Country

Indicator

Value type

Values present

Gross domestic savings (% of GDP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	38.5	+0.0752%	9
Albania	17.6	-0.826%	84
Argentina	16.9	-4.51%	87
Armenia	22.7	+10.9%	56
Australia	26.5	-6.43%	36
Austria	25.3	-7.26%	42
Azerbaijan	30.2	-6.22%	26
Belgium	24	-3.03%	51
Benin	32.1	+7.46%	22
Burkina Faso	20.6	+33.6%	69
Bangladesh	24	-7%	52
Bulgaria	22.7	-4.92%	58
Bahamas	22.8	-2.99%	55
Bosnia & Herzegovina	11.4	-9.18%	101
Belarus	24.2	-14.2%	50
Bermuda	43.4	+0.5%	7
Brazil	17.4	-3.47%	85
Brunei	48.5	-2.17%	5
Botswana	22.6	-18.8%	59
Central African Republic	-4.32	-24.1%	126
Canada	23	-3.85%	54
Switzerland	37.2	-0.329%	13
Chile	26.8	+10.6%	35
Côte d’Ivoire	25	+10.7%	45
Cameroon	15	-3.61%	94
Congo - Kinshasa	29.2	-0.238%	28
Congo - Brazzaville	39.2	-8.36%	8
Colombia	12.1	+3.67%	100
Comoros	-12.8	-3.23%	129
Cape Verde	4.58	+224%	115
Costa Rica	21.4	+3.81%	64
Cyprus	22.5	+3.86%	60
Czechia	32.6	-1.25%	20
Germany	24.9	-3.02%	46
Djibouti	8.22	-33%	106
Denmark	32.4	+4.74%	21
Dominican Republic	20.8	+0.644%	66
Ecuador	21.8	+5.06%	62
Egypt	6.15	-56.9%	111
Spain	24.8	-0.707%	47
Estonia	25.7	-0.796%	39
Ethiopia	14.3	-3.18%	96
Finland	22.7	-0.677%	57
France	21.2	+0.207%	65
Gabon	54.2	-1.25%	4
United Kingdom	16.9	+3.84%	86
Georgia	15.2	-7.16%	92
Ghana	11.1	+22.7%	102
Guinea	19.2	+20.8%	75
Gambia	8.35	+24.4%	105
Guinea-Bissau	5.22	+10.6%	114
Equatorial Guinea	18.7	-11.3%	76
Greece	12.8	+6.91%	99
Guatemala	1.13	-19.1%	119
Hong Kong SAR China	19.8	+18.4%	73
Honduras	-1.52	-18.7%	124
Croatia	20.4	-4.95%	71
Haiti	-5.51	-13.8%	128
Hungary	29.2	-4.23%	29
Indonesia	36.9	-3.28%	14
India	28.4	-3.86%	32
Ireland	58.9	-1.14%	1
Iran	36.6	-0.627%	15
Iraq	38.5	-9.89%	10
Iceland	25.4	-0.926%	41
Israel	25.9	-10.5%	37
Italy	24.6	+1.18%	48
Kenya	13	+16.8%	98
Cambodia	34.4	+2.41%	17
Libya	30.6	-5.36%	25
Sri Lanka	24.3	+8.5%	49
Lithuania	25.6	-0.952%	40
Luxembourg	47.9	-1.09%	6
Latvia	18.6	-10.9%	78
Macao SAR China	58.8	+4.78%	2
Morocco	20.7	+0.961%	67
Moldova	-4.71	+27.8%	127
Madagascar	14.9	+16.5%	95
Mexico	18.5	-2.77%	79
North Macedonia	15.3	-7.82%	91
Mali	15	+3.76%	93
Malta	36.2	-1.01%	16
Montenegro	5.86	-34.6%	112
Mongolia	33.9	-20.1%	18
Mozambique	13.9	+189%	97
Mauritius	16.7	+5.39%	88
Malaysia	27.2	-1.36%	34
Namibia	-0.727	-116%	122
Niger	29	+28.3%	31
Nicaragua	7.09	-19.8%	107
Netherlands	31.4	+0.819%	24
Norway	37.9	-4.55%	12
Nepal	6.22	-13.8%	110
Pakistan	6.26	-1.87%	109
Peru	25	+10.4%	44
Philippines	9.35	+0.931%	103
Poland	21.7	-7.45%	63
Puerto Rico	15.8	+9.01%	90
Portugal	21.9	+1.27%	61
Paraguay	20.4	-6.73%	70
Palestinian Territories	-16.2	-15.7%	130
Romania	18.2	-13.3%	80
Russia	32.1	-2.15%	23
Rwanda	18.1	+96.8%	82
Saudi Arabia	33.7	-5.24%	19
Sudan	2.8	+46.1%	117
Senegal	17.8	+6.31%	83
Singapore	57.9	-0.847%	3
Sierra Leone	6.9	-16.4%	108
El Salvador	1.21	-31.6%	118
Somalia	-31.6	+2.49%	131
Serbia	19.5	-3.87%	74
Slovakia	20.3	-4.94%	72
Slovenia	27.7	-3.1%	33
Sweden	29	+0.467%	30
Seychelles	-0.863	-113%	123
Chad	30	+8.46%	27
Togo	8.58	-8.07%	104
Thailand	25.1	-3.14%	43
Tunisia	5.23	+138%	113
Turkey	25.8	-6.2%	38
Tanzania	37.9	+2.13%	11
Uganda	23.8	+26.2%	53
Ukraine	-0.29	-89.8%	121
Uruguay	20.7	-1.2%	68
United States	18.6	-0.0809%	77
Uzbekistan	18.1	+6.05%	81
Samoa	1.06	-118%	120
Kosovo	3.38	+3.16%	116
South Africa	15.9	-2.05%	89
Zimbabwe	-3.99	-146%	125

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.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.GDS.TOTL.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))

Compare

Gross domestic savings (% of GDP)

Year: 2024

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