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Compute IFF Estimates.R
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Compute IFF Estimates.R
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# Compute IFF Estimates
# Alice Lepissier
# alice.lepissier@gmail.com
# Originally prepared for the United Nations Economic Commission for Africa (UNECA)
## ## ## ## ## ## ## ## ## ## ##
# INDEX ####
## ## ## ## ## ## ## ## ## ## ##
# Preamble
# Create Variables
# .. Create dependent variable and predictors
# .. Variable transformation
# Remove Outliers
# .. Truncate panel and remove CIF ratios greater than 100
# .. Estimate regression
# .. Identify and remove outliers
# Mis-Invoicing Estimates
# .. Estimate CIF rates
# .. Compute fitted values when predictors are 0
# .. Compute FOB imports
# .. Estimate fixed effects regression
# .. Estimate fixed effects regression (robustness check)
# .. Harmonization procedure
# .. Harmonization procedure (robustness check)
# .. Compute IFF
# .. Tariff Evasion Analysis
## ## ## ## ## ## ## ## ## ## ##
# PREAMBLE ####
## ## ## ## ## ## ## ## ## ## ##
setwd("/home/alepissier/IFFe/") # Virtual server
data.disk <- "/scratch/alepissier/IFFe/"
# source("Scripts/Data Preparation.R")
library(car)
library(GGally)
library(ggridges)
library(kableExtra)
library(lfe)
library(reshape2)
library(scales)
library(stargazer)
library(tidyverse)
options(scipen = 999)
## ## ## ## ## ## ## ## ## ## ##
# CREATE VARIABLES ####
## ## ## ## ## ## ## ## ## ## ##
load(paste0(data.disk, "Data/Panel/panel.Rdata"))
length(unique(panel$id)) == nrow(panel)
# TRUE, 23266944 obs
panel %>%
filter(pExport_value == 0 & is.na(pReExport_value)) %>% nrow
# There is no case where a mirrored Re-Export value is missing while
# the mirrored Export value exists.
panel <- panel %>%
mutate(NetExport_value = Export_value - ReExport_value,
pNetExport_value = pExport_value - pReExport_value)
panel <- panel %>%
select(-ends_with("quantity"), -ends_with("weight"))
# .. Create dependent variables and predictors ####
panel <- panel %>%
mutate(ratio_CIF = Import_value / pNetExport_value,
ratio_CIF_mirror = pImport_value / NetExport_value) %>%
filter(is.finite(ratio_CIF)) %>%
filter(is.finite(ratio_CIF_mirror))
nrow(panel)
# 5762590
summary(panel$ratio_CIF)
hist(panel$ratio_CIF)
summary(panel$ratio_CIF_mirror)
hist(panel$ratio_CIF_mirror)
panel <- panel %>%
mutate(ln.ratio_CIF = log(ratio_CIF),
ln.ratio_CIF_mirror = log(ratio_CIF_mirror)) %>%
filter(is.finite(ln.ratio_CIF)) %>%
filter(is.finite(ln.ratio_CIF_mirror))
nrow(panel)
# 4821618
summary(panel$year)
panel <- panel %>%
group_by(reporter.ISO, partner.ISO, commodity.code) %>%
mutate(ln.ratio_CIF_lag = dplyr::lag(ln.ratio_CIF, n = 1),
ln.ratio_CIF_lag_mirror = dplyr::lag(ln.ratio_CIF_mirror, n = 1)) %>%
ungroup() %>%
filter(is.finite(ln.ratio_CIF_lag)) %>%
filter(is.finite(ln.ratio_CIF_lag_mirror))
nrow(panel)
# 4341968
summary(panel$year)
panel <- panel %>%
group_by(reporter.ISO, partner.ISO, commodity.code) %>%
mutate(Import_fut = dplyr::lead(Import_value, n = 1),
pImport_fut = dplyr::lead(pImport_value, n = 1)) %>%
ungroup() %>%
filter(is.finite(Import_fut)) %>%
filter(is.finite(pImport_fut))
nrow(panel)
# 3944277
summary(panel$year)
panel <- panel %>%
mutate(FutImport_misrep = Import_fut / Import_value,
ReExport_misrep = pReExport_value / Import_value,
FutImport_misrep_mirror = pImport_fut / pImport_value,
ReExport_misrep_mirror = ReExport_value / pImport_value) %>%
