option has been created to convert these bases into euros in order to standardize valuations.
## Path
PathTest<-getwd( ) # "C:/Users/SJOITL/Desktop/temporaire/test/"
PathTemp<-getwd( ) # "C:/Users/SJOITL/Desktop/temporaire/"
# Assign Paths
MyPathFIGARO <- paste0(PathTemp, "ixi/")
MyPathFIGARO_out <- PathTest
MyPathWIODLR <- PathTemp
MyPathWIODLR_out <- PathTest
MyPathWIOD <- PathTemp
MyPathWIOD_out <- PathTest
MyPathICIO <- PathTemp
MyPathICIO_out <- PathTest
# Choose the lines to launch according to your option's preferences
# Normalize MRIO FIGARO
Path_FIG22ixi <- MyPathFIGARO
Path_out <- MyPathFIGARO_out
# Annual database option
av_create_FIGAROixi_2022(Path_FIG22ixi, Path_out, OptAnnual = TRUE)
# Unique database for all years option
av_create_FIGAROixi_2022(Path_FIG22ixi, Path_out, OptAnnual = FALSE)
# Normalize MRIO LR-WIOD (unique database only as it has limited size, and raw data is also a unique database)
Path_WIODLR <- MyPathWIODLR
Path_out <- MyPathWIODLR_out
# Euro calculation option
av_create_LRWIOD_2022(Path_WIODLR, Path_out, ConvertToEuro = TRUE)
# keep dollars option
av_create_LRWIOD_2022(Path_WIODLR, Path_out, ConvertToEuro = FALSE)
# Normalize MRIO WIOD
Path_WIOD <- MyPathWIOD
Path_out <- MyPathWIOD_out
# Annual database option + Euro calculation option
av_create_WIOD_2016(Path_WIOD, Path_out, OptAnnual = TRUE, ConvertToEuro = TRUE)
# Annual database option + keep dollars option
av_create_WIOD_2016(Path_WIOD, Path_out, OptAnnual = TRUE, ConvertToEuro = FALSE)
# Unique database for all years option + Euro calculation option
av_create_WIOD_2016(Path_WIOD, Path_out, OptAnnual = FALSE, ConvertToEuro = TRUE)
# Unique database for all years option + keep dollars option
av_create_WIOD_2016(Path_WIOD, Path_out, OptAnnual = FALSE, ConvertToEuro = FALSE)
# Normalize MRIO ICIO
Path_ICIO <- MyPathICIO
Path_out <- MyPathICIO_out
# Annual database option + Euro calculation option
av_create_ICIO_2021(Path_ICIO, Path_out, OptAnnual = TRUE, ConvertToEuro = TRUE)
# Annual database option + keep dollars option
av_create_ICIO_2021(Path_ICIO, Path_out, OptAnnual = TRUE, ConvertToEuro = FALSE)
# Unique database for all years option + Euro calculation option
av_create_ICIO_2021(Path_ICIO, Path_out, OptAnnual = FALSE, ConvertToEuro = TRUE)
# Unique database for all years option + keep dollars option
av_create_ICIO_2021(Path_ICIO, Path_out, OptAnnual = FALSE, ConvertToEuro = FALSE)
# You can check that each database created has the same variable names.UE27 aggregation :
A function is available to convert these databases into an aggregated version, where each of the 27 EU countries is aggregated into an EU27 item. These bases are necessary to calculate the made-in of EU27 (which cannot be aggregated a posteriori to the made-in calculations of each country. This function only works with normalized data (see last chunk)
Note that UE27 aggregated MRIO are smaller than non-aggregated, therefore unique database can be used even if non-aggregated are annual databases.
# Example for FIGARO MRIO (easy to change to others)
MyPathFIGARO <- PathTest
PathFIGARO_out <- PathTest
# Choose the lines to launch according to your option's preferences
# Annual database option
# Case where UE27 databases are also annual
for (k in 2010:2020) {
MyDl <- readRDS(paste0(MyPathFIGARO, "/BDn_FIG_", k, ".rds"))
MyDlUE27 <- av_dl_UE27(MyDl, OptSaveRDS = paste0(PathFIGARO_out, "/BDn_FIG_", k, "_UE27"))
}
# Case where UE27 databases become a unique database for all years
for (k in 2010:2020) {
MyDl <- readRDS(paste0(MyPathFIGARO, "/BDn_FIG_", k, ".rds"))
MyDlUE27 <- av_dl_UE27(MyDl, OptSaveRDS = "NO")
if (k == 2010) {
MyDlUE27_ALL <- MyDlUE27
} else {
MyDlUE27_ALL <- rbind(MyDlUE27_ALL, MyDlUE27)
}
}
saveRDS(MyDlUE27_ALL, paste0(PathFIGARO_out, "/BDn_FIG_UE27"))
# Unique database for all years option
MyDl <- readRDS(paste0(MyPathFIGARO, "/BDn_FIG.rds"))
MyDlUE27 <- av_dl_UE27(MyDl, OptSaveRDS = "NO") # No RDS saved
MyDlUE27 <- av_dl_UE27(MyDl, OptSaveRDS = paste0(PathFIGARO_out, "/BDn_FIG_UE27")) # With RDS savedAt this step, you have normalized data, therefore you can use Avionic2 functions.
We show below one of the main function that identify each component of a MRIO (function CompoMRIO) and can add extensions like carbon emissions.
