IBRD loans and IDA credits (DOD, current US$)

Source: worldbank.org, 01.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 297,385,000 -1.99% 89
Angola Angola 3,885,277,000 +20.1% 31
Albania Albania 1,400,053,000 +6.89% 54
Argentina Argentina 9,888,761,000 +7.42% 18
Armenia Armenia 1,716,577,000 +3.5% 48
Azerbaijan Azerbaijan 1,333,216,000 -10.4% 55
Burundi Burundi 118,000,000 -2.98% 104
Benin Benin 1,976,654,000 +27.1% 47
Burkina Faso Burkina Faso 2,417,008,000 +10.5% 41
Bangladesh Bangladesh 19,772,007,000 +8.48% 4
Bosnia & Herzegovina Bosnia & Herzegovina 1,318,771,000 -7.33% 56
Belarus Belarus 1,004,558,000 +1.15% 62
Belize Belize 36,418,000 +1.41% 114
Bolivia Bolivia 1,534,505,000 +6.21% 52
Brazil Brazil 15,578,606,000 -3.01% 7
Bhutan Bhutan 438,353,000 +12.4% 84
Botswana Botswana 539,645,000 +3.62% 80
Central African Republic Central African Republic 124,222,000 -1.15% 103
China China 15,447,169,000 -3.86% 8
Côte d’Ivoire Côte d’Ivoire 4,189,122,000 +34.4% 30
Cameroon Cameroon 2,543,482,000 +13.9% 38
Congo - Kinshasa Congo - Kinshasa 2,996,855,000 +41% 36
Congo - Brazzaville Congo - Brazzaville 628,863,000 +15.7% 75
Colombia Colombia 15,797,180,000 +4.47% 5
Comoros Comoros 45,359,000 +37.1% 112
Cape Verde Cape Verde 600,543,000 +9.88% 76
Costa Rica Costa Rica 1,550,232,000 -0.725% 50
Djibouti Djibouti 273,621,000 +19.1% 90
Dominica Dominica 158,173,000 +30.3% 99
Dominican Republic Dominican Republic 1,527,904,000 +30.1% 53
Ecuador Ecuador 5,239,832,000 +20.4% 23
Egypt Egypt 12,313,867,000 -0.0364% 14
Eritrea Eritrea 419,255,000 +0.845% 86
Ethiopia Ethiopia 11,983,593,000 +6.89% 16
Fiji Fiji 478,940,000 +2.24% 82
Gabon Gabon 659,641,000 +1.69% 73
Georgia Georgia 2,124,922,000 +5.77% 45
Ghana Ghana 5,393,107,000 +13.6% 21
Guinea Guinea 655,180,000 +6.29% 74
Gambia Gambia 128,488,000 +2.15% 102
Guinea-Bissau Guinea-Bissau 213,791,000 +14.3% 94
Grenada Grenada 248,930,000 +17.6% 92
Guatemala Guatemala 2,060,837,000 -2.54% 46
Guyana Guyana 172,444,000 +46.8% 98
Honduras Honduras 970,213,000 +3.68% 64
Indonesia Indonesia 22,216,296,000 +7.7% 2
India India 39,283,871,000 +2.68% 1
Iran Iran 135,228,000 +4.42% 101
Iraq Iraq 3,291,174,000 -4.89% 33
Jamaica Jamaica 988,083,000 -4.21% 63
Jordan Jordan 4,457,170,000 +12.3% 25
Kazakhstan Kazakhstan 3,383,267,000 -5.39% 32
Kenya Kenya 12,464,757,000 +12.8% 12
Kyrgyzstan Kyrgyzstan 720,807,000 +13.2% 72
Cambodia Cambodia 1,315,356,000 +40.5% 57
Laos Laos 835,672,000 +10.5% 69
Lebanon Lebanon 1,010,500,000 +19.5% 61
Liberia Liberia 827,972,000 +17.7% 70
St. Lucia St. Lucia 172,897,000 +10.3% 97
Sri Lanka Sri Lanka 4,388,015,000 +14.3% 26