filter(is.finite(FutImport_misrep) & is.finite(ReExport_misrep)) %>%
filter(is.finite(FutImport_misrep_mirror) & is.finite(ReExport_misrep_mirror))
nrow(panel)
# 3944277
tariff_mirror <- panel %>%
select(reporter.ISO, partner.ISO, commodity.code, year, tariff) %>%
rename(tariff_mirror = tariff)
panel <- left_join(panel, tariff_mirror,
by = c("reporter.ISO" = "partner.ISO",
"partner.ISO" = "reporter.ISO",
"commodity.code" = "commodity.code",
"year" = "year"))
rm(tariff_mirror)
panel <- panel %>%
filter(complete.cases(ratio_CIF, ln.ratio_CIF, ln.ratio_CIF_lag,
ratio_CIF_mirror, ln.ratio_CIF_mirror, ln.ratio_CIF_lag_mirror,
FutImport_misrep, ReExport_misrep,
FutImport_misrep_mirror, ReExport_misrep_mirror,
tariff, tariff_mirror,
dist, contig, rLandlocked, pLandlocked,
rCorruption, pCorruption, rRegulatory.qual, pRegulatory.qual))
nrow(panel)
# 2559456
# .. Variable transformation ####
panel <- panel %>%
mutate(dist.sq = I(dist^2))
panel <- panel %>%
mutate(rCorruption = 100 - rCorruption,
pCorruption = 100 - pCorruption,
rRegulatory.qual = 100 - rRegulatory.qual,
pRegulatory.qual = 100 - pRegulatory.qual) %>%
rename(rPoorRegulation = rRegulatory.qual,
pPoorRegulation = pRegulatory.qual)
hist(panel$ln.ratio_CIF)
hist(panel$FutImport_misrep)
hist(log(panel$FutImport_misrep))
hist(panel$ReExport_misrep)
hist(log(panel$ReExport_misrep))
hist(panel$tariff)
hist(log(panel$tariff))
hist(panel$rCorruption)
hist(panel$pCorruption)
hist(panel$rPoorRegulation)
hist(panel$pPoorRegulation)
hist(panel$dist)
hist(panel$dist.sq)
ihs <- function(x){
x <- log(x + sqrt(x^2 + 1))
return(x)
}
# Check whether there are zeros in the data
summary(log(panel$FutImport_misrep))
# Fine to log
summary(log(panel$ReExport_misrep))
# Need inverse hyperbolic sine transformation
summary(log(panel$tariff))
# Need inverse hyperbolic sine transformation
summary(log(panel$FutImport_misrep_mirror))
# Fine to log
summary(log(panel$ReExport_misrep_mirror))
# Need inverse hyperbolic sine transformation
summary(log(panel$tariff_mirror))
# Need inverse hyperbolic sine transformation
panel <- panel %>%
mutate(ln.FutImport_misrep = log(FutImport_misrep),
ihs.ReExport_misrep = ihs(ReExport_misrep),
ihs.tariff = ihs(tariff),
ln.FutImport_misrep_mirror = log(FutImport_misrep_mirror),
ihs.ReExport_misrep_mirror = ihs(ReExport_misrep_mirror),
ihs.tariff_mirror = ihs(tariff_mirror))
hist(panel$ln.FutImport_misrep)
hist(panel$ihs.ReExport_misrep)
hist(panel$ihs.tariff)
hist(panel$ln.FutImport_misrep_mirror)
hist(panel$ihs.ReExport_misrep_mirror)
hist(panel$ihs.tariff_mirror)
rm(ihs)
nrow(panel)
# 2559456
save(panel, file = paste0(data.disk, "Data/Panel/panel_clean.Rdata"))
## ## ## ## ## ## ## ## ## ## ##
# REMOVE OUTLIERS ####
## ## ## ## ## ## ## ## ## ## ##
# .. Truncate panel and remove CIF ratios greater than 100 ####
summary(panel$ratio_CIF)
summary(panel$ratio_CIF_mirror)
panel %>% filter(ratio_CIF > 10^4) %>% nrow
# 3849
panel %>% filter(ratio_CIF > 10^3) %>% nrow
# 15264
panel %>% filter(ratio_CIF_mirror > 10^4) %>% nrow
# 3849
panel %>% filter(ratio_CIF_mirror > 10^3) %>% nrow
# 15264
panel <- panel %>%
filter(ratio_CIF < 10^2) %>%
filter(ratio_CIF_mirror < 10^2)
nrow(panel)
# 2451110
# .. Estimate regression ####
fit <- lm(ln.ratio_CIF ~ dist + dist.sq +
contig +
rLandlocked +
pLandlocked +
ln.FutImport_misrep +
ihs.ReExport_misrep +
ln.ratio_CIF_lag +
tariff +
rCorruption + pCorruption +
rPoorRegulation + pPoorRegulation +
factor(year),
data = panel)
summary(fit)
mean(exp(fitted(fit)))
# 1.719294
max(panel$ratio_CIF)