Note : These code are used intermediary but are not necessary to reproduce working paper illustrations
# Example with FIGARO data in 2010 (can easily be subset from the unique database for all years if necessary)
DT <- readRDS(paste0(PathTest, "BDn_FIG_2010.rds"))
# Build the components of MRIO
MRIO_FIG_2010 <- CompoMRIO(DT, typeCompo = "OptFullOptionsBonus", OptTab = FALSE)
# You can also access quickly to each component
Output <- CompoMRIO(DT, typeCompo = "PROD")
TechCoeff <- CompoMRIO(DT, typeCompo = "A", OptTab = TRUE)
TechCoeff2 <- CompoMRIO(DT, typeCompo = "A", OptTab = FALSE) # As a long format and not wide
FinDemand <- CompoMRIO(DT, typeCompo = "DF_TOT", OptTab = FALSE)
VA <- CompoMRIO(DT, typeCompo = "VA", OptTab = FALSE)
# You can add extensions
MRIO2 <- av_extend_MRIO_dw(MRIO_dw = MRIO_FIG_2010, "FIGARO", TypExtension = "StressVA") # Stressor Value Added
MRIO3 <- av_extend_MRIO_dw(MRIO_dw = MRIO_FIG_2010, "FIGARO", TypExtension = "StressEmi", Path1 = PathTemp) # Stressor CO2 EmissionsWe also show below how you can calculate content of value added embedded in final demand, and you can calculate footprints.
Note : These code are used intermediary but are not necessary to reproduce working paper illustrations
# Example with FIGARO data in 2010 (can easily be subset from the unique database for all years if necessary)
DT <- readRDS(paste0(PathTest, "BDn_FIG_2010.rds"))
# Build the components of MRIO and add VA and CO2 emissions stressors
MRIO_FIG_2010 <- CompoMRIO(DT, typeCompo = "OptFullOptionsBonus", OptTab = FALSE)
MRIO_FIG_2010_ext <- av_extend_MRIO_dw(MRIO_dw = MRIO_FIG_2010, "FIGARO", TypExtension = "StressEmi", Path1 = PathTemp) # Stressor CO2 Emissions
MRIO_FIG_2010_ext <- av_extend_MRIO_dw(MRIO_dw = MRIO_FIG_2010_ext, "FIGARO", TypExtension = "StressVA", Path1 = PathTemp) # Stressor Value Added
# Calculation of contents embedded in final demand, for France
ContentVA <- Contenus(MRIO_FIG_2010_ext, "VA", MethContenu = "MatDF", EmprPays = "FRA")
ContentEmi <- Contenus(MRIO_FIG_2010_ext, "Emi", MethContenu = "MatDF", EmprPays = "FRA")
# Calculation of footprints (content embedded in final demand as a matrix), for France
FootprintVA <- Contenus(MRIO_FIG_2010_ext, "VA", MethContenu = "DiagDFpays", EmprPays = "FRA")
FootprintEmi <- Contenus(MRIO_FIG_2010_ext, "Emi", MethContenu = "DiagDFpays", EmprPays = "FRA")
# Check that sums of production approach is the same that sums of consumption approach (usually called footprint)
print(sum(EmpreinteVA[["Empreinte_Conso"]]))
print(sum(EmpreinteVA[["Empreinte_Production"]]))
print(sum(EmpreinteEmi[["Empreinte_Conso"]]))
print(sum(EmpreinteEmi[["Empreinte_Production"]]))2. Calculation of Made-In indicators and content of VA embedded in exports.
We need to build indicators databases before plotting and so on.
We distinguish the case of a single database for each MRIO, and the case of one database per year for each MRIO.
Here is the example of FIGARO MRIO, but easy to adapt to others.
##### MADE-IN indicators
### Annual database option
# loading of individual databases.
FIG2010 <- readRDS(paste0(PathTest, "BDn_FIG_2010.rds"))
FIG2011 <- readRDS(paste0(PathTest, "BDn_FIG_2011.rds"))
FIG2012 <- readRDS(paste0(PathTest, "BDn_FIG_2012.rds"))
FIG2013 <- readRDS(paste0(PathTest, "BDn_FIG_2013.rds"))
FIG2014 <- readRDS(paste0(PathTest, "BDn_FIG_2014.rds"))
FIG2015 <- readRDS(paste0(PathTest, "BDn_FIG_2015.rds"))
FIG2016 <- readRDS(paste0(PathTest, "BDn_FIG_2016.rds"))
FIG2017 <- readRDS(paste0(PathTest, "BDn_FIG_2017.rds"))
FIG2018 <- readRDS(paste0(PathTest, "BDn_FIG_2018.rds"))
FIG2019 <- readRDS(paste0(PathTest, "BDn_FIG_2019.rds"))
FIG2020 <- readRDS(paste0(PathTest, "BDn_FIG_2010.rds"))
ListFIG <- list(FIG2010, FIG2011, FIG2012, FIG2013, FIG2014, FIG2015, FIG2016, FIG2017, FIG2018, FIG2019, FIG2020)
UE27_FIG2010 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2010.rds"))
UE27_FIG2011 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2011.rds"))
UE27_FIG2012 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2012.rds"))
UE27_FIG2013 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2013.rds"))
UE27_FIG2014 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2014.rds"))
UE27_FIG2015 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2015.rds"))
UE27_FIG2016 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2016.rds"))
UE27_FIG2017 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2017.rds"))
UE27_FIG2018 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2018.rds"))
UE27_FIG2019 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2019.rds"))
UE27_FIG2020 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2010.rds"))
ListUE27_FIG <- list(UE27_FIG2010, UE27_FIG2011, UE27_FIG2012, UE27_FIG2013, UE27_FIG2014, UE27_FIG2015, UE27_FIG2016, UE27_FIG2017, UE27_FIG2018, UE27_FIG2019, UE27_FIG2020)
BaseFIG <- FIG2020 # not used here, just to fill parameters
# Option with rates results only
MadeInFIG_rates <- BoucleAnneesMADEINs(BaseFIG, Optdl = TRUE, 2010:2020, OptDonneesBrutes = FALSE, OptAnnual = TRUE, ListdlAnnual = ListFIG)
saveRDS(MadeInFIG_rates, "MadeInFIG_rates.rds")
# Option with levels (numerator and denominator) often useful for manipulations
MadeInFIG_levels <- BoucleAnneesMADEINs(BaseFIG, Optdl = TRUE, 2010:2020, OptDonneesBrutes = TRUE, OptAnnual = TRUE, ListdlAnnual = ListFIG)
saveRDS(MadeInFIG_levels, "MadeInFIG_levels.rds")
## Calculation for EU27 : be careful the calculation cannot be done by only agregating, you need to apply functions to EU27 format data.
