Age dependency ratio, young (% of working-age population)

Source: worldbank.org, 03.09.2025

Year: 2024

Flag Country Value Value change, % Rank
Aruba Aruba 25.5 -1.47% 155
Afghanistan Afghanistan 78.4 -1.36% 12
Angola Angola 84 -0.836% 9
Albania Albania 25.4 -0.555% 157
Andorra Andorra 16.5 -2.73% 213
United Arab Emirates United Arab Emirates 19.6 -1.58% 203
Argentina Argentina 32.7 -3.2% 110
Armenia Armenia 28.7 -1.31% 127
American Samoa American Samoa 41.6 -1.85% 86
Antigua & Barbuda Antigua & Barbuda 25.3 -2.12% 159
Australia Australia 27.7 -0.803% 135
Austria Austria 21.8 +0.0396% 193
Azerbaijan Azerbaijan 31.3 -2.37% 119
Burundi Burundi 84.7 -2.67% 7
Belgium Belgium 25.2 -1.63% 160
Benin Benin 75.1 -1.13% 15
Burkina Faso Burkina Faso 75.3 -2.42% 14
Bangladesh Bangladesh 42.7 -1.6% 83
Bulgaria Bulgaria 22.8 -0.228% 181
Bahrain Bahrain 24.2 -1.52% 168
Bahamas Bahamas 25.5 -2.46% 156
Bosnia & Herzegovina Bosnia & Herzegovina 20.1 -0.759% 202
Belarus Belarus 24.7 -1.93% 164
Belize Belize 38.9 -1.87% 92
Bermuda Bermuda 21 -1.1% 197
Bolivia Bolivia 46.1 -1.59% 75
Brazil Brazil 28.4 -1.15% 129
Barbados Barbados 26 -1.09% 148
Brunei Brunei 28.8 -0.731% 126
Bhutan Bhutan 28.8 -3.14% 125
Botswana Botswana 50.3 -0.603% 64
Central African Republic Central African Republic 100 -0.551% 1
Canada Canada 23.2 -0.886% 174
Switzerland Switzerland 23 +0.131% 179
Chile Chile 24.6 -2.37% 165
China China 23.1 -3.83% 178
Côte d’Ivoire Côte d’Ivoire 72.3 -1.62% 22
Cameroon Cameroon 74.3 -1.29% 16
Congo - Kinshasa Congo - Kinshasa 90.5 -0.214% 4
Congo - Brazzaville Congo - Brazzaville 71.5 -1.71% 25
Colombia Colombia 29 -1.47% 124
Comoros Comoros 63.6 -1.23% 42
Cape Verde Cape Verde 37.8 -3.9% 97
Costa Rica Costa Rica 27.3 -2.79% 140
Cuba Cuba 22.4 -1.2% 186
Curaçao Curaçao 21.9 -1.8% 192
Cayman Islands Cayman Islands 21.6 -0.188% 194
Cyprus Cyprus 23.2 +0.563% 175
Czechia Czechia 24.1 -1.73% 169
Germany Germany 22.1 +0.597% 189
Djibouti Djibouti 44.2 -1.8% 78
Dominica Dominica 25.8 -1.34% 151
Denmark Denmark 24.7 -0.926% 163
Dominican Republic Dominican Republic 40.6 -1.3% 90
Algeria Algeria 48.1 -1.18% 69
Ecuador Ecuador 36.4 -2.63% 100
Egypt Egypt 50.9 -1.82% 63
Eritrea Eritrea 66.1 -2.23% 35
Spain Spain 19.6 -2.31% 204
Estonia Estonia 24.8 -1.99% 162
Ethiopia Ethiopia 67.7 -1.2% 31
Finland Finland 23.8 -1.99% 172
Fiji Fiji 40.8 -1.29% 88
France France 26.9 -1.48% 143
Faroe Islands Faroe Islands 32 -1.23% 115
Micronesia (Federated States of) Micronesia (Federated States of) 51.2 -0.975% 62
Gabon Gabon 61.4 -0.714% 47
United Kingdom United Kingdom 27.1 -1.23% 141
Georgia Georgia 32.7 -1.03% 111
Ghana Ghana 59.2 -1.46% 52
Gibraltar Gibraltar 27.3 -0.812% 139
