Goods exports (BoP, current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Flag Country Value Value change, % Rank
Angola Angola 36,795,038,476 -0.243% 57
Albania Albania 1,816,570,405 -8.45% 94
Argentina Argentina 79,776,898,593 +19.4% 42
Armenia Armenia 12,935,242,414 +49.6% 74
Antigua & Barbuda Antigua & Barbuda 72,129,663 -16% 107
Australia Australia 341,773,717,960 -8.11% 21
Austria Austria 205,062,759,777 -4% 29
Azerbaijan Azerbaijan 25,992,028,000 -11% 62
Belgium Belgium 377,278,566,095 -3.25% 19
Bangladesh Bangladesh 47,196,357,650 -10.1% 50
Bulgaria Bulgaria 46,034,370,000 -2.19% 51
Bahrain Bahrain 24,278,457,447 -2.16% 63
Bahamas Bahamas 873,793,415 +1.34% 97
Bosnia & Herzegovina Bosnia & Herzegovina 8,401,072,501 -1.98% 78
Belarus Belarus 39,496,300,000 +1.18% 54
Belize Belize 482,478,850 -1.14% 101
Brazil Brazil 339,856,399,962 -1.15% 22
Brunei Brunei 11,073,238,079 -1.42% 75
Bhutan Bhutan 656,254,321 -11.4% 99
Canada Canada 568,755,306,467 -0.278% 12
Switzerland Switzerland 495,224,171,478 +1.29% 13
Chile Chile 99,165,252,561 +6.66% 36
China China 3,408,990,836,685 +7.23% 1
Colombia Colombia 51,086,101,456 -2.96% 49
Cape Verde Cape Verde 327,839,007 +27.6% 104
Costa Rica Costa Rica 20,656,218,378 +9.37% 65
Cyprus Cyprus 4,394,490,618 -6.85% 87
Czechia Czechia 196,720,190,756 +0.0905% 30
Germany Germany 1,477,518,262,473 -1.85% 3
Djibouti Djibouti 4,076,525,066 -14.6% 88
Dominica Dominica 22,038,606 -15% 112
Denmark Denmark 172,296,088,704 +7.3% 32
Dominican Republic Dominican Republic 13,872,100,000 +7.12% 72
Ecuador Ecuador 34,699,028,759 +10.2% 58
Spain Spain 421,986,205,385 +0.572% 17
Estonia Estonia 19,122,533,277 -1.8% 68
Finland Finland 83,314,509,855 -7.64% 39
France France 671,019,694,286 -1.27% 7
United Kingdom United Kingdom 467,345,990,497 -4.88% 14
Georgia Georgia 8,621,585,743 +6.19% 77
Gambia Gambia 363,318,172 +11.5% 103
Greece Greece 52,614,126,856 -2.72% 48
Grenada Grenada 70,414,737 -3.83% 108
Guatemala Guatemala 13,329,123,280 +2.16% 73
Hong Kong SAR China Hong Kong SAR China 631,152,679,075 +9.5% 8
Honduras Honduras 5,673,307,366 -5.21% 83
Croatia Croatia 21,906,435,391 +5.51% 64
Hungary Hungary 128,199,006,512 -5.64% 33
Indonesia Indonesia 261,838,569,241 +1.61% 25
India India 447,166,486,233 +2.65% 15
Iceland Iceland 7,017,672,662 +2.35% 81
Israel Israel 70,206,400,000 -3.81% 45
Italy Italy 623,626,905,634 -0.539% 9
Jamaica Jamaica 1,867,644,681 -6.71% 93
Japan Japan 694,482,454,393 -2.84% 6
Kazakhstan Kazakhstan 80,080,042,005 -0.214% 41
Cambodia Cambodia 26,751,672,606 +13.5% 61
St. Kitts & Nevis St. Kitts & Nevis 33,698,430 +17.3% 111
South Korea South Korea 696,196,100,000 +8.18% 5
Kuwait Kuwait 77,491,357,207 -7.94% 43
St. Lucia St. Lucia 136,744,099 -5.06% 106
Lesotho Lesotho 967,621,610 +11.4% 96
Lithuania Lithuania 38,699,062,862 -2.23% 56
Luxembourg Luxembourg 30,961,048,142 +3.18% 60
Latvia Latvia 19,753,997,162 -2.06% 66
Moldova Moldova 3,013,520,000 -12% 89
Maldives Maldives 382,706,586 -9.18% 102
Mexico Mexico 617,764,074,730 +4.08% 10
North Macedonia North Macedonia 7,281,964,499 -6.92% 80
Malta Malta 4,581,614,361 +5.67% 86
Montenegro Montenegro 713,795,944 -7.79% 98
Mozambique Mozambique 8,211,288,446 -0.787% 79
Malaysia Malaysia 248,386,278,673 +7.33% 27
Namibia Namibia 4,599,128,167 -1.29% 85
Nigeria Nigeria 52,970,842,473 -5.11% 47
Nicaragua Nicaragua 6,836,600,000 +2.22% 82
Netherlands Netherlands 723,199,994,052 -1.18% 4
Norway Norway 172,354,587,824 -2.96% 31
Nepal Nepal 1,902,351,796 +39.5% 92
New Zealand New Zealand 43,293,805,832 +1.77% 53
Pakistan Pakistan 32,122,000,000 +11.5% 59
Panama Panama 19,108,479,008 -5.09% 69
Peru Peru 76,171,928,524 +13.5% 44
Philippines Philippines 55,011,626,463 -0.443% 46
Poland Poland 359,865,000,000 -0.795% 20
Portugal Portugal 81,937,268,496 +1.96% 40
Paraguay Paraguay 14,741,285,806 -8.58% 71
Palestinian Territories Palestinian Territories 2,370,515,527 -6.04% 91
Qatar Qatar 95,044,230,769 -2.75% 37
Romania Romania 93,320,174,902 -0.264% 38
Russia Russia 433,091,670,000 +1.96% 16
Saudi Arabia Saudi Arabia 305,620,223,846 -4.56% 23
Singapore Singapore 583,031,406,690 +4.21% 11
Solomon Islands Solomon Islands 510,244,191 +18.4% 100
El Salvador El Salvador 5,586,296,814 +1.19% 84
Suriname Suriname 2,581,977,001 +9.42% 90
Slovakia Slovakia 106,792,255,538 -1.74% 35
Slovenia Slovenia 45,636,500,300 +1.91% 52
Sweden Sweden 222,023,441,682 -0.386% 28
Thailand Thailand 297,048,556,090 +5.81% 24
Tajikistan Tajikistan 1,422,920,504 -23.6% 95
Timor-Leste Timor-Leste 195,980,071 -69% 105
Tonga Tonga 10,832,572 -16.5% 113
Trinidad & Tobago Trinidad & Tobago 9,827,201,192 -5.31% 76
Turkey Turkey 257,507,000,000 +2.59% 26
Ukraine Ukraine 38,888,000,000 +12.1% 55
Uruguay Uruguay 16,380,815,424 +8.74% 70
United States United States 2,083,245,000,000 +1.86% 2
Uzbekistan Uzbekistan 19,626,075,710 +0.0484% 67
St. Vincent & Grenadines St. Vincent & Grenadines 59,192,511 +19.7% 109
Vietnam Vietnam 405,532,000,000 +14.3% 18
Samoa Samoa 42,233,693 -2.62% 110
South Africa South Africa 111,690,416,671 +1.15% 34 

                    
# 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.MRCH.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.MRCH.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))