BaseFIG <- FIG2020 # not used here, just to fill parameters
# Option with rates results only
MadeInFIG_rates <- BoucleAnneesMADEINs(BaseFIG, Optdl = TRUE, 2010:2020, OptDonneesBrutes = FALSE, OptAnnual = TRUE, ListdlAnnual = ListUE27_FIG)
saveRDS(MadeInFIG_rates, "MadeInFIG_rates_UE27.rds")
# Option with levels (numerator and denominator) often useful for manipulations
MadeInFIG_levels <- BoucleAnneesMADEINs(BaseFIG, Optdl = TRUE, 2010:2020, OptDonneesBrutes = TRUE, OptAnnual = TRUE, ListdlAnnual = ListUE27_FIG)
saveRDS(MadeInFIG_levels, "MadeInFIG_levels_UE27.rds")
### Unique database for all years option
# loading of the full database.
BaseFIG <- readRDS(paste0(PathTest, "BDn_FIG.rds"))
# Option with rates results only
MadeInFIG_rates <- BoucleAnneesMADEINs(BaseFIG, Optdl = TRUE, 2010:2020, OptDonneesBrutes = FALSE, OptAnnual = FALSE)
saveRDS(MadeInFIG_rates, "MadeInFIG_rates.rds")
# Option with levels (numerator and denominator) often useful for manipulations
MadeInFIG_levels <- BoucleAnneesMADEINs(BaseFIG, Optdl = TRUE, 2010:2020, OptDonneesBrutes = TRUE, OptAnnual = FALSE)
saveRDS(MadeInFIG_levels, "MadeInFIG_levels.rds")
## Calculation for EU27 : be careful the calculation cannot be done by only agregating, you need to apply functions to EU27 format data.
# loading of the full database.
BaseFIG_UE27 <- readRDS(paste0(PathTest, "BDn_FIG_UE27.rds"))
# Option with rates results only
MadeInFIG_rates <- BoucleAnneesMADEINs(BaseFIG_UE27, Optdl = TRUE, 2010:2020, OptDonneesBrutes = FALSE, OptAnnual = FALSE)
saveRDS(MadeInFIG_rates, "MadeInFIG_rates_UE27.rds")
# Option with levels (numerator and denominator) often useful for manipulations
MadeInFIG_levels <- BoucleAnneesMADEINs(BaseFIG_UE27, Optdl = TRUE, 2010:2020, OptDonneesBrutes = TRUE, OptAnnual = FALSE)
saveRDS(MadeInFIG_levels, "MadeInFIG_levels_UE27.rds")
##### CONTENT OF VA EMBEDDED IN EXPORTS
### Annual database option
# loading of individual databases.
FIG2010 <- readRDS(paste0(PathTest, "BDn_FIG_2010.rds"))
FIG2011 <- readRDS(paste0(PathTest, "BDn_FIG_2011.rds"))
FIG2012 <- readRDS(paste0(PathTest, "BDn_FIG_2012.rds"))
FIG2013 <- readRDS(paste0(PathTest, "BDn_FIG_2013.rds"))
FIG2014 <- readRDS(paste0(PathTest, "BDn_FIG_2014.rds"))
FIG2015 <- readRDS(paste0(PathTest, "BDn_FIG_2015.rds"))
FIG2016 <- readRDS(paste0(PathTest, "BDn_FIG_2016.rds"))
FIG2017 <- readRDS(paste0(PathTest, "BDn_FIG_2017.rds"))
FIG2018 <- readRDS(paste0(PathTest, "BDn_FIG_2018.rds"))
FIG2019 <- readRDS(paste0(PathTest, "BDn_FIG_2019.rds"))
FIG2020 <- readRDS(paste0(PathTest, "BDn_FIG_2010.rds"))
ListFIG <- list(FIG2010, FIG2011, FIG2012, FIG2013, FIG2014, FIG2015, FIG2016, FIG2017, FIG2018, FIG2019, FIG2020)
UE27_FIG2010 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2010.rds"))
UE27_FIG2011 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2011.rds"))
UE27_FIG2012 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2012.rds"))
UE27_FIG2013 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2013.rds"))
UE27_FIG2014 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2014.rds"))
UE27_FIG2015 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2015.rds"))
UE27_FIG2016 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2016.rds"))
UE27_FIG2017 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2017.rds"))
UE27_FIG2018 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2018.rds"))
UE27_FIG2019 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2019.rds"))
UE27_FIG2020 <- readRDS(paste0(PathTest, "BDn_UE27_FIG_2010.rds"))
ListUE27_FIG <- list(UE27_FIG2010, UE27_FIG2011, UE27_FIG2012, UE27_FIG2013, UE27_FIG2014, UE27_FIG2015, UE27_FIG2016, UE27_FIG2017, UE27_FIG2018, UE27_FIG2019, UE27_FIG2020)
List_Countries_1 <- c("FRA", "DEU", "ITA", "ESP", "GBR") # bench FRA
List_Countries_2 <- c("UE27", "USA", "CHN", "JPN") # bench UE
# Note that 3333 is not used here with these options (just for fill)
Loop_Countries_1 <- BouclePaysEtAnneesContVAdesExports(ListUE27_FIG, Optdl = TRUE, 3333, ListCountries = List_Countries_1, OptUE27 = FALSE, OptAnnual = TRUE)
Loop_Countries_2 <- BouclePaysEtAnneesContVAdesExports(ListUE27_FIG, Optdl = TRUE, 3333, ListCountries = List_Countries_2, OptUE27 = TRUE, OptAnnual = TRUE)
# Save
saveRDS(Loop_Countries_1, "ContentVAExports_FRAandnearly.rds")
saveRDS(Loop_Countries_2, "ContentVAExports_UE27andBigAreas.rds")
