Portfolio investment, bonds (PPG + PNG) (NFL, current US$)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Angola Angola 0 -100% 21
Albania Albania 590,952,000 +7.58% 15
Argentina Argentina -2,613,709,000 -18% 50
Armenia Armenia -169,869,000 -123% 35
Azerbaijan Azerbaijan 0 -100% 21
Benin Benin 0 21
Bangladesh Bangladesh -314,510,000 -23.1% 41
Bosnia & Herzegovina Bosnia & Herzegovina -181,660,000 37
Belarus Belarus -681,267,000 +357% 44
Belize Belize -5,767,000 -99% 25
Bolivia Bolivia -183,399,000 -649% 38
Brazil Brazil 5,443,079,000 -216% 3
China China -29,282,319,000 -47.9% 54
Côte d’Ivoire Côte d’Ivoire -55,541,000 0% 31
Cameroon Cameroon -47,098,000 -92% 30
Congo - Brazzaville Congo - Brazzaville -27,234,000 0% 28
Colombia Colombia 4,269,879,000 -389% 4
Costa Rica Costa Rica 1,450,000,000 12
Dominica Dominica -1,563,000 -37.8% 24
Dominican Republic Dominican Republic 1,632,234,000 -40.4% 9
Ecuador Ecuador -64,000,000 +18.5% 32
Egypt Egypt 1,434,542,000 -192% 13
Gabon Gabon -24,442,000 0% 27
Georgia Georgia -748,702,000 +32.1% 46
Ghana Ghana 0 -100% 21
Grenada Grenada -10,561,000 0% 26
Guatemala Guatemala 1,565,000,000 -13.1% 11
Guyana Guyana 0 21
Honduras Honduras -166,666,000 0% 34
Indonesia Indonesia 3,517,960,000 -123% 5
India India 548,291,000 -142% 16
Iraq Iraq -1,000,000,000 0% 49
Jamaica Jamaica -188,199,000 -14.5% 39
Jordan Jordan 1,920,000,000 -343% 7
Kazakhstan Kazakhstan -627,324,000 -57% 43
Kenya Kenya 0 21
Laos Laos 105,641,000 -72.3% 19
Lebanon Lebanon -300,000,000 40
St. Lucia St. Lucia -200,000 0% 22
Sri Lanka Sri Lanka 0 -100% 21
Morocco Morocco 2,500,000,000 -267% 6
Mexico Mexico 14,135,809,000 +1,472% 1
North Macedonia North Macedonia 54,175,000 -12,787% 20
Montenegro Montenegro 0 21
Mongolia Mongolia -767,825,000 -60.2% 47
Mauritius Mauritius 450,134,000 +58.9% 17
Nigeria Nigeria -850,000,000 -352% 48
Pakistan Pakistan 0 21
Peru Peru -3,573,499,000 +983% 52
Philippines Philippines 1,632,209,000 -41% 10
Paraguay Paraguay 192,146,000 -326% 18
Rwanda Rwanda -113,899,000 +1,041% 33
Senegal Senegal 0 21
El Salvador El Salvador -604,130,000 +1.79% 42
Serbia Serbia 1,750,195,000 +389% 8
Thailand Thailand -5,124,871,000 +250% 53
Tajikistan Tajikistan 0 21
Tunisia Tunisia -700,401,000 +58.7% 45
Turkey Turkey 6,630,476,000 -398% 2
Ukraine Ukraine -170,904,000 -33.9% 36
Uzbekistan Uzbekistan 1,107,835,000 -32.3% 14
St. Vincent & Grenadines St. Vincent & Grenadines -1,043,000 -15.5% 23
Vietnam Vietnam -40,081,000 -113% 29
South Africa South Africa -3,431,559,000 -21% 51
Zambia Zambia 0 21
Zimbabwe Zimbabwe 0 21 

                    
# 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.BOND.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.BOND.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))