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Risk Analysis for Overall Secrecy Score.R
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# Risk Analysis for Overall Secrecy Score
# Alice Lepissier
# alice.lepissier@gmail.com
## ## ## ## ## ## ## ## ## ## ##
# INDEX ####
## ## ## ## ## ## ## ## ## ## ##
# Preamble
# Data Preparation
# VIE for Overall Secrecy
# .. Jurisdiction-level scores
# .... Calculate Total of flow/stock per reporter per year
# .... Calculate Vulnerabilities per reporter per year
# .... Calculate Share of Vulnerability per reporter per year
# .... Calculate Intensities per reporter per year
# .... Calculate Exposures per reporter per year
# .. Region-level scores
# .... Calculate Total of flow/stock per region per year
# .... Calculate Vulnerabilities per region per year
# .... Calculate Intensities per region per year
# .... Calculate Exposures per region per year
# .. Income group-level scores
# .... Calculate Total of flow/stock per income group per year
# .... Calculate Vulnerabilities per income group per year
# .... Calculate Intensities per income group per year
# .... Calculate Exposures per income group per year
# .. Check zero values in measures due to summing over NAs
# .... For Totals
# .... For Vulnerabilities
# .... For Intensities
# .... For Exposures
# .. Remove infinite values
# Average Measures Graphs
# .. Calculate average measures across years per reporter
# .. Extract conduits
# .. Plot country-risk graphs
# .... With NAs labelled, disaggregated
# .... Without NAs labelled, disaggregated
# .... With NAs labelled, aggregated
# .. Plot jurisdiction-level scores
# .... For conduits
# .... By region, excluding conduits
# .... By region, including conduits
# .... By region, including conduits, all flows/stocks
# .... By region, including conduits, all flows/stocks, aggregated
# .... By income group, excluding conduits
# .... By income group, including conduits
# .... Export
# .. Calculate average measures across years per region
# .... Plot region-level scores
# .... Plot region-level scores, all flows/stocks
# .... Plot region-level scores, all flows/stocks, aggregated
# .... Export
# .. Calculate average measures across years per income group
# .... Plot income group-level scores
# .... Plot income group-level scores, all flows/stocks
# .... Plot income group-level scores, all flows/stocks, aggregated
# .... Export
# .. Housekeeping
# Time Series Graphs
# .. For regions
# .... Calculate average V for each measure in each year
# .... Calculate average I for each measure in each year
# .... Calculate average E for each measure in each year
# .... Plot
# .... Export
# .. For income groups
# .... Calculate average V for each measure in each year
# .... Calculate average I for each measure in each year
# .... Calculate average E for each measure in each year
# .... Plot
# .... Export
# .. Housekeeping
# Yearly snapshot graphs
# .. Plot country-risk graphs
# .... With NAs labelled
# .. Plot jurisdiction-level scores
# .... By region
# .... By income group
# .. Plot region-level scores
# .. Plot income group-level scores
# .. Housekeeping
# Export results
## ## ## ## ## ## ## ## ## ## ##
# PREAMBLE ####
## ## ## ## ## ## ## ## ## ## ##
setwd("C:/cloudstorage/googledrive/Projects/Tax Justice Network/Consultancy 2 - summer 18/Risk-based IFF/") # Alice work
#setwd("D:/Google Drive/Projects/Tax Justice Network/Consultancy 2 - summer 18/Risk-based IFF/") # Alice laptop
library(plyr) # Must load before dplyr
library(dplyr)
library(ggpubr)
library(ggplot2)
library(gmodels)
library(RColorBrewer)
library(rebus)
library(reshape2)
library(scales)
library(tidyr)
library(tidyverse)
library(WDI)
library(wesanderson)
library(xlsx)
## ## ## ## ## ## ## ## ## ## ##
# DATA PREPARATION ####
## ## ## ## ## ## ## ## ## ## ##
#source("Scripts/Data Preparation.R")
load("Data/panel.RData")
## ## ## ## ## ## ## ## ## ## ##
# VIE FOR OVERALL SECRECY ####
## ## ## ## ## ## ## ## ## ## ##
panelSJ <- subset(panel, pSS != FALSE)
vars <- c("dClaims", "Liabilities",
"DIdI", "DII", "DIdO", "DIO",
"PIA", "PIL", "PIdL",
"Export", "Import")
# .. Jurisdiction-level scores ####
V <- paste0("V", vars)
I <- paste0("I", vars)
# .... Calculate Total of flow/stock per reporter per year ####
panelSJ <- panelSJ %>%
group_by(reporter.ISO, year) %>%
mutate_at(.vars = vars,
.fun = funs(Tot = sum(abs(.), na.rm = T))) %>%
ungroup()
names(panelSJ) <- sub("^(.*)_(.*)$", "\\2\\1", names(panelSJ))
# .... Calculate Vulnerabilities per reporter per year ####
panelSJ <- panelSJ %>%
group_by(reporter.ISO, year) %>%
mutate_at(.vars = vars,
.fun = funs(V = sum((abs(.) * pSecrecyScore)/sum(abs(.), na.rm = T), na.rm = T))) %>%
ungroup()
names(panelSJ) <- sub("^(.*)_(.*)$", "\\2\\1", names(panelSJ))
# .... Calculate Share of Vulnerability per reporter per year ####
panelSJ_Share <- panelSJ %>%
group_by(reporter.ISO, year) %>%
mutate_at(.vars = vars,
.fun = funs(VShare = (abs(.) * pSecrecyScore)/sum(abs(.) * pSecrecyScore, na.rm = T),
VShareNumerator = abs(.) * pSecrecyScore)) %>%
ungroup()
names(panelSJ_Share) <- sub("^(.*)_(.*)$", "\\2\\1", names(panelSJ_Share))
panelSJ_Share <- panelSJ_Share %>%
group_by(reporter.ISO, year) %>%
mutate_at(.vars = paste0("VShare", vars),
.fun = funs(VShareSum = sum(., na.rm = T))) %>%
ungroup()
names(panelSJ_Share) <- sub("^(.*)_(.*)$", "\\2\\1", names(panelSJ_Share))
names(panelSJ_Share) <- gsub("VShareSumVShare", "VShareSum", names(panelSJ_Share))
grep("VShareSum", names(panelSJ_Share))
for (c in 83:93){
z <- which(panelSJ_Share[, c+33] == 0)
zeroTot <- panelSJ_Share[z, ]
print(unique(zeroTot[, c]))
