Country

Indicator

Value type

Values present

Gross savings (current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	24,055,871,299	-0.0982%	47
Albania	5,984,935,077	+15.7%	70
Argentina	96,196,360,930	-6.31%	31
Armenia	5,331,852,615	+16.6%	72
Australia	405,384,081,338	-2.17%	10
Austria	129,804,292,826	-4.74%	27
Azerbaijan	20,338,656,059	-4.51%	52
Belgium	159,466,973,402	-0.119%	22
Bangladesh	154,703,622,919	+1.33%	23
Bulgaria	21,169,343,969	+0.597%	50
Bosnia & Herzegovina	6,112,943,654	+0.779%	69
Belarus	17,749,043,373	-7.79%	57
Brazil	315,383,306,047	-3.89%	16
Brunei	7,351,019,648	+6.16%	65
Canada	479,455,297,956	+0.563%	9
Switzerland	303,157,125,551	+3.49%	17
Chile	71,863,067,569	+6.21%	33
Colombia	57,851,149,726	+24.7%	39
Cape Verde	542,695,944	+27%	82
Costa Rica	14,056,661,945	+13.6%	59
Cyprus	3,868,966,161	+5.92%	75
Czechia	95,838,075,640	-9.71%	32
Germany	1,257,895,327,504	+0.822%	2
Djibouti	382,763,505	-35.6%	83
Denmark	148,878,712,790	+12%	24
Dominican Republic	29,203,147,270	+0.436%	45
Ecuador	29,808,967,188	+9.87%	43
Spain	404,794,451,345	+5.62%	11
Estonia	10,217,489,327	+6.39%	61
Finland	67,144,568,793	+2.54%	34
France	685,981,250,640	+6.18%	4
United Kingdom	560,101,211,220	+10.4%	6
Georgia	6,248,332,198	+4.47%	68
Gambia	731,397,831	+7.73%	81
Greece	29,514,029,520	+28.4%	44
Guatemala	22,185,702,150	+8.87%	48
Hong Kong SAR China	116,572,224,332	+24.2%	29
Honduras	6,590,877,665	+8.1%	66
Croatia	21,084,186,686	+2.9%	51
Hungary	57,855,127,866	+2.62%	38
Indonesia	483,916,805,184	-1.61%	8
India	1,174,153,990,212	+4.3%	3
Iceland	8,043,575,055	-2.93%	64
Israel	144,274,756,776	-5.68%	25
Italy	551,784,276,633	+3.82%	7
Cambodia	18,285,255,346	+11.2%	56
Lithuania	19,415,923,744	+5.63%	53
Luxembourg	17,190,217,294	+17.3%	58
Latvia	8,283,461,132	-2.27%	63
Moldova	927,293,626	-36.7%	79
Mexico	352,430,915,509	-2.18%	14
North Macedonia	4,377,232,943	-7.47%	74
Malta	5,277,309,716	+5.11%	73
Montenegro	915,880,010	-25%	80
Mozambique	1,757,441,233	+202%	77
Malaysia	99,319,880,667	+3.49%	30
Namibia	1,156,161,963	-15.3%	78
Nicaragua	5,517,358,132	+3.87%	71
Netherlands	358,757,142,653	+4.24%	13
Norway	199,145,183,140	-4.58%	19
Nepal	18,656,371,731	+25.5%	55
Pakistan	51,184,022,625	+17%	41
Peru	63,232,967,063	+19.2%	36
Philippines	134,976,801,247	+13%	26
Poland	163,375,440,507	+4.08%	21
Portugal	66,791,732,871	+11.6%	35
Paraguay	8,388,411,529	-1.6%	62
Palestinian Territories	2,284,663,119	-2.16%	76
Romania	61,005,241,484	-8.72%	37
Russia	665,766,326,578	+3.55%	5
Saudi Arabia	366,917,950,430	-5.71%	12
Singapore	220,468,288,483	+12.8%	18
El Salvador	6,562,175,989	-1.09%	67
Slovakia	24,608,223,606	-1.64%	46
Slovenia	18,763,077,574	+0.972%	54
Sweden	193,026,524,600	+4.42%	20
Thailand	126,562,683,164	-3.12%	28
Turkey	325,852,582,343	+9.76%	15
Ukraine	21,609,699,883	-6.74%	49
Uruguay	11,687,981,586	+6.47%	60
United States	5,294,183,000,000	+4.08%	1
Uzbekistan	32,522,694,695	+19.1%	42
Samoa	275,788,452	+37.6%	84
South Africa	53,305,486,937	-2.31%	40

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.GNS.ICTR.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.GNS.ICTR.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 savings (current US$)

Year: 2024

Acquire full dataset with Python or R from source