Country

Indicator

Value type

Values present

Current account balance (% of GDP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	7.85	+59.2%	12
Albania	-2.38	+98.7%	75
Argentina	0.993	-131%	46
Armenia	-3.87	+67.4%	87
Antigua & Barbuda	-8.15	-39.7%	98
Australia	-1.96	+554%	72
Austria	2.42	+82.8%	36
Azerbaijan	6.29	-45.3%	14
Belgium	-0.855	+22.3%	61
Bangladesh	0.416	-58.6%	48
Bulgaria	-1.79	+105%	71
Bahrain	4.78	-18.2%	23
Bahamas	-6.65	-5.03%	96
Bosnia & Herzegovina	-4.15	+79.2%	90
Belarus	-2.53	+66.4%	78
Belize	-1.47	+128%	68
Brazil	-2.81	+120%	81
Brunei	14.4	+12%	6
Bhutan	-20.5	-35.7%	109
Canada	-0.462	-27.1%	56
Switzerland	5.04	-5.09%	21
Chile	-1.47	-53%	67
China	2.26	+56.9%	38
Colombia	-1.77	-21.7%	69
Cape Verde	3.65	-244%	29
Costa Rica	-1.35	-5.51%	66
Cyprus	-8.39	-25.7%	100
Czechia	1.75	-1,490%	43
Germany	5.73	+3.14%	18
Djibouti	14.9	-18.9%	5
Dominica	-23.2	-31.5%	111
Denmark	13	+32.3%	8
Dominican Republic	-3.35	-8.59%	83
Ecuador	5.68	+210%	20
Spain	3.03	+14.1%	32
Estonia	-1.15	-34.6%	64
Finland	0.31	-158%	50
France	0.392	-139%	49
United Kingdom	-2.65	-24.5%	79
Georgia	-4.42	-20.5%	91
Gambia	-2.97	-40.8%	82
Greece	-6.38	+3.47%	95
Grenada	-19.5	+5.82%	108
Guatemala	2.94	-4.34%	33
Hong Kong SAR China	12.9	+51.9%	9
Honduras	-4.61	+15.8%	92
Croatia	-1.13	-250%	63
Hungary	2.28	+549%	37
Indonesia	-0.607	+307%	58
India	-0.829	-5.65%	60
Iceland	-2.53	-373%	77
Israel	3.09	-14.9%	31
Italy	1.13	+697%	45
Jamaica	3.41	+16.3%	30
Japan	4.82	+29.8%	22
Kazakhstan	-1.28	-64.4%	65
Cambodia	0.479	-63.3%	47
St. Kitts & Nevis	-15.9	+37%	104
Kuwait	29.1	-6.21%	1
St. Lucia	-2.52	+60.6%	76
Lesotho	3.72	-152%	28
Lithuania	2.48	+125%	35
Luxembourg	13.8	+22.7%	7
Latvia	-2.12	-45.7%	73
Moldova	-16	+41.5%	105
Maldives	-18	-15.2%	107
Mexico	-0.323	+3.29%	54
North Macedonia	-2.24	-725%	74
Malta	5.69	-11.4%	19
Montenegro	-17.4	+54.1%	106
Mozambique	-11.1	+5.8%	101
Malaysia	1.69	+8.24%	44
Namibia	-15.4	+0.71%	103
Nigeria	9.17	+419%	11
Nicaragua	4.15	-49.6%	27
Netherlands	9.92	+0.779%	10
Norway	17.1	-2.05%	4
Nepal	4.55	+1,174%	24
New Zealand	-6.14	-8.18%	94
Pakistan	0.187	-161%	53
Panama	1.94	-163%	42
Peru	2.21	+569%	39
Philippines	-3.79	+33.9%	86
Poland	0.196	-89.1%	52
Portugal	2.17	+288%	40
Paraguay	-3.75	+815%	84
Palestinian Territories	-21.1	+30.3%	110
Qatar	17.5	+2.18%	3
Romania	-8.36	+19.8%	99
Russia	2.87	+20.1%	34
Saudi Arabia	-0.459	-116%	55
Singapore	17.5	-0.834%	2
Solomon Islands	-3.76	-64.9%	85
El Salvador	-1.79	+64.6%	70
Suriname	0.197	-95.4%	51
Slovakia	-2.75	+215%	80
Slovenia	4.46	-0.332%	25
Sweden	7.42	+6.44%	13
Thailand	2.11	+46.6%	41
Tajikistan	6.24	+30.9%	15
Timor-Leste	-28.2	+230%	112
Tonga	-3.91	-33.8%	89
Trinidad & Tobago	4.23	-63.5%	26
Turkey	-0.754	-78.9%	59
Ukraine	-7.21	+36.6%	97
Uruguay	-1.01	-70%	62
United States	-3.88	+18.9%	88
Uzbekistan	-4.99	-34.3%	93
St. Vincent & Grenadines	-13.5	-19.6%	102
Vietnam	5.89	-0.969%	17
Samoa	6.05	+41.3%	16
South Africa	-0.596	-63.1%	57

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.CAB.XOKA.GD.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 <- 'BN.CAB.XOKA.GD.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

Current account balance (% of GDP)

Year: 2024

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