Country

Indicator

Value type

Values present

External balance on goods and services (% of GDP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	13.5	-8.14%	11
Albania	-6.85	+32.7%	93
Argentina	2.51	-320%	43
Armenia	0.554	-168%	54
Australia	2.1	-52.1%	47
Austria	3.31	+56.6%	41
Azerbaijan	9.12	-36.9%	20
Belgium	-0.0131	-97.8%	60
Benin	-2.96	-36.4%	75
Burkina Faso	-6.45	-9.07%	90
Bangladesh	-5.86	+25.4%	83
Bulgaria	2.33	-43.3%	45
Bahamas	-3.68	+297%	78
Bosnia & Herzegovina	-14.3	+21.9%	108
Belarus	-1.79	-482%	69
Bermuda	32.1	+19.5%	6
Brazil	0.498	-78.1%	56
Brunei	15.4	-7.07%	9
Botswana	-14.9	+246%	111
Central African Republic	-16.9	+15.8%	117
Canada	-0.256	-480%	62
Switzerland	10.2	-9.9%	18
Chile	3.59	+300%	36
China	2.85	+35.6%	42
Côte d’Ivoire	0.529	-115%	55
Cameroon	-6.41	+60.7%	89
Congo - Kinshasa	-4.25	+55%	80
Congo - Brazzaville	12.4	-29.6%	13
Colombia	-4.94	+4.62%	81
Comoros	-24.6	-1.7%	126
Cape Verde	-11.4	-36.5%	103
Costa Rica	5.73	+6.97%	24
Cyprus	3.58	+242%	37
Czechia	6.56	+31.3%	23
Germany	3.86	-3.74%	35
Djibouti	12.5	-19.2%	12
Denmark	10.8	+32.6%	16
Dominican Republic	-6.25	-13.2%	88
Ecuador	3.35	+2,401%	39
Egypt	-6.85	+206%	92
Spain	4.28	+9.01%	32
Estonia	0.564	-34.1%	53
Ethiopia	-6.21	-16.2%	87
Finland	0.645	+105%	52
France	-0.735	-63.3%	65
Micronesia (Federated States of)	-41.7	+0.032%	133
Gabon	36.1	-3.39%	3
United Kingdom	-1.13	+5.45%	66
Georgia	-7.61	-11.7%	94
Ghana	1.15	-236%	51
Guinea	-12	+58.8%	105
Gambia	-30.6	+31.9%	131
Guinea-Bissau	-15.7	+10.3%	116
Equatorial Guinea	9.79	-16.8%	19
Greece	-5.36	+13%	82
Guatemala	-15.6	+3.14%	115
Hong Kong SAR China	4.06	+592%	33
Honduras	-24	-0.177%	124
Croatia	-3.12	+70.1%	77
Haiti	-15.4	-23.7%	114
Hungary	5.55	+21.6%	26
Indonesia	1.79	-17.4%	50
India	-2.31	+10.5%	71
Ireland	42.2	+28.2%	2
Iran	-3.92	+3.87%	79
Iraq	0.279	-97.2%	57
Iceland	-1.14	-844%	68
Israel	2.42	-14.6%	44
Italy	2.29	+60.3%	46
Jordan	-14.5	+8.57%	110
Kenya	-8.02	-8.03%	97
Cambodia	-0.72	+70.8%	64
Kiribati	-88.6	-7.45%	136
Libya	15.7	+7.92%	8
Sri Lanka	-2.62	+21.6%	74
Lithuania	5.18	+32.7%	28
Luxembourg	32.8	+5.48%	5
Latvia	-2.56	-31%	73
Macao SAR China	44.4	+9.03%	1
Morocco	-9.19	+10.8%	101
Moldova	-25.8	+8.71%	128
Madagascar	-7.72	+8.34%	96
Mexico	-1.14	-20.6%	67
North Macedonia	-13.1	+0.663%	106
Mali	-5.87	-25.4%	84
Malta	17.4	+3.96%	7
Montenegro	-22.7	+21.7%	123
Mongolia	-0.615	-106%	63
Mozambique	-10.2	-32.3%	102
Mauritius	-11.6	+15.5%	104
Malaysia	5.34	+4.77%	27
Namibia	-26.3	+12%	129
Niger	10.3	+1,002%	17
Nicaragua	-17.6	+32.3%	118
Netherlands	12.1	+8.3%	14
Norway	13.9	-10.1%	10
Nepal	-25.3	-8.25%	127
Nauru	-79.2	+38.9%	135
Pakistan	-6.67	-12.7%	91
Peru	5.6	+60.6%	25
Philippines	-14.3	+1.36%	109
Poland	4.01	-30.1%	34
Puerto Rico	9.11	+61.7%	21
Portugal	1.82	+58.2%	49
Paraguay	-2.43	-241%	72
Palestinian Territories	-39.2	-10.9%	132
Romania	-6.1	+27.5%	86
Russia	4.33	+4.19%	31
Rwanda	-8.31	-44.2%	99
Saudi Arabia	3.52	-44.2%	38
Sudan	-0.0753	-70.8%	61
Senegal	-15.1	-40.1%	112
Singapore	35.2	-6.01%	4
Sierra Leone	-22.6	+76.3%	122
El Salvador	-19.1	+1.15%	121
Somalia	-54.3	+2.07%	134
Serbia	-6.08	+39.9%	85
Slovakia	0.202	-86.8%	59
Slovenia	6.59	+2.86%	22
Sweden	4.39	+9.15%	30
Seychelles	-18	+28.7%	119
Chad	10.9	-14.5%	15
Togo	-13.7	+0.668%	107
Thailand	3.35	+62.4%	40
Tunisia	-8.12	+45.8%	98
Turkey	0.268	-111%	58
Tanzania	-1.85	-51.5%	70
Uganda	-7.66	-24.5%	95
Ukraine	-18.9	-9.45%	120
Uruguay	5.06	+50.1%	29
United States	-3.09	+7.58%	76
Uzbekistan	-15.2	-11.9%	113
Samoa	-24.5	-26.1%	125
Kosovo	-30.4	-0.822%	130
South Africa	2	+507%	48
Zimbabwe	-8.46	+10.8%	100

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.ZS'

# 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.ZS'

# 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 (% of GDP)

Year: 2024

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