### Unique database for all years option
# loading of the full database.
BaseFIG <- readRDS(paste0(PathTest, "BDn_FIG.rds"))
BaseFIG_UE27 <- readRDS(paste0(PathTest, "BDn_FIG_UE27.rds"))
List_Countries_1 <- c("FRA", "DEU", "ITA", "ESP", "GBR") # bench FRA
List_Countries_2 <- c("UE27", "USA", "CHN", "JPN") # bench UE
Loop_Countries_1 <- BouclePaysEtAnneesContVAdesExports(ListUE27_FIG, Optdl = TRUE, 2010:2020, ListCountries = List_Countries_1, OptUE27 = FALSE, OptAnnual = FALSE)
Loop_Countries_2 <- BouclePaysEtAnneesContVAdesExports(ListUE27_FIG, Optdl = TRUE, 2010:2020, ListCountries = List_Countries_2, OptUE27 = TRUE, OptAnnual = FALSE)
# Save
saveRDS(Loop_Countries_1, "ContentVAExports_FRAandnearly.rds")
saveRDS(Loop_Countries_2, "ContentVAExports_UE27andBigAreas.rds")Agreggation of Manuf categories and Made-In calculation
There is a function to calculate in aggregate ‘Manuf’ classification for each of our 4 MRIO
# Data loading (results of made-in in level)
resMadeIn_LRWIOD <- readRDS(paste0(PathTemp, "RESULT_MADEINs_LRWIOD_brut.rds"))
resMadeIn_WIOD <- readRDS(paste0(PathTemp, "RESULT_MADEINs_WIOD_brut.rds"))
resMadeIn_FIGARO <- readRDS(paste0(PathTemp, "RESULT_MADEINs_FIGARO_brut.rds"))
resMadeIn_ICIO <- readRDS(paste0(PathTemp, "RESULT_MADEINs_ICIO_brut.rds"))
# Calculation (and save) in Manuf classification
MadeIn_Manuf_classif <- MadeIn_Manuf(resMadeIn_LRWIOD, resMadeIn_WIOD, resMadeIn_FIGARO, resMadeIn_ICIO, OptSaveRDS = TRUE)Made-In (with UE27 and Manuf classif) : Adjustments on series : retropolation
### Retropolation according to the working paper final series
# You need first to calculate Made-In of manuf in rate and in level (see previous chunk)
resMadeIn_manuf <- readRDS(paste0(PathTemp, "RESULT_MADEINs_Manuf.rds"))
MadeIn_levels <- readRDS(paste0(PathTemp, "RESULT_MADEINs_Manuf_Niveau.rds"))
resMadeIn_LRWIOD <- readRDS(paste0(PathTemp, "RESULT_MADEINs_LRWIOD.rds"))
resMadeIn_WIOD <- readRDS(paste0(PathTemp, "RESULT_MADEINs_WIOD.rds"))
resMadeIn_FIGARO <- readRDS(paste0(PathTemp, "RESULT_MADEINs_FIGARO.rds"))
RetroMadeIn <- MadeIn_Retropolation(resMadeIn_manuf, MadeIn_levels, resMadeIn_LRWIOD, resMadeIn_WIOD, resMadeIn_FIGARO, resMadeIn_ICIO, OptSaveRDS = TRUE)
# Rétropolation Made-In UE27
resMadeInUE27_LRWIOD <- readRDS(paste0(PathTemp, "RESULT_MADEINsUE27_LRWIOD.rds"))
resMadeInUE27_WIOD <- readRDS(paste0(PathTemp, "RESULT_MADEINsUE27_WIOD.rds"))
resMadeInUE27_FIGARO <- readRDS(paste0(PathTemp, "RESULT_MADEINsUE27_FIGARO.rds"))
resMadeInUE27_ICIO <- readRDS(paste0(PathTemp, "RESULT_MADEINsUE27_ICIO.rds"))
RetroPo_MadeIn_UE27 <- MadeIn_Retropolation_UE27(resMadeInUE27_LRWIOD, resMadeInUE27_WIOD, resMadeInUE27_FIGARO, resMadeInUE27_ICIO, OptSaveRDS = TRUE)Content of VA embedded in exports : Adjustments on series : retropolation
# Retropolation of contents in VA of exports
ContVAdesExports_LRWIOD <- readRDS(paste0(PathTest, "RESULT_ContVAdesExports_LRWIOD.rds"))
ContVAdesExports_WIOD <- readRDS(paste0(PathTest, "RESULT_ContVAdesExports_WIOD.rds"))
ContVAdesExports_FIGARO <- readRDS(paste0(PathTest, "RESULT_ContVAdesExports_FIGARO.rds"))
Retropo_ContVA <- ContentVAExports_Retropolation(ContVAdesExports_LRWIOD, ContVAdesExports_WIOD, ContVAdesExports_FIGARO, OptSaveRDS = TRUE)