# 99.99301
mean(panel$ratio_CIF)
# 3.098722
# .. Identify and remove outliers ####
panel$CD <- cooks.distance(fit)
summary(panel$CD)
while(max(panel$CD) > 2){
panel <- panel %>%
filter(CD <= 2)
fit <- lm(ln.ratio_CIF ~ dist + dist.sq +
contig +
rLandlocked +
pLandlocked +
ln.FutImport_misrep +
ihs.ReExport_misrep +
ln.ratio_CIF_lag +
tariff +
rCorruption + pCorruption +
rPoorRegulation + pPoorRegulation +
factor(year),
data = panel)
panel$CD <- cooks.distance(fit)
}
nrow(panel)
# 2451110
summary(panel$CD)
Bonferonni.out <- outlierTest(fit, n.max = 10000)
obs <- as.numeric(names(Bonferonni.out[[1]]))
outliers <- panel[c(obs), ]
mean(outliers$ratio_CIF)
# 3.9108
mean(panel$ratio_CIF)
# 3.098722
panel <- panel[-c(obs),]
mean(panel$ratio_CIF)
# 3.097565
fit <- lm(ln.ratio_CIF ~ dist + dist.sq +
contig +
rLandlocked +
pLandlocked +
ln.FutImport_misrep +
ihs.ReExport_misrep +
ln.ratio_CIF_lag +
tariff +
rCorruption + pCorruption +
rPoorRegulation + pPoorRegulation +
factor(year),
data = panel)
Bonferonni.out <- outlierTest(fit, n.max = 10000)
obs <- as.numeric(names(Bonferonni.out[[1]]))
outliers <- panel[c(obs), ]
mean(outliers$ratio_CIF)
# 6.117861
mean(panel$ratio_CIF)
# 3.097565
panel <- panel[-c(obs),]
mean(panel$ratio_CIF)
# 3.096567
fit <- lm(ln.ratio_CIF ~ dist + dist.sq +
contig +
rLandlocked +
pLandlocked +
ln.FutImport_misrep +
ihs.ReExport_misrep +
ln.ratio_CIF_lag +
tariff +
rCorruption + pCorruption +
rPoorRegulation + pPoorRegulation +
factor(year),
data = panel)
Bonferonni.out <- outlierTest(fit, n.max = 10000)
obs <- as.numeric(names(Bonferonni.out[[1]]))
outliers <- panel[c(obs), ]
mean(outliers$ratio_CIF)
# 5.767423
mean(panel$ratio_CIF)
# 3.096567
panel <- panel[-c(obs),]
mean(panel$ratio_CIF)
# 3.09642
mean(panel$ratio_CIF_mirror)
# 3.097388
rm(Bonferonni.out, outliers, obs)
nrow(panel)
# 2446679
save(panel, file = paste0(data.disk, "Data/Panel/panel_nooutliers.Rdata"))
## ## ## ## ## ## ## ## ## ## ##
# MIS-INVOICING ESTIMATES ####
## ## ## ## ## ## ## ## ## ## ##
# .. Estimate CIF rates ####
load(paste0(data.disk, "Data/Panel/panel_nooutliers.Rdata"))
nrow(panel)
# 2446679
max(panel$ratio_CIF)
# 99.99301
hist(panel$ratio_CIF)
summary(panel$ratio_CIF)
max(panel$ratio_CIF_mirror)
# 99.99301
hist(panel$ratio_CIF_mirror)
summary(panel$ratio_CIF_mirror)
fit <- lm(ln.ratio_CIF ~ dist + dist.sq +
contig +
rLandlocked +
pLandlocked +
ln.FutImport_misrep +
ihs.ReExport_misrep +
ln.ratio_CIF_lag +
tariff +
rCorruption + pCorruption +
rPoorRegulation + pPoorRegulation +
factor(year),
data = panel)
summary(fit)
mean(exp(fitted(fit)))
# 1.730972
panel <- panel %>%
mutate(dist_t = dist / 10^3,
dist_t.sq = dist_t * dist_t)
summary(panel$dist_t)
# Final results will remain unchanged, but distance is taken in 1000s of km
# rather than km to avoid tiny coefficients which are hard to read.
fit_paper <- lm(ln.ratio_CIF ~ dist_t + dist_t.sq +
contig +
rLandlocked +
pLandlocked +
ln.FutImport_misrep +
ihs.ReExport_misrep +
ln.ratio_CIF_lag +
tariff +
rCorruption + pCorruption +
rPoorRegulation + pPoorRegulation +
factor(year),
data = panel)
summary(fit_paper)
mean(exp(fitted(fit_paper)))
# 1.730972
fit_mirror <- lm(ln.ratio_CIF_mirror ~ dist + dist.sq +
contig +
rLandlocked +
pLandlocked +
ln.FutImport_misrep_mirror +
ihs.ReExport_misrep_mirror +
ln.ratio_CIF_lag_mirror +
tariff_mirror +
rCorruption + pCorruption +
rPoorRegulation + pPoorRegulation +
factor(year),
data = panel)
summary(fit_mirror)
mean(exp(fitted(fit_mirror)))
