Current education expenditure, primary (% of total expenditure in primary public institutions)

Source: worldbank.org, 03.09.2025

Year: 2021

Flag Country Value Value change, % Rank
United Arab Emirates United Arab Emirates 75.5 +7.99% 72
Argentina Argentina 96.4 -1.68% 23
Armenia Armenia 93.5 +8.22% 39
Australia Australia 88.3 +0.209% 58
Austria Austria 91.2 +1.12% 47
Azerbaijan Azerbaijan 85.1 +3.91% 63
Belgium Belgium 95.6 +0.586% 31
Bulgaria Bulgaria 95.4 +0.298% 32
Bosnia & Herzegovina Bosnia & Herzegovina 96.7 +1.12% 19
Bolivia Bolivia 96.9 -0.564% 18
Brazil Brazil 94.4 -1.25% 36
Barbados Barbados 96 -1.26% 28
Switzerland Switzerland 88.7 +0.7% 54
Chile Chile 96 -0.24% 26
Côte d’Ivoire Côte d’Ivoire 99.7 +1.01% 5
Costa Rica Costa Rica 97.9 -0.762% 13
Cayman Islands Cayman Islands 100 0% 1
Cyprus Cyprus 96.6 -0.425% 21
Czechia Czechia 91.8 +1.67% 46
Germany Germany 88.3 -2.18% 57
Denmark Denmark 90.3 +0.706% 50
Dominican Republic Dominican Republic 94.7 +12.1% 35
Ecuador Ecuador 99.9 -0.106% 2
Egypt Egypt 85.9 62
Spain Spain 96.7 -0.351% 20
Estonia Estonia 83.9 +1.54% 66
Finland Finland 86.6 +3.27% 61
France France 92.9 -0.531% 43
United Kingdom United Kingdom 93.3 +0.664% 40
Guatemala Guatemala 97.3 +0.972% 15
Croatia Croatia 96.2 +2.63% 25
Hungary Hungary 93.2 -1.31% 41
Ireland Ireland 89.4 +0.5% 53
Iceland Iceland 92.8 -1.08% 44
Israel Israel 88.4 +0.0927% 56
Italy Italy 96.4 -0.168% 22
Jamaica Jamaica 99.3 +0.993% 7
Jordan Jordan 90.1 +2.2% 51
Japan Japan 86.9 +4.9% 60
St. Kitts & Nevis St. Kitts & Nevis 100 0% 1
South Korea South Korea 83 +3.12% 67
Lithuania Lithuania 94.4 +4.75% 37
Luxembourg Luxembourg 88.5 -1.46% 55
Latvia Latvia 87.1 +4.04% 59
Monaco Monaco 80 -14.2% 70
Moldova Moldova 90.7 +0.742% 49
Mexico Mexico 98.4 -0.0238% 12
Mali Mali 99.4 +0.249% 6
Malta Malta 97.8 +0.866% 14
Malaysia Malaysia 98.8 -0.648% 9
Norway Norway 84.5 +0.799% 64
New Zealand New Zealand 84.2 -1.59% 65
Oman Oman 98.8 -0.126% 8
Peru Peru 81.9 -6.3% 68
Poland Poland 95.2 -0.222% 33
Portugal Portugal 99.8 +0.124% 4
Paraguay Paraguay 93.6 -0.924% 38
Romania Romania 95.8 -0.332% 30
Rwanda Rwanda 77.3 -11.7% 71
Saudi Arabia Saudi Arabia 98.8 10
Senegal Senegal 98.5 -0.432% 11
Singapore Singapore 94.7 +2.65% 34
Sierra Leone Sierra Leone 73.8 -2.8% 73
El Salvador El Salvador 80.4 -13% 69
San Marino San Marino 99.8 -0.00805% 3
Slovakia Slovakia 96.2 -0.061% 24
Slovenia Slovenia 93 -0.977% 42
Sweden Sweden 92.5 -2.81% 45
Turks & Caicos Islands Turks & Caicos Islands 100 0% 1
Trinidad & Tobago Trinidad & Tobago 95.9 +2.29% 29
Turkey Turkey 90.9 -0.505% 48
Ukraine Ukraine 96 +1.12% 27
Uruguay Uruguay 97.1 +0.212% 17
United States United States 89.9 +0.526% 52
Uzbekistan Uzbekistan 100 0% 1
South Africa South Africa 97.2 +1.11% 16 

                    
# 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.XPD.CPRM.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 <- 'SE.XPD.CPRM.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))