Customs and other import duties (% of tax revenue)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Albania Albania 2.17 -0.255% 52
United Arab Emirates United Arab Emirates 0.0024 -68.1% 67
Argentina Argentina 6.67 +0.396% 29
Armenia Armenia 3.54 +16% 41
Burkina Faso Burkina Faso 12.9 -6.72% 13
Bulgaria Bulgaria 0.0458 -4.69% 65
Bahamas Bahamas 10.1 -12.6% 20
Bosnia & Herzegovina Bosnia & Herzegovina 0.000434 +491% 68
Belarus Belarus 15 +45.8% 9
Brazil Brazil 3.52 -11.4% 43
Botswana Botswana 28 0% 3
Switzerland Switzerland 1.64 -4.74% 55
Chile Chile 1.17 +10.5% 58
China China 2.68 -14.7% 47
Côte d’Ivoire Côte d’Ivoire 11.8 -14.2% 15
Colombia Colombia 2.07 -25.6% 54
Costa Rica Costa Rica 2.59 -2.84% 48
Dominican Republic Dominican Republic 5.2 -10.7% 35
Ethiopia Ethiopia 25.6 +5.54% 5
Fiji Fiji 13.4 -17.5% 11
France France 0.00598 -0.121% 66
United Kingdom United Kingdom 0.679 -15.2% 61
Georgia Georgia 0.792 +4.9% 60
Guinea-Bissau Guinea-Bissau 11.5 -6.3% 16
Guatemala Guatemala 4.3 -2.99% 36
Iceland Iceland 0.608 -11.3% 62
Israel Israel 0.418 -38% 63
Jordan Jordan 3.88 +0.904% 38
Kazakhstan Kazakhstan 3.77 +28.8% 39
Kenya Kenya 6.15 +2.46% 31
Kyrgyzstan Kyrgyzstan 9.76 +19.8% 21
Cambodia Cambodia 11.2 -0.0407% 17
Kiribati Kiribati 2.32 -70.3% 49
South Korea South Korea 2.08 -15.5% 53
Sri Lanka Sri Lanka 14.3 -26.6% 10
Morocco Morocco 5.35 +13.4% 34
Moldova Moldova 3.52 -3.25% 42
Madagascar Madagascar 9.26 +1,663% 24
Mexico Mexico 2.27 -6.47% 50
North Macedonia North Macedonia 7.01 +4.28% 28
Mauritius Mauritius 1.39 -0.929% 56
Malaysia Malaysia 1.32 -13.3% 57
Namibia Namibia 32.3 +27% 2
Nicaragua Nicaragua 3.67 +3.73% 40
Norway Norway 0.251 +19.6% 64
Philippines Philippines 25.8 -3.83% 4
Papua New Guinea Papua New Guinea 2.19 -23.3% 51
Paraguay Paraguay 9.35 -1.59% 23
Russia Russia 5.8 +40.1% 32
Rwanda Rwanda 9.42 +33% 22
Saudi Arabia Saudi Arabia 6.21 +7.02% 30
Senegal Senegal 12.9 -16.9% 12
El Salvador El Salvador 7.05 -11.1% 27
San Marino San Marino 0.956 -11.4% 59
Somalia Somalia 67.6 +3.58% 1
Togo Togo 20.3 +1.85% 7
Thailand Thailand 4.18 +9.8% 37
Tajikistan Tajikistan 10.8 +21.2% 18
Tonga Tonga 9.04 +14.3% 25
Turkey Turkey 3.02 -10.9% 45
Tanzania Tanzania 19.1 +3% 8
Uganda Uganda 10.8 +5.16% 19
Ukraine Ukraine 3.42 +28.5% 44
Uruguay Uruguay 5.55 -9.96% 33
United States United States 2.77 -11.4% 46
Uzbekistan Uzbekistan 8.01 +42.6% 26
Vanuatu Vanuatu 21 -0.246% 6
Samoa Samoa 12.3 +2.09% 14 

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'GC.TAX.IMPT.ZS'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)
                    
                
                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'GC.TAX.IMPT.ZS'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))