# 1.719214
# Final results will remain unchanged, but distance is taken in 1000s of km
# rather than km to avoid tiny coefficients which are hard to read.
fit_mirror_paper <- lm(ln.ratio_CIF_mirror ~ dist_t + dist_t.sq +
contig +
rLandlocked +
pLandlocked +
ln.FutImport_misrep_mirror +
ihs.ReExport_misrep_mirror +
ln.ratio_CIF_lag_mirror +
tariff_mirror +
rCorruption + pCorruption +
rPoorRegulation + pPoorRegulation +
factor(year),
data = panel)
summary(fit_mirror_paper)
mean(exp(fitted(fit_mirror_paper)))
# 1.719214
# Check non-linear relationship with distance
d <- panel %>% distinct(dist) %>%
mutate(y = coef(fit)["dist"]*dist + coef(fit)["dist.sq"]*dist^2)
ggplot(d, aes(x = dist, y = y)) +
geom_line()
# Check for multicollinearity
kable(vif(fit), digits = 3, format = "rst")
kable(vif(fit_mirror), digits = 3, format = "rst")
vars <- panel %>%
select(all_of(c("dist", "dist.sq",
# "contig",
# "rLandlocked",
# "pLandlocked",
"ln.FutImport_misrep",
"ihs.ReExport_misrep",
"ln.ratio_CIF_lag",
"tariff",
"rCorruption", "pCorruption",
"rPoorRegulation", "pPoorRegulation")))
# mutate(vars("contig", "rLandlocked", "pLandlocked"),
# ~as.numeric(.))
cor(vars)
save(fit, file = "Results/fit")
save(fit_mirror, file = "Results/fit_mirror")
stargazer(fit, type = "html", style = "aer",
out = "Results/Regression table.html")
stargazer(fit_mirror, type = "html", style = "aer",
out = "Results/Regression table.html")
stargazer(fit_paper, fit_mirror_paper, style = "aer")
# .. Compute fitted values when predictors are 0 ####
IFF.preds <- c("tariff", "tariff_mirror",
"rCorruption", "pCorruption",
"rPoorRegulation", "pPoorRegulation")
coef_illicit <- coef(fit)
for (v in 1:length(coef_illicit)){
if (!(names(coef_illicit)[v] %in% IFF.preds)){
coef_illicit[v] <- 0
}
}
coef_illicit
panel$fitted_IFF <- as.numeric(exp(model.matrix(fit) %*% coef_illicit))
mean(panel$fitted_IFF)
# 0.9756726
coef_illicit_mirror <- coef(fit_mirror)
for (v in 1:length(coef_illicit_mirror)){
if (!(names(coef_illicit_mirror)[v] %in% IFF.preds)){
coef_illicit_mirror[v] <- 0
}
}
coef_illicit_mirror
panel$fitted_IFF_mirror <- as.numeric(exp(model.matrix(fit_mirror) %*% coef_illicit_mirror))
mean(panel$fitted_IFF_mirror)
# 0.9685555
coef_licit <- coef(fit)
for (v in 1:length(coef_licit)){
if ((names(coef_licit)[v] %in% IFF.preds)){
coef_licit[v] <- 0
}
}
coef_licit
panel$fitted_nonIFF <- as.numeric(exp(model.matrix(fit) %*% coef_licit))
mean(panel$fitted_nonIFF)
# 1.76583
coef_licit_mirror <- coef(fit_mirror)
for (v in 1:length(coef_licit_mirror)){
if ((names(coef_licit_mirror)[v] %in% IFF.preds)){
coef_licit_mirror[v] <- 0
}
}
coef_licit_mirror
panel$fitted_nonIFF_mirror <- as.numeric(exp(model.matrix(fit_mirror) %*% coef_licit_mirror))
mean(panel$fitted_nonIFF_mirror)
# 1.767022
mean(exp(log(exp(model.matrix(fit) %*% coef_licit)) +
log(exp(model.matrix(fit) %*% coef_illicit))))
# 1.730972
panel$fitted <- exp(fitted(fit))
mean(panel$fitted)
# 1.730972
round(mean(exp(log(exp(model.matrix(fit) %*% coef_licit)) +
log(exp(model.matrix(fit) %*% coef_illicit)))), 10) ==
round(mean(exp(fitted(fit))), 10)
# TRUE
panel$resid <- exp(resid(fit))
panel$fitted_mirror <- exp(fitted(fit_mirror))
