Country

Indicator

Value type

Values present

External balance on goods and services (current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	10,829,119,878	-13%	31
Albania	-1,862,119,342	+53.2%	89
Argentina	15,889,079,544	-315%	25
Armenia	142,859,471	-173%	59
Australia	36,729,293,880	-51.4%	13
Austria	17,273,722,889	+59.6%	22
Azerbaijan	6,774,058,824	-35.3%	35
Belgium	-87,239,866	-97.7%	61
Benin	-635,776,350	-30.6%	75
Burkina Faso	-1,500,497,207	+4.01%	84
Bangladesh	-26,357,644,330	+29.1%	129
Bulgaria	2,609,474,169	-37.9%	45
Bahamas	-581,900,000	+311%	74
Bosnia & Herzegovina	-4,062,299,869	+25.3%	104
Belarus	-1,359,747,051	-500%	81
Bermuda	2,882,900,000	+25.1%	44
Brazil	10,856,517,290	-78.2%	30
Brunei	2,377,201,822	-4.8%	46
Botswana	-2,887,134,465	+246%	95
Central African Republic	-465,266,245	+24.7%	73
Canada	-5,745,750,821	-492%	111
Switzerland	95,742,532,387	-5.66%	6
Chile	11,845,972,478	+293%	28
China	533,713,513,222	+39.1%	1
Côte d’Ivoire	458,038,706	-116%	56
Cameroon	-3,290,967,271	+67.4%	98
Congo - Kinshasa	-3,007,630,309	+63.6%	96
Congo - Brazzaville	1,944,745,263	-27.8%	49
Colombia	-20,688,762,303	+19.5%	124
Comoros	-379,586,819	+6.25%	70
Cape Verde	-314,959,435	-30.8%	67
Costa Rica	5,466,033,886	+17.9%	37
Cyprus	1,302,134,453	+267%	51
Czechia	22,645,853,224	+32%	18
Germany	179,680,569,715	-0.883%	4
Djibouti	512,102,945	-15.7%	55
Denmark	46,428,394,342	+39.9%	11
Dominican Republic	-7,764,731,649	-10.4%	115
Ecuador	4,181,712,600	+2,474%	41
Egypt	-26,643,864,091	+201%	130
Spain	73,742,581,336	+15.9%	8
Estonia	241,249,618	-31.8%	58
Finland	1,933,131,517	+108%	50
France	-23,235,028,570	-61.9%	126
Micronesia (Federated States of)	-196,400,000	+6.23%	64
Gabon	7,527,803,015	+0.523%	33
United Kingdom	-41,250,509,365	+14%	132
Georgia	-2,568,994,366	-3.14%	92
Ghana	953,399,973	-240%	53
Guinea	-3,044,583,519	+79.6%	97
Gambia	-768,209,323	+38%	76
Guinea-Bissau	-333,026,708	+12.5%	68
Equatorial Guinea	1,249,393,561	-13.9%	52
Greece	-13,777,256,225	+19.4%	119
Guatemala	-17,612,103,505	+11.9%	123
Hong Kong SAR China	16,511,629,606	+639%	23
Honduras	-8,906,890,933	+7.78%	116
Croatia	-2,884,598,424	+86.5%	94
Haiti	-3,896,207,609	-3.04%	103
Hungary	12,371,537,888	+26.7%	27
Indonesia	25,049,322,656	-15.9%	17
India	-90,572,737,249	+18.8%	134
Ireland	243,604,877,819	+34.3%	2
Iran	-17,145,345,954	+12.2%	121
Iraq	778,980,385	-97%	54
Iceland	-382,939,692	-892%	71
Israel	13,063,808,228	-9.89%	26
Italy	54,327,706,392	+65%	10
Jordan	-7,712,253,521	+13.4%	114
Kenya	-9,984,700,944	+5.99%	117
Cambodia	-333,665,530	+87%	69
Kiribati	-272,793,830	-1.28%	66
Libya	7,334,059,295	+11.6%	34
Sri Lanka	-2,597,644,514	+43.8%	93
Lithuania	4,395,860,974	+41.1%	39
Luxembourg	30,547,176,466	+12.3%	15
Latvia	-1,114,293,374	-29.5%	78
Macao SAR China	22,272,833,658	+19.5%	20
Morocco	-14,185,782,126	+18.5%	120
Moldova	-4,702,789,931	+18.4%	107
Madagascar	-1,345,125,479	+18.9%	80
Mexico	-21,123,031,930	-18%	125
North Macedonia	-2,189,554,295	+6.55%	90
Mali	-1,559,617,681	-19.4%	85
Malta	4,228,108,052	+13.8%	40
Montenegro	-1,829,872,551	+30.4%	88
Mongolia	-145,038,254	-107%	63
Mozambique	-2,287,316,734	-27.6%	91
Mauritius	-1,732,340,041	+22.4%	87
Malaysia	22,545,523,833	+10.6%	19
Namibia	-3,522,381,730	+20.7%	101
Niger	2,020,107,751	+1,190%	48
Nicaragua	-3,466,084,428	+46.3%	100
Netherlands	148,477,705,562	+15.2%	5
Norway	67,203,815,432	-9.99%	9
Nepal	-10,851,312,837	-4.07%	118
Nauru	-127,060,360	+47.1%	62
Pakistan	-24,893,517,780	-3.58%	128
Peru	16,194,388,066	+74%	24
Philippines	-66,189,443,106	+7.06%	133
Poland	36,658,868,368	-21.4%	14
Puerto Rico	11,469,800,000	+71.9%	29
Portugal	5,612,694,745	+68.6%	36
Paraguay	-1,079,351,355	-245%	77
Palestinian Territories	-5,379,400,000	-31.6%	109
Romania	-23,346,751,044	+39.1%	127
Russia	94,019,176,890	+9.34%	7
Rwanda	-1,184,347,481	-44.5%	79
Saudi Arabia	43,606,933,333	-43.3%	12
Sudan	-37,591,104	-63.4%	60
Senegal	-4,867,762,305	-37%	108
Singapore	192,576,893,401	+1.79%	3
Sierra Leone	-1,705,671,180	+108%	86
El Salvador	-6,768,230,000	+5.67%	113
Somalia	-6,577,585,550	+12.7%	112
Serbia	-5,412,585,013	+53.2%	110
Slovakia	285,784,162	-86%	57
Slovenia	4,774,501,474	+7.82%	38
Sweden	26,794,070,912	+13.7%	16
Seychelles	-390,807,755	+27.5%	72
Chad	2,242,479,007	-7.66%	47
Togo	-1,360,609,215	+8.95%	82
Thailand	17,650,738,217	+65.7%	21
Tunisia	-4,336,687,891	+61.6%	106
Turkey	3,551,005,971	-113%	43
Tanzania	-1,459,125,191	-51.7%	83
Uganda	-4,108,584,745	-16.9%	105
Ukraine	-36,112,248,772	-4.69%	131
Uruguay	4,093,921,667	+55.8%	42
United States	-903,051,000,000	+13.3%	135
Uzbekistan	-17,469,962,042	-1.36%	122
Samoa	-261,673,077	-15.9%	65
Kosovo	-3,391,281,277	+5.62%	99
South Africa	7,993,576,161	+538%	32
Zimbabwe	-3,739,012,677	+39%	102

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 = 'NE.RSB.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 <- 'NE.RSB.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

External balance on goods and services (current US$)

Year: 2024

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