Country

Indicator

Value type

Values present

Total reserves minus gold (current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	14,242,892,505	+2.16%	61
Albania	6,228,580,528	-0.00002%	80
United Arab Emirates	231,690,115,820	+25.5%	9
Argentina	24,380,645,449	+28.4%	50
Armenia	3,685,220,143	+2.15%	95
Antigua & Barbuda	358,440,632	-1.63%	128
Australia	54,455,310,269	-3.8%	36
Austria	11,918,528,062	-5.76%	66
Azerbaijan	12,699,124,300	-7.63%	65
Belgium	22,374,333,716	-13.1%	52
Bangladesh	20,196,792,193	-3.49%	55
Bulgaria	40,269,659,586	-7.69%	42
Bahrain	4,557,337,759	-5.22%	90
Bosnia & Herzegovina	9,126,638,850	+0.228%	73
Belarus	4,392,189,758	-3.16%	91
Belize	498,087,321	+5.14%	124
Bolivia	87,151,700	-63.8%	135
Brazil	318,856,510,332	-7.96%	8
Brunei	4,032,248,416	-3.57%	92
Bhutan	932,629,244	+44%	116
Botswana	3,455,661,776	-27.3%	98
Canada	119,778,473,397	+1.89%	18
Switzerland	822,130,485,596	+3.42%	3
Chile	44,382,397,504	-4.27%	39
China	3,264,803,869,739	-1.11%	1
Colombia	61,505,803,384	+4.73%	34
Comoros	322,435,665	-0.288%	130
Cape Verde	783,105,558	+1.45%	118
Costa Rica	14,177,018,014	+7.2%	62
Cyprus	921,268,672	+6.22%	117
Czechia	141,988,317,940	-2.98%	17
Germany	96,792,003,856	-3.59%	21
Djibouti	348,725,427	-30.5%	129
Dominica	155,971,242	-15%	134
Denmark	102,822,670,523	-2.03%	20
Dominican Republic	13,423,522,737	-13.4%	63
Algeria	68,448,330,429	-1.81%	32
Ecuador	4,703,151,586	+74.3%	87
Egypt	34,277,830,660	+38.8%	46
Spain	84,153,911,869	-0.313%	26
Estonia	2,053,834,340	-20.3%	106
Ethiopia	3,784,387,425	+86.6%	94
Finland	14,321,732,953	+4.7%	60
Fiji	1,596,997,610	+3.28%	109
France	78,428,799,614	-0.975%	30
United Kingdom	148,569,354,567	-5.57%	16
Georgia	3,848,485,592	-23.1%	93
Greece	5,609,616,295	-6.86%	83
Grenada	423,263,144	+4.73%	125
Guatemala	23,834,241,452	+14.3%	51
Guyana	1,009,772,164	+12.8%	114
Honduras	7,977,882,379	+6.41%	75
Croatia	3,335,632,833	+5.02%	100
Haiti	2,565,745,293	+4.03%	104
Hungary	37,193,970,854	-5.73%	44
Indonesia	149,117,682,033	+5.65%	15
India	569,544,279,505	-0.864%	4
Ireland	11,687,942,512	-3.46%	68
Iraq	87,047,970,882	-15.3%	24
Iceland	6,236,489,889	+9.85%	79
Israel	214,543,676,670	+4.83%	13
Italy	84,874,502,826	+0.0649%	25
Jordan	15,929,034,365	+11%	59
Japan	1,159,702,379,069	-6.37%	2
Kazakhstan	21,979,739,005	+33.6%	54
Kenya	10,065,144,725	+37.1%	70
Kyrgyzstan	1,890,318,643	+4.63%	107
Cambodia	18,607,696,938	+8.45%	56
St. Kitts & Nevis	294,747,745	+3.03%	132
South Korea	409,457,248,117	-1.1%	6
Kuwait	44,103,517,198	-6.92%	40
Lebanon	9,240,157,310	+9.09%	71
Libya	80,591,801,678	-2.55%	29
St. Lucia	406,063,791	-4.3%	126
Sri Lanka	6,054,549,136	+38.4%	82
Lesotho	1,007,862,057	+18%	115
Lithuania	6,918,508,682	+19.6%	77
Luxembourg	2,601,640,416	-8.03%	102
Latvia	4,582,765,661	+1.49%	89
Macao SAR China	29,391,736,000	+5.84%	47
Morocco	35,278,753,636	+1.2%	45
Moldova	5,477,297,217	+0.534%	84
Madagascar	2,784,591,093	+5.8%	101
Maldives	673,885,913	+14.1%	121
Mexico	221,944,176,978	+7.56%	11
North Macedonia	4,673,715,191	+2.54%	88
Malta	1,401,984,072	+16.4%	111
Montenegro	1,741,494,537	+10.7%	108
Mongolia	4,891,417,145	+11.5%	86
Mozambique	3,512,608,804	+4.05%	97
Mauritius	7,464,581,565	+16.2%	76
Malaysia	112,967,830,258	+1.88%	19
Namibia	3,355,524,015	+13.5%	99
Nigeria	38,612,493,467	+20.5%	43
Nicaragua	6,104,867,094	+12.1%	81
Netherlands	27,753,363,986	-5.02%	48
Norway	81,241,559,662	+0.972%	28
New Zealand	22,065,156,554	+42.5%	53
Oman	17,722,180,397	+2.45%	57
Pakistan	12,977,150,810	+37.4%	64
Panama	6,855,505,288	+1.46%	78
Peru	76,337,730,572	+10.5%	31
Philippines	95,250,799,159	+2.2%	22
Poland	185,514,883,619	+9.13%	14
Portugal	10,331,816,204	+4.66%	69
Palestinian Territories	1,328,000,000	+0.363%	112
Qatar	44,692,252,847	-0.34%	38
Romania	64,698,509,621	-2.16%	33
Rwanda	2,406,450,702	+31.2%	105
Saudi Arabia	436,769,021,308	+0.0555%	5
Singapore	365,494,398,351	+6.07%	7
El Salvador	3,589,659,290	+20.1%	96
Serbia	26,445,152,929	+6.12%	49
Suriname	1,530,494,164	+22.5%	110
Slovakia	11,793,061,858	+28.4%	67
Slovenia	2,566,300,985	+18.8%	103
Sweden	52,023,373,807	-0.958%	37
Seychelles	773,677,576	+13.3%	119
Thailand	217,261,360,075	+4.31%	12
Timor-Leste	736,966,607	-5.76%	120
Tonga	377,298,749	-4.85%	127
Trinidad & Tobago	5,437,606,534	-11.2%	85
Tunisia	8,769,595,098	-0.187%	74
Turkey	90,858,657,478	-1.99%	23
Ukraine	41,484,541,773	+7.15%	41
Uruguay	17,369,289,710	+6.89%	58
United States	227,759,698,199	-2.71%	10
Uzbekistan	9,144,960,133	-7.93%	72
St. Vincent & Grenadines	316,823,949	+12.9%	131
Vietnam	83,081,854,928	-9.93%	27
Vanuatu	614,649,779	-4.52%	122
Samoa	507,740,281	+13.6%	123
Kosovo	1,310,096,687	+5.19%	113
South Africa	54,912,456,859	+1.36%	35
Zimbabwe	261,372,935	+253%	133

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 = 'FI.RES.XGLD.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 <- 'FI.RES.XGLD.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

Total reserves minus gold (current US$)

Year: 2024

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