PNG, commercial banks and other creditors (NFL, current US$)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Angola Angola -1,076,517,000 -83% 79
Albania Albania 288,487,000 +235% 30
Argentina Argentina 1,043,178,000 -475% 16
Armenia Armenia 209,539,000 +171% 32
Azerbaijan Azerbaijan -181,750,000 +46.6% 71
Burkina Faso Burkina Faso -67,260,000 -85% 60
Bangladesh Bangladesh 1,260,591,000 +354% 13
Bosnia & Herzegovina Bosnia & Herzegovina 474,127,000 +1,445% 25
Belarus Belarus -1,679,075,000 +128% 82
Belize Belize -4,301,000 +80% 51
Bolivia Bolivia -78,393,000 -85.4% 62
Brazil Brazil 16,209,979,000 -48% 2
Botswana Botswana -4,087,000 -54.6% 50
China China -21,508,394,000 -42% 86
Côte d’Ivoire Côte d’Ivoire -142,144,000 -27.7% 69
Cameroon Cameroon -158,004,000 +138% 70
Congo - Brazzaville Congo - Brazzaville 265,000 -82.8% 47
Colombia Colombia 7,785,331,000 +13.3% 3
Costa Rica Costa Rica 705,663,000 -25.1% 21
Dominica Dominica -3,194,000 +9.99% 49
Dominican Republic Dominican Republic 646,333,000 +22.7% 23
Algeria Algeria 62,550,000 +371% 37
Ecuador Ecuador 42,493,000 -214% 38
Egypt Egypt 980,463,000 +6,281% 17
Fiji Fiji 80,952,000 -370% 34
Georgia Georgia 374,848,000 -28.9% 27
Ghana Ghana 570,009,000 -142% 24
Guinea Guinea -15,000,000 -114% 54
Guatemala Guatemala -63,582,000 -96.7% 59
Guyana Guyana 667,515,000 +2,761% 22
Honduras Honduras -59,520,000 -63.5% 58
Haiti Haiti 1,667,000 0% 45
Indonesia Indonesia -1,545,266,000 -132% 81
India India 25,164,625,000 -357% 1
Jamaica Jamaica -502,460,000 -272% 75
Jordan Jordan 24,785,000 -105% 42
Kazakhstan Kazakhstan 5,955,423,000 -993% 4
Kenya Kenya -138,549,000 +105% 68
Kyrgyzstan Kyrgyzstan 121,508,000 -48.2% 33
Cambodia Cambodia -737,312,000 -144% 76
Laos Laos 1,852,638,000 -683% 11
Lebanon Lebanon -2,293,129,000 -9.05% 85
Liberia Liberia 39,701,000 +818% 39
Sri Lanka Sri Lanka 906,039,000 +221% 18
Lesotho Lesotho -48,580,000 +418% 56
Morocco Morocco -93,015,000 -66.7% 65
Moldova Moldova 420,000 -99.2% 46
Madagascar Madagascar -8,666,000 0% 52
Maldives Maldives 8,720,000 -544% 43
Mexico Mexico -2,210,257,000 +137% 84
North Macedonia North Macedonia 296,275,000 +195% 29
Myanmar (Burma) Myanmar (Burma) 77,501,000 -63.5% 35
Montenegro Montenegro -90,501,000 -72.7% 64
Mongolia Mongolia 1,960,755,000 +7.4% 10
Mozambique Mozambique 2,423,745,000 -26% 9
Mauritius Mauritius -282,091,000 -123% 72
Nigeria Nigeria -873,692,000 -147% 77
Nicaragua Nicaragua 7,881,000 -118% 44
Nepal Nepal -73,244,000 -170% 61
Pakistan Pakistan 1,248,968,000 +91.4% 14
Peru Peru 1,213,315,000 -24% 15
Philippines Philippines 4,179,137,000 +277% 6
Papua New Guinea Papua New Guinea -1,784,555,000 -1.95% 83
Paraguay Paraguay -98,591,000 +10.3% 66
Rwanda Rwanda 340,408,000 +174% 28
Senegal Senegal 3,458,297,000 +65.6% 8
Solomon Islands Solomon Islands 30,471,000 +181% 41
El Salvador El Salvador -131,776,000 -131% 67
Serbia Serbia 1,721,749,000 +220% 12
Suriname Suriname -296,664,000 +378% 73
Eswatini Eswatini -56,526,000 +108% 57
Thailand Thailand -1,011,241,000 -211% 78
Tajikistan Tajikistan 72,297,000 +75.7% 36
Turkmenistan Turkmenistan -10,847,000 -54.2% 53
Tunisia Tunisia -440,559,000 +7.28% 74
Turkey Turkey 3,486,765,000 +178% 7
Tanzania Tanzania 905,000,000 +175% 19
Uganda Uganda -1,354,170,000 -367% 80
Ukraine Ukraine 436,989,000 -112% 26
Uzbekistan Uzbekistan 5,430,967,000 +95.7% 5
Vietnam Vietnam 859,308,000 -86.9% 20
Samoa Samoa -413,000 -45.5% 48
Kosovo Kosovo 238,780,000 +12.5% 31
South Africa South Africa 34,627,000 -98.6% 40
Zambia Zambia -89,679,000 -76.3% 63
Zimbabwe Zimbabwe -22,629,000 -90.8% 55 

                    
# 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.PNGC.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.PNGC.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))