Guinea Guinea 73.5 -1.41% 18
Gambia Gambia 71 -1.85% 26
Guinea-Bissau Guinea-Bissau 66.8 -1.86% 33
Equatorial Guinea Equatorial Guinea 63.2 -0.757% 43
Greece Greece 21 -2.57% 198
Grenada Grenada 28.5 -2.09% 128
Greenland Greenland 30.4 +1.01% 122
Guatemala Guatemala 49.7 -2.43% 65
Guam Guam 42.3 +0.585% 84
Guyana Guyana 45.5 -0.411% 77
Hong Kong SAR China Hong Kong SAR China 15.7 -0.566% 214
Honduras Honduras 47.1 -1.59% 72
Croatia Croatia 22.1 -1.28% 188
Haiti Haiti 48.7 -1.67% 68
Hungary Hungary 22.3 -0.442% 187
Indonesia Indonesia 36.1 -1.49% 102
Isle of Man Isle of Man 22.7 -1.85% 184
India India 36.1 -2.03% 103
Ireland Ireland 28.3 -2.59% 130
Iran Iran 32.4 -1.85% 113
Iraq Iraq 61 -2.18% 50
Iceland Iceland 26.7 -1.61% 146
Israel Israel 45.7 -0.857% 76
Italy Italy 18.7 -1.84% 210
Jamaica Jamaica 25.6 -2.37% 154
Jordan Jordan 47.3 -2.33% 71
Japan Japan 19.5 -1.82% 206
Kazakhstan Kazakhstan 47.4 -0.274% 70
Kenya Kenya 61.2 -2.54% 48
Kyrgyzstan Kyrgyzstan 52.2 -1.13% 60
Cambodia Cambodia 46.5 -1.23% 73
Kiribati Kiribati 56.8 -0.86% 53
St. Kitts & Nevis St. Kitts & Nevis 25.8 -0.258% 153
South Korea South Korea 15.1 -2.93% 216
Kuwait Kuwait 23.2 -2.35% 177
Laos Laos 46.4 -1.52% 74
Lebanon Lebanon 41.1 -2.31% 87
Liberia Liberia 69.1 -1.93% 27
Libya Libya 40.6 -2.55% 89
St. Lucia St. Lucia 24.1 -1.88% 170
Liechtenstein Liechtenstein 21.9 +0.146% 191
Sri Lanka Sri Lanka 33.4 -1.43% 109
Lesotho Lesotho 56 -1.76% 55
Lithuania Lithuania 22.6 -1.02% 185
Luxembourg Luxembourg 22.9 +0.205% 180
Latvia Latvia 24.5 -1.58% 166
Macao SAR China Macao SAR China 19.4 -0.477% 207
Saint Martin (French part) Saint Martin (French part) 35.4 +3.33% 106
Morocco Morocco 38.7 -1.68% 93
Monaco Monaco 26.8 +2.45% 145
Moldova Moldova 30.9 -0.409% 120
Madagascar Madagascar 68.5 -1.22% 30
Maldives Maldives 25.9 -2.49% 150
Mexico Mexico 36.4 -1.85% 99
Marshall Islands Marshall Islands 56.6 -0.618% 54
North Macedonia North Macedonia 25.8 -1.02% 152
Mali Mali 89.6 -1.34% 5
Malta Malta 19.5 -0.491% 205
Myanmar (Burma) Myanmar (Burma) 35.5 -0.878% 105
Montenegro Montenegro 28.3 -0.876% 131
Mongolia Mongolia 51.4 -1.63% 61
Northern Mariana Islands Northern Mariana Islands 32.2 -1.96% 114
Mozambique Mozambique 84.3 -1.16% 8
Mauritania Mauritania 79 -1.33% 11
Mauritius Mauritius 20.7 -1.7% 200
Malawi Malawi 71.8 -2.55% 24
Malaysia Malaysia 30.9 -2.55% 121
Namibia Namibia 62.3 -1.19% 44
New Caledonia New Caledonia 31.7 -0.853% 117
Niger Niger 91.7 -1.44% 2
Nigeria Nigeria 73.3 -1.96% 19
Nicaragua Nicaragua 43.8 -1.96% 81
Netherlands Netherlands 23.3 -0.662% 173
Norway Norway 24.9 -2.03% 161
Nepal Nepal 43.7 -1.21% 82
Nauru Nauru 63.6 -1.45% 41
New Zealand New Zealand 28 -1.18% 132
