PPG, official creditors (NFL, US$)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Afghanistan Afghanistan -10,229,000 +13.1% 92
Angola Angola -424,887,142 -1,345% 113
Albania Albania 59,489,550 -12.6% 67
Argentina Argentina 2,696,815,331 +198% 11
Armenia Armenia 56,396,283 -74.2% 68
Azerbaijan Azerbaijan -403,511,278 -15.3% 112
Burundi Burundi 26,734,118 +35.5% 78
Benin Benin 402,703,081 -7.11% 40
Burkina Faso Burkina Faso 375,677,771 +30.8% 41
Bangladesh Bangladesh 7,361,472,910 -11.7% 2
Bosnia & Herzegovina Bosnia & Herzegovina 50,126,488 -145% 71
Belarus Belarus -660,382,343 +16.9% 117
Belize Belize 50,515,139 +101% 70
Bolivia Bolivia 454,862,191 -33% 35
Brazil Brazil 934,653,215 -287% 22
Bhutan Bhutan 124,052,859 -16.1% 59
Botswana Botswana -100,497,511 -223% 103
Central African Republic Central African Republic 11,714,620 +164% 84
China China -1,410,123,670 -8.95% 119
Côte d’Ivoire Côte d’Ivoire 1,799,862,851 -5.18% 14
Cameroon Cameroon 77,231,633 -85.3% 64
Congo - Kinshasa Congo - Kinshasa 890,752,723 +183% 24
Congo - Brazzaville Congo - Brazzaville -64,864,541 +20% 99
Colombia Colombia 731,692,782 -73.7% 28
Comoros Comoros 17,976,368 -39.6% 81
Cape Verde Cape Verde 7,709,991 -87.1% 86
Costa Rica Costa Rica -139,125,482 -108% 107
Djibouti Djibouti 281,618,014 +748% 48
Dominica Dominica 27,941,829 -18.5% 77
Dominican Republic Dominican Republic 1,172,346,854 +216% 17
Algeria Algeria -97,990,857 -8.16% 102
Ecuador Ecuador 1,010,505,087 +5.88% 21
Egypt Egypt 3,502,522,470 +28.1% 7
Eritrea Eritrea -8,941,274 -29.3% 91
Ethiopia Ethiopia 2,683,446,249 +966% 12
Fiji Fiji 5,792,562 -98.7% 87
Gabon Gabon -136,761,198 -308% 105
Georgia Georgia 419,288,925 -18% 37
Ghana Ghana 461,623,129 -59.6% 34
Guinea Guinea 72,342,876 -22.6% 66
Gambia Gambia 88,678,739 +31.7% 61
Guinea-Bissau Guinea-Bissau 30,783,971 +159% 74
Grenada Grenada 49,514,107 +80.5% 72
Guatemala Guatemala -205,078,210 -196% 110
Guyana Guyana 207,808,506 +2.06% 50
Honduras Honduras 15,059,180 -97.5% 83
Haiti Haiti -29,690,000 -231% 98
Indonesia Indonesia 2,961,968,899 +76% 9
India India 6,711,052,827 +0.187% 3
Iran Iran -19,990,504 -146% 95
Iraq Iraq -523,384,547 -20% 114
Jamaica Jamaica -180,841,407 +450% 109
Jordan Jordan 1,303,262,815 +182% 16
Kazakhstan Kazakhstan -282,194,012 -274% 111
Kenya Kenya 867,025,557 -19.9% 26
Kyrgyzstan Kyrgyzstan 159,342,991 -17.6% 56
Cambodia Cambodia 1,096,753,410 +9.7% 19
Laos Laos 51,268,031 -61.3% 69
Lebanon Lebanon 172,985,523 +32% 52
Liberia Liberia 159,178,660 +23.1% 57
St. Lucia St. Lucia 123,629,081 +1,412% 60
Sri Lanka Sri Lanka 3,005,169,621 +104% 8
Lesotho Lesotho 28,630,347 -27.8% 76
Morocco Morocco 615,314,459 -69% 31
Moldova Moldova 312,554,121 -41.1% 46
Madagascar Madagascar 503,170,628 +25.4% 32
Maldives Maldives 369,611,061 +185% 42
Mexico Mexico -1,743,912,615 -684% 120
North Macedonia North Macedonia 344,147,829 +1,204% 44
Mali Mali 170,408,981 -14.9% 54
Myanmar (Burma) Myanmar (Burma) -24,822,615 -87.2% 96
Montenegro Montenegro -103,158,911 +520% 104
Mongolia Mongolia 84,589,501 -69.3% 62
Mozambique Mozambique 296,238,884 -441% 47
Mauritania Mauritania -74,236,435 -40.8% 100
Mauritius Mauritius 253,218,028 +525% 49
Malawi Malawi 313,887,029 +18.4% 45
Niger Niger 80,089,744 -87.3% 63
Nigeria Nigeria 4,804,214,676 +57.1% 4
Nicaragua Nicaragua 360,111,429 -1.74% 43
Nepal Nepal 640,606,843 +8.23% 29
Pakistan Pakistan 3,682,257,096 +57.1% 6
Peru Peru 1,087,882,934 +11.1% 20
Philippines Philippines 4,765,623,886 +35% 5
Papua New Guinea Papua New Guinea 410,230,152 -57.3% 39
Paraguay Paraguay 814,184,584 -39.3% 27
Rwanda Rwanda 917,401,019 +102% 23
Sudan Sudan -138,983,718 -5.71% 106
Senegal Senegal 873,847,672 +50.4% 25
Solomon Islands Solomon Islands 43,519,807 +105% 73
Sierra Leone Sierra Leone -7,836,297 +198% 90
El Salvador El Salvador 1,101,284,497 +367% 18
Somalia Somalia -15,038,427 +4.64% 94
Serbia Serbia 427,613,433 -70.5% 36
São Tomé & Príncipe São Tomé & Príncipe 24,713,446 -1,232% 79
Suriname Suriname 203,314,844 -4.3% 51
Eswatini Eswatini 18,969,851 -87.8% 80
Syria Syria 0 88
Chad Chad 166,981,904 +798% 55
Togo Togo 172,457,782 +111% 53
Thailand Thailand -629,593,004 -745% 116
Tajikistan Tajikistan 16,688,215 +24,260% 82
Turkmenistan Turkmenistan -555,414,214 +68.1% 115
Timor-Leste Timor-Leste 10,073,498 -51% 85
Tonga Tonga -12,327,758 +59.6% 93
Tunisia Tunisia 1,325,882,275 +515% 15
Turkey Turkey -157,985,433 -129% 108
Tanzania Tanzania 2,015,613,919 +45.7% 13
Uganda Uganda 488,657,002 +6.52% 33
Ukraine Ukraine 24,835,637,728 +46.7% 1
Uzbekistan Uzbekistan 2,761,880,767 -8.5% 10
St. Vincent & Grenadines St. Vincent & Grenadines 74,273,325 +25.4% 65
Vietnam Vietnam -1,218,424,853 -0.177% 118
Vanuatu Vanuatu -2,291,229 -107% 89
Samoa Samoa -29,620,225 +16.1% 97
Kosovo Kosovo 30,198,423 -61.6% 75
Yemen Yemen -79,406,977 +14.6% 101
South Africa South Africa 627,831,383 -54.6% 30
Zambia Zambia 134,595,406 -84.5% 58
Zimbabwe Zimbabwe 417,014,483 +50.9% 38 

                    
# 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.NFL.OFFT.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.NFL.OFFT.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))