Average transaction cost of sending remittances from a specific country (%)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
United Arab Emirates United Arab Emirates 3.08 +30.7% 24
Australia Australia 2.88 -15.9% 31
Austria Austria 1.82 -14.1% 41
Belgium Belgium 3.81 -40.2% 19
Bahrain Bahrain 3.84 +62.1% 18
Brazil Brazil 4.6 -26.9% 11
Canada Canada 2.97 -9.31% 28
Switzerland Switzerland 4.11 -5.07% 16
Chile Chile 5.14 -2.28% 9
Côte d’Ivoire Côte d’Ivoire 3.05 +52.7% 25
Cameroon Cameroon 11.7 +17.4% 3
Costa Rica Costa Rica 2.83 +50.7% 32
Czechia Czechia 2.89 -9.27% 30
Germany Germany 3.02 -8.64% 27
Dominican Republic Dominican Republic 7.42 -30.9% 7
Spain Spain 2.24 -1.88% 36
France France 2.9 -1.79% 29
United Kingdom United Kingdom 3.61 +35.3% 22
India India 0.538 +31.1% 44
Israel Israel 13.4 -2.68% 1
Italy Italy 2.19 -44% 37
Jordan Jordan 2.27 -7.46% 35
Japan Japan 2.62 -5.04% 33
Kenya Kenya 5.26 +31% 8
South Korea South Korea 4.19 +778% 15
Kuwait Kuwait 2.42 -2.35% 34
Malaysia Malaysia 2.05 +38% 39
Netherlands Netherlands 3.03 -10.3% 26
Norway Norway 1.16 +29.4% 43
New Zealand New Zealand 3.9 +22.3% 17
Oman Oman 4.44 +13.2% 13
Pakistan Pakistan 7.77 -16.5% 5
Portugal Portugal 2.07 -65.5% 38
Qatar Qatar 4.31 +19.1% 14
Rwanda Rwanda 7.55 -1.18% 6
Saudi Arabia Saudi Arabia 4.52 +280% 12
Senegal Senegal 1.38 -61.1% 42
Singapore Singapore 1.97 +66.8% 40
Sweden Sweden 4.87 +3.64% 10
Thailand Thailand 3.61 -45% 21
Turkey Turkey 9.96 -32.3% 4
Tanzania Tanzania 12.8 +54.5% 2
United States United States 3.51 +28.8% 23
South Africa South Africa 3.75 -8.46% 20 

                    
# 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 = 'SI.RMT.COST.OB.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 <- 'SI.RMT.COST.OB.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))