# Retropolation of contents in VA of exports UE27
resMadeInUE27_LRWIOD <- readRDS(paste0(PathTest, "RESULT_ContVAdesExportsUE27_LRWIOD.rds"))
resMadeInUE27_WIOD <- readRDS(paste0(PathTest, "RESULT_ContVAdesExportsUE27_WIOD.rds"))
resMadeInUE27_FIGARO <- readRDS(paste0(PathTest, "RESULT_ContVAdesExportsUE27_FIGARO.rds"))
resMadeInUE27_ICIO <- readRDS(paste0(PathTest, "RESULT_ContVAdesExportsUE27_ICIO.rds"))
Retropo_ContVA_UE27 <- ContentVAExports_Retropolation_UE27(resMadeInUE27_LRWIOD, resMadeInUE27_WIOD, resMadeInUE27_FIGARO, resMadeInUE27_ICIO, OptGraph = TRUE, OptSaveRDS = TRUE)Calculation of the content of foreign VA in French final demand, by country of origin
# Calculs par pays de provenance ATTENTION : la base de départ byCountry a été construite pour la FRA, on pourrait la construire pour d'autres pays
# Loading of normalized MRIO
BDn_LR_WIOD <- readRDS("Sorties/BDn_LR_WIOD.rds")
BDn_WIOD <- readRDS("Sorties/BDn_WIOD.rds")
BDn_FIG <- readRDS("Sorties/BDn_FIG.rds")
BDn_ICIO <- readRDS("Sorties/BDn_ICIO.rds")
# Calculation of the content of foreign VA in French final demand by country of origin
Build_MadeIn_byOrigin(BDn_LR_WIOD, BDn_WIOD, BDn_FIG, BDn_ICIO, SelectCountry = "FRA", OptSaveRDS = TRUE)
SelectCountries <- c("FRA", "DEU", "GBR", "ESP", "ITA", "USA", "CHN")
RESULT_MADEINs_byCountry <- readRDS(paste0(PathTemp, "RESULT_MADEINs_byCountry.rds"))
MadeIn_FRA_byOrigin <- MadeIn_byOrigin(RESULT_MADEINs_byCountry, SelectCountries = c("FRA", "DEU", "GBR", "ESP", "ITA", "USA", "CHN"), MadeInOf = "FRA", OptSaveRDS = FALSE)$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
3. Illustrations of the working paper
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Analysis of the content of foreign VA in French final demand by country of origin Note : see previous chunk to build the result database
DT <- readRDS("Sorties/RESULT_MADEINs_byCountry.rds")
# Data manipulations
DT_FRA <- DT[Col_Country == "FRA" & Lig_Country != "FRA", ]
DT_FRA_TotByCountry <- DT_FRA[, sum(value), by = c("year", "base")]
DT_FRA_tab <- dcast(DT_FRA, Lig_Country + base ~ year, var.value = "value")
DT_FRA <- DT_FRA[DT_FRA_TotByCountry, on = .(year, base)]
DT_FRA <- DT_FRA[, V1 := value / V1 * 100]
setnames(DT_FRA, "V1", "valuePct")
print(DT_FRA[, sum(valuePct), by = c("year", "base")]) # Controle Somme = 100
DT_FRA_Pct <- DT_FRA[, value := NULL]
DT_FRA_tabPct <- dcast(DT_FRA_Pct, Lig_Country + base ~ year, var.value = "valuePct")
# Countries to select for this graph
Select_Country <- c("FRA", "DEU", "GBR", "ESP", "ITA", "USA", "NLD", "CHN", "BEL")
DT_FRA_Pct_LRWIOD <- DT_FRA_Pct[base == "LRWIOD" & year %in% 1965:1999 & Lig_Country %in% Select_Country, ]
DT_FRA_Pct_WIOD <- DT_FRA_Pct[base == "WIOD" & year %in% 2000:2009 & Lig_Country %in% Select_Country, ]
DT_FRA_Pct_FIGARO <- DT_FRA_Pct[base == "FIGARO" & year %in% 2010:2020 & Lig_Country %in% Select_Country, ]
DT_FRA_Pct_select <- rbind(DT_FRA_Pct_LRWIOD, DT_FRA_Pct_WIOD, DT_FRA_Pct_FIGARO)
DT_FRA_Pct_Interm <- DT_FRA_Pct_select[, sum(valuePct), by = c("year", "Col_Country", "base")]
DT_FRA_Pct_Interm <- DT_FRA_Pct_Interm[, valuePct := 100 - V1][, Lig_Country := "ROW"] # ROW calculation : be careful because countries are different between MRIO, so ROW need to be recalculated.