Lesotho Lesotho 456,706,000 +7.02% 83
Morocco Morocco 9,422,527,000 +8.69% 19
Moldova Moldova 1,067,321,000 +15.1% 60
Madagascar Madagascar 2,387,771,000 +16.9% 42
Maldives Maldives 97,695,000 +2.63% 106
Mexico Mexico 14,617,898,000 -6.27% 9
North Macedonia North Macedonia 874,626,000 +15% 67
Mali Mali 2,165,789,000 +6.53% 44
Myanmar (Burma) Myanmar (Burma) 1,565,920,000 -4.6% 49
Montenegro Montenegro 204,494,000 -1.74% 95
Mongolia Mongolia 815,784,000 +2.19% 71
Mozambique Mozambique 3,010,343,000 +0.895% 35
Mauritania Mauritania 424,024,000 +10.2% 85
Mauritius Mauritius 113,232,000 -13.4% 105
Malawi Malawi 1,549,633,000 +17.5% 51
Niger Niger 2,460,811,000 +6.53% 39
Nigeria Nigeria 15,611,402,000 +11.5% 6
Nicaragua Nicaragua 915,466,000 +3.37% 65
Nepal Nepal 4,367,464,000 +5.12% 27
Pakistan Pakistan 19,990,093,000 +10.6% 3
Peru Peru 5,284,212,000 +8.73% 22
Philippines Philippines 12,424,491,000 +16.7% 13
Papua New Guinea Papua New Guinea 568,547,000 +4.34% 79
Paraguay Paraguay 1,118,784,000 +26.2% 59
Rwanda Rwanda 3,270,212,000 +21.3% 34
Sudan Sudan 258,971,000 -12% 91
Senegal Senegal 4,357,273,000 +19% 28
Solomon Islands Solomon Islands 78,991,000 +36.2% 110
Sierra Leone Sierra Leone 479,349,000 +2.85% 81
El Salvador El Salvador 875,197,000 -0.477% 66
Somalia Somalia 93,510,000 -10.6% 109
Serbia Serbia 2,425,630,000 -0.579% 40
São Tomé & Príncipe São Tomé & Príncipe 9,631,000 -2.31% 118
Suriname Suriname 19,622,000 +246% 116
Eswatini Eswatini 192,434,000 +9.91% 96
Syria Syria 14,052,000 0% 117
Chad Chad 150,518,000 -3.13% 100
Togo Togo 575,065,000 +59.7% 78
Thailand Thailand 583,000,000 -12.5% 77
Tajikistan Tajikistan 374,576,000 +1.61% 87
Turkmenistan Turkmenistan 20,000,000 0% 115
Timor-Leste Timor-Leste 55,233,000 +4.29% 111
Tonga Tonga 41,508,000 +1.82% 113
Tunisia Tunisia 4,315,065,000 +4.34% 29
Turkey Turkey 12,036,786,000 +6.88% 15
Tanzania Tanzania 10,988,591,000 +19.1% 17
Uganda Uganda 4,675,099,000 +4.74% 24
Ukraine Ukraine 13,584,309,000 +54.8% 11
Uzbekistan Uzbekistan 6,571,729,000 +18.9% 20
St. Vincent & Grenadines St. Vincent & Grenadines 229,205,000 +6.96% 93
Vietnam Vietnam 14,532,411,000 -3.82% 10
Vanuatu Vanuatu 96,033,000 -2.22% 108
Samoa Samoa 96,336,000 -2.83% 107
Kosovo Kosovo 372,990,000 +9.09% 88
Yemen Yemen 1,154,693,000 -5.71% 58
South Africa South Africa 2,567,677,000 -7.86% 37
Zambia Zambia 2,220,849,000 +8.49% 43
Zimbabwe Zimbabwe 869,402,000 +0.331% 68 

                    
# 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.DOD.MWBG.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.DOD.MWBG.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))