Exports of goods, services and primary income (BoP, current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Flag Country Value Value change, % Rank
Angola Angola 37,595,189,736 +0.136% 66
Albania Albania 10,886,457,062 +9.6% 83
Argentina Argentina 103,058,203,570 +15.5% 46
Armenia Armenia 19,341,905,230 +27.5% 75
Antigua & Barbuda Antigua & Barbuda 1,337,916,382 +10.8% 102
Australia Australia 496,776,076,122 -2.94% 21
Austria Austria 349,778,607,415 +0.618% 28
Azerbaijan Azerbaijan 36,706,645,000 -2% 67
Belgium Belgium 635,407,225,113 -2.07% 17
Bangladesh Bangladesh 54,441,152,065 -8.27% 57
Bulgaria Bulgaria 65,963,860,000 -0.581% 53
Bahrain Bahrain 46,864,627,660 +3.48% 61
Bahamas Bahamas 6,871,675,632 +12.6% 89
Bosnia & Herzegovina Bosnia & Herzegovina 13,447,684,278 +1.85% 79
Belarus Belarus 51,006,921,382 +3.73% 59
Belize Belize 1,661,728,324 +6.74% 99
Brazil Brazil 423,587,148,510 +0.671% 24
Brunei Brunei 12,482,176,136 -0.387% 80
Bhutan Bhutan 973,417,791 +7.67% 104
Canada Canada 909,435,501,564 +2.39% 11
Switzerland Switzerland 888,519,486,493 +3.29% 13
Chile Chile 123,237,056,033 +6.36% 44
China China 4,102,204,607,329 +7.72% 2
Colombia Colombia 78,776,677,573 +1.46% 49
Cape Verde Cape Verde 1,204,744,196 +19.1% 103
Costa Rica Costa Rica 38,023,309,976 +9.53% 65
Cyprus Cyprus 66,007,703,081 +4.24% 52
Czechia Czechia 257,278,250,999 +1.8% 33
Germany Germany 2,415,281,576,865 +0.693% 3
Djibouti Djibouti 5,523,909,768 -10.4% 93
Dominica Dominica 224,162,694 +13.7% 112
Denmark Denmark 349,214,300,672 +8.9% 29
Dominican Republic Dominican Republic 29,991,300,000 +10.2% 72
Ecuador Ecuador 38,761,300,024 +7.73% 64
Spain Spain 769,975,222,530 +5.42% 15
Estonia Estonia 35,986,977,053 +2.63% 68
Finland Finland 157,351,429,222 -0.838% 37
France France 1,489,248,125,896 +4.37% 5
United Kingdom United Kingdom 1,642,942,621,923 +3.2% 4
Georgia Georgia 18,091,003,593 +6.04% 76
Gambia Gambia 947,125,488 +28.3% 105
Greece Greece 120,457,557,986 +1.97% 45
Grenada Grenada 891,186,075 +4.54% 106
Guatemala Guatemala 20,466,081,420 +6.16% 74
Hong Kong SAR China Hong Kong SAR China 1,022,332,474,457 +9.66% 9
Honduras Honduras 9,860,131,675 -4.4% 85
Croatia Croatia 53,771,180,164 +6.06% 58
Hungary Hungary 198,754,020,465 -2.36% 34
Indonesia Indonesia 310,879,988,067 +3.91% 31
India India 872,908,923,247 +7.62% 14
Iceland Iceland 15,160,037,065 +1.98% 78
Israel Israel 171,951,800,000 -0.698% 35
Italy Italy 916,138,492,942 +1.38% 10
Jamaica Jamaica 7,694,046,789 -1.75% 88
Japan Japan 1,356,550,363,617 +1.58% 7
Kazakhstan Kazakhstan 96,941,041,857 +2.26% 47
Cambodia Cambodia 32,321,047,134 +14.4% 69
St. Kitts & Nevis St. Kitts & Nevis 549,672,867 -11.1% 109
South Korea South Korea 906,472,500,000 +8.08% 12
Kuwait Kuwait 129,287,978,357 -2.95% 41
St. Lucia St. Lucia 1,656,968,673 +12.8% 100
Lesotho Lesotho 1,597,872,990 +9.21% 101
Lithuania Lithuania 66,372,709,639 +3.6% 51
Luxembourg Luxembourg 574,860,997,990 +4.63% 18
Latvia Latvia 31,366,726,155 +0.386% 71
Moldova Moldova 6,852,210,000 -1.55% 90
Maldives Maldives 5,460,337,528 +10.7% 94
Mexico Mexico 702,745,151,212 +4.67% 16
North Macedonia North Macedonia 10,700,805,196 -1.81% 84
Malta Malta 49,859,311,128 +2.63% 60
Montenegro Montenegro 4,147,024,531 -1.76% 96
Mozambique Mozambique 9,759,889,101 +0.248% 86
Malaysia Malaysia 322,537,949,513 +9.76% 30
Namibia Namibia 6,553,289,785 +6.85% 91
Nigeria Nigeria 61,412,994,229 -2.06% 56
Nicaragua Nicaragua 8,512,018,290 -0.748% 87
Netherlands Netherlands 1,436,390,877,665 +0.331% 6
Norway Norway 303,383,669,423 +1.48% 32
Nepal Nepal 4,711,599,972 +67.9% 95
New Zealand New Zealand 69,671,278,110 +4.23% 50
Pakistan Pakistan 41,238,794,534 +11.7% 63
Panama Panama 41,635,517,245 +0.29% 62
Peru Peru 88,994,639,936 +14.6% 48
Philippines Philippines 124,732,519,159 +3.98% 43
Poland Poland 503,630,000,000 +1.63% 19
Portugal Portugal 163,750,610,225 +5.5% 36
Paraguay Paraguay 17,877,650,486 -5.67% 77
Palestinian Territories Palestinian Territories 3,885,898,095 -42.7% 97
Qatar Qatar 144,653,296,703 -0.846% 39
Romania Romania 145,927,528,230 +0.0747% 38
Russia Russia 500,193,570,000 +0.147% 20
Saudi Arabia Saudi Arabia 401,064,781,454 -0.469% 25
Singapore Singapore 1,178,197,520,393 +8.86% 8
Solomon Islands Solomon Islands 711,818,641 +16.3% 108
El Salvador El Salvador 12,034,527,313 +8.69% 82
Suriname Suriname 2,866,172,568 +11.1% 98
Slovakia Slovakia 125,450,563,198 -1.17% 42
Slovenia Slovenia 62,521,450,953 +2.59% 55
Sweden Sweden 435,536,680,535 +3.94% 22
Thailand Thailand 384,008,520,727 +8.67% 26
Tajikistan Tajikistan 6,544,946,652 +19.9% 92
Timor-Leste Timor-Leste 854,116,835 -30.1% 107
Tonga Tonga 192,634,043 +21.1% 113
Trinidad & Tobago Trinidad & Tobago 12,168,779,064 -4.63% 81
Turkey Turkey 382,298,000,000 +4.57% 27
Ukraine Ukraine 65,296,000,000 +2.67% 54
Uruguay Uruguay 25,842,393,271 +6.83% 73
United States United States 4,625,320,000,000 +3.97% 1
Uzbekistan Uzbekistan 31,840,592,925 +4.85% 70
St. Vincent & Grenadines St. Vincent & Grenadines 437,515,409 +22.8% 110
Vietnam Vietnam 434,925,000,000 +14.6% 23
Samoa Samoa 405,072,200 +7.18% 111
South Africa South Africa 138,309,935,802 +1.38% 40 

                    
# 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 = 'BX.GSR.TOTL.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 <- 'BX.GSR.TOTL.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))