Country

Indicator

Value type

Values present

Adjusted savings: gross savings (% of GNI)

Source: worldbank.org, 03.09.2025

Year: 2021

Country	Value	Value change, %	Rank
Aruba	15.4	+178%	108
Angola	43.7	+31.4%	8
Albania	19.6	+42.5%	87
Argentina	22	+25.9%	74
Armenia	17	+4.91%	99
Australia	26.1	+7.6%	58
Austria	28.1	-1.2%	49
Azerbaijan	33.5	+41.3%	22
Belgium	26.2	+4%	57
Bangladesh	34.2	-4.1%	19
Bulgaria	21.9	+3.38%	77
Bahamas	7.94	-34.8%	126
Bosnia & Herzegovina	20.9	+24.7%	81
Belarus	30.4	+6.54%	36
Belize	27.1	-4.62%	51
Bermuda	40.4	+5.82%	9
Bolivia	14.2	+12.7%	113
Brazil	17.9	+19.7%	95
Brunei	49.4	+0.0381%	4
Bhutan	14.3	-23.8%	112
Botswana	23.1	+3.52%	68
Canada	23.1	+20.9%	69
Switzerland	36	+10.1%	15
Chile	19.9	-6.46%	85
China	45.3	+2.31%	6
Cameroon	14.4	+6.49%	111
Congo - Kinshasa	25.5	+10.8%	60
Colombia	13.8	-6.93%	116
Comoros	13.9	+40.5%	115
Cape Verde	33	-4.57%	24
Costa Rica	16.8	+12.7%	100
Cyprus	12.4	+14.8%	118
Czechia	29.5	+2.17%	42
Germany	29.6	+4.7%	40
Denmark	31	+4.6%	30
Dominican Republic	29.3	+21.8%	44
Algeria	37.4	+19.2%	12
Ecuador	25.8	+2.24%	59
Egypt	7.94	-30.9%	127
Spain	21.7	+3.44%	78
Estonia	31.1	+8.09%	29
Ethiopia	25.2	-7.53%	61
Finland	24.8	+0.341%	62
France	23.7	+12.1%	67
United Kingdom	16	+11.4%	104
Georgia	9.19	-22.6%	121
Ghana	22.3	-20.6%	71
Guinea	2.16	-32.2%	132
Gambia	36.6	+36.5%	13
Greece	10.7	+49.4%	119
Guatemala	19.6	+5.48%	86
Hong Kong SAR China	27	+10.3%	53
Honduras	20.9	-10%	82
Croatia	24.6	+6.76%	63
Haiti	14.9	-21.8%	110
Hungary	27.4	+1.75%	50
Indonesia	34	+12.5%	20
India	30.7	+6.1%	34
Ireland	51.3	+5.84%	2
Iraq	30.8	+47.6%	31
Iceland	16	-8.62%	105
Israel	29.6	+0.0888%	41
Italy	22.7	+6.73%	70
Jamaica	34.9	+25.6%	17
Jordan	8.33	-17%	124
Japan	27.1	-0.919%	52
Kazakhstan	29.1	+1.22%	46
Kenya	16.7	+8.51%	101
Kyrgyzstan	21.9	-31.9%	76
Cambodia	30.2	+5.42%	39
South Korea	36.2	+0.755%	14
Lebanon	-2.94	-216%	133
Lithuania	21.6	-1.09%	80
Luxembourg	32.8	+13%	25
Latvia	22	-10.5%	75
Macao SAR China	31.4	-8.55%	27
Morocco	29.2	+4.47%	45
Moldova	16.4	+1.65%	103
Madagascar	8.91	+5.76%	122
Maldives	33.1	+242%	23
Mexico	24.2	-5.98%	64
North Macedonia	30.5	+8.27%	35
Malta	31.2	+15.7%	28
Montenegro	17.1	+232%	98
Mongolia	23.8	+31.9%	66
Mozambique	15.2	-9.13%	109
Mauritania	38.9	+5.87%	10
Mauritius	8.36	+60.4%	123
Malaysia	26.8	+10%	54
Namibia	7.28	-59.3%	129
Nigeria	35.2	+18.7%	16
Nicaragua	21.6	-11.8%	79
Netherlands	29.3	+5.3%	43
Norway	38.6	+27.9%	11
Nepal	32.5	+0.198%	26
New Zealand	18.8	-6.71%	93
Oman	18.8	+53.7%	92
Pakistan	14.1	-4.79%	114
Panama	30.7	+13.2%	33
Peru	22.2	+10.3%	72
Philippines	19.5	-15.4%	88
Poland	20.4	-3.19%	83
Portugal	19.4	+7.87%	89
Paraguay	24	+1.42%	65
Palestinian Territories	15.9	+52%	106
Qatar	52.2	+22.4%	1
Romania	19.3	-2.74%	90
Russia	30.2	+12.4%	38
Rwanda	15.7	+23.5%	107
Sudan	6.99	-78.4%	130
Singapore	50.3	+8.4%	3
El Salvador	18.1	-13.1%	94
Serbia	22.1	+3.92%	73
Slovakia	19.3	-2.85%	91
Slovenia	26.3	-5.67%	56
Sweden	30.3	+1.4%	37
Eswatini	20	+15.1%	84
Seychelles	7.41	+22.4%	128
Thailand	28.8	+0.499%	48
Timor-Leste	-10.8	-221%	134
Tonga	4.32	-80%	131
Tunisia	8.22	+30.1%	125
Turkey	30.8	+13.4%	32
Uganda	10.1	-28.9%	120
Ukraine	12.8	+7.05%	117
Uruguay	17.9	+2.56%	96
United States	17.9	-6.17%	97
Uzbekistan	33.5	-0.477%	21
Vietnam	34.4	-0.192%	18
Vanuatu	44.1	-5.76%	7
Samoa	28.8	-16.3%	47
Kosovo	26.7	+2.92%	55
South Africa	16.5	+13.9%	102
Zambia	47	+0.947%	5

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.ADJ.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.ADJ.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))

Compare

Adjusted savings: gross savings (% of GNI)

Year: 2021

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