}
# All the 0 values in the shares of Vulnerabilities are due to Vulnerabilities of 0,
# which are themselves due to NAs in the original data.
# We can convert them back to NAs.
# na.rm = T is needed when summing otherwise a single NA value will return NAs (even though there may be valid other values).
missing <- apply(subset(panelSJ, select = c(dClaims, Liabilities,
DII, DIdO,
PIA, PIdL,
Export, Import)), MARGIN = 1, function(x) sum(is.na(x))) == 8
panelSJ_Share <- panelSJ_Share[which(missing == FALSE),]
panelSJ_Share <- panelSJ_Share %>%
select(id:pIncome,
pSecrecyScore,
dClaims, Liabilities,
DII, DIdO,
PIA, PIdL,
Export, Import,
VdClaims:VImport,
VSharedClaims:VShareImport,
VShareNumeratordClaims:VShareNumeratorImport) %>%
arrange(id)
dClaims <- panelSJ_Share %>%
filter(!is.na(dClaims)) %>%
select(id:pIncome,
pSecrecyScore,
dClaims,
VdClaims,
VSharedClaims,
VShareNumeratordClaims) %>%
arrange(reporter, year, -VSharedClaims)
write.csv(dClaims, "Results/Vulnerability shares/dClaims.csv", row.names = FALSE)
Liabilities <- panelSJ_Share %>%
filter(!is.na(Liabilities)) %>%
select(id:pIncome,
pSecrecyScore,
Liabilities,
VLiabilities,
VShareLiabilities,
VShareNumeratorLiabilities) %>%
arrange(reporter, year, -VShareLiabilities)
write.csv(Liabilities, "Results/Vulnerability shares/Liabilities.csv", row.names = FALSE)
DII <- panelSJ_Share %>%
filter(!is.na(DII)) %>%
select(id:pIncome,
pSecrecyScore,
DII,
VDII,
VShareDII,
VShareNumeratorDII) %>%
arrange(reporter, year, -VShareDII)
write.csv(DII, "Results/Vulnerability shares/DII.csv", row.names = FALSE)
DIdO <- panelSJ_Share %>%
filter(!is.na(DIdO)) %>%
select(id:pIncome,
pSecrecyScore,
DIdO,
VDIdO,
VShareDIdO,
VShareNumeratorDIdO) %>%
arrange(reporter, year, -VShareDIdO)
write.csv(DIdO, "Results/Vulnerability shares/DIdO.csv", row.names = FALSE)
PIA <- panelSJ_Share %>%
filter(!is.na(PIA)) %>%
select(id:pIncome,
pSecrecyScore,
PIA,
VPIA,
VSharePIA,
VShareNumeratorPIA) %>%
arrange(reporter, year, -VSharePIA)
write.csv(PIA, "Results/Vulnerability shares/PIA.csv", row.names = FALSE)
PIdL <- panelSJ_Share %>%
filter(!is.na(PIdL)) %>%
select(id:pIncome,
pSecrecyScore,
PIdL,
VPIdL,
VSharePIdL,
VShareNumeratorPIdL) %>%
arrange(reporter, year, -VSharePIdL)
write.csv(PIdL, "Results/Vulnerability shares/PIdL.csv", row.names = FALSE)
Export <- panelSJ_Share %>%
filter(!is.na(Export)) %>%
select(id:pIncome,
pSecrecyScore,
Export,
VExport,
VShareExport,
VShareNumeratorExport) %>%
arrange(reporter, year, -VShareExport)
write.csv(Export, "Results/Vulnerability shares/Export.csv", row.names = FALSE)
Import <- panelSJ_Share %>%
filter(!is.na(Import)) %>%
select(id:pIncome,
pSecrecyScore,
Import,
VImport,
VShareImport,
VShareNumeratorImport) %>%
arrange(reporter, year, -VShareImport)
write.csv(Import, "Results/Vulnerability shares/Import.csv", row.names = FALSE)
rm(zeroTot, c, z, missing,
dClaims, Liabilities, DII, DIdO, PIA, PIdL, Export, Import,
panelSJ_Share)
# .... Calculate Intensities per reporter per year ####
panelSJ <- panelSJ %>%
group_by(reporter.ISO, year) %>%
mutate_at(.vars = paste("Tot", vars, sep = ""),
.fun = funs(I = abs( . / rGDP))) %>%
ungroup()
names(panelSJ) <- sub("^(.*)_(.*)$", "\\2\\1", names(panelSJ))
names(panelSJ) <- gsub("ITot", "I", names(panelSJ))
# .... Calculate Exposures per reporter per year ####
n <- ncol(panelSJ)
for (var in 1:length(vars)){
panelSJ[, n + var] <- panelSJ[[V[var]]] * panelSJ[[I[var]]]
