Country

Indicator

Value type

Values present

Gross domestic savings (current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	30,929,617,718	-5.21%	47
Albania	4,783,451,375	+14.5%	93
Argentina	106,811,815,779	-6.4%	33
Armenia	5,860,235,785	+18.8%	87
Australia	464,739,248,500	-5.13%	10
Austria	132,060,199,742	-5.45%	28
Azerbaijan	22,441,647,059	-3.78%	57
Belgium	159,574,919,556	-0.036%	24
Benin	6,896,565,400	+17.3%	84
Burkina Faso	4,799,426,902	+52.9%	92
Bangladesh	107,835,911,368	-4.3%	31
Bulgaria	25,449,185,825	+4.2%	53
Bahamas	3,608,300,000	+0.58%	97
Bosnia & Herzegovina	3,228,989,402	-6.71%	98
Belarus	18,380,674,957	-10.1%	66
Bermuda	3,896,200,000	+5.2%	96
Brazil	379,389,162,608	-3.98%	14
Brunei	7,494,089,237	+0.22%	83
Botswana	4,382,924,796	-18.8%	94
Central African Republic	-118,970,031	-18.3%	122
Canada	516,444,266,538	-0.842%	8
Switzerland	348,499,957,502	+4.37%	15
Chile	88,598,951,087	+8.85%	34
Côte d’Ivoire	21,639,471,701	+20.3%	59
Cameroon	7,682,392,922	+0.399%	82
Congo - Kinshasa	20,643,367,736	+5.33%	61
Congo - Brazzaville	6,164,717,196	-5.97%	86
Colombia	50,829,634,622	+18.5%	42
Comoros	-198,078,686	+4.59%	123
Cape Verde	126,715,360	+253%	117
Costa Rica	20,406,515,063	+14.4%	62
Cyprus	8,184,324,974	+11.4%	79
Czechia	112,495,746,621	-0.72%	30
Germany	1,160,498,031,941	-0.139%	2
Djibouti	335,972,716	-30.1%	115
Denmark	139,340,670,014	+10.5%	26
Dominican Republic	25,789,047,307	+3.84%	52
Ecuador	27,224,852,700	+8.12%	51
Egypt	23,921,801,408	-57.6%	55
Spain	426,469,810,873	+5.59%	11
Estonia	10,969,295,023	+2.74%	74
Finland	68,103,379,094	+0.968%	38
France	670,287,484,692	+3.83%	5
Gabon	11,306,837,977	+2.74%	73
United Kingdom	615,511,279,702	+12.3%	6
Georgia	5,137,210,010	+1.89%	90
Ghana	9,199,945,807	+26.2%	75
Guinea	4,855,385,466	+36.6%	91
Gambia	209,366,310	+30.2%	116
Guinea-Bissau	110,677,366	+12.9%	118
Equatorial Guinea	2,392,671,833	-8.18%	106
Greece	32,967,148,647	+12.9%	45
Guatemala	1,273,676,858	-12.3%	109
Hong Kong SAR China	80,611,027,940	+26.5%	35
Honduras	-563,681,642	-12.2%	125
Croatia	18,863,056,069	+4.21%	65
Haiti	-1,389,087,549	+9.5%	127
Hungary	65,024,659,719	-0.259%	40
Indonesia	514,800,085,261	-1.51%	9
India	1,109,978,752,749	+3.39%	3
Ireland	340,258,401,365	+3.52%	18
Iran	159,885,213,150	+7.3%	23
Iraq	107,568,866,039	-6.28%	32
Iceland	8,507,171,820	+5.41%	78
Israel	139,975,977,650	-5.57%	25
Italy	584,725,308,637	+4.17%	7
Kenya	16,186,555,292	+34.5%	69
Cambodia	15,947,498,535	+12.1%	70
Libya	14,253,649,677	-2.13%	71
Sri Lanka	24,081,807,656	+28.3%	54
Lithuania	21,719,731,633	+5.35%	58
Luxembourg	44,669,674,176	+5.27%	43
Latvia	8,090,591,599	-8.9%	80
Macao SAR China	29,527,807,664	+14.8%	49
Morocco	32,012,206,504	+7.96%	46
Moldova	-856,834,300	+39.1%	126
Madagascar	2,593,033,596	+27.8%	102
Mexico	343,307,869,365	+0.426%	16
North Macedonia	2,552,956,093	-2.43%	104
Mali	3,984,909,356	+12.1%	95
Malta	8,801,553,513	+8.4%	77
Montenegro	472,723,713	-29.9%	112
Mongolia	7,998,829,946	-7.34%	81
Mozambique	3,121,809,525	+210%	99
Mauritius	2,496,882,721	+11.8%	105
Malaysia	114,757,328,807	+4.14%	29
Namibia	-97,256,761	-117%	121
Niger	5,669,811,333	+50.1%	89
Nicaragua	1,397,158,130	-11.3%	107
Netherlands	385,246,268,340	+7.21%	13
Norway	183,372,469,950	-4.4%	21
Nepal	2,670,428,585	-9.91%	101
Pakistan	23,367,022,625	+8.35%	56
Peru	72,360,433,582	+19.6%	36
Philippines	43,143,023,479	+6.6%	44
Poland	198,145,189,024	+4.2%	20
Puerto Rico	19,884,700,000	+15.9%	64
Portugal	67,610,446,793	+7.91%	39
Paraguay	9,072,980,063	-3.84%	76
Palestinian Territories	-2,222,000,000	-35.3%	129
Romania	69,665,448,852	-5.37%	37
Russia	697,306,018,731	+2.68%	4
Rwanda	2,574,251,397	+95.7%	103
Saudi Arabia	416,488,266,667	-3.77%	12
Sudan	1,396,307,990	+82.7%	108
Senegal	5,744,282,711	+11.8%	88
Singapore	317,029,964,086	+7.38%	19
Sierra Leone	520,611,396	-1.54%	111
El Salvador	426,480,000	-28.6%	113
Somalia	-3,827,145,110	+13.1%	130
Serbia	17,361,012,750	+5.28%	67
Slovakia	28,746,424,409	+0.649%	50
Slovenia	20,108,123,399	+1.57%	63
Sweden	177,060,122,346	+4.69%	22
Seychelles	-18,708,090	-113%	120
Chad	6,179,395,298	+17.1%	85
Togo	852,031,016	-0.507%	110
Thailand	132,066,640,641	-1.17%	27
Tunisia	2,795,696,921	+163%	100
Turkey	341,559,582,343	+11%	17
Tanzania	29,867,347,811	+1.77%	48
Uganda	12,750,225,914	+38.9%	72
Ukraine	-552,300,117	-89.3%	124
Uruguay	16,721,248,393	+2.56%	68
United States	5,442,862,000,000	+5.2%	1
Uzbekistan	20,809,681,906	+18.8%	60
Samoa	11,315,667	-121%	119
Kosovo	376,606,910	+9.86%	114
South Africa	63,725,622,989	+2.98%	41
Zimbabwe	-1,764,816,180	-158%	128

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 = 'NY.GDS.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 <- 'NY.GDS.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

Gross domestic savings (current US$)

Year: 2024

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