Multilateral debt service (TDS, current US$)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 13,220,000 +10% 109
Angola Angola 376,602,127 +52.1% 47
Albania Albania 291,892,294 +25.3% 52
Argentina Argentina 3,537,087,953 +39.7% 7
Armenia Armenia 299,230,738 +35.8% 49
Azerbaijan Azerbaijan 751,452,680 +9.47% 30
Burundi Burundi 19,849,127 +3.69% 106
Benin Benin 299,210,608 +71.8% 50
Burkina Faso Burkina Faso 173,623,545 +20% 62
Bangladesh Bangladesh 2,273,516,377 +21.3% 12
Bosnia & Herzegovina Bosnia & Herzegovina 515,050,185 +44.3% 37
Belarus Belarus 470,861,971 +37.8% 41
Belize Belize 70,454,114 +36.4% 83
Bolivia Bolivia 945,717,439 +37% 22
Brazil Brazil 4,619,188,203 +26.5% 5
Bhutan Bhutan 44,632,103 +20.4% 95
Botswana Botswana 211,940,666 +40.4% 60
Central African Republic Central African Republic 5,164,971 +24.6% 113
China China 4,677,743,485 +43.1% 4
Côte d’Ivoire Côte d’Ivoire 331,641,558 +32.3% 48
Cameroon Cameroon 231,913,162 +42.2% 57
Congo - Kinshasa Congo - Kinshasa 150,050,088 -23% 65
Congo - Brazzaville Congo - Brazzaville 108,530,091 +2.83% 73
Colombia Colombia 2,302,988,542 +32.3% 11
Comoros Comoros 5,642,319 +878% 111
Cape Verde Cape Verde 54,625,845 +27.1% 90
Costa Rica Costa Rica 810,397,000 +66.5% 27
Djibouti Djibouti 39,399,355 +12.5% 97
Dominica Dominica 14,670,067 +20% 108
Dominican Republic Dominican Republic 758,871,476 +68.2% 29
Algeria Algeria 79,561,352 +2.58% 81
Ecuador Ecuador 1,635,243,123 +31.2% 15
Egypt Egypt 5,370,114,736 +24.8% 2
Eritrea Eritrea 5,443,206 -1.9% 112
Ethiopia Ethiopia 497,404,950 +43.3% 39
Fiji Fiji 94,961,000 +165% 77
Gabon Gabon 257,635,769 +64% 55
Georgia Georgia 455,022,182 +56.1% 42
Ghana Ghana 286,793,333 +21.5% 53
Guinea Guinea 52,276,209 -1.68% 91
Gambia Gambia 27,867,458 +19% 100
Guinea-Bissau Guinea-Bissau 30,489,328 -28% 99
Grenada Grenada 22,864,959 +6.39% 104
Guatemala Guatemala 562,575,456 +10.8% 33
Guyana Guyana 63,731,271 +26.3% 87
Honduras Honduras 488,055,374 +38.4% 40
Haiti Haiti 5,021,000 -13.4% 115
Indonesia Indonesia 4,239,411,158 +36.3% 6
India India 8,542,016,674 +58.1% 1
Iran Iran 5,105,793 -81.6% 114
Iraq Iraq 523,120,000 +40.9% 36
Jamaica Jamaica 383,641,071 +46.5% 46
Jordan Jordan 864,742,677 +81.4% 24
Kazakhstan Kazakhstan 1,033,521,008 +77% 21
Kenya Kenya 1,217,605,011 +12.7% 19
Kyrgyzstan Kyrgyzstan 117,024,535 +12.2% 72
Cambodia Cambodia 140,833,320 +15.5% 67
Laos Laos 123,226,065 +8.04% 69
Lebanon Lebanon 189,994,047 +77.7% 61
Liberia Liberia 33,816,661 +19.2% 98
St. Lucia St. Lucia 26,984,780 +17.5% 101
Sri Lanka Sri Lanka 922,265,820 +35.5% 23
