Trained teachers in lower secondary education, male (% of male teachers)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
United Arab Emirates United Arab Emirates 100 0% 1
Armenia Armenia 75.2 +4.18% 19
Azerbaijan Azerbaijan 99.5 +0.181% 2
Burkina Faso Burkina Faso 65.4 -5.74% 23
Bangladesh Bangladesh 68 +1.36% 21
Bahrain Bahrain 100 0% 1
Bahamas Bahamas 86.5 +3.4% 14
Belarus Belarus 98.4 -0.0779% 4
Belize Belize 68 +7.86% 22
Barbados Barbados 48.6 +1.39% 27
Brunei Brunei 89.1 +2.83% 11
Cameroon Cameroon 61.7 +35.4% 25
Cuba Cuba 100 0% 1
Cayman Islands Cayman Islands 98 -2.04% 5
Djibouti Djibouti 72.4 -27.6% 20
Dominican Republic Dominican Republic 0 -100% 33
Ecuador Ecuador 76.4 +1.42% 18
Indonesia Indonesia 36.5 -6.76% 30
India India 91.6 +3.74% 9
Jordan Jordan 100 0% 1
Cambodia Cambodia 100 0% 1
Kiribati Kiribati 79.4 -0.714% 16
Lebanon Lebanon 41 29
St. Lucia St. Lucia 62 -3.16% 24
Macao SAR China Macao SAR China 91.3 -1.46% 10
Morocco Morocco 100 0% 1
Monaco Monaco 78.9 -6.25% 17
Moldova Moldova 100 0% 1
Mali Mali 43.6 -19.1% 28
Mongolia Mongolia 34.5 -1.14% 31
Niger Niger 21.7 +12.4% 32
Nepal Nepal 96 +9.85% 7
Oman Oman 98.8 -1.17% 3
Palau Palau 92.9 0% 8
Palestinian Territories Palestinian Territories 100 0% 1
Qatar Qatar 100 0% 1
Rwanda Rwanda 86.1 +0.0193% 15
El Salvador El Salvador 96.7 +6.02% 6
San Marino San Marino 0 33
Chad Chad 60.8 -22.8% 26
Thailand Thailand 100 0% 1
Uzbekistan Uzbekistan 100 0% 1
Venezuela Venezuela 88.3 12
Vietnam Vietnam 87.3 +4.85% 13 

                    
# 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.SEC.TCAQ.LO.MA.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.SEC.TCAQ.LO.MA.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))