Oman Oman 34 -2.75% 108
Pakistan Pakistan 62.1 -1.4% 46
Panama Panama 37.9 -1.64% 96
Peru Peru 35.9 -1.82% 104
Philippines Philippines 41.8 -3.33% 85
Palau Palau 25.9 -1.35% 149
Papua New Guinea Papua New Guinea 53.1 -1.29% 59
Poland Poland 22.8 -1.49% 182
Puerto Rico Puerto Rico 18.3 -2.98% 211
North Korea North Korea 27.6 +0.315% 136
Portugal Portugal 20.4 -0.197% 201
Paraguay Paraguay 44.1 -0.709% 80
Palestinian Territories Palestinian Territories 65.5 -1.33% 37
French Polynesia French Polynesia 27.5 -3.25% 137
Qatar Qatar 18.1 -0.576% 212
Romania Romania 24.4 -0.909% 167
Russia Russia 26.4 -0.845% 147
Rwanda Rwanda 63.6 -1.43% 40
Saudi Arabia Saudi Arabia 32.5 -1.36% 112
Sudan Sudan 72 -0.594% 23
Senegal Senegal 65.6 -1.87% 36
Singapore Singapore 15.7 -0.249% 215
Solomon Islands Solomon Islands 62.2 -1.68% 45
Sierra Leone Sierra Leone 64.9 -1.84% 38
El Salvador El Salvador 37 -1.98% 98
San Marino San Marino 18.8 -3.02% 209
Somalia Somalia 91.7 -0.298% 3
Serbia Serbia 22.7 +0.215% 183
South Sudan South Sudan 67 -3.78% 32
São Tomé & Príncipe São Tomé & Príncipe 64.9 -1.89% 39
Suriname Suriname 38.6 -1.23% 94
Slovakia Slovakia 24 -0.404% 171
Slovenia Slovenia 23.2 -1.09% 176
Sweden Sweden 27.3 -1.62% 138
Eswatini Eswatini 53.5 -1.26% 58
Sint Maarten Sint Maarten 21.3 -2.64% 195
Seychelles Seychelles 27.9 -0.907% 133
Syria Syria 44.1 -5.49% 79
Turks & Caicos Islands Turks & Caicos Islands 22.1 -0.63% 190
Chad Chad 88.9 -2.67% 6
Togo Togo 69 -1.43% 29
Thailand Thailand 21.1 -1.83% 196
Tajikistan Tajikistan 60.5 -0.742% 51
Turkmenistan Turkmenistan 49.3 -0.37% 67
Timor-Leste Timor-Leste 55.1 -2.68% 56
Tonga Tonga 61.2 -0.803% 49
Trinidad & Tobago Trinidad & Tobago 25.4 -1.12% 158
Tunisia Tunisia 36.2 -1.67% 101
Turkey Turkey 31.4 -1.94% 118
Tuvalu Tuvalu 54.6 +2.3% 57
Tanzania Tanzania 78.2 -0.78% 13
Uganda Uganda 80.2 -1.55% 10
Ukraine Ukraine 20.7 -2.85% 199
Uruguay Uruguay 27.9 -2.34% 134
United States United States 26.8 -1.19% 144
Uzbekistan Uzbekistan 49.3 +1.4% 66
St. Vincent & Grenadines St. Vincent & Grenadines 31.9 -1.68% 116
Venezuela Venezuela 39.4 -2.82% 91
British Virgin Islands British Virgin Islands 19.1 -4.56% 208
U.S. Virgin Islands U.S. Virgin Islands 27.1 +0.564% 142
Vietnam Vietnam 34.3 -1.55% 107
Vanuatu Vanuatu 66.8 -1.16% 34
Samoa Samoa 69.1 -0.839% 28
Kosovo Kosovo 29.9 -2.96% 123
Yemen Yemen 73 -0.382% 21
South Africa South Africa 38.4 -0.809% 95
Zambia Zambia 73.3 -1.97% 20
Zimbabwe Zimbabwe 73.6 -1.92% 17 

                    
# 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 = 'SP.POP.DPND.YG'

# 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 <- 'SP.POP.DPND.YG'

# 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))