DT_FRA_Pct_Interm <- DT_FRA_Pct_Interm[, V1 := NULL]
DT_FRA_Pct_select <- rbind(DT_FRA_Pct_select, DT_FRA_Pct_Interm)
# Stacked + percent
ggplot(DT_FRA_Pct_select, aes(fill = Lig_Country, y = valuePct, x = year)) +
geom_bar(position = "stack", stat = "identity") +
scale_fill_viridis(discrete = T) +
ggtitle("Content of foreign VA in French final demand by country of origin") +
theme_ipsum() +
xlab("")
###############################################################################
######### Function graph by country
GraphMadeIn_byCountry <- function(pays, Select_Country = c("FRA", "DEU", "GBR", "ESP", "ITA", "USA", "NLD", "CHN", "BEL")) {
DT <- readRDS("RESULT_MADEINs_byCountry.rds")
DT_pays <- DT[Col_Country == pays & Lig_Country != pays, ]
DT_pays_TotByCountry <- DT_pays[, sum(value), by = c("year", "base")]
DT_pays_tab <- dcast(DT_pays, Lig_Country + base ~ year, var.value = "value")
DT_pays <- DT_pays[DT_pays_TotByCountry, on = .(year, base)]
DT_pays <- DT_pays[, V1 := value / V1 * 100]
setnames(DT_pays, "V1", "valuePct")
DT_pays_Pct <- DT_pays[, value := NULL]
DT_pays_tabPct <- dcast(DT_pays_Pct, Lig_Country + base ~ year, var.value = "valuePct")
DT_pays_Pct_LRWIOD <- DT_pays_Pct[base == "LRWIOD" & year %in% 1965:1999 & Lig_Country %in% Select_Country, ]
DT_pays_Pct_WIOD <- DT_pays_Pct[base == "WIOD" & year %in% 2000:2009 & Lig_Country %in% Select_Country, ]
DT_pays_Pct_FIGARO <- DT_pays_Pct[base == "FIGARO" & year %in% 2010:2020 & Lig_Country %in% Select_Country, ]
DT_pays_Pct_select <- rbind(DT_pays_Pct_LRWIOD, DT_pays_Pct_WIOD, DT_pays_Pct_FIGARO)
DT_pays_Pct_Interm <- DT_pays_Pct_select[, sum(valuePct), by = c("year", "Col_Country", "base")]
DT_pays_Pct_Interm <- DT_pays_Pct_Interm[, valuePct := 100 - V1][, Lig_Country := "ROW"]
DT_pays_Pct_Interm <- DT_pays_Pct_Interm[, V1 := NULL]
DT_pays_Pct_select <- rbind(DT_pays_Pct_select, DT_pays_Pct_Interm)
# Stacked + percent
p <- ggplot(DT_pays_Pct_select, aes(fill = Lig_Country, y = valuePct, x = year)) +
geom_bar(position = "stack", stat = "identity") +
scale_fill_viridis(discrete = T) +
ggtitle(paste0("Content of foreign VA in French final demand by country of origin ", pays)) +
theme_ipsum() +
xlab("")
print(p)
ggsave(paste0("MadeIn_ComplByCountry_", pays, ".png"), plot = last_plot(), device = "png")
}
GraphMadeIn_byCountry("FRA")
GraphMadeIn_byCountry("ESP")
GraphMadeIn_byCountry("ITA")
GraphMadeIn_byCountry("GBR")
GraphMadeIn_byCountry("DEU")
GraphMadeIn_byCountry("CHN")
GraphMadeIn_byCountry("USA")
GraphMadeIn_byCountry("BEL")
GraphMadeIn_byCountry("GRC")
GraphMadeIn_byCountry("JPN")Another graph for the content of foreign VA in French final demand by country of origin
Select_Country <- list("AUT", "BEL", "BGR", "CYP", "CZE", "DEU", "DNK", "ESP", "EST", "FIN", "FRA", "GRC", "HRV", "HUN", "IRL", "ITA", "LTU", "LUX", "LVA", "MLT", "NLD", "POL", "PRT", "ROU", "SVK", "SVN", "SWE")
DT <- readRDS(paste0(PathTemp, "RESULT_MADEINs_byCountry.rds"))
DT_pays <- DT[Col_Country == "FRA" & Lig_Country != "FRA", ]
DT_pays_TotByCountry <- DT_pays[, sum(value), by = c("year", "base")]
DT_pays_tab <- dcast(DT_pays, Lig_Country + base ~ year, var.value = "value")
DT_pays <- DT_pays[DT_pays_TotByCountry, on = .(year, base)]
DT_pays <- DT_pays[, V1 := value / V1 * 100]
setnames(DT_pays, "V1", "valuePct")
DT_pays_Pct <- DT_pays[, value := NULL]
DT_pays_tabPct <- dcast(DT_pays_Pct, Lig_Country + base ~ year, var.value = "valuePct")
DT_pays_Pct_LRWIOD <- DT_pays_Pct[base == "LRWIOD" & year %in% 1965:1999 & Lig_Country %in% Select_Country, ]
DT_pays_Pct_WIOD <- DT_pays_Pct[base == "WIOD" & year %in% 2000:2009 & Lig_Country %in% Select_Country, ]
DT_pays_Pct_FIGARO <- DT_pays_Pct[base == "FIGARO" & year %in% 2010:2020 & Lig_Country %in% Select_Country, ]
DT_pays_Pct_select <- rbind(DT_pays_Pct_LRWIOD, DT_pays_Pct_WIOD, DT_pays_Pct_FIGARO)
DT_pays_Pct_Interm <- DT_pays_Pct_select[, sum(valuePct), by = c("year", "Col_Country", "base")]
DT_pays_Pct_Interm <- DT_pays_Pct_Interm[, valuePct := 100 - V1][, Lig_Country := "ROW"]
DT_pays_Pct_Interm <- DT_pays_Pct_Interm[, V1 := NULL]
DT_pays_Pct_select <- rbind(DT_pays_Pct_select, DT_pays_Pct_Interm)
DT_pays_Pct_select_tab <- dcast(DT_pays_Pct_select, year ~ Lig_Country, value.var = "valuePct")
print(rowSums(DT_pays_Pct_select_tab[, 2:8]))
