Taxes on exports (% of tax revenue)

Source: worldbank.org, 19.12.2024

Year: 2021

Flag Country Value Value change, % Rank
Argentina Argentina 17 +38% 2
Azerbaijan Azerbaijan 0.0296 +1.87% 29
Burundi Burundi 0.0245 -35% 30
Burkina Faso Burkina Faso 0.0181 -28.1% 31
Bangladesh Bangladesh 0.000218 -99.4% 38
Bahamas Bahamas 5.95 +108% 6
Belarus Belarus 10.8 -18.1% 5
Brazil Brazil 0.0125 +140% 34
Botswana Botswana 0.00299 +18.7% 36
Central African Republic Central African Republic 0.364 -10.6% 16
Côte d’Ivoire Côte d’Ivoire 12 -4.52% 4
Cameroon Cameroon 1.36 +5.97% 10
Congo - Kinshasa Congo - Kinshasa 0.306 +11.7% 17
Congo - Brazzaville Congo - Brazzaville 1.56 +24.8% 9
Costa Rica Costa Rica 0.111 -8.98% 24
Fiji Fiji 0.535 +18.1% 15
Equatorial Guinea Equatorial Guinea 0.899 +108% 13
Indonesia Indonesia 2.24 +572% 8
Kazakhstan Kazakhstan 18.3 +42.5% 1
Kyrgyzstan Kyrgyzstan 0.0166 +47.6% 32
Cambodia Cambodia 0.241 +91.4% 18
Laos Laos 0.124 +34.4% 23
Sri Lanka Sri Lanka 0.171 +3.46% 21
Maldives Maldives 0.213 +84,687% 19
Mexico Mexico 0.00000 -40.9% 39
Mongolia Mongolia 0.000599 -70.4% 37
Malaysia Malaysia 1.18 +145% 12
Namibia Namibia 0.659 +16.1% 14
Norway Norway 0.0329 -23.4% 28
Nepal Nepal 0.0377 +106% 26
Papua New Guinea Papua New Guinea 3.59 -12.3% 7
Russia Russia 16.1 +62.7% 3
Togo Togo 0.205 +2.76% 20
Thailand Thailand 0.0106 +90.1% 35
Uganda Uganda 0.0162 -82.7% 33
Ukraine Ukraine 0.127 +299% 22
Vanuatu Vanuatu 1.35 +5,424% 11
South Africa South Africa 0.0358 +809% 27
Zambia Zambia 0.0415 +18.3% 25 

                    
# 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.EXPT.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.EXPT.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))