colnames(panelSJ)[n + var] <- paste0("E", vars[var])
}
rm(n, var)
# .. Region-level scores ####
wrV <- paste0("wrV", vars)
wrI <- paste0("wrI", vars)
# ... Calculate Total of flow/stock per region per year ####
panelSJ <- panelSJ %>%
group_by(rRegion, year) %>%
mutate_at(.vars = vars,
.fun = funs(wrTot = sum(abs(.), na.rm = T))) %>%
ungroup()
names(panelSJ) <- sub("^(.*)_(.*)$", "\\2\\1", names(panelSJ))
# .... Calculate Vulnerabilities per region per year ####
panelSJ <- panelSJ %>%
group_by(rRegion, year) %>%
mutate_at(.vars = vars,
.fun = funs(wrV = sum((abs(.) * pSecrecyScore)/sum(abs(.), na.rm = T), na.rm = T))) %>%
ungroup()
names(panelSJ) <- sub("^(.*)_(.*)$", "\\2\\1", names(panelSJ))
# .... Calculate Intensities per region per year ####
panelSJ <- panelSJ %>%
group_by(rRegion, year) %>%
do({
sum_value = sum(distinct(., reporter, year, rGDP)$rGDP, na.rm = TRUE);
mutate(., wrrGDP = sum_value)
})
panelSJ <- panelSJ %>%
group_by(rRegion, year) %>%
mutate_at(.vars = paste("wrTot", vars, sep = ""),
.fun = funs(wrI = abs( . / wrrGDP))) %>%
ungroup()
names(panelSJ) <- sub("^(.*)_(.*)$", "\\2\\1", names(panelSJ))
names(panelSJ) <- gsub("wrIwrTot", "wrI", names(panelSJ))
# .... Calculate Exposures per region per year ####
n <- ncol(panelSJ)
for (var in 1:length(vars)){
panelSJ[, n + var] <- panelSJ[[wrV[var]]] * panelSJ[[wrI[var]]]
colnames(panelSJ)[n + var] <- paste0("wrE", vars[var])
}
rm(n, var)
# .. Income group-level scores ####
wiV <- paste0("wiV", vars)
wiI <- paste0("wiI", vars)
# ... Calculate Total of flow/stock per income group per year ####
panelSJ <- panelSJ %>%
group_by(rIncome, year) %>%
mutate_at(.vars = vars,
.fun = funs(wiTot = sum(abs(.), na.rm = T))) %>%
ungroup()
names(panelSJ) <- sub("^(.*)_(.*)$", "\\2\\1", names(panelSJ))
# .... Calculate Vulnerabilities per income group per year ####
panelSJ <- panelSJ %>%
group_by(rIncome, year) %>%
mutate_at(.vars = vars,
.fun = funs(wiV = sum((abs(.) * pSecrecyScore)/sum(abs(.), na.rm = T), na.rm = T))) %>%
ungroup()
names(panelSJ) <- sub("^(.*)_(.*)$", "\\2\\1", names(panelSJ))
# .... Calculate Intensities per income group per year ####
panelSJ <- panelSJ %>%
group_by(rIncome, year) %>%
do({
sum_value = sum(distinct(., reporter, year, rGDP)$rGDP, na.rm = TRUE);
mutate(., wirGDP = sum_value)
})
panelSJ <- panelSJ %>%
group_by(rIncome, year) %>%
mutate_at(.vars = paste("wiTot", vars, sep = ""),
.fun = funs(wiI = abs( . / wirGDP))) %>%
ungroup()
names(panelSJ) <- sub("^(.*)_(.*)$", "\\2\\1", names(panelSJ))
names(panelSJ) <- gsub("wiIwiTot", "wiI", names(panelSJ))
# .... Calculate Exposures per income group per year ####
n <- ncol(panelSJ)
for (var in 1:length(vars)){
panelSJ[, n + var] <- panelSJ[[wiV[var]]] * panelSJ[[wiI[var]]]
colnames(panelSJ)[n + var] <- paste0("wiE", vars[var])
}
rm(n, var)
rm(V, I, wrV, wrI, wiV, wiI, vars)
rm(panel)
# .. Check zero values in measures due to summing over NAs ####
summary(panelSJ[,73:206])
# .... For Totals ####
grep("Tot", names(panelSJ))
for (c in 11:23){
z <- which(panelSJ[, c+61] == 0) # increment is -40 if panel w/o KFSIs
zeroTot <- panelSJ[z, ]
print(unique(zeroTot[, c]))
}
grep("wrTot", names(panelSJ))
for (c in 11:21){
z <- which(panelSJ[, c+105] == 0)
zeroTot <- panelSJ[z, ]
print(unique(zeroTot[, c]))
}
grep("wiTot", names(panelSJ))
for (c in 11:21){
z <- which(panelSJ[, c+150] == 0)
zeroTot <- panelSJ[z, ]
print(unique(zeroTot[, c]))
