Adjusted net national income per capita (constant 2015 US$)

Source: worldbank.org, 03.09.2025

Year: 2021

Flag Country Value Value change, % Rank
Angola Angola 1,977 -4.37% 99
Albania Albania 4,033 +12.3% 71
Argentina Argentina 10,784 +12.2% 41
Armenia Armenia 3,562 +2.38% 77
Austria Austria 36,086 +3.2% 13
Burundi Burundi 183 -1.95% 140
Belgium Belgium 34,703 +5.51% 16
Benin Benin 1,053 +5.53% 115
Burkina Faso Burkina Faso 587 -7.92% 128
Bangladesh Bangladesh 1,772 +6.53% 102
Bulgaria Bulgaria 8,074 +8.82% 45
Bahamas Bahamas 23,262 +13.5% 22
Bosnia & Herzegovina Bosnia & Herzegovina 5,290 +10.8% 61
Belarus Belarus 5,336 +4% 59
Belize Belize 4,619 +12.1% 66
Bolivia Bolivia 2,513 -1.87% 93
Brazil Brazil 6,478 +0.93% 54
Brunei Brunei 22,591 -3.24% 23
Bhutan Bhutan 2,594 -0.315% 92
Botswana Botswana 4,983 +1.7% 63
Central African Republic Central African Republic 371 -2.29% 138
Canada Canada 36,665 +8.2% 12
Switzerland Switzerland 63,885 +7.43% 2
Chile Chile 10,447 +5.8% 43
China China 7,861 +6.79% 46
Côte d’Ivoire Côte d’Ivoire 1,857 +0.812% 100
Cameroon Cameroon 1,207 -3.7% 113
Congo - Kinshasa Congo - Kinshasa 373 -9.35% 137
Congo - Brazzaville Congo - Brazzaville 775 +32.5% 121
Colombia Colombia 5,423 +6.48% 58
Comoros Comoros 1,330 +2.81% 109
Cape Verde Cape Verde 2,922 +6.87% 88
Costa Rica Costa Rica 11,460 +4.77% 40
Cyprus Cyprus 22,186 +3.7% 24
Czechia Czechia 14,650 +5.72% 32
Germany Germany 36,016 +2.5% 14
Djibouti Djibouti 2,619 +7.44% 91
Denmark Denmark 51,316 +6.18% 5
Dominican Republic Dominican Republic 7,079 +6.4% 51
Algeria Algeria 3,069 +1.95% 83
Ecuador Ecuador 4,258 +0.291% 68
Egypt Egypt 3,098 +0.0471% 82
Spain Spain 21,791 +5.68% 25
Estonia Estonia 17,264 +8.79% 27
Ethiopia Ethiopia 729 +3.65% 123
Finland Finland 38,530 +3.74% 10
France France 31,681 +8.67% 18
Gabon Gabon 5,323 +2.55% 60
Georgia Georgia 4,182 +7.53% 70
Ghana Ghana 1,596 -5.35% 106
Guinea Guinea 678 -2.34% 126
Gambia Gambia 539 -1.61% 130
Guinea-Bissau Guinea-Bissau 531 +3.59% 131
Equatorial Guinea Equatorial Guinea 2,306 +13.2% 97
Greece Greece 15,728 +9.85% 29
Guatemala Guatemala 3,639 +2.28% 75
Honduras Honduras 2,114 +5.24% 98
Croatia Croatia 12,579 +11.3% 37
Haiti Haiti 1,259 -2.35% 111
Hungary Hungary 12,168 +3.68% 39
Indonesia Indonesia 2,924 +4.63% 87
India India 1,703 +9.57% 103
Ireland Ireland 41,799 +16% 9
Iran Iran 3,865 +9.16% 73
Iraq Iraq 3,774 +18.6% 74
Iceland Iceland 42,627 +0.943% 8
Israel Israel 34,571 +6.94% 17
Italy Italy 25,952 +6.75% 20
Japan Japan 27,018 -0.802% 19
Kazakhstan Kazakhstan 7,422 -7.22% 48
Kenya Kenya 1,494 +7.88% 108