# Graph -> Excel ?
write.xlsx(DT_pays_Pct_select_tab, "MadeIn_provenance_pour Graph.xlsx", sheetName = "serie", col.names = TRUE, row.names = TRUE)
write.xlsx(DT_pays_Pct_select_tab, "SortieOuestFrance.xlsx", sheetName = "Made in FRA Provenance", col.names = TRUE, row.names = TRUE, append = TRUE)Analysis of Made-In France in comparison of countries of same size
MadeInTotaux <- readRDS(paste0(PathTemp, "RESULT_MADEINs_TotauxAvecNiveaux.rds"))
MadeInTotaux_select <- MadeInTotaux[Col_Country %in% c("FRA", "DEU", "GBR", "ESP", "ITA"), c("year", "Col_Country", "value")]
MadeInTotaux_select <- MadeInTotaux_select[, year := as.numeric(as.character(year))]
theme_set(theme_minimal(base_family = "Lato"))
ggplot(MadeInTotaux_select, aes(x = year, y = value, group = Col_Country, color = Col_Country)) + #
geom_line(size = 1.5, alpha = 0.9, linetype = 1) +
theme(
plot.title = element_text(face = "bold", size = 12),
legend.background = element_rect(fill = "white", size = 4, colour = "white"),
legend.justification = c(0, 0),
legend.position = c(0, 0),
axis.ticks = element_line(colour = "grey70", size = 0.2),
panel.grid.major = element_line(colour = "grey70", size = 0.2),
panel.grid.minor = element_blank(),
panel.grid = element_line(
color = "#8ccde3",
size = 0.75,
linetype = 2
)
) +
scale_x_continuous(n.breaks = 20) +
ggtitle("Made-in for France and comparable countries")
ggsave(paste0("MadeIn_Graph1_FRA.png"), plot = last_plot(), device = "png")
###################### Table of figures
MadeInTotaux_select_tab <- dcast(MadeInTotaux_select, Col_Country ~ year, value.vars = "value")
MadeInTotaux_select_tab3 <- MadeInTotaux_select_tab # init
MadeInTotaux_select_tab3[, 2:ncol(MadeInTotaux_select_tab3)] <- round(MadeInTotaux_select_tab3[, 2:ncol(MadeInTotaux_select_tab3)], 3)
MadeInTotaux_select_tab3 <- setDF(MadeInTotaux_select_tab3)
MadeInTotaux_select_tab3 <- GetRownamesFromFirstCol(MadeInTotaux_select_tab3)
MadeInTotaux_select_tab3 <- MadeInTotaux_select_tab3[, c("1965", "1975", "1985", "2000", "2005", "2010", "2015", "2020")]
pander::pander(MadeInTotaux_select_tab3)
pdf(paste0("MadeIn_Tab1_FRA.pdf")) # , height=15, width=8.5
grid.table(MadeInTotaux_select_tab3)
dev.off()
write.xlsx(MadeInTotaux_select_tab3, "SortieOuestFrance.xlsx", sheetName = "Made in FRA", col.names = TRUE, row.names = TRUE, append = TRUE)Same analysis for EU27 and other comparable areas
MadeInTotaux <- readRDS(paste0(PathTemp, "RESULT_MADEINs_UE27.rds"))
MadeInTotaux_select <- MadeInTotaux[Col_Country %in% c("UE27", "USA", "CHN", "IND", "JPN", "BRA"), c("year", "Col_Country", "value")]
MadeInTotaux_select <- MadeInTotaux_select[, year := as.numeric(as.character(year))]
theme_set(theme_minimal(base_family = "Lato"))
ggplot(MadeInTotaux_select, aes(x = year, y = value, group = Col_Country, color = Col_Country)) + #
geom_line(size = 1.5, alpha = 0.9, linetype = 1) +
theme(
plot.title = element_text(face = "bold", size = 12),
legend.background = element_rect(fill = "white", size = 4, colour = "white"),
legend.justification = c(0, 0),
legend.position = c(0, 0),
axis.ticks = element_line(colour = "grey70", size = 0.2),
panel.grid.major = element_line(colour = "grey70", size = 0.2),
panel.grid.minor = element_blank(),
panel.grid = element_line(
color = "#8ccde3",
size = 0.75,
linetype = 2
)
) +
scale_x_continuous(n.breaks = 20) +
ggtitle("Made-in for EU27 and other comparable areas")
ggsave(paste0("MadeIn_GraphCompl_GrandsPays.png"), plot = last_plot(), device = "png")
###################### Table of figures
MadeInTotaux_select_tab <- dcast(MadeInTotaux_select, Col_Country ~ year, value.vars = "value")
MadeInTotaux_select_tab3 <- MadeInTotaux_select_tab # init
MadeInTotaux_select_tab3[, 2:ncol(MadeInTotaux_select_tab3)] <- round(MadeInTotaux_select_tab3[, 2:ncol(MadeInTotaux_select_tab3)], 3)
MadeInTotaux_select_tab3 <- setDF(MadeInTotaux_select_tab3)
MadeInTotaux_select_tab3 <- GetRownamesFromFirstCol(MadeInTotaux_select_tab3)
MadeInTotaux_select_tab3 <- MadeInTotaux_select_tab3[, c("1965", "1975", "1985", "2000", "2005", "2010", "2015", "2020")]
pander::pander(MadeInTotaux_select_tab3)
pdf(paste0("MadeIn_TabCompl_GrandsPays.pdf")) # , height=15, width=8.5
grid.table(MadeInTotaux_select_tab3)
dev.off()
