Country

Indicator

Value type

Values present

Net trade in goods and services (BoP, current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	14,241,387,650	+7.3%	29
Albania	-1,848,064,175	+44.9%	79
Argentina	16,900,782,717	-281%	25
Armenia	-468,702,340	+142%	70
Antigua & Barbuda	31,318,769	-136%	57
Australia	19,823,436,844	-66.6%	22
Austria	14,898,841,398	+45.8%	28
Azerbaijan	6,774,099,000	-35.3%	37
Belgium	-5,568,857,935	+42.2%	93
Bangladesh	-21,111,378,001	+47.8%	105
Bulgaria	2,631,660,000	-38.2%	46
Bahrain	8,259,042,553	+3.62%	34
Bahamas	-297,869,650	+13.6%	67
Bosnia & Herzegovina	-4,060,945,565	+25.2%	89
Belarus	-1,293,052,644	-608%	78
Belize	-83,919,656	+126%	58
Brazil	11,283,090,117	-77%	33
Brunei	2,372,398,525	-4.95%	47
Bhutan	-568,591,172	-37%	71
Canada	-5,947,032,090	-539%	94
Switzerland	92,504,654,755	-5.44%	6
Chile	11,883,616,133	+274%	32
China	538,958,018,244	+39.8%	1
Colombia	-9,766,708,285	+25.6%	99
Cape Verde	-314,995,202	-30.9%	68
Costa Rica	6,310,784,270	+19.7%	39
Cyprus	1,317,833,476	+260%	49
Czechia	22,518,192,686	+32.4%	20
Germany	174,799,183,479	-1.14%	3
Djibouti	484,898,406	-20.2%	51
Dominica	-174,779,840	-23.5%	61
Denmark	46,450,895,106	+40%	12
Dominican Republic	-7,581,300,000	-12.5%	97
Ecuador	4,498,211,130	+1,588%	43
Spain	73,856,232,904	+15.9%	8
Estonia	262,145,352	-25.2%	52
Finland	1,890,301,304	+105%	48
France	-3,311,261,737	-92.4%	87
United Kingdom	-41,224,292,470	+13.7%	109
Georgia	-2,593,149,355	-1.88%	83
Gambia	-710,819,730	+11.8%	72
Greece	-13,984,812,965	+16.4%	100
Grenada	-131,638,556	+36.9%	59
Guatemala	-17,579,310,780	+11.9%	104
Hong Kong SAR China	16,518,374,546	+636%	26
Honduras	-8,882,297,543	+9.37%	98
Croatia	-3,259,264,201	+86.6%	85
Hungary	12,425,921,823	+26.3%	31
Indonesia	21,449,490,424	-25%	21
India	-101,034,797,310	+17%	112
Iceland	-382,278,038	-633%	69
Israel	12,810,200,000	-9.83%	30
Italy	61,620,081,678	+77.8%	10
Jamaica	-2,400,772,587	-7.33%	82
Japan	-42,600,298,380	-41.5%	110
Kazakhstan	17,662,721,866	-2.97%	24
Cambodia	-2,616,768,810	+56.9%	84
St. Kitts & Nevis	-138,542,834	+54.6%	60
South Korea	76,425,300,000	+605%	7
Kuwait	28,188,883,808	-12%	18
St. Lucia	154,233,444	+21.3%	54
Lesotho	-1,099,698,565	-7.67%	75
Lithuania	4,404,370,172	+41.1%	44
Luxembourg	42,171,155,231	+9.6%	14
Latvia	-1,116,135,565	-29.4%	76
Moldova	-4,701,120,000	+18.3%	90
Maldives	68,023,386	-165%	55
Mexico	-16,268,563,735	-34.8%	102
North Macedonia	-2,198,664,602	+6.86%	81
Malta	4,739,928,988	+18.2%	41
Montenegro	-1,848,846,671	+32.3%	80
Mozambique	-1,129,454,668	-36.4%	77
Malaysia	22,698,293,285	+11.1%	19
Namibia	-3,312,449,512	+22.1%	88
Nigeria	-194,385,706	-96.2%	62
Nicaragua	-3,302,581,710	+45.4%	86
Netherlands	148,313,760,177	+15.1%	4
Norway	66,737,994,417	-10.7%	9
Nepal	-14,032,620,092	+20.8%	101
New Zealand	-6,199,833,773	-33.9%	95
Pakistan	-26,624,779,552	+21.8%	107
Panama	6,489,366,214	+228%	38
Peru	16,164,915,867	+75.8%	27
Philippines	-54,164,616,659	+13.2%	111
Poland	36,634,000,000	-23.3%	15
Portugal	7,258,325,860	+68%	36
Paraguay	-981,473,459	-234%	74
Palestinian Territories	-5,379,455,681	-34.6%	92
Qatar	55,524,450,549	-1.79%	11
Romania	-23,322,218,788	+37.7%	106
Russia	93,826,740,000	+9.66%	5
Saudi Arabia	43,885,705,969	-44.3%	13
Singapore	192,576,845,361	+1.79%	2
Solomon Islands	-214,251,464	-36.5%	64
El Salvador	-6,768,233,250	+5.67%	96
Suriname	222,216,301	-32.6%	53
Slovakia	64,857,364	-97.2%	56
Slovenia	4,576,156,270	+5.16%	42
Sweden	29,326,001,914	+16.7%	17
Thailand	17,772,045,139	+70.2%	23
Tajikistan	-5,289,347,985	+38.2%	91
Timor-Leste	-919,259,512	+96.7%	73
Tonga	-273,376,514	-5.12%	66
Trinidad & Tobago	896,603,025	-61.5%	50
Turkey	5,734,000,000	-120%	40
Ukraine	-35,911,000,000	-5.2%	108
Uruguay	4,211,887,073	+56.8%	45
United States	-917,838,000,000	+16.9%	113
Uzbekistan	-17,450,961,300	-0.829%	103
St. Vincent & Grenadines	-215,996,803	-11.4%	65
Vietnam	30,711,000,000	-12.8%	16
Samoa	-206,019,343	-3.99%	63
South Africa	8,039,494,195	+560%	35

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.GNFS.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.GNFS.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 and services (BoP, current US$)

Year: 2024

Acquire full dataset with Python or R from source