Country

Indicator

Value type

Values present

Adjusted savings: mineral depletion (% of GNI)

Source: worldbank.org, 03.09.2025

Year: 2021

Country	Value	Value change, %	Rank
Aruba	0		88
Afghanistan	0		88
Angola	0		88
Albania	0.0634	+363%	65
Argentina	0.457	+163%	50
Armenia	3.33	+296%	21
Antigua & Barbuda	0		88
Australia	4.47	+148%	18
Austria	0		88
Azerbaijan	0.26	+49.5%	55
Burundi	0		88
Belgium	0		88
Benin	0		88
Burkina Faso	12.6	+265%	4
Bangladesh	0		88
Bulgaria	0.474	+76.5%	49
Bahamas	0		88
Bosnia & Herzegovina	0.0317	+43.5%	72
Belarus	0		88
Belize	0		88
Bermuda	0		88
Bolivia	4.65	+1,280%	15
Brazil	1.48	+307%	32
Barbados	0		88
Brunei	0		88
Bhutan	0.00655	+16.8%	81
Botswana	0.19	+265%	58
Central African Republic	0.557	+324%	46
Canada	0.79	+482%	42
Switzerland	0		88
Chile	9.41	+252%	6
China	0.322	+184%	52
Côte d’Ivoire	1.89	+245%	28
Cameroon	0		88
Congo - Kinshasa	22.7	+388%	1
Congo - Brazzaville	0		88
Colombia	0.671	+169%	44
Comoros	0		88
Cape Verde	0		88
Costa Rica	0		88
Curaçao	0		88
Cyprus	0.0101	+78.4%	78
Czechia	0		88
Germany	0	-100%	88
Djibouti	0		88
Dominica	0		88
Denmark	0		88
Dominican Republic	1.42	+76.4%	33
Algeria	0.00298	+97%	84
Ecuador	0		88
Egypt	0		88
Spain	0.0384	+354%	69
Estonia	0		88
Ethiopia	0.206	+228%	57
Finland	0.0491	+480%	67
Fiji	0.927	+145%	39
France	0		88
Faroe Islands	0		88
Micronesia (Federated States of)	0		88
Gabon	0.0207	+235%	74
United Kingdom	0.00004	+110%	87
Georgia	1.06	+66.1%	37
Ghana	4.36	+125%	19
Guinea	0		88
Gambia	0		88
Guinea-Bissau	0		88
Equatorial Guinea	0		88
Greece	0.0361	+286%	71
Grenada	0		88
Guatemala	0.537	+170%	48
Guyana	7.64	+282%	9
Hong Kong SAR China	0		88
Honduras	0.323	+53,469%	51
Croatia	0		88
Haiti	0		88
Hungary	0		88
Indonesia	1.25	+328%	35
India	0.946	+112%	38
Ireland	0.0368	+196%	70
Iran	1.69	+180%	31
Iraq	0		88
Iceland	0		88
Israel	0		88
Italy	0		88
Jamaica	0		88
Jordan	0		88
Japan	0.00538	-77.3%	82
Kazakhstan	6.51	+120%	12
Kenya	0.00446	+228%	83
Kyrgyzstan	9.22	+94.2%	7
Cambodia	0		88
Kiribati	0		88
St. Kitts & Nevis	0		88
South Korea	0.000372	-98.7%	86
Laos	1.85	+211%	29
Lebanon	0		88
Liberia	4.48		17
Libya	0		88
St. Lucia	0		88
Sri Lanka	0		88
Lesotho	0		88
Lithuania	0		88
Luxembourg	0		88
Latvia	0		88
Macao SAR China	0		88
Morocco	0.123	+58.7%	61
Moldova	0		88
Madagascar	0		88
Maldives	0		88
Mexico	0.845	+215%	41
Marshall Islands	0		88
North Macedonia	0		88
Mali	13.2	+138%	3
Malta	0		88
Myanmar (Burma)	0.759	+285%	43
Montenegro	0.0109	-9.46%	76
Mongolia	11.5	+263%	5
Mozambique	0.111	+249%	63
Mauritania	6.61	+2,366%	11
Mauritius	0		88
Malawi	0.000471	+53.1%	85
Malaysia	0		88
Namibia	2.18	+220%	27
Niger	0.632	+293%	45
Nigeria	0.00683	+56.7%	80
Nicaragua	2.22	+5,527%	26
Netherlands	0		88
Norway	0		88
Nepal	0		88
Nauru	0		88
New Zealand	0.0758	+159%	64
Oman	0		88
Pakistan	0.0222	+98%	73
Panama	1.33	+800%	34
Peru	7.22	+286%	10
Philippines	1.13	+301%	36
Palau	0		88
Papua New Guinea	9.15	+348%	8
Poland	0.175	+193%	59
Puerto Rico	0		88
Portugal	0.116	+88.7%	62
Paraguay	0		88
Palestinian Territories	0		88
Qatar	0		88
Romania	0.0161	+146%	75
Russia	0.895	+87.1%	40
Rwanda	0		88
Sudan	5.35	+69.5%	14
Senegal	2.29	+139%	25
Singapore	0		88
Solomon Islands	0		88
Sierra Leone	0.167	+5,197%	60
El Salvador	0		88
Somalia	0		88
Serbia	0.238	+256%	56
São Tomé & Príncipe	0		88
Suriname	0		88
Slovakia	0.00922	+41.2%	79
Slovenia	0		88
Sweden	0.556	+417%	47
Eswatini	0		88
Seychelles	0		88
Turks & Caicos Islands	0		88
Chad	0		88
Togo	3.77	+219%	20
Thailand	0		88
Tajikistan	4.49	+88.1%	16
Timor-Leste	0		88
Tonga	0		88
Trinidad & Tobago	0		88
Tunisia	0		88
Turkey	0.28	+171%	54
Tuvalu	0		88
Tanzania	3.13	+243%	22
Uganda	0		88
Ukraine	1.74	+4,747%	30
Uruguay	0.0103		77
United States	0.0606	+319%	66
Uzbekistan	5.54	+18.6%	13
St. Vincent & Grenadines	0		88
Vietnam	0.0405	+358%	68
Vanuatu	0		88
Samoa	0		88
Kosovo	0.296	+126%	53
South Africa	2.65	+159%	24
Zambia	21	+276%	2
Zimbabwe	3.07	+79.3%	23

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.DMIN.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.DMIN.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: mineral depletion (% of GNI)

Year: 2021

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