Country

Indicator

Value type

Values present

Reserves and related items (BoP, current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	910,853,540	+9.95%	35
Albania	439,817,767	-61%	48
Argentina	5,173,245,170	-131%	14
Armenia	274,262,938	-156%	53
Antigua & Barbuda	-1,016,450	-96.2%	74
Australia	3,399,165,117	+166%	17
Austria	-353,919,082	-91.9%	91
Azerbaijan	-766,182,955	-132%	97
Belgium	-2,741,065,104	+22.7%	104
Bangladesh	-1,131,737,533	-77.2%	100
Bulgaria	-959,765,903	-127%	99
Bahrain	-228,961,291	-177%	88
Bahamas	242,142,468	-806%	57
Bosnia & Herzegovina	799,714,994	+430%	36
Belarus	-77,239,661	-74.1%	81
Belize	10,559,177	-208%	70
Brazil	-26,421,602,620	-224%	110
Brunei	-107,238,494	-84.8%	82
Bhutan	-28,884,507	-91.7%	77
Canada	5,498,278,304	-28.3%	13
Switzerland	26,145,736,959	-120%	3
Chile	-2,572,373,903	-138%	103
China	-60,497,384,785	-1,505%	111
Colombia	6,051,360,281	+255%	12
Cape Verde	34,950,093	+22%	66
Costa Rica	-118,594,170	-103%	83
Cyprus	73,960,732	-1,788%	64
Czechia	1,152,165,758	-26%	31
Germany	-1,554,128,087	-264%	101
Djibouti	-148,909,747	+83.4%	84
Dominica	-24,990,206	+34.5%	76
Denmark	1,432,030,475	-73.2%	29
Dominican Republic	-1,775,154,731	-248%	102
Ecuador	1,037,726,237	-125%	32
Spain	1,452,034,781	-77.5%	28
Estonia	-345,656,001	-225%	90
Finland	686,132,527	+1,675%	40
France	1,619,679,176	-107%	26
United Kingdom	-3,851,737,409	-22.1%	107
Georgia	-631,488,105	+7,070%	95
Gambia	-228,774,147	+68%	87
Greece	-281,241,345	-150%	89
Grenada	23,179,230	-35.5%	68
Guatemala	2,930,725,001	+224%	20
Hong Kong SAR China	-11,456,188,755	+12.4%	109
Honduras	453,140,253	-146%	47
Croatia	252,546,717	-129%	56
Hungary	235,520,233	-91.7%	58
Indonesia	7,189,541,702	+14.2%	10
India	16,389,914,107	-57.1%	4
Iceland	721,990,598	-297%	39
Israel	8,247,433,181	-3,465%	9
Italy	2,263,773,698	-23.6%	23
Jamaica	583,074,341	+75.4%	44
Japan	-66,514,447,489	-323%	112
Kazakhstan	-892,820,262	-84.9%	98
Cambodia	432,745,083	+491%	49
St. Kitts & Nevis	8,147,642	-201%	71
South Korea	-3,072,700,000	+3.84%	106
Kuwait	-2,961,941,569	+583%	105
St. Lucia	-17,374,757	-128%	75
Lesotho	258,577,560	+1,449%	55
Lithuania	1,535,811,420	+130%	27
Luxembourg	-158,368,440	-346%	85
Latvia	35,108,777	-86.3%	65
Moldova	-167,682,680	-127%	86
Maldives	83,565,512	-135%	62
Mexico	13,904,307,987	+84.5%	5
North Macedonia	272,771,970	-60.1%	54
Malta	211,270,481	-1,271%	60
Montenegro	304,091,340	-160%	52
Mozambique	101,694,307	-84.9%	61
Malaysia	3,846,081,179	-178%	16
Namibia	628,816,930	+167%	42
Nigeria	8,335,870,672	-403%	8
Nicaragua	722,790,156	-28.7%	38
Netherlands	-765,050,070	-171%	96
Norway	-36,033,477	-101%	79
Nepal	4,756,984,153	+36.2%	15
New Zealand	8,663,628,733	+658%	7
Pakistan	2,470,076,338	-27.6%	22
Panama	365,574,950	+57,853%	50
Peru	7,079,785,972	-355%	11
Philippines	609,606,342	-83.4%	43
Poland	29,227,368,046	+40.2%	2
Portugal	565,631,035	-206%	45
Paraguay	-487,362,664	-236%	92
Palestinian Territories	5,500,000	-98.7%	72
Qatar	3,007,984,168	-22.6%	19
Romania	970,980,041	-93.2%	33
Saudi Arabia	925,513,340	-104%	34
Singapore	29,682,447,026	-51.4%	1
Solomon Islands	16,212,502	-34.2%	69
El Salvador	799,230,595	+72.1%	37
Suriname	214,860,726	+253%	59
Slovakia	2,818,089,526	+327%	21
Slovenia	357,993,999	-31,462%	51
Sweden	3,384,565,084	-159%	18
Thailand	12,413,243,984	+388%	6
Tajikistan	683,758,985	-8.9%	41
Timor-Leste	-43,817,838	-10.9%	80
Tonga	5,348,749	-75.8%	73
Trinidad & Tobago	-620,533,634	+5.79%	94
Turkey	495,570,087	-124%	46
Ukraine	-30,743,140	-100%	78
Uruguay	1,160,043,008	+36.6%	30
United States	2,110,690,362	+5,806%	25
Uzbekistan	-612,558,275	-77.2%	93
St. Vincent & Grenadines	33,417,867	-188%	67
Vietnam	-9,129,012,945	-262%	108
Samoa	75,018,846	-14.1%	63
South Africa	2,125,231,291	-1,094%	24

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.RES.INCL.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.RES.INCL.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

Reserves and related items (BoP, current US$)

Year: 2024

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