Kyrgyzstan Kyrgyzstan 850 -7.11% 120
Cambodia Cambodia 1,292 -2.42% 110
Kiribati Kiribati 2,503 -6.39% 94
South Korea South Korea 25,458 +3.26% 21
Lebanon Lebanon 4,213 -16.4% 69
Libya Libya 7,383 +29.9% 49
Sri Lanka Sri Lanka 3,890 +0.657% 72
Lesotho Lesotho 961 -2% 118
Lithuania Lithuania 15,051 +2.17% 31
Luxembourg Luxembourg 65,458 +10.4% 1
Latvia Latvia 13,539 +6.09% 36
Morocco Morocco 2,965 +6.08% 85
Moldova Moldova 3,423 +10.3% 78
Madagascar Madagascar 376 +3.47% 136
Maldives Maldives 5,960 +21.4% 55
Mexico Mexico 7,064 +1.74% 52
North Macedonia North Macedonia 4,938 +2.45% 65
Mali Mali 602 -10.2% 127
Montenegro Montenegro 6,908 +14.4% 53
Mongolia Mongolia 3,006 -10.6% 84
Mozambique Mozambique 431 -0.9% 134
Mauritania Mauritania 1,820 -4.52% 101
Malaysia Malaysia 7,438 -0.448% 47
Namibia Namibia 3,339 -4.46% 79
Niger Niger 505 +2.86% 132
Nicaragua Nicaragua 1,685 +0.123% 105
Netherlands Netherlands 38,230 +4.75% 11
Norway Norway 63,306 +15.1% 3
Nepal Nepal 1,046 +5.57% 116
New Zealand New Zealand 34,749 +2.27% 15
Oman Oman 12,327 -2.93% 38
Pakistan Pakistan 1,550 +5.85% 107
Panama Panama 10,595 -1.51% 42
Peru Peru 5,230 +2.06% 62
Philippines Philippines 2,951 -3.08% 86
Poland Poland 13,654 +6.59% 35
Portugal Portugal 16,322 +4.07% 28
Paraguay Paraguay 5,760 +5.43% 57
Romania Romania 9,695 +4.57% 44
Russia Russia 7,310 -2.32% 50
Rwanda Rwanda 719 +8.47% 125
Senegal Senegal 1,242 +2.57% 112
Singapore Singapore 48,061 +17.7% 6
Solomon Islands Solomon Islands 1,698 -5.76% 104
Sierra Leone Sierra Leone 563 -5.94% 129
El Salvador El Salvador 3,282 +7.14% 81
Somalia Somalia 306 +1.61% 139
Serbia Serbia 5,806 +6.69% 56
Slovakia Slovakia 14,390 +2.63% 33
Slovenia Slovenia 19,635 +6.85% 26
Sweden Sweden 46,079 +3.9% 7
Eswatini Eswatini 2,463 +2.77% 95
Seychelles Seychelles 13,912 +6.61% 34
Chad Chad 436 +5.48% 133
Togo Togo 735 +0.118% 122
Tajikistan Tajikistan 1,152 -0.307% 114
Timor-Leste Timor-Leste 394 -66.3% 135
Tonga Tonga 4,552 -7.64% 67
Tunisia Tunisia 3,299 +4.4% 80
Tanzania Tanzania 899 +0.934% 119
Uganda Uganda 721 -0.301% 124
Ukraine Ukraine 2,446 +6.57% 96
Uruguay Uruguay 15,464 +3.27% 30
United States United States 52,311 +5.34% 4
Vietnam Vietnam 2,831 -0.28% 89
Vanuatu Vanuatu 2,691 -5.65% 90
Samoa Samoa 3,616 -5.07% 76
South Africa South Africa 4,947 +5.2% 64
Zimbabwe Zimbabwe 1,021 +36.6% 117 

                    
# 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.NNTY.PC.KD'

# 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.NNTY.PC.KD'

# 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))