}
# All the 0 values in the totals for flows/stocks are due to summing only NAs with na.rm = T.
# We can convert them back to NAs.
# na.rm = T is needed when summing otherwise a single NA value will return NAs (even though there may be valid other values).
cols <- grep("Tot", names(panelSJ))
panelSJ[, cols][panelSJ[, cols] == 0] <- NA
rm(zeroTot, c, cols, z)
# .... For Vulnerabilities ####
grep("V", names(panelSJ))
for (c in 11:21){
z <- which(panelSJ[, c+72] == 0)
zeroTot <- panelSJ[z, ]
print(unique(zeroTot[, c]))
}
grep("wrV", names(panelSJ))
for (c in 11:21){
z <- which(panelSJ[, c+116] == 0)
zeroTot <- panelSJ[z, ]
print(unique(zeroTot[, c]))
}
grep("wiV", names(panelSJ))
for (c in 11:21){
z <- which(panelSJ[, c+161] == 0)
zeroTot <- panelSJ[z, ]
print(unique(zeroTot[, c]))
}
# All the 0 values in the Vulnerabilities for flows/stocks are due to summing only NAs with na.rm = T.
# We can convert them back to NAs.
# na.rm = T is needed when summing otherwise a single NA value will return NAs (even though there may be valid other values).
cols <- grep("V", names(panelSJ))
panelSJ[, cols][panelSJ[, cols] == 0] <- NA
rm(zeroTot, c, cols, z)
# .... For Intensities ####
colnames(panelSJ)
for (c in 11:21){
z <- which(panelSJ[, c+83] == 0)
zeroTot <- panelSJ[z, ]
print(unique(zeroTot[, c]))
}
grep("wrI", names(panelSJ))
for (c in 11:21){
z <- which(panelSJ[, c+128] == 0)
zeroTot <- panelSJ[z, ]
print(unique(zeroTot[, c]))
}
grep("wiI", names(panelSJ))
for (c in 11:21){
z <- which(panelSJ[, c+173] == 0)
zeroTot <- panelSJ[z, ]
print(unique(zeroTot[, c]))
}
# All the 0 values in the Intensities for flows/stocks are due to summing only NAs with na.rm = T.
# We can convert them back to NAs.
# na.rm = T is needed when summing otherwise a single NA value will return NAs (even though there may be valid other values).
colnames(panelSJ)
cols <- c(95:105,140:150,185:195)
panelSJ[, cols][panelSJ[, cols] == 0] <- NA
rm(zeroTot, c, cols, z)
# .... For Exposures ####
colnames(panelSJ)
for (c in 11:21){
z <- which(panelSJ[, c+94] == 0)
zeroTot <- panelSJ[z, ]
print(unique(zeroTot[, c]))
}
grep("wrE", names(panelSJ))
for (c in 11:21){
z <- which(panelSJ[, c+139] == 0)
zeroTot <- panelSJ[z, ]
print(unique(zeroTot[, c]))
}
grep("wiE", names(panelSJ))
for (c in 11:21){
z <- which(panelSJ[, c+184] == 0)
zeroTot <- panelSJ[z, ]
print(unique(zeroTot[, c]))
}
# All the 0 values in the Exposures for flows/stocks are due to summing only NAs with na.rm = T.
# We can convert them back to NAs.
# na.rm = T is needed when summing otherwise a single NA value will return NAs (even though there may be valid other values).
