Country

Indicator

Value type

Values present

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

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Afghanistan	9.02	-11.4%	7
Angola	12.8	+56.2%	2
Albania	5.6	-7.97%	18
Armenia	4.36	+5.39%	29
Bangladesh	7.65	+122%	13
Bulgaria	3.46	-32.2%	39
Bosnia & Herzegovina	3.86	+73.6%	34
Bolivia	3.45	-21.4%	41
Brazil	2.42	+5.31%	61
Botswana	8.41	+22%	9
China	2.83	-8.96%	51
Côte d’Ivoire	0.743	+97.3%	88
Cameroon	3.02	+26%	48
Congo - Kinshasa	1.98	-75.1%	75
Colombia	2.43	-2.09%	60
Comoros	1.74	-40.8%	80
Cape Verde	3.44	-49%	42
Costa Rica	2.26	-18.6%	65
Cuba	19.6	+15.2%	1
Dominican Republic	2.52	+31.8%	57
Algeria	7.83	-11.5%	12
Ecuador	2.3	+649%	64
Egypt	3.3	-16%	46
Eritrea	9.16	-2.45%	6
Ethiopia	3.41	-14.1%	44
Fiji	3.22	+1.03%	47
Ghana	2.01	+41%	73
Gambia	6.76	+22.2%	14
Guatemala	2.56	-8.25%	55
Guyana	7.93	+4.03%	11
Honduras	2.62	-6.42%	54
Croatia	0.266	-74.7%	91
Haiti	4.7	-10.8%	24
Hungary	1.61	-12%	83
Indonesia	2.44	-16.1%	59
India	1.76	-0.814%	79
Jamaica	3.59	-7.02%	37
Jordan	3.35	-14.4%	45
Kenya	3.86	+78.3%	35
Kyrgyzstan	4.22	-35.7%	30
Cambodia	2.12	-50.8%	68
Laos	4.87	+1.85%	23
Lebanon	5.28	-24.2%	21
Liberia	2.83	-1.45%	50
Sri Lanka	2.19	+0.583%	66
Lesotho	1.82	+24.7%	78
Lithuania	1.4	-4.98%	84
Morocco	4.17	-15.9%	31
Moldova	2.01	-44.1%	72
Madagascar	2.93	-65.3%	49
Mexico	2.65	+98%	53
North Macedonia	2.11	-6.17%	69
Mali	1.73	-2.84%	81
Myanmar (Burma)	4.04	-143%	33
Mozambique	3.79	-41.4%	36
Malawi	-49.7	+374%	92
Malaysia	0.984	+21.2%	85
Nigeria	4.64	+33.6%	26
Nicaragua	2.33	+38.1%	63
Nepal	2.53	+12.7%	56
Pakistan	2.14	+0.951%	67
Panama	2.49	+32.8%	58
Peru	3.57	-5.33%	38
Philippines	1.67	+16.6%	82
Poland	0.873	-12.5%	86
Paraguay	4.66	-30.2%	25
Palestinian Territories	1.97	0%	76
Romania	0.873	-39.4%	87
Rwanda	5.76	+100%	17
Senegal	0.72	-45.8%	89
Sierra Leone	10.3	+4.37%	3
El Salvador	2		74
Somalia	6.01	-14.3%	16
Serbia	1.94	+1.1%	77
South Sudan	6.47	+152%	15
Eswatini	1.82	0%	78
Togo	2.06	+304%	70
Thailand	2.02	-0.272%	71
Tajikistan	10.3	-20%	4
Tonga	4.1	-13.1%	32
Tunisia	5.54	-37.9%	19
Turkey	2.34	-4.05%	62
Tanzania	5.3	+322%	20
Uganda	8.86	+61.6%	8
Ukraine	0.718	-59.6%	90
Vietnam	3.46	+5.35%	40
Vanuatu	9.49	+17.9%	5
Samoa	4.59	+44.6%	27
Kosovo	3.42	-10.2%	43
Yemen	2.81	-18.8%	52
South Africa	4.55	+121%	28
Zambia	8.13	+48.8%	10
Zimbabwe	5.26	+51%	22

Python
R

                    
# 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.IB.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.IB.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))

Compare

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

Year: 2023

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