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

Source: worldbank.org, 03.09.2025

Year: 2021

Flag Country Value Value change, % Rank
United Arab Emirates United Arab Emirates 76.7 +8.99% 72
Argentina Argentina 97.3 -1.84% 11
Armenia Armenia 97.1 +0.338% 13
Australia Australia 91.1 +2.45% 42
Austria Austria 90.1 -2.52% 48
Azerbaijan Azerbaijan 97.5 +0.942% 7
Belgium Belgium 93.7 +0.281% 30
Bulgaria Bulgaria 91.8 +0.0591% 39
Bosnia & Herzegovina Bosnia & Herzegovina 96.4 -0.67% 15
Belarus Belarus 92.1 -0.465% 35
Bolivia Bolivia 94.7 -1.49% 25
Brazil Brazil 97.4 -0.0172% 10
Barbados Barbados 98.3 -1.61% 4
Canada Canada 92.3 -0.887% 33
Chile Chile 93.5 +0.0506% 31
Côte d’Ivoire Côte d’Ivoire 86.1 +21.2% 62
Costa Rica Costa Rica 93.4 -0.0733% 32
Cyprus Cyprus 95 -0.279% 22
Czechia Czechia 84 -7.24% 64
Germany Germany 91.1 +0.832% 43
Denmark Denmark 94.5 +1.18% 26
Ecuador Ecuador 90.8 -2.3% 45
Spain Spain 86.7 -1.11% 60
Estonia Estonia 91.9 +1.22% 38
Finland Finland 94.9 -0.913% 24
France France 91 -0.591% 44
Guatemala Guatemala 95 +5.57% 21
Croatia Croatia 89.2 +4.08% 52
Hungary Hungary 89.4 +13.2% 51
Ireland Ireland 97.4 +0.422% 8
Iceland Iceland 98 +0.0375% 6
Israel Israel 77.6 -4.56% 71
Italy Italy 90.6 +0.21% 46
Jamaica Jamaica 99.9 +0.421% 2
Jordan Jordan 36.5 +3.29% 77
Japan Japan 87.3 +0.196% 59
Kyrgyzstan Kyrgyzstan 98 -1.4% 5
South Korea South Korea 86.4 +2.36% 61
Lithuania Lithuania 89.2 +14.3% 53
Luxembourg Luxembourg 97.3 -0.00307% 12
Latvia Latvia 92.1 +6.24% 37
Macao SAR China Macao SAR China 84.1 +5.11% 63
Monaco Monaco 94.3 27
Moldova Moldova 93.8 +0.0906% 28
Mexico Mexico 97.4 +0.207% 9
Mali Mali 90.2 -3.58% 47
Malta Malta 73.9 +0.409% 73
Malaysia Malaysia 82.2 -4.16% 66
Netherlands Netherlands 88.5 -0.311% 54
Norway Norway 92.1 +4.25% 36
New Zealand New Zealand 87.8 +0.861% 56
Oman Oman 66.8 -5.61% 76
Peru Peru 83.9 -0.645% 65
Poland Poland 87.7 +0.0725% 57
Portugal Portugal 93.8 -0.938% 29
Paraguay Paraguay 96.1 +0.591% 18
Palestinian Territories Palestinian Territories 87.4 58
Romania Romania 91.6 -3.46% 41
Rwanda Rwanda 66.8 -10% 75
Saudi Arabia Saudi Arabia 100 1
Senegal Senegal 73 +6.3% 74
Singapore Singapore 95 +2.96% 23
Sierra Leone Sierra Leone 81 -18.8% 69
El Salvador El Salvador 81.8 -17.6% 67
San Marino San Marino 96.1 -0.0697% 17
Serbia Serbia 96.8 +0.451% 14
Slovakia Slovakia 89.5 -3.48% 49
Slovenia Slovenia 89.5 -0.565% 50
Sweden Sweden 96.3 +0.122% 16
Chad Chad 81.1 -18.2% 68
Turkmenistan Turkmenistan 99.9 +3.16% 3
Trinidad & Tobago Trinidad & Tobago 80.9 -6.84% 70
Turkey Turkey 88 -3.19% 55
Ukraine Ukraine 95.4 -0.119% 20
Uruguay Uruguay 92.3 -0.474% 34
United States United States 91.7 +0.335% 40
Uzbekistan Uzbekistan 100 0% 1
South Africa South Africa 96 +0.572% 19 

                    
# 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.CTER.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.CTER.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))