colnames(panelSJ)
cols <- c(106:116,151:161,196:206)
panelSJ[, cols][panelSJ[, cols] == 0] <- NA
rm(zeroTot, c, cols, z)
# .. Remove infinite values ####
panelSJ[panelSJ == "Inf"] <- NA
panelSJ <- panelSJ %>%
select(id, reporter, reporter.ISO, partner, partner.ISO, year, everything()) %>%
arrange(id)
save(panelSJ, file = "Data/panelSJ.RData")
write.csv(panelSJ, "Results/panelSJ_Secrecy Score.csv", row.names = FALSE)
load("Data/panelSJ.RData")
## ## ## ## ## ## ## ## ## ## ##
# AVERAGE MEASURES GRAPHS ####
## ## ## ## ## ## ## ## ## ## ##
measure <- c("xV", "xI", "xE")
wrmeasure <- c("xwrV", "xwrI", "xwrE")
wimeasure <- c("xwiV", "xwiI", "xwiE")
measure.label <- c("Vulnerability", "Intensity", "Exposure")
region <- c("af", "am", "as", "eu", "oc", "nr")
regionno <- c("afno", "amno", "asno", "euno", "ocno", "nrno")
region.label <- c("Africa", "Americas", "Asia", "Europe", "Oceania", "No Region")
incomegroup <- c("HIC", "UMC", "LMC", "LIC", "ni")
incomegroupno <- c("HICno", "UMCno", "LMCno", "LICno", "nino")
incomegroup.label <- c("HIC", "UMC", "LMC", "LIC", "No Income Group")
flowstock <- c("Banking Positions", "Direct Investment", "Portfolio Investment", "Trade")
vars <- list()
vars$BankingPositions <- c("dClaims", "Liabilities")
vars$DirectInvestment <- c("DII", "DIdO")
vars$PortInvestment <- c("PIA", "PIdL")
vars$Trade <- c("Export", "Import")
var.labels <- list()
var.labels$BankingPositions <- c("Claims (derived)", "Liabilities")
var.labels$DirectInvestment <- c("Inward", "Outward (derived)")
var.labels$PortInvestment <- c("Assets", "Liabilities (derived)")
var.labels$Trade <- c("Exports", "Imports")
cols <- list()
cols$BankingPositions <- c("#899DA4", "#C93312")
cols$DirectInvestment <- c("#7294D4", "#FD6467")
cols$PortInvestment <- c("#00A08A", "#F98400")
cols$Trade <- c("#FAD510", "#800080")
# .. Calculate average measures across years per reporter ####
measures <- c("VdClaims", "VLiabilities",
"VDIdI", "VDII", "VDIdO", "VDIO",
"VPIA", "VPIL", "VPIdL",
"VExport", "VImport",
"IdClaims", "ILiabilities",
"IDIdI", "IDII", "IDIdO", "IDIO",
"IPIA", "IPIL", "IPIdL",
"IExport", "IImport",
"EdClaims", "ELiabilities",
"EDIdI", "EDII", "EDIdO", "EDIO",
"EPIA", "EPIL", "EPIdL",
"EExport", "EImport")
yraverage <- panelSJ %>%
group_by(reporter) %>%
distinct(year, .keep_all = TRUE) %>%
mutate_at(.vars = measures,
.fun = funs(x = mean(., na.rm = T))) %>%
ungroup()
names(yraverage) <- sub("^(.*)_(.*)$", "\\2\\1", names(yraverage))
yraverage <- yraverage %>%
select(reporter, rRegion, rIncome, starts_with("x")) %>%
distinct(reporter, .keep_all = TRUE)
yraverage$xVBanking <- rowMeans(subset(yraverage,
select = c(xVdClaims, xVLiabilities)),
na.rm = T)
yraverage$xVDirectInv <- rowMeans(subset(yraverage,
select = c(xVDII, xVDIdO)),
na.rm = T)
yraverage$xVPortInv <- rowMeans(subset(yraverage,
select = c(xVPIA, xVPIdL)),
na.rm = T)
yraverage$xVTrade <- rowMeans(subset(yraverage,
select = c(xVExport, xVImport)),
na.rm = T)
yraverage$xIBanking <- rowMeans(subset(yraverage,
select = c(xIdClaims, xILiabilities)),
na.rm = T)
yraverage$xIDirectInv <- rowMeans(subset(yraverage,
select = c(xIDII, xIDIdO)),
na.rm = T)
yraverage$xIPortInv <- rowMeans(subset(yraverage,
select = c(xIPIA, xIPIdL)),
na.rm = T)
yraverage$xITrade <- rowMeans(subset(yraverage,
select = c(xIExport, xIImport)),
na.rm = T)
yraverage$xEBanking <- rowMeans(subset(yraverage,
select = c(xEdClaims, xELiabilities)),
na.rm = T)
yraverage$xEDirectInv <- rowMeans(subset(yraverage,
select = c(xEDII, xEDIdO)),
na.rm = T)
yraverage$xEPortInv <- rowMeans(subset(yraverage,
select = c(xEPIA, xEPIdL)),
na.rm = T)
yraverage$xETrade <- rowMeans(subset(yraverage,
select = c(xEExport, xEImport)),
na.rm = T)
# .. Extract conduits ####
choose.cut <- "c90"
pctiles <- apply(yraverage[, 4:ncol(yraverage)], 2, quantile, probs = c(0.75, 0.8, 0.9, 0.95, 0.99), na.rm = TRUE)
pctiles <- as.data.frame(t(pctiles))
pctiles$variable <- row.names(pctiles)
yraverage <- melt(yraverage,
id.vars = c("reporter", "rRegion", "rIncome"),
measure.vars = c("xVdClaims", "xVLiabilities",
"xVDIdI", "xVDII", "xVDIdO", "xVDIO",
"xVPIA", "xVPIL", "xVPIdL",
"xVExport", "xVImport",
"xIdClaims", "xILiabilities",