mean(panel$fitted_mirror)
# 1.719214
panel$resid_mirror <- exp(resid(fit_mirror))
rm(coef_licit, coef_illicit, coef_licit_mirror, coef_illicit_mirror,
d, v, IFF.preds)
# .. Compute FOB imports ####
summary(panel$fitted)
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.000 0.827 1.091 1.731 1.465 3464.136
summary(panel$fitted_IFF)
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.1130 0.9412 0.9824 0.9757 1.0082 1.0730
summary(panel$fitted_nonIFF)
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.000 0.855 1.117 1.766 1.494 3436.165
noCIF <- c("BRA", "KHM", "CAN", "GIN", "PRY",
"ZAF", "UKR", "USA", "MLI", "TJK")
panel <- panel %>%
mutate(FOB_Import = ifelse(reporter.ISO %in% noCIF,
Import_value, Import_value / fitted),
pFOB_Import = ifelse(reporter.ISO %in% noCIF,
pImport_value, pImport_value / fitted_mirror),
FOB_Import_stripresid = ifelse(reporter.ISO %in% noCIF,
Import_value, Import_value / (fitted * resid)),
pFOB_Import_stripresid = ifelse(reporter.ISO %in% noCIF,
pImport_value, pImport_value / (fitted_mirror * resid_mirror)),
FOB_Import_stripnonIFF = ifelse(reporter.ISO %in% noCIF,
Import_value, Import_value / fitted_nonIFF),
pFOB_Import_stripnonIFF = ifelse(reporter.ISO %in% noCIF,
pImport_value, pImport_value / fitted_nonIFF_mirror),
FOB_Import_stripnonIFFresid = ifelse(reporter.ISO %in% noCIF,
Import_value, Import_value / (fitted_nonIFF * resid)),
pFOB_Import_stripnonIFFresid = ifelse(reporter.ISO %in% noCIF,
pImport_value, pImport_value / (fitted_nonIFF_mirror * resid_mirror)),
FOB_Import_stripIFF = ifelse(reporter.ISO %in% noCIF,
Import_value, Import_value / fitted_IFF),
pFOB_Import_stripIFF = ifelse(reporter.ISO %in% noCIF,
pImport_value, pImport_value / fitted_IFF_mirror))
# Checks
check <- panel %>% filter(!(reporter.ISO %in% noCIF))
# V_{ij}^{M, FOB} = V_{ji}^X * exp(epsilonhat)
sum(round(check$FOB_Import) ==
round(check$pNetExport_value * check$resid)) == nrow(check)
# TRUE
# V_{ij}^{M, FOB, resid} = V_{ji}^X
sum(round(check$FOB_Import_stripresid) ==
round(check$pNetExport_value)) == nrow(check)
# TRUE
# V_{ij}^{M, FOB, nonIFF} = V_{ji}^X * exp(lambdahat) * exp(epsilonhat)
sum(round(check$FOB_Import_stripnonIFF, -2) ==
round(check$pNetExport_value *
check$fitted_IFF * check$resid, -2)) == nrow(check)
# TRUE
# V_{ij}^{M, FOB, nonIFFresid} = V_{ji}^X * exp(lambdahat)
sum(round(check$FOB_Import_stripnonIFFresid, -3) ==
round(check$pNetExport_value *
check$fitted_IFF, -3)) == nrow(check)
# TRUE
# V_{ij}^{M, FOB, IFF} = V_{ji}^X * exp(betahat) * exp(epsilonhat)
sum(round(check$FOB_Import_stripIFF, -3) ==
round(check$pNetExport_value *
check$fitted_nonIFF * check$resid, -3)) == nrow(check)
# TRUE
# V_{ji}^{M, FOB} = V_{ij}^X * exp(epsilonhat)
sum(round(check$pFOB_Import) ==
round(check$NetExport_value * check$resid_mirror)) == nrow(check)
# TRUE
# V_{ji}^{M, FOB, resid} = V_{ij}^X
sum(round(check$pFOB_Import_stripresid) ==
round(check$NetExport_value)) == nrow(check)
# TRUE
# V_{ji}^{M, FOB, nonIFF} = V_{ij}^X * exp(lambdahat) * exp(epsilonhat)
sum(round(check$pFOB_Import_stripnonIFF, -2) ==
round(check$NetExport_value *
check$fitted_IFF_mirror * check$resid_mirror, -2)) == nrow(check)
