Country

Indicator

Value type

Values present

Net trade in goods (BoP, current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	22,604,796,440	+3.69%	21
Albania	-6,051,403,664	+23.2%	85
Argentina	22,376,764,306	-862%	22
Armenia	-2,304,484,723	-8.36%	64
Antigua & Barbuda	-653,472,199	-0.689%	54
Australia	45,103,349,938	-46.3%	13
Austria	8,579,119,507	+98.6%	30
Azerbaijan	8,824,944,000	-31.1%	29
Belgium	5,241,463,523	+73.4%	33
Bangladesh	-16,450,582,784	+70.4%	98
Bulgaria	-5,790,020,000	+36.3%	83
Bahrain	3,607,978,723	-19.9%	35
Bahamas	-3,722,018,786	+15.9%	73
Bosnia & Herzegovina	-6,499,855,004	+14.6%	87
Belarus	-4,700,900,000	+86%	78
Belize	-879,523,647	+13.1%	58
Brazil	65,842,294,657	-28.6%	11
Brunei	3,708,947,272	-2.34%	34
Bhutan	-628,894,317	-10.9%	52
Canada	-4,960,971,145	+816%	79
Switzerland	127,287,136,416	+1.88%	5
Chile	21,032,531,463	+52.3%	23
China	767,976,198,456	+29.3%	1
Colombia	-9,160,354,810	+34.6%	92
Cape Verde	-845,576,462	-5.33%	57
Costa Rica	-2,509,594,115	-20.5%	65
Cyprus	-7,403,694,463	-7.65%	89
Czechia	17,996,722,959	+37.9%	26
Germany	255,019,028,086	+3.86%	2
Djibouti	41,450,097	-85.7%	42
Dominica	-208,711,975	-12.9%	45
Denmark	40,540,232,627	+34.2%	16
Dominican Republic	-15,935,900,000	+0.458%	97
Ecuador	6,813,038,587	+209%	32
Spain	-34,958,066,874	-6.78%	105
Estonia	-2,910,212,732	+18.1%	67
Finland	7,396,910,777	-23.7%	31
France	-62,978,904,803	-23.5%	109
United Kingdom	-288,728,599,256	+11.4%	112
Georgia	-6,470,152,546	+6.46%	86
Gambia	-1,025,006,568	+16.8%	59
Greece	-38,580,996,406	+8.03%	107
Grenada	-491,408,669	+6.5%	51
Guatemala	-15,802,379,220	+9.99%	96
Hong Kong SAR China	-1,964,866,675	-87.8%	61
Honduras	-8,903,274,603	+6.56%	91
Croatia	-19,602,333,121	+3.44%	99
Hungary	1,516,971,105	-511%	39
Indonesia	39,931,601,513	-13.7%	17
India	-279,236,787,767	+13.8%	111
Iceland	-2,278,196,556	+8.49%	63
Israel	-26,322,900,000	+27.9%	102
Italy	69,145,166,155	+74.9%	10
Jamaica	-4,199,379,244	-4.55%	74
Japan	-24,254,044,103	-50.2%	101
Kazakhstan	18,883,784,472	-4.84%	25
Cambodia	-4,495,586,540	+50.5%	75
St. Kitts & Nevis	-370,375,644	+6.9%	47
South Korea	100,126,900,000	+166%	7
Kuwait	44,047,951,671	-13.9%	14
St. Lucia	-711,323,125	+16.9%	56
Lesotho	-700,107,560	-11.9%	55
Lithuania	-5,109,650,730	+3.91%	80
Luxembourg	1,588,167,260	+311%	38
Latvia	-3,564,936,607	-9.71%	71
Moldova	-5,619,920,000	+15.4%	82
Maldives	-3,076,406,071	+7.03%	68
Mexico	-8,245,882,693	+48.6%	90
North Macedonia	-3,352,462,152	+17.7%	70
Malta	-2,723,507,295	+2.89%	66
Montenegro	-3,573,513,679	+10.6%	72
Mozambique	-163,858,809	-81.9%	44
Malaysia	25,666,709,568	-14.3%	19
Namibia	-2,171,373,013	+40.4%	62
Nigeria	13,166,183,305	+63%	27
Nicaragua	-3,294,700,000	+22.4%	69
Netherlands	107,087,142,934	+16.4%	6
Norway	74,098,245,265	-9.12%	9
Nepal	-13,060,693,536	+24.8%	94
New Zealand	-5,237,979,666	-29.5%	81
Pakistan	-23,553,000,000	+20.6%	100
Panama	-6,032,141,440	-39%	84
Peru	24,080,574,871	+40.4%	20
Philippines	-68,744,346,165	+4.1%	110
Poland	-6,904,000,000	-235%	88
Portugal	-27,332,830,760	-0.0189%	103
Paraguay	-1,094,048,117	-240%	60
Palestinian Territories	-4,503,230,632	-36.8%	76
Qatar	62,437,362,637	-8.59%	12
Romania	-35,606,742,987	+13.7%	106
Russia	132,967,310,000	+9.29%	4
Saudi Arabia	90,273,218,071	-29.6%	8
Singapore	148,133,409,834	-5.79%	3
Solomon Islands	-98,468,458	-50.4%	43
El Salvador	-9,508,769,534	+7.39%	93
Suriname	931,324,087	+18.3%	40
Slovakia	-433,660,907	-134%	50
Slovenia	644,213,160	+37.4%	41
Sweden	37,462,591,343	+13.6%	18
Thailand	19,273,530,151	-0.544%	24
Tajikistan	-4,513,347,236	+35.9%	77
Timor-Leste	-644,176,704	+359%	53
Tonga	-220,990,046	+0.163%	46
Trinidad & Tobago	2,321,784,761	-38.3%	37
Turkey	-56,258,000,000	-34.8%	108
Ukraine	-30,403,000,000	+4.35%	104
Uruguay	3,469,084,377	+56.6%	36
United States	-1,212,993,000,000	+14.1%	113
Uzbekistan	-13,533,889,535	-8.92%	95
St. Vincent & Grenadines	-385,671,004	+9.29%	48
Vietnam	43,043,000,000	-2.24%	15
Samoa	-405,485,109	+5.07%	49
South Africa	11,861,927,173	+114%	28

Python
R

                    
# 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.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 <- 'BN.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))

Compare

Net trade in goods (BoP, current US$)

Year: 2024

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