Gross savings (% of GNI)

Source: worldbank.org, 03.09.2025

Year: 2024

Flag Country Value Value change, % Rank
Angola Angola 32.6 +3.96% 10
Albania Albania 22.2 -0.266% 49
Argentina Argentina 15.5 -4.46% 70
Armenia Armenia 21.5 +10% 53
Australia Australia 23.9 -4.46% 39
Austria Austria 24.8 -6.83% 33
Azerbaijan Azerbaijan 28.4 -7.49% 19
Belgium Belgium 23.6 -3.42% 42
Bangladesh Bangladesh 33 -1.83% 9
Bulgaria Bulgaria 19.8 -8.32% 58
Bosnia & Herzegovina Bosnia & Herzegovina 21.6 -2.15% 52
Belarus Belarus 23.9 -12.8% 40
Brazil Brazil 14.9 -3.48% 72
Brunei Brunei 46.1 +1.74% 2
Canada Canada 21.7 -2.72% 51
Switzerland Switzerland 33.4 -1.02% 8
Chile Chile 22.9 +8.85% 46
Colombia Colombia 14.1 +8.5% 73
Cape Verde Cape Verde 20 +17.3% 55
Costa Rica Costa Rica 15.9 +3.38% 68
Cyprus Cyprus 11.9 +0.0861% 77
Czechia Czechia 29 -7.46% 18
Germany Germany 26.1 -2.14% 27
Djibouti Djibouti 9.28 -37.2% 81
Denmark Denmark 33.5 +5.55% 7
Dominican Republic Dominican Republic 24.8 -1.73% 32
Ecuador Ecuador 24.6 +7.15% 35
Spain Spain 23.6 -0.646% 41
Estonia Estonia 24.4 +1.72% 36
Finland Finland 22.3 +0.948% 48
France France 21.2 +2.23% 54
United Kingdom United Kingdom 15.5 +2.49% 69
Georgia Georgia 19.8 -6.09% 59
Gambia Gambia 29.5 +3.32% 15
Greece Greece 11.7 +20.4% 78
Guatemala Guatemala 19.9 -0.0755% 56
Hong Kong SAR China Hong Kong SAR China 26.2 +15.2% 26
Honduras Honduras 19.3 +0.407% 63
Croatia Croatia 22.7 -6.83% 47
Hungary Hungary 26.6 -1.94% 23
Indonesia Indonesia 35.6 -3.38% 6
India India 30.4 -2.93% 12
Iceland Iceland 24.1 -6.88% 38
Israel Israel 26.9 -10.4% 22
Italy Italy 23.4 +0.958% 44
Cambodia Cambodia 40.2 +1.15% 4
Lithuania Lithuania 23.5 -1.29% 43
Luxembourg Luxembourg 26 +7.82% 28
Latvia Latvia 19.4 -5.79% 62
Moldova Moldova 5.05 -41.5% 84
Mexico Mexico 19.6 -4.81% 60
North Macedonia North Macedonia 27.7 -12.5% 21
Malta Malta 25.3 -3.36% 30
Montenegro Montenegro 11.4 -29.1% 79
Mozambique Mozambique 8.83 +190% 83
Malaysia Malaysia 24.3 -1.68% 37
Namibia Namibia 8.94 -22.6% 82
Nicaragua Nicaragua 29.5 -5.6% 17
Netherlands Netherlands 29.6 -1.41% 14
Norway Norway 39.3 -4.31% 5
Nepal Nepal 42.8 +19.5% 3
Pakistan Pakistan 14.1 +6.69% 74
Peru Peru 23.2 +10.5% 45
Philippines Philippines 25.9 +4.99% 29
Poland Poland 18.5 -7.97% 64
Portugal Portugal 22 +3.91% 50
Paraguay Paraguay 19.5 -4.88% 61
Palestinian Territories Palestinian Territories 13.7 +26.5% 75
Romania Romania 16.4 -16.2% 67
Russia Russia 31 -1.31% 11
Saudi Arabia Saudi Arabia 29.5 -7.03% 16
Singapore Singapore 48.1 +1.49% 1
El Salvador El Salvador 19.8 -5.26% 57
Slovakia Slovakia 17.8 -6.94% 66
Slovenia Slovenia 26.2 -3.46% 25
Sweden Sweden 30.3 +0.101% 13
Thailand Thailand 24.7 -4.76% 34
Turkey Turkey 24.9 -7.08% 31
Ukraine Ukraine 11.3 -9.07% 80
Uruguay Uruguay 15.4 +1.92% 71
United States United States 18.1 -0.98% 65
Uzbekistan Uzbekistan 28 +6.28% 20
Samoa Samoa 26.2 +21% 24
South Africa South Africa 13.6 -6.5% 76 

                    
# 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.GN.ZS'

# 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.GN.ZS'

# 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))