Average time to clear exports through customs (days)

Source: worldbank.org, 03.09.2025

Year: 2024

Flag Country Value Value change, % Rank
Armenia Armenia 12.5 +3.9% 7
Azerbaijan Azerbaijan 1.54 -47% 46
Belgium Belgium 3.14 -48.8% 36
Benin Benin 4.8 -73.1% 23
Burkina Faso Burkina Faso 3.19 -59.1% 35
Bahrain Bahrain 3.45 32
Bhutan Bhutan 3.1 -75.6% 38
Canada Canada 3.47 31
China China 3.33 -56.1% 34
Cameroon Cameroon 5.31 -21.2% 20
Congo - Kinshasa Congo - Kinshasa 4.64 -26% 24
Congo - Brazzaville Congo - Brazzaville 1.92 44
Cyprus Cyprus 9.37 -33.6% 11
Czechia Czechia 2.63 -30.2% 41
Ecuador Ecuador 7.92 -13.7% 15
Spain Spain 4.4 -37.2% 26
United Kingdom United Kingdom 5.54 18
Equatorial Guinea Equatorial Guinea 14 2
Ireland Ireland 2.49 -23.9% 42
Iceland Iceland 7.97 14
Israel Israel 10.1 +88.5% 9
Italy Italy 5.23 -4.58% 21
Jamaica Jamaica 10 +23% 10
Jordan Jordan 2.71 -42.1% 40
Kazakhstan Kazakhstan 13 +44.7% 4
South Korea South Korea 15.4 +115% 1
Laos Laos 3.12 -56.3% 37
Latvia Latvia 5.07 +141% 22
Moldova Moldova 3.42 +47.3% 33
Mali Mali 7.06 -57.2% 16
Malta Malta 3.54 +0.546% 30
Malaysia Malaysia 12.8 +181% 5
Namibia Namibia 3.79 -7.87% 27
Papua New Guinea Papua New Guinea 12.6 +27.6% 6
Senegal Senegal 5.48 -23% 19
Serbia Serbia 1.79 +11.8% 45
Slovenia Slovenia 5.55 +17.2% 17
Sweden Sweden 3.66 +24.5% 28
Eswatini Eswatini 1 -75.9% 47
Tajikistan Tajikistan 4.4 +110% 25
Turkmenistan Turkmenistan 8.68 12
Tonga Tonga 14 +489% 3
Tunisia Tunisia 3.65 -45.8% 29
Turkey Turkey 10.9 +317% 8
Uruguay Uruguay 2.93 +40.8% 39
United States United States 8.3 13
Uzbekistan Uzbekistan 2.27 -38.5% 43 

                    
# 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 = 'IC.CUS.DURS.EX'

# 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 <- 'IC.CUS.DURS.EX'

# 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))