"xIDIdI", "xIDII", "xIDIdO", "xIDIO",
"xIPIA", "xIPIL", "xIPIdL",
"xIExport", "xIImport",
"xEdClaims", "xELiabilities",
"xEDIdI", "xEDII", "xEDIdO", "xEDIO",
"xEPIA", "xEPIL", "xEPIdL",
"xEExport", "xEImport",
"xVBanking", "xVDirectInv", "xVPortInv", "xVTrade",
"xIBanking", "xIDirectInv", "xIPortInv", "xITrade",
"xEBanking", "xEDirectInv", "xEPortInv", "xETrade"))
yraverage <- subset(yraverage, !is.na(value) & !is.infinite(value))
yraverage <- merge(yraverage, pctiles,
by = "variable",
all.x = TRUE)
colnames(yraverage)[colnames(yraverage) == "75%"] <- "c75"
colnames(yraverage)[colnames(yraverage) == "80%"] <- "c80"
colnames(yraverage)[colnames(yraverage) == "90%"] <- "c90"
colnames(yraverage)[colnames(yraverage) == "95%"] <- "c95"
colnames(yraverage)[colnames(yraverage) == "99%"] <- "c99"
conduits <- subset(yraverage, value > eval(as.name(paste(choose.cut))))
conduits <- conduits[order(conduits$variable, conduits$reporter), ]
write.csv(conduits, "Results/VIE conduits_Secrecy Score.csv", row.names = FALSE)
af <- subset(yraverage, rRegion == "Africa")
am <- subset(yraverage, rRegion == "Americas")
as <- subset(yraverage, rRegion == "Asia")
eu <- subset(yraverage, rRegion == "Europe")
oc <- subset(yraverage, rRegion == "Oceania")
nr <- subset(yraverage, rRegion == "")
HIC <- subset(yraverage, rIncome == "HIC")
UMC <- subset(yraverage, rIncome == "UMC")
LMC <- subset(yraverage, rIncome == "LMC")
LIC <- subset(yraverage, rIncome == "LIC")
ni <- subset(yraverage, rIncome == "")
afno <- subset(yraverage, rRegion == "Africa" & value <= eval(as.name(paste(choose.cut))))
amno <- subset(yraverage, rRegion == "Americas" & value <= eval(as.name(paste(choose.cut))))
asno <- subset(yraverage, rRegion == "Asia" & value <= eval(as.name(paste(choose.cut))))
euno <- subset(yraverage, rRegion == "Europe" & value <= eval(as.name(paste(choose.cut))))
ocno <- subset(yraverage, rRegion == "Oceania" & value <= eval(as.name(paste(choose.cut))))
nrno <- subset(yraverage, rRegion == "" & value <= eval(as.name(paste(choose.cut))))
HICno <- subset(yraverage, rIncome == "HIC" & value <= eval(as.name(paste(choose.cut))))
UMCno <- subset(yraverage, rIncome == "UMC" & value <= eval(as.name(paste(choose.cut))))
LMCno <- subset(yraverage, rIncome == "LMC" & value <= eval(as.name(paste(choose.cut))))
LICno <- subset(yraverage, rIncome == "LIC" & value <= eval(as.name(paste(choose.cut))))
nino <- subset(yraverage, rIncome == "" & value <= eval(as.name(paste(choose.cut))))
# .. Plot country-risk graphs ####
countryrisk <- spread(yraverage[, 1:5], variable, value)
countryrisk <- gather(countryrisk, variable, value, 4:ncol(countryrisk),
factor_key = TRUE)
reporters <- unique(panelSJ$reporter)
reporters.string <- iconv(reporters, from = "UTF-8", to = "ASCII//TRANSLIT")
var.labels.all <- setNames(rep(c("Banking Claims (derived)", "Banking Liabilities",
"FDI Inward", "FDI Outward (derived)",
"Portfolio Assets", "Portfolio Liabilities (derived)",
"Exports", "Imports"), 3),
paste0(c(rep("xV", 8),
rep("xI", 8),
rep("xE", 8)),
rep(c("dClaims", "Liabilities",
"DII", "DIdO",
"PIA", "PIdL",
"Export", "Import"), 3)))
col.labels.all <- setNames(rep(c("#C93312", "#899DA4",
"#FD6467", "#7294D4",
"#F98400", "#00A08A",
"#800080", "#FAD510"), 3),
paste0(c(rep("xV", 8),
rep("xI", 8),
rep("xE", 8)),
rep(c("dClaims", "Liabilities",
"DII", "DIdO",
"PIA", "PIdL",
"Export", "Import"), 3)))
vars.all <- paste("dClaims", "Liabilities",
"DII", "DIdO",
"PIA", "PIdL",
"Export", "Import", sep = "|")
# .... With NAs labelled, disaggregated ####
for (m in 1:length(measure)){
for (r in 1:length(reporters)){
dat <- countryrisk %>% filter(str_detect(variable, paste0("^",measure[m])) &
str_detect(variable, vars.all) &
reporter == reporters[r])
na.coord <- as.character(dat[which(is.na(dat$value)), 4])
g <- ggplot(dat,
aes(x = fct_rev(variable), y = value,
fill = variable)) +
geom_col() +
coord_flip() +
geom_text(aes(label = ifelse(startsWith(as.character(dat$variable), "xI"),
round(value, 3),
round(value))),
size = 4, hjust = 0.5, fontface = "bold",
nudge_y = ifelse(startsWith(as.character(dat$variable), "xV"),
0.07*max(dat$value, na.rm = T),
ifelse(startsWith(as.character(dat$variable), "xI"),