# TRUE
# V_{ji}^{M, FOB, nonIFFresid} = V_{ij}^X * exp(lambdahat)
sum(round(check$pFOB_Import_stripnonIFFresid, -3) ==
round(check$NetExport_value *
check$fitted_IFF_mirror, -3)) == nrow(check)
# TRUE
# V_{ji}^{M, FOB, IFF} = V_{ij}^X * exp(betahat) * exp(epsilonhat)
sum(round(check$pFOB_Import_stripIFF, -3) ==
round(check$NetExport_value *
check$fitted_nonIFF_mirror * check$resid_mirror, -3)) == nrow(check)
# TRUE
rm(check)
# .. Estimate fixed effects regression ####
panel <- panel %>%
mutate(M_dist = abs(log(FOB_Import/pNetExport_value))) %>%
filter(is.finite(M_dist)) %>%
mutate(X_dist = abs(log(pFOB_Import/NetExport_value))) %>%
filter(is.finite(X_dist)) %>%
mutate(M_dist_rob = abs(log(FOB_Import_stripresid/pNetExport_value))) %>%
filter(is.finite(M_dist_rob)) %>%
mutate(X_dist_rob = abs(log(pFOB_Import_stripresid/NetExport_value))) %>%
filter(is.finite(X_dist_rob))
nrow(panel)
# 2446679
# Doing the filtering on robust distances in the same chunk
# does not truncate the regular sample
FE_M.out <- felm(M_dist ~ 0| reporter.ISO + partner.ISO + year + commodity.code,
data = panel)
FE_M <- getfe(FE_M.out, se = T)
save(FE_M, file = "Results/FE_M.Rdata")
FE_X.out <- felm(X_dist ~ 0| reporter.ISO + partner.ISO + year + commodity.code,
data = panel)
FE_X <- getfe(FE_X.out, se = T)
save(FE_X, file = "Results/FE_X.Rdata")
load("Results/FE_M.Rdata")
load("Results/FE_X.Rdata")
FE_M <- FE_M %>%
group_by(fe) %>%
mutate(min = min(effect)) %>%
ungroup()
FE_M$sigma <- pi/2*(FE_M$effect - (FE_M$min + 2*FE_M$se))
attr(FE_M$sigma, "extra") <- NULL
FE_X <- FE_X %>%
group_by(fe) %>%
mutate(min = min(effect)) %>%
ungroup()
FE_X$sigma <- pi/2*(FE_X$effect - (FE_X$min + 2*FE_X$se))
attr(FE_X$sigma, "extra") <- NULL
# .. Estimate fixed effects regression (robustness check) ####
FE_M_rob.out <- felm(M_dist_rob ~ 0| reporter.ISO + partner.ISO + year + commodity.code,
data = panel)
FE_M_rob <- getfe(FE_M_rob.out, se = T)
save(FE_M_rob, file = "Results/FE_M_rob.Rdata")
FE_X_rob.out <- felm(X_dist_rob ~ 0| reporter.ISO + partner.ISO + year + commodity.code,
data = panel)
FE_X_rob <- getfe(FE_X_rob.out, se = T)
save(FE_X_rob, file = "Results/FE_X_rob.Rdata")
load("Results/FE_M_rob.Rdata")
load("Results/FE_X_rob.Rdata")
FE_M_rob <- FE_M_rob %>%
group_by(fe) %>%
mutate(min = min(effect)) %>%
ungroup()
FE_M_rob$sigma <- pi/2*(FE_M_rob$effect - (FE_M_rob$min + 2*FE_M_rob$se))
attr(FE_M_rob$sigma, "extra") <- NULL
FE_X_rob <- FE_X_rob %>%
group_by(fe) %>%
mutate(min = min(effect)) %>%
ungroup()
FE_X_rob$sigma <- pi/2*(FE_X_rob$effect - (FE_X_rob$min + 2*FE_X_rob$se))
attr(FE_X_rob$sigma, "extra") <- NULL
# .. Harmonization procedure ####
panel <- left_join(panel, FE_M %>%
filter(fe == "reporter.ISO") %>%
select(idx, sigma) %>%
mutate(idx = as.character(idx)),
by = c("reporter.ISO" = "idx")) %>%
rename(rSigma_M = sigma)
panel <- left_join(panel, FE_M %>%
filter(fe == "partner.ISO") %>%
select(idx, sigma) %>%
mutate(idx = as.character(idx)),
by = c("partner.ISO" = "idx")) %>%
rename(pSigma_M = sigma)
panel <- left_join(panel, FE_X %>%
filter(fe == "reporter.ISO") %>%
select(idx, sigma) %>%
mutate(idx = as.character(idx)),
