Trained teachers in primary education, male (% of male teachers)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Andorra Andorra 100 0% 1
United Arab Emirates United Arab Emirates 100 0% 1
Armenia Armenia 75.1 +4.08% 36
Azerbaijan Azerbaijan 99 -0.766% 4
Burkina Faso Burkina Faso 85.7 +2.07% 26