0.1*max(dat$value, na.rm = T),
0.05*max(dat$value, na.rm = T)))) +
ylab(paste0(measure.label[m], " Score")) +
scale_fill_manual(values = col.labels.all,
labels = var.labels.all) +
scale_y_continuous(labels = comma) +
scale_x_discrete(labels = var.labels.all) +
theme(axis.title.y = element_blank()) +
guides(fill = FALSE) +
ggtitle(reporters[r])
if (!is_empty(na.coord)) {
g <- g + annotate("text", x = na.coord, y = 0,
label = "NA", fontface = "bold")
}
ggsave(g,
file = paste0("Figures/Overall Secrecy Score/Jurisdiction scores/Country risk profiles/Disaggregated/", measure[m], "_", reporters.string[r],".png"),
width = 6, height = 5, units = "in")
}
}
# .... Without NAs labelled, disaggregated ####
for (m in 1:length(measure)){
for (r in 1:length(reporters)){
g <- ggplot(yraverage %>% filter(str_detect(variable, paste0("^",measure[m])) &
str_detect(variable, vars.all) &
reporter == reporters[r]),
aes(x = fct_rev(variable), y = value, fill = (variable))) +
geom_col() + coord_flip() +
ylab(paste0(measure.label[m], " Score")) +
scale_fill_manual(values = col.labels.all,
labels = var.labels.all) +
scale_y_continuous(labels = comma) +
scale_x_discrete(labels = var.labels.all) +
theme(axis.title.y = element_blank()) +
guides(fill = FALSE) +
ggtitle(reporters[r])
ggsave(g,
file = paste0("Figures/Overall Secrecy Score/Jurisdiction scores/Country risk profiles/Disaggregated/No labels/", measure[m], "_", reporters.string[r],".pdf"),
width = 6, height = 5, units = "in")
}
}
# .... With NAs labelled, aggregated ####
var.labels.all <- setNames(rep(c("Banking",
"Direct Investment",
"Portfolio Investment",
"Trade"), 3),
paste0(c(rep("xV", 4),
rep("xI", 4),
rep("xE", 4)),
rep(c("Banking",
"DirectInv",
"PortInv",
"Trade"), 3)))
vars.all <- paste("Banking",
"DirectInv",
"PortInv",
"Trade", sep = "|")
for (m in 1:length(measure)){
for (r in 1:length(reporters)){
dat <- countryrisk %>% filter(str_detect(variable, paste0("^",measure[m])) &
str_detect(variable, vars.all) &
reporter == reporters[r])
na.coord <- as.character(dat[which(is.na(dat$value)), 4])
g <- ggplot(dat,
aes(x = fct_rev(variable), y = value,
fill = variable)) +
geom_col() +
coord_flip() +
geom_text(aes(label = ifelse(startsWith(as.character(dat$variable), "xI"),
round(value, 3),
round(value))),
size = 4, hjust = 0.5, fontface = "bold",
nudge_y = ifelse(startsWith(as.character(dat$variable), "xV"),
0.07*max(dat$value, na.rm = T),
ifelse(startsWith(as.character(dat$variable), "xI"),
0.1*max(dat$value, na.rm = T),
0.05*max(dat$value, na.rm = T)))) +
ylab(paste0(measure.label[m], " Score")) +
scale_fill_manual(values = rev(wes_palette("Chevalier1")),
labels = var.labels.all) +
scale_y_continuous(labels = comma) +
scale_x_discrete(labels = var.labels.all) +
theme(axis.title.y = element_blank()) +
guides(fill = FALSE) +
ggtitle(reporters[r])
if (!is_empty(na.coord)) {
g <- g + annotate("text", x = na.coord, y = 0,
label = "NA", fontface = "bold")
}
ggsave(g,
file = paste0("Figures/Overall Secrecy Score/Jurisdiction scores/Country risk profiles/Aggregated/", measure[m], "_", reporters.string[r],".png"),
width = 6, height = 5, units = "in")
}
}
# .. Plot jurisdiction-level scores ####
# .... For conduits ####
for (m in 1:length(measure)){
for (f in 1:length(flowstock)){
g <- ggplot(conduits %>%
filter((variable == paste0(measure[m], vars[[f]][1]) |
variable == paste0(measure[m], vars[[f]][2])) &
value != 0) %>%
distinct(reporter, variable, .keep_all = TRUE),
aes(x = reorder(reporter, value, sum), y = value, fill = fct_rev(variable))) +
geom_col() +
coord_flip() +
labs(title = paste0(measure.label[m], " of ", flowstock[f], " in Conduits"),
x = "Reporting country",
y = paste0(measure.label[m], " Score")) +
guides(fill = guide_legend(title = NULL, reverse = TRUE)) +
scale_fill_manual(labels = rev(c(var.labels[[f]][1], var.labels[[f]][2])),
values = c(cols[[f]][1], cols[[f]][2])) +
scale_y_continuous(labels = comma)
ggsave(g,
file = paste0("Figures/Overall Secrecy Score/Jurisdiction scores/Conduits/", measure[m], "_", names(vars)[f], "_Conduits",".pdf"),
width = 6, height = 5, units = "in")
ggsave(g,
