Net capital account (BoP, current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Flag Country Value Value change, % Rank
Angola Angola -109,047,004 -4,614% 80
Albania Albania -170,794,823 -440% 85
Argentina Argentina 249,847,602 +45.8% 26
Armenia Armenia 34,023,215 -43.7% 50
Antigua & Barbuda Antigua & Barbuda 26,481,481 -2.04% 54
Australia Australia -227,543,869 -44.7% 89
Austria Austria -1,043,899,781 -147% 95
Azerbaijan Azerbaijan -1,445,000 -85% 70
Belgium Belgium -417,713,123 -132% 92
Bangladesh Bangladesh 616,932,740 +27.7% 17
Bulgaria Bulgaria 1,951,770,000 +22.1% 9
Bahrain Bahrain 231,914,894 -33.7% 27
Bosnia & Herzegovina Bosnia & Herzegovina 198,525,383 +11.2% 31
Belarus Belarus 8,033,010 -98.5% 61
Belize Belize 6,369,430 -74.7% 62
Brazil Brazil -16,269,901,932 +43.1% 102
Bhutan Bhutan 76,593,443 -36.1% 42
Canada Canada -434,342,488 -10.9% 93
Switzerland Switzerland -199,807,874 -105% 88
Chile Chile 24,864,928 -56.4% 55
China China -109,090,721 -63.3% 81
Cape Verde Cape Verde 41,041,896 +119% 47
Costa Rica Costa Rica 24,256,202 +12.2% 56
Cyprus Cyprus -138,581,834 +291% 84
Czechia Czechia 5,999,697,115 +50.3% 3
Germany Germany -22,039,138,473 -23.6% 103
Djibouti Djibouti 31,746,389 -9.66% 51
Dominica Dominica 210,300,214 +5.91% 30
Denmark Denmark -1,444,702,770 -0.124% 97
Ecuador Ecuador 79,294,018 +2.07% 40
Spain Spain 19,964,165,007 +14.1% 1
Estonia Estonia 711,490,524 +39.3% 15
Finland Finland -181,147,718 -39.8% 86
France France 6,362,248,282 -16.9% 2
United Kingdom United Kingdom -7,058,901,684 +9.31% 101
Georgia Georgia 28,000,125 -27.8% 53
Gambia Gambia 117,879,070 -17.2% 37
Greece Greece -32,903,209 -101% 74
Grenada Grenada 220,126,264 +73.2% 29
Hong Kong SAR China Hong Kong SAR China -87,794,043 -112% 77
Honduras Honduras 6,200,000 -65.6% 63
Croatia Croatia 1,332,079,940 -44.6% 10
Hungary Hungary 830,254,673 -57.3% 13
Indonesia Indonesia 40,006,562 +44.2% 48
India India -68,515,184 -44% 76
Iceland Iceland -27,579,602 -10.4% 73
Israel Israel 584,700,000 -1.6% 18
Italy Italy -633,580,237 -103% 94
Jamaica Jamaica -9,785,554 -47.8% 72
Japan Japan -1,454,952,892 -47.9% 98
Kazakhstan Kazakhstan 76,776,189 -94% 41
Cambodia Cambodia 125,606,599 -9.98% 36
St. Kitts & Nevis St. Kitts & Nevis 36,298,360 -56.9% 49
South Korea South Korea 303,300,000 +544% 23
Kuwait Kuwait -105,591,840 -12.5% 79
St. Lucia St. Lucia 41,533,515 +12.6% 46
Lesotho Lesotho 309,496,018 -16.2% 22
Lithuania Lithuania 1,328,576,149 +4.13% 11
Luxembourg Luxembourg -199,386,036 +191% 87
Latvia Latvia 671,245,936 -17.5% 16
Moldova Moldova 81,840,000 -0.0122% 39
Mexico Mexico -68,436,723 +278% 75
North Macedonia North Macedonia 9,432,868 -1,060% 59
Malta Malta 348,824,484 +26.3% 21
Montenegro Montenegro -580,538 +1,118% 69
Mozambique Mozambique 266,930,097 -39.6% 25
Malaysia Malaysia -2,254,543 -96.1% 71
Namibia Namibia 162,257,133 +20.8% 33
Nicaragua Nicaragua 28,700,000 -60.9% 52
Netherlands Netherlands -3,648,112,245 +44.1% 99
Norway Norway -1,105,518,996 +45% 96
Nepal Nepal 68,621,189 +45.5% 44
New Zealand New Zealand -365,847,037 -124% 91
Pakistan Pakistan 164,000,000 -1.8% 32
Panama Panama 2,646,955 -71.1% 65
Philippines Philippines 72,290,264 -2.91% 43
Poland Poland 2,538,000,000 +93% 8
Portugal Portugal 3,453,172,293 -15.7% 7
Paraguay Paraguay 220,265,241 +29% 28
Palestinian Territories Palestinian Territories 365,518,915 -9.59% 20
Qatar Qatar -118,681,319 -11.1% 82
Romania Romania 4,567,432,718 -36.5% 6
Russia Russia -257,860,000 -81.9% 90
Saudi Arabia Saudi Arabia -4,408,888,889 -33.3% 100
Solomon Islands Solomon Islands 95,705,521 -27.4% 38
El Salvador El Salvador 286,323,738 -14.9% 24
Suriname Suriname 3,155,000 +216% 64
Slovakia Slovakia 1,090,154,537 +52.3% 12
Slovenia Slovenia -101,199,013 -1,079% 78
Sweden Sweden 142,563,382 -297% 35
Thailand Thailand 757,147,976 -62% 14
Tajikistan Tajikistan 426,172,296 -11.5% 19
Timor-Leste Timor-Leste 1,336,824 -68.6% 67
Tonga Tonga 45,846,809 -30.4% 45
Trinidad & Tobago Trinidad & Tobago 476,191 +55% 68
Turkey Turkey -126,000,000 -38.2% 83
Ukraine Ukraine 5,116,000,000 +3,428% 5
Uruguay Uruguay 2,517,538 -68.2% 66
United States United States 5,267,000,000 -183% 4
Uzbekistan Uzbekistan 8,328,714 -1.31% 60
St. Vincent & Grenadines St. Vincent & Grenadines 18,851,852 -17.9% 57
Samoa Samoa 143,320,455 +22.8% 34
South Africa South Africa 13,388,531 +4.64% 58 

                    
# 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 = 'BN.TRF.KOGT.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 <- 'BN.TRF.KOGT.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))