by = c("reporter.ISO" = "idx")) %>%
rename(rSigma_X = sigma)
panel <- left_join(panel, FE_X %>%
filter(fe == "partner.ISO") %>%
select(idx, sigma) %>%
mutate(idx = as.character(idx)),
by = c("partner.ISO" = "idx")) %>%
rename(pSigma_X = sigma)
panel <- panel %>%
mutate(w_r_M = (exp(pSigma_M^2)*(exp(pSigma_M^2) - 1))/
(exp(rSigma_M^2)*(exp(rSigma_M^2)- 1) +
exp(pSigma_M^2)*(exp(pSigma_M^2) - 1)),
w_p_M = (exp(rSigma_M^2)*(exp(rSigma_M^2) - 1))/
(exp(rSigma_M^2)*(exp(rSigma_M^2)- 1) +
exp(pSigma_M^2)*(exp(pSigma_M^2) - 1)))
summary(panel$w_r_M)
summary(panel$w_p_M)
panel <- panel %>%
mutate(w_r_X = (exp(pSigma_X^2)*(exp(pSigma_X^2) - 1))/
(exp(rSigma_X^2)*(exp(rSigma_X^2)- 1) +
exp(pSigma_X^2)*(exp(pSigma_X^2) - 1)),
w_p_X = (exp(rSigma_X^2)*(exp(rSigma_X^2) - 1))/
(exp(rSigma_X^2)*(exp(rSigma_X^2)- 1) +
exp(pSigma_X^2)*(exp(pSigma_X^2) - 1)))
summary(panel$w_r_X)
summary(panel$w_p_X)
ggplot(data = panel %>%
select(starts_with("w_r") | starts_with("w_p")) %>%
melt(),
aes(x = value, y = variable)) +
geom_density_ridges()
panel %>%
select(starts_with("w_r") | starts_with("w_p")) %>%
melt() %>%
count(variable)
panel <- panel %>%
mutate(w_M = w_r_M + w_p_M,
w_X = w_r_X + w_p_X)
summary(panel$w_M)
summary(panel$w_X)
panel <- panel %>%
mutate(RV_M = w_r_M*FOB_Import + w_p_M*pNetExport_value,
RV_X = w_r_X*NetExport_value + w_p_X*pFOB_Import)
# .. Harmonization procedure (robustness check) ####
panel <- left_join(panel, FE_M_rob %>%
filter(fe == "reporter.ISO") %>%
select(idx, sigma) %>%
mutate(idx = as.character(idx)),
by = c("reporter.ISO" = "idx")) %>%
rename(rSigma_M_rob = sigma)
panel <- left_join(panel, FE_M_rob %>%
filter(fe == "partner.ISO") %>%
select(idx, sigma) %>%
mutate(idx = as.character(idx)),
by = c("partner.ISO" = "idx")) %>%
rename(pSigma_M_rob = sigma)
panel <- left_join(panel, FE_X_rob %>%
filter(fe == "reporter.ISO") %>%
select(idx, sigma) %>%
mutate(idx = as.character(idx)),
by = c("reporter.ISO" = "idx")) %>%
rename(rSigma_X_rob = sigma)
panel <- left_join(panel, FE_X_rob %>%
filter(fe == "partner.ISO") %>%
select(idx, sigma) %>%
mutate(idx = as.character(idx)),
by = c("partner.ISO" = "idx")) %>%
rename(pSigma_X_rob = sigma)
panel <- panel %>%
mutate(w_r_M_rob = (exp(pSigma_M_rob^2)*(exp(pSigma_M_rob^2) - 1))/
(exp(rSigma_M_rob^2)*(exp(rSigma_M_rob^2)- 1) +
exp(pSigma_M_rob^2)*(exp(pSigma_M_rob^2) - 1)),
w_p_M_rob = (exp(rSigma_M_rob^2)*(exp(rSigma_M_rob^2) - 1))/
(exp(rSigma_M_rob^2)*(exp(rSigma_M_rob^2)- 1) +
exp(pSigma_M_rob^2)*(exp(pSigma_M_rob^2) - 1)))
summary(panel$w_r_M_rob)
summary(panel$w_p_M_rob)
panel <- panel %>%
mutate(w_r_X_rob = (exp(pSigma_X_rob^2)*(exp(pSigma_X_rob^2) - 1))/
(exp(rSigma_X_rob^2)*(exp(rSigma_X_rob^2)- 1) +
exp(pSigma_X_rob^2)*(exp(pSigma_X_rob^2) - 1)),
w_p_X_rob = (exp(rSigma_X_rob^2)*(exp(rSigma_X_rob^2) - 1))/
(exp(rSigma_X_rob^2)*(exp(rSigma_X_rob^2)- 1) +
exp(pSigma_X_rob^2)*(exp(pSigma_X_rob^2) - 1)))
summary(panel$w_r_X_rob)
summary(panel$w_p_X_rob)
ggplot(data = panel %>%
select(starts_with("w_r") | starts_with("w_p")) %>%
select(ends_with("rob")) %>%
melt(),
aes(x = value, y = variable)) +
geom_density_ridges()
panel %>%
select(starts_with("w_r") | starts_with("w_p")) %>%