Lesotho Lesotho 48,960,376 +7.03% 92
Morocco Morocco 1,984,821,640 +22.2% 14
Moldova Moldova 502,293,634 +330% 38
Madagascar Madagascar 104,235,641 +23.3% 74
Maldives Maldives 64,831,239 +43% 86
Mexico Mexico 4,837,920,000 +182% 3
North Macedonia North Macedonia 225,543,910 +32.1% 58
Mali Mali 157,951,424 +5.99% 63
Myanmar (Burma) Myanmar (Burma) 130,312,231 +18.3% 68
Montenegro Montenegro 74,038,906 +20.7% 82
Mongolia Mongolia 253,354,320 +53.8% 56
Mozambique Mozambique 142,491,570 +3.74% 66
Mauritania Mauritania 213,938,863 +13.6% 59
Mauritius Mauritius 66,709,603 +31.2% 84
Malawi Malawi 65,757,807 +15.8% 85
Niger Niger 89,108,208 -44.3% 78
Nigeria Nigeria 856,286,529 +34.2% 25
Nicaragua Nicaragua 526,694,021 +42.8% 35
Nepal Nepal 291,977,194 +18.1% 51
Pakistan Pakistan 2,926,674,353 +35.5% 9
Peru Peru 1,387,222,441 +49.2% 17
Philippines Philippines 2,485,556,092 +99.9% 10
Papua New Guinea Papua New Guinea 264,051,104 +67.5% 54
Paraguay Paraguay 618,394,566 +69.9% 32
Rwanda Rwanda 122,207,690 +48.3% 70
Sudan Sudan 81,541,313 -2.18% 80
Senegal Senegal 434,414,097 +35.5% 43
Solomon Islands Solomon Islands 4,846,686 +1.82% 117
Sierra Leone Sierra Leone 47,960,032 +3.94% 93
El Salvador El Salvador 773,503,041 +37.3% 28
Somalia Somalia 16,017,206 +4.18% 107
Serbia Serbia 720,245,108 +21.4% 31
São Tomé & Príncipe São Tomé & Príncipe 2,181,423 -21.8% 119
Suriname Suriname 102,220,928 +24.8% 75
Eswatini Eswatini 46,693,104 +40.7% 94
Syria Syria 0 120
Chad Chad 24,256,176 +11.6% 103
Togo Togo 61,623,953 -2.5% 89
Thailand Thailand 527,536,000 +257% 34
Tajikistan Tajikistan 120,406,919 +22.5% 71
Turkmenistan Turkmenistan 102,008,145 +67.3% 76
Timor-Leste Timor-Leste 21,269,000 +46.7% 105
Tonga Tonga 3,198,439 +0.821% 118
Tunisia Tunisia 1,252,339,303 +14.5% 18
Turkey Turkey 3,228,208,307 +10.1% 8
Tanzania Tanzania 411,380,764 +29.7% 44
Uganda Uganda 408,991,097 +31.7% 45
Ukraine Ukraine 1,447,187,256 +66.4% 16
Uzbekistan Uzbekistan 1,074,219,128 +96.4% 20
St. Vincent & Grenadines St. Vincent & Grenadines 25,993,886 +0.647% 102
Vietnam Vietnam 2,163,348,393 +17.8% 13
Vanuatu Vanuatu 4,902,000 +22.4% 116
Samoa Samoa 12,543,259 +0.236% 110
Kosovo Kosovo 40,001,025 +2.21% 96
Yemen Yemen 88,639,000 +0.936% 79
South Africa South Africa 847,527,783 +43.4% 26
Zambia Zambia 154,926,274 +31.9% 64
Zimbabwe Zimbabwe 62,317,585 +695% 88 

                    
# 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 = 'DT.TDS.MLAT.CD'

# 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 <- 'DT.TDS.MLAT.CD'

# 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))