Country

Indicator

Value type

Values present

Net financial account (BoP, current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	7,028,783,740	+27.6%	25
Albania	-651,574,063	+860%	69
Argentina	4,060,699,339	-115%	29
Armenia	-524,585,156	+5.58%	67
Antigua & Barbuda	-150,017,292	-52.3%	60
Australia	-34,906,047,569	-2,051%	110
Austria	8,379,985,185	+55.2%	23
Azerbaijan	4,562,977,000	-52.7%	28
Belgium	-1,813,793,412	-6.03%	83
Bangladesh	-2,213,578,450	-166%	87
Bulgaria	-727,540,000	-168%	71
Bahrain	2,019,148,936	+405%	35
Bahamas	-904,756,832	-27.4%	74
Bosnia & Herzegovina	-845,134,406	+271%	73
Belarus	-1,169,782,037	-4,687%	77
Belize	-102,814,083	+126%	58
Brazil	-85,755,035,940	+126%	112
Brunei	1,685,548,147	+46.4%	36
Bhutan	-203,655,541	-71.1%	63
Canada	-14,041,176,096	+18.6%	102
Switzerland	49,474,858,114	-19.5%	14
Chile	-7,104,022,157	-15.2%	98
China	433,909,503,554	+77.7%	1
Colombia	-5,580,730,080	-28.9%	96
Cape Verde	140,666,208	-576%	51
Costa Rica	-737,388,239	+20.8%	72
Cyprus	-2,996,853,366	-3.69%	92
Czechia	8,849,190,095	+119%	22
Germany	259,443,352,952	+23%	2
Djibouti	-230,670,539	+10.2%	64
Dominica	7,445,594	-111%	55
Denmark	38,403,737,165	+16.7%	15
Dominican Republic	-5,037,700,000	-4.24%	95
Ecuador	7,100,710,893	+1,447%	24
Spain	75,310,103,509	+16.4%	8
Estonia	217,366,894	-121%	48
Finland	972,373,891	-117%	42
France	-23,813,581,898	-38.3%	107
United Kingdom	-80,091,996,697	-29.9%	111
Georgia	-1,484,602,031	-11.9%	80
Gambia	-337,201,806	+26.3%	66
Greece	-14,448,586,005	+48.8%	103
Grenada	-60,029,669	-68.2%	57
Guatemala	2,783,285,630	-1.67%	31
Hong Kong SAR China	62,435,846,287	+103%	10
Honduras	-1,280,603,172	-7%	79
Croatia	1,041,324,607	-76.3%	40
Hungary	1,295,334,854	-270%	37
Indonesia	-10,266,439,189	+190%	101
India	-31,294,079,957	-2.22%	109
Iceland	1,021,988,484	-280%	41
Israel	19,775,700,000	-3.65%	18
Italy	55,370,748,036	+62.1%	11
Jamaica	1,269,059,397	+20.3%	38
Japan	170,436,525,577	-2.07%	3
Kazakhstan	-2,011,422,402	-74.5%	84
Cambodia	-689,490,314	+55.8%	70
St. Kitts & Nevis	-170,287,501	+73.4%	61
South Korea	95,208,800,000	+197%	6
Kuwait	49,857,281,679	-1.43%	13
St. Lucia	-40,994,108	+31.6%	56
Lesotho	180,225,648	-45.4%	49
Lithuania	2,292,004,533	-41.3%	34
Luxembourg	13,134,653,210	+27.6%	19
Latvia	546,683,164	+679%	44
Moldova	-2,824,011,689	+71%	90
Maldives	-1,118,776,128	-9.97%	75
Mexico	-3,474,808,561	-56%	94
North Macedonia	-242,621,998	-325%	65
Malta	2,317,610,905	+11.8%	33
Montenegro	-1,235,876,271	+49.5%	78
Mozambique	-2,283,554,659	+21.4%	88
Malaysia	6,800,414,810	-692%	26
Namibia	-1,736,207,328	+1.07%	82
Nigeria	12,119,731,836	-166%	20
Nicaragua	-572,106,197	-230%	68
Netherlands	114,504,632,063	+14.5%	4
Norway	63,009,190,753	-29.1%	9
Nepal	3,292,017,705	+77.3%	30
New Zealand	-134,435,411	-98.8%	59
Pakistan	643,000,000	-132%	43
Panama	-2,956,623,203	+2.43%	91
Peru	5,058,598,215	-368%	27
Philippines	-17,029,204,241	+71.4%	104
Poland	-6,824,000,000	-143%	97
Portugal	10,499,706,136	+71.8%	21
Paraguay	-2,165,411,194	+258%	85
Palestinian Territories	-2,190,255,040	+29.5%	86
Qatar	36,343,131,868	-0.184%	16
Romania	-22,983,063,068	+84.8%	106
Russia	53,009,890,000	+37%	12
Saudi Arabia	-24,562,827,460	-376%	108
Singapore	95,621,922,319	+5.92%	5
Solomon Islands	41,078,851	-143%	53
El Salvador	-1,496,962,756	+61.4%	81
Suriname	179,736,052	+228%	50
Slovakia	-2,681,341,706	-4.24%	89
Slovenia	2,701,980,260	+15.2%	32
Sweden	83,870,289,214	+128%	7
Thailand	28,552,497,260	+98%	17
Tajikistan	1,081,733,932	+11%	39
Timor-Leste	446,280,949	+1,161%	46
Tonga	27,600,673	+78.2%	54
Trinidad & Tobago	487,344,452	-52.9%	45
Turkey	-22,795,000,000	-55.7%	105
Ukraine	-8,352,000,000	+6.38%	100
Uruguay	414,566,483	-121%	47
United States	-1,268,835,000,000	+37.3%	113
Uzbekistan	-7,415,057,100	-20.9%	99
St. Vincent & Grenadines	-172,021,443	-15.8%	62
Vietnam	-1,138,000,000	-113%	76
Samoa	105,213,332	-27.9%	52
South Africa	-3,390,309,388	+12.9%	93

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.FIN.TOTL.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.FIN.TOTL.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 financial account (BoP, current US$)

Year: 2024

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