Country

Indicator

Value type

Values present

Adjusted savings: net national savings (current US$)

Source: worldbank.org, 03.09.2025

Year: 2021

Country	Value	Value change, %	Rank
Aruba	97,335,194	-159%	116
Angola	22,010,083,433	+81.9%	42
Albania	457,580,112	-192%	102
Argentina	49,375,088,602	+135%	28
Armenia	542,245,996	+29.6%	101
Australia	134,411,783,154	+60.3%	10
Austria	41,296,046,142	+7.17%	33
Azerbaijan	13,293,492,406	+105%	51
Belgium	41,284,643,865	+48.5%	34
Bangladesh	144,695,225,047	+7.1%	9
Bulgaria	6,049,633,181	+38.1%	68
Bahamas	227,709,078	-58.2%	111
Bosnia & Herzegovina	1,343,322,912	+312%	93
Belarus	8,855,693,446	+27.6%	59
Belize	180,305,206	-15.6%	112
Bermuda	2,778,324,523	+18.1%	83
Bolivia	682,054,278	+154%	98
Brazil	-15,896,717,479	-76.1%	134
Brunei	5,410,759,977	+12.7%	72
Bhutan	302,160,135	-21.1%	109
Botswana	434,797,192	+45%	103
Canada	130,074,511,723	+595%	11
Switzerland	95,837,042,543	+71.1%	19
Chile	5,148,692,383	-36.8%	74
China	3,299,021,688,260	+29.4%	1
Cameroon	1,345,222,314	+64%	92
Congo - Kinshasa	10,991,241,833	+25.4%	53
Colombia	8,827,302,906	-13%	60
Comoros	88,757,120	+159%	117
Cape Verde	402,433,324	+4.43%	105
Costa Rica	6,496,988,544	+21.2%	67
Cyprus	-549,801,081	-18.5%	125
Czechia	19,283,162,689	+32.8%	46
Germany	472,132,090,888	+25.4%	3
Denmark	60,793,076,424	+29.7%	25
Dominican Republic	19,861,497,347	+51.3%	44
Algeria	46,257,175,319	+45.9%	29
Ecuador	6,647,647,116	+12.2%	65
Egypt	7,146,435,992	-62.3%	62
Spain	67,733,240,684	+48.7%	23
Estonia	5,002,786,054	+44.2%	75
Ethiopia	19,751,974,015	-4.8%	45
Finland	18,643,972,223	+20.8%	47
France	149,163,686,767	+243%	8
United Kingdom	24,968,332,342	-144%	40
Georgia	-510,690,046	+193%	124
Ghana	9,300,856,436	-29.7%	58
Guinea	-1,465,063,572	+38.8%	131
Gambia	406,380,058	+122%	104
Greece	-8,440,299,722	-49.2%	133
Guatemala	6,647,651,326	+28.2%	64
Hong Kong SAR China	36,148,238,477	+57.8%	36
Honduras	4,111,126,191	-1.82%	79
Croatia	4,341,754,994	+58.8%	78
Haiti	1,970,700,013	-0.898%	85
Hungary	16,334,814,152	+17.7%	48
Indonesia	157,759,463,324	+56.9%	7
India	580,564,673,173	+28.9%	2
Ireland	63,863,392,073	+86.3%	24
Iraq	41,990,691,395	+130%	32
Iceland	-291,314,692	-243%	121
Israel	69,989,208,563	+21.5%	22
Italy	98,521,976,656	+118%	17
Jamaica	4,082,264,554	+46.7%	80
Jordan	1,860,657,988	-18.4%	86
Japan	96,194,268,674	-38.8%	18
Kazakhstan	29,408,697,755	+10.9%	38
Kenya	6,012,016,918	+78%	69
Kyrgyzstan	560,746,946	-59.3%	100
Cambodia	4,968,809,905	+9.4%	76
South Korea	284,549,346,346	+10.7%	5
Lebanon	-5,578,368,169	-2.17%	132
Lithuania	5,834,269,888	+24.6%	70
Luxembourg	9,658,640,871	+70.4%	55
Latvia	102,614,458	-87.2%	115
Macao SAR China	7,034,632,470	-13%	63
Morocco	26,959,815,044	+25.1%	39
Moldova	589,760,534	+12.1%	99
Madagascar	385,405,132	+61.3%	106
Maldives	1,039,321,249	-1,209%	97
Mexico	44,645,259,713	-21.4%	31
North Macedonia	1,837,119,456	+36.8%	87
Malta	2,644,136,069	+68.2%	84
Montenegro	303,436,654	-188%	108
Mongolia	1,339,275,341	+140%	94
Mozambique	-1,239,366,949	+61.9%	129
Mauritania	3,094,412,886	+28.1%	82
Mauritius	-378,174,955	-50.7%	122
Malaysia	20,139,904,240	+75.7%	43
Namibia	-658,118,279	-207%	126
Nigeria	100,364,782,651	+30.1%	16
Nicaragua	1,628,956,157	-10.5%	89
Netherlands	116,722,432,698	+36.4%	13
Norway	108,222,915,619	+176%	14
Nepal	9,393,328,890	+7.5%	57
New Zealand	8,742,920,116	-14.4%	61
Oman	9,497,556,863	+189%	56
Pakistan	34,760,437,857	+12.4%	37
Panama	11,211,573,749	+28.2%	52
Peru	24,956,228,679	+19.4%	41
Philippines	38,352,298,603	-27.6%	35
Poland	56,842,610,985	+11.4%	27
Portugal	-1,216,474,464	-73.8%	127
Paraguay	6,549,635,283	+13.5%	66
Palestinian Territories	1,451,122,677	+644%	91
Qatar	60,424,513,913	+74.4%	26
Romania	9,976,288,435	-3.95%	54
Russia	276,643,521,258	+48.3%	6
Rwanda	268,440,896	-1,737%	110
Sudan	-5,165,841	-100%	118
Singapore	102,900,012,158	+42.9%	15
El Salvador	1,175,054,212	-34.7%	96
Serbia	3,919,244,026	+21.9%	81
Slovakia	1,833,402,539	-16.1%	88
Slovenia	4,727,351,192	+8.06%	77
Sweden	88,160,090,893	+20.4%	20
Eswatini	104,306,290	-348%	114
Seychelles	-52,433,511	-18.1%	120
Thailand	45,663,406,119	+1.87%	30
Timor-Leste	-445,719,266	-716%	123
Tonga	-21,883,145	-132%	119
Tunisia	-1,228,339,808	-49.8%	128
Turkey	124,502,001,480	+49.5%	12
Uganda	1,535,701,992	-38.9%	90
Ukraine	-1,405,286,066	-28.7%	130
Uruguay	5,360,239,050	+6.75%	73
United States	388,420,071,604	-24%	4
Uzbekistan	13,490,155,670	+12.5%	50
Vietnam	70,008,294,091	+3.42%	21
Vanuatu	385,010,777	-1.62%	107
Samoa	169,301,317	-22.1%	113
Kosovo	1,319,266,533	+24.7%	95
South Africa	14,437,137,581	-595%	49
Zambia	5,498,642,440	+14.8%	71

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.NNAT.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.ADJ.NNAT.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

Adjusted savings: net national savings (current US$)

Year: 2021

Acquire full dataset with Python or R from source