file = paste0("Figures/Overall Secrecy Score/Jurisdiction scores/Conduits/PNG/", measure[m], "_", names(vars)[f], "_Conduits",".png"),
width = 6, height = 5, units = "in")
}
}
# .... By region, excluding conduits ####
for (m in 1:length(measure)){
for (r in 1:length(regionno)){
for (f in 1:length(flowstock)){
viz <- get(regionno[r]) %>%
filter((variable == paste0(measure[m], vars[[f]][1]) |
variable == paste0(measure[m], vars[[f]][2])) &
value != 0)
g <- ggplot(viz %>%
distinct(reporter, variable, .keep_all = TRUE),
aes(x = reorder(reporter, value, sum), y = value, fill = fct_rev(variable))) +
geom_col() +
coord_flip() +
labs(title = paste0(measure.label[m], " of ", flowstock[f], " in ", region.label[r]),
x = "Reporting country",
y = paste0(measure.label[m], " Score")) +
guides(fill = guide_legend(title = NULL, reverse = TRUE)) +
scale_fill_manual(labels = rev(c(var.labels[[f]][1], var.labels[[f]][2])),
values = c(cols[[f]][1], cols[[f]][2])) +
scale_y_continuous(labels = comma)
if (viz %>% distinct(reporter) %>% nrow >= 45){
g <- g + theme(axis.text.y = element_text(size = rel(0.9)))
}
if (viz %>% distinct(reporter) %>% nrow >= 48){
g <- g + theme(axis.text.y = element_text(size = rel(0.7)))
}
ggsave(g,
file = paste0("Figures/Overall Secrecy Score/Jurisdiction scores/By region/Excluding conduits/", measure[m], "_", names(vars)[f], "_", region.label[r], "_No Conduits", ".pdf"),
width = 6, height = 5, units = "in")
ggsave(g,
file = paste0("Figures/Overall Secrecy Score/Jurisdiction scores/By region/Excluding conduits/PNG/", measure[m], "_", names(vars)[f], "_", region.label[r], "_No Conduits", ".png"),
width = 6, height = 5, units = "in")
}
}
}
# .... By region, including conduits ####
for (m in 1:length(measure)){
for (r in 1:length(region)){
for (f in 1:length(flowstock)){
viz <- get(region[r]) %>%
filter((variable == paste0(measure[m], vars[[f]][1]) |
variable == paste0(measure[m], vars[[f]][2])) &
value != 0)
g <- ggplot(viz %>%
distinct(reporter, variable, .keep_all = TRUE),
aes(x = reorder(reporter, value, sum), y = value, fill = fct_rev(variable))) +
geom_col() +
coord_flip() +
labs(title = paste0(measure.label[m], " of ", flowstock[f], " in ", region.label[r]),
x = "Reporting country",
y = paste0(measure.label[m], " Score")) +
guides(fill = guide_legend(title = NULL, reverse = TRUE)) +
scale_fill_manual(labels = rev(c(var.labels[[f]][1], var.labels[[f]][2])),
values = c(cols[[f]][1], cols[[f]][2])) +
scale_y_continuous(labels = comma)
if (viz %>% distinct(reporter) %>% nrow >= 45){
g <- g + theme(axis.text.y = element_text(size = rel(0.9)))
}
if (viz %>% distinct(reporter) %>% nrow >= 48){
g <- g + theme(axis.text.y = element_text(size = rel(0.7)))
}
ggsave(g,
file = paste0("Figures/Overall Secrecy Score/Jurisdiction scores/By region/Including conduits/", measure[m], "_", names(vars)[f], "_", region.label[r],".pdf"),
width = 6, height = 5, units = "in")
ggsave(g,
file = paste0("Figures/Overall Secrecy Score/Jurisdiction scores/By region/Including conduits/PNG/", measure[m], "_", names(vars)[f], "_", region.label[r],".png"),
width = 6, height = 5, units = "in")
}
}
}
# .... By region, including conduits, all flows/stocks ####
co <- c("#C93312", "#899DA4", "#FD6467", "#7294D4", "#F98400", "#00A08A", "#800080", "#FAD510")
v.labels <- c("Claims (derived)", "Liabilities",
"Inward", "Outward (derived)",
"Assets", "Liabilities (derived)",
"Exports", "Imports")
for (m in 1:length(measure)){
for (r in 1:length(region)){
viz <- get(region[r]) %>%
filter((variable == paste0(measure[m], vars[[1]][1]) |
variable == paste0(measure[m], vars[[1]][2]) |
variable == paste0(measure[m], vars[[2]][1]) |
variable == paste0(measure[m], vars[[2]][2]) |
variable == paste0(measure[m], vars[[3]][1]) |
variable == paste0(measure[m], vars[[3]][2]) |
variable == paste0(measure[m], vars[[4]][1]) |
variable == paste0(measure[m], vars[[4]][2])) &
value != 0)
g <- ggplot(viz %>%
distinct(reporter, variable, .keep_all = TRUE),
aes(x = reorder(reporter, value, sum), y = value, fill = variable)) +
geom_col() +
coord_flip() +
labs(title = paste0(measure.label[m], " of all flows/stocks in ", region.label[r]),
x = "Reporting country",
y = paste0(measure.label[m], " Score")) +