select(ends_with("rob")) %>%
melt() %>%
count(variable)
panel <- panel %>%
mutate(w_M_rob = w_r_M_rob + w_p_M_rob,
w_X_rob = w_r_X_rob + w_p_X_rob)
summary(panel$w_M_rob)
summary(panel$w_X_rob)
panel <- panel %>%
mutate(RV_M_rob = w_r_M_rob*FOB_Import_stripresid + w_p_M_rob*pNetExport_value,
RV_X_rob = w_r_X_rob*NetExport_value + w_p_X_rob*pFOB_Import_stripresid)
# .. Compute IFF ####
panel <- panel %>%
mutate(Imp_IFF = FOB_Import_stripnonIFF - RV_M,
Exp_IFF = RV_X - NetExport_value,
Imp_IFF_rob = FOB_Import_stripnonIFFresid - RV_M_rob,
Exp_IFF_rob = RV_X_rob - NetExport_value)
summary(panel$Imp_IFF)
summary(panel$Exp_IFF)
summary(panel$Imp_IFF_rob)
summary(panel$Exp_IFF_rob)
ggplot(data = panel %>%
select(c(Imp_IFF, Exp_IFF)) %>%
melt(),
aes(x = value, y = variable)) +
geom_density_ridges()
panel %>%
filter(Imp_IFF > 2*10^10) %>%
distinct(reporter.ISO)
# 1 GBR
# 2 CHN
# 3 USA
# 4 CZE
# 5 AGO
# 6 IRL
# 7 CHE
# 8 DNK
# 9 HKG
# 10 THA
panel %>%
filter(Exp_IFF > 2*10^10) %>%
distinct(reporter.ISO)
# 1 PER
# 2 TTO
# 3 YEM
# 4 ARG
# 5 BOL
# 6 CIV
# 7 TZA
# 8 COG
ggplot(data = panel %>%
select(c(RV_M, RV_X)) %>%
melt(),
aes(x = value, y = variable)) +
geom_density_ridges()
ggplot(data = panel %>%
select(c(RV_M, FOB_Import_stripnonIFF, Imp_IFF)) %>%
melt() %>%
filter(value < 10^6 & value > -10^6),
aes(x = value, y = variable)) +
geom_density_ridges()
ggplot(data = panel %>%
select(c(RV_X, NetExport_value, Exp_IFF)) %>%
melt() %>%
filter(value < 10^6 & value > -10^6),
aes(x = value, y = variable)) +
geom_density_ridges()
panel %>%
filter(duplicated(panel$id)) %>% nrow
# 0
rm(fit, fit_mirror, noCIF,
FE_M, FE_X, FE_M.out, FE_X.out,
FE_M_rob, FE_X_rob, FE_M_rob.out, FE_X_rob.out)
save(panel, file = "Results/panel_results.Rdata")
## ## ## ## ## ## ## ## ## ## ##
# TARIFF EVASION ANALYSIS ####
## ## ## ## ## ## ## ## ## ## ##
load("Results/panel_results.Rdata")
load(paste0(data.disk, "Data/WDI/WDI.Rdata"))
# Import under-invoicing
Imp.under <- panel %>%
select(reporter, reporter.ISO, rIncome, rRegion, rDev, rHDI,
partner, partner.ISO, pIncome, pRegion, pDev, pHDI,
tariff, Imp_IFF,
year, commodity.code,
Import_value, GDP) %>%
mutate(Imp_IFF_GDP = Imp_IFF / GDP) %>%
filter(Imp_IFF < 0) %>%
filter(tariff < 100) %>%
left_join(WDI %>% select(ISO3166.3, GNPpc, year),
by = c("reporter.ISO" = "ISO3166.3",
"year" = "year"))
fit1 <- felm(abs(Imp_IFF_GDP) ~ tariff + Import_value + GDP |
commodity.code + reporter.ISO + partner.ISO,
data = Imp.under)
summary(fit1)
stargazer(fit1, type = "html", style = "aer",
out = "Results/Import under-invoicing regression.html")
# Interact development status
# Imp.under <- Imp.under %>%
# mutate(rIncome = factor(rIncome,
# levels = c("LIC", "LMC", "UMC", "HIC")))
Imp.under <- Imp.under %>%
mutate(rIncome = as.factor(rIncome))
levels(Imp.under$rIncome)
fit2 <- lm(abs(Imp_IFF) ~ tariff*rIncome + Import_value + GDP,
data = Imp.under)
summary(fit2)
stargazer(fit2, type = "html", style = "aer",
out = "Results/Tariff evasion regression.html")
fit3 <- lm(abs(Imp_IFF_GDP) ~ tariff*rIncome + Import_value + GDP,
data = Imp.under)
summary(fit3)
# Import over-invoicing
Imp.over <- panel %>%