write.xlsx(MadeInTotaux_select_tab3, "SortieOuestFrance.xlsx", sheetName = "Made in UE", col.names = TRUE, row.names = TRUE)Analysis by industry structure for the French economy
MadeInSectors <- readRDS(paste0(PathTemp, "RESULT_MADEINs_SectorsAvecNiveaux.rds"))
MadeInSectors_select <- MadeInSectors[Col_Country %in% c("FRA"), c("year", "Col_Country", "value", "Lig_Indus", "TypeManuf")]
MadeInSectors_select <- MadeInSectors_select[, year := as.numeric(as.character(year))]
MadeInSectors_select1 <- MadeInSectors_select[TypeManuf == "manufCT", ]
MadeInSectors_select2 <- MadeInSectors_select[TypeManuf == "manufIP19", ]
MadeInSectors_select <- MadeInSectors_select[Lig_Indus %in% c("Agri", "Enrj", "Manuf", "ManufNonFab", "ManufFabC3C5", "Constru", "Services", "ServMarch", "ServNonMarch"), ]
theme_set(theme_minimal(base_family = "Lato"))
ggplot(MadeInSectors_select, aes(x = year, y = value, group = Lig_Indus, color = Lig_Indus)) + #
geom_line(size = 1.5, alpha = 0.9, linetype = 1) +
theme(
plot.title = element_text(face = "bold", size = 12),
legend.background = element_rect(fill = "white", size = 4, colour = "white"),
legend.justification = c(0, 0),
legend.position = c(0, 0),
axis.ticks = element_line(colour = "grey70", size = 0.2),
panel.grid.major = element_line(colour = "grey70", size = 0.2),
panel.grid.minor = element_blank(),
panel.grid = element_line(
color = "#8ccde3",
size = 0.75,
linetype = 2
)
) +
scale_x_continuous(n.breaks = 20) +
ggtitle("Made-in France by industry")
ggsave(paste0("MadeIn_Graph2_FRAsectors.png"), plot = last_plot(), device = "png")
###################### Table of figures
MadeInSectors_select2 <- unique(MadeInSectors_select[, TypeManuf := NULL])
MadeInSectors_select_tab <- dcast(MadeInSectors_select2, Col_Country + Lig_Indus ~ year, value.vars = "value")
MadeInSectors_select_tab <- MadeInSectors_select_tab[, Col_Country := paste0(Col_Country, "_", Lig_Indus)]
MadeInSectors_select_tab <- MadeInSectors_select_tab[, Lig_Indus := NULL]
MadeInSectors_select_tab3 <- MadeInSectors_select_tab # init
MadeInSectors_select_tab3[, 2:ncol(MadeInSectors_select_tab3)] <- round(MadeInSectors_select_tab3[, 2:ncol(MadeInSectors_select_tab3)], 3)
MadeInSectors_select_tab3 <- setDF(MadeInSectors_select_tab3)
MadeInSectors_select_tab3 <- GetRownamesFromFirstCol(MadeInSectors_select_tab3)
MadeInSectors_select_tab3 <- MadeInSectors_select_tab3[, c("1965", "1975", "1985", "2000", "2005", "2010", "2015", "2020")]
pander::pander(MadeInSectors_select_tab3)
pdf(paste0("MadeIn_Tab2_FRAsectors..pdf")) # , height=15, width=8.5
grid.table(MadeInSectors_select_tab3)
dev.off()
#######################
ggplot(MadeInSectors_select1, aes(x = year, y = value, group = Lig_Indus, color = Lig_Indus)) + #
geom_line(size = 1.5, alpha = 0.9, linetype = 1) +
theme(
plot.title = element_text(face = "bold", size = 12),
legend.background = element_rect(fill = "white", size = 4, colour = "white"),
legend.justification = c(0, 0),
legend.position = c(0, 0),
axis.ticks = element_line(colour = "grey70", size = 0.2),
panel.grid.major = element_line(colour = "grey70", size = 0.2),
panel.grid.minor = element_blank(),
panel.grid = element_line(
color = "#8ccde3",
size = 0.75,
linetype = 2
)
) +
scale_x_continuous(n.breaks = 20) +
ggtitle("Made-in France : decompo 1")
ggplot(MadeInSectors_select2, aes(x = year, y = value, group = Lig_Indus, color = Lig_Indus)) + #
geom_line(size = 1.5, alpha = 0.9, linetype = 1) +
theme(
plot.title = element_text(face = "bold", size = 12),
legend.background = element_rect(fill = "white", size = 4, colour = "white"),
legend.justification = c(0, 0),
legend.position = c(0, 0),
axis.ticks = element_line(colour = "grey70", size = 0.2),
panel.grid.major = element_line(colour = "grey70", size = 0.2),
panel.grid.minor = element_blank(),
panel.grid = element_line(
color = "#8ccde3",
size = 0.75,
linetype = 2
)
) +
scale_x_continuous(n.breaks = 20) +
ggtitle("Made-in France : decompo 2")Content of VA in exports
# Loading of data
Data1 <- readRDS("RESULT_ContenusVAExport.rds")
Data2 <- readRDS("RESULT_ContenusVAExport_UE27.rds")
# Plots
ggplot(data = Data1[CountryREF == "FRA", ], aes(x = year, y = value, color = CountryREF)) +
geom_line()
ggplot(data = Data2[CountryREF == "FRA", ], aes(x = year, y = value, color = CountryREF)) +
geom_line()