Children out of school, primary

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Albania Albania 17,039 +25.7% 51
Andorra Andorra 18 -94.4% 84
United Arab Emirates United Arab Emirates 919 -99% 73
Armenia Armenia 12,859 -17.6% 54
Azerbaijan Azerbaijan 9,457 -65.5% 56
Burkina Faso Burkina Faso 1,642,690 +32.5% 5
Bangladesh Bangladesh 765,936 +963% 9
Bahrain Bahrain 8,791 -12.6% 59
Bahamas Bahamas 8,775 +16.9% 60
Bosnia & Herzegovina Bosnia & Herzegovina 25,308 +3.97% 46
Belarus Belarus 19,818 -31% 49
Belize Belize 5,716 +31.3% 63
Bolivia Bolivia 58,597 +22.9% 32
Barbados Barbados 1,694 +33.2% 68
Brunei Brunei 1,468 -3.74% 69
Côte d’Ivoire Côte d’Ivoire 175,494 -60% 15
Cameroon Cameroon 169,740 -3.35% 17
Congo - Brazzaville Congo - Brazzaville 158,517 -15.8% 20
Comoros Comoros 28,200 -14.9% 45
Cuba Cuba 34,271 +4.01% 39
Curaçao Curaçao 366 +679% 76
Cayman Islands Cayman Islands 754 +8.33% 75
Dominica Dominica 825 +6.31% 74
Dominican Republic Dominican Republic 158,549 +82.8% 19
Algeria Algeria 46,538 +58.8% 36
Ecuador Ecuador 85,933 +177% 26
Ethiopia Ethiopia 4,260,424 -1.52% 1
Fiji Fiji 2,020 +6.29% 67
Georgia Georgia 8,335 +90.5% 61
Guatemala Guatemala 174,671 -2.23% 16
Guyana Guyana 12,963 -17.8% 53
Honduras Honduras 256,362 +0.679% 13
Indonesia Indonesia 202,815 -41.3% 14
India India 1,722,440 +853% 3
Jamaica Jamaica 36,233 +61% 38
Jordan Jordan 30,737 +44% 43
Kazakhstan Kazakhstan 68,819 -49.6% 30
Cambodia Cambodia 79,658 -68.5% 28
Kiribati Kiribati 2,332 +58.6% 66
Laos Laos 60,021 -0.7% 31
Lebanon Lebanon 158,713 +8.82% 18
St. Lucia St. Lucia 982 +216% 72
Lesotho Lesotho 96,388 +7.43% 23
Macao SAR China Macao SAR China 6,221 -12% 62
Morocco Morocco 29,239 +35.4% 44
Maldives Maldives 4,455 +58.6% 64
Mali Mali 1,299,315 -13.5% 7
Montenegro Montenegro 80 +25% 82
Mongolia Mongolia 20,170 -0.826% 48
Mauritius Mauritius 1,399 -72.7% 70
Malaysia Malaysia 57,976 -60.1% 33
Niger Niger 1,671,295 +0.243% 4
Nicaragua Nicaragua 81,007 +25% 27
Nepal Nepal 105,716 +255% 22
Nauru Nauru 76 -65.3% 83
Oman Oman 15,701 -61.3% 52
Panama Panama 45,724 +151% 37
Peru Peru 9,076 -1.09% 58
Philippines Philippines 1,326,378 -0.963% 6
Palau Palau 184 -29.5% 78
Puerto Rico Puerto Rico 32,434 +227% 40
Paraguay Paraguay 95,489 +4.23% 25
Palestinian Territories Palestinian Territories 49,867 -1.26% 34
Russia Russia 381,448 +90.6% 12
Rwanda Rwanda 9,134 +397% 57
Senegal Senegal 753,550 +1.57% 10
Solomon Islands Solomon Islands 32,389 +49.7% 41
El Salvador El Salvador 95,977 -8.38% 24
San Marino San Marino 104 +2.97% 80
Suriname Suriname 31,544 +151% 42
Seychelles Seychelles 280 +48.9% 77
Syria Syria 643,032 -23.8% 11
Chad Chad 768,751 -4.41% 8
Togo Togo 10,682 -17.6% 55
Thailand Thailand 71,223 +355% 29
Tonga Tonga 80.6 -77.3% 81
Trinidad & Tobago Trinidad & Tobago 47,132 +281% 35
Tunisia Tunisia 25,079 +15.1% 47
Tuvalu Tuvalu 14 -63.2% 85
Tanzania Tanzania 2,011,644 +10.2% 2
Uzbekistan Uzbekistan 130,836 +21.3% 21
St. Vincent & Grenadines St. Vincent & Grenadines 154 -18.5% 79
Venezuela Venezuela 17,885 -95.9% 50
Vanuatu Vanuatu 4,449 +6.95% 65
Samoa Samoa 1,020 +209% 71 

                    
# 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 = 'SE.PRM.UNER'

# 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 <- 'SE.PRM.UNER'

# 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))