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

Source: worldbank.org, 03.09.2025

Year: 2021

Flag Country Value Value change, % Rank
United Arab Emirates United Arab Emirates 82.4 +6.1% 68
Argentina Argentina 96.3 -1.87% 22
Armenia Armenia 95 +6.16% 31
Australia Australia 90 +0.429% 54
Austria Austria 96.8 +0.259% 17
Azerbaijan Azerbaijan 90.4 +1.05% 50
Belgium Belgium 96.4 +0.139% 21
Bulgaria Bulgaria 95.3 -0.776% 29
Bosnia & Herzegovina Bosnia & Herzegovina 97.9 -0.253% 12
Bolivia Bolivia 95.4 -0.985% 28
Brazil Brazil 82.6 -14.5% 67
Barbados Barbados 96 +0.13% 23
Switzerland Switzerland 92.4 +0.292% 43
Chile Chile 94.1 -0.606% 36
Côte d’Ivoire Côte d’Ivoire 93.4 -1.22% 39
Costa Rica Costa Rica 99.4 +0.0228% 4
Cayman Islands Cayman Islands 100 0% 1
Cyprus Cyprus 97.4 -0.287% 14
Czechia Czechia 92.1 +1.12% 45
Germany Germany 91.8 +0.311% 47
Denmark Denmark 94.6 +1.02% 34
Dominican Republic Dominican Republic 90.1 +21.3% 53
Ecuador Ecuador 99.9 -0.106% 2
Egypt Egypt 84 -11.9% 66
Spain Spain 96.7 -0.541% 18
Estonia Estonia 87.2 -1.93% 62
Finland Finland 87.6 +1.71% 60
France France 90 -1.12% 56
United Kingdom United Kingdom 89.2 -0.113% 58
Guatemala Guatemala 93.4 +1.34% 40
Hungary Hungary 92 -0.148% 46
Ireland Ireland 90.3 -1.88% 51
Iceland Iceland 95.7 -0.543% 26
Israel Israel 91.1 -0.0405% 48
Italy Italy 95.8 -0.00773% 25
Jamaica Jamaica 97.4 -0.774% 15
Jordan Jordan 90.1 +2.94% 52
Japan Japan 87.8 +0.706% 59
St. Kitts & Nevis St. Kitts & Nevis 51.5 -48.5% 74
South Korea South Korea 82.4 +0.408% 69
Lithuania Lithuania 92.8 +0.807% 42
Luxembourg Luxembourg 87.5 -1.69% 61
Latvia Latvia 85.9 +5.06% 65
Monaco Monaco 47.6 -51.2% 75
Moldova Moldova 91.1 +1.49% 49
Mexico Mexico 98.1 +0.116% 10
Mali Mali 98.4 +2.28% 9
Malta Malta 94.2 +3.76% 35
Malaysia Malaysia 96.5 -2.73% 20
Norway Norway 86.8 -0.0685% 64
New Zealand New Zealand 87.2 -1.28% 63
Oman Oman 98.8 -0.126% 5
Peru Peru 80.4 -8.43% 70
Poland Poland 95 +0.193% 30
Portugal Portugal 95 -1.08% 32
Paraguay Paraguay 93.7 -0.882% 37
Romania Romania 94.7 -0.156% 33
Rwanda Rwanda 60.4 -32.8% 73
Saudi Arabia Saudi Arabia 98.8 6
Senegal Senegal 98.7 +0.281% 7
Singapore Singapore 98.1 +1% 11
Sierra Leone Sierra Leone 79.4 -0.168% 71
El Salvador El Salvador 75.5 -12.7% 72
San Marino San Marino 99.8 +0.0175% 3
Slovakia Slovakia 95.6 +0.251% 27
Slovenia Slovenia 93.2 +1.11% 41
Sweden Sweden 93.6 -1.4% 38
Turks & Caicos Islands Turks & Caicos Islands 90 +9.5% 55
Turkmenistan Turkmenistan 97.8 13
Trinidad & Tobago Trinidad & Tobago 98.5 +2.71% 8
Turkey Turkey 92.4 +0.781% 44
Ukraine Ukraine 96 +1.12% 24
Uruguay Uruguay 97.2 -1.05% 16
United States United States 89.9 +0.526% 57
Uzbekistan Uzbekistan 100 1
South Africa South Africa 96.5 -0.265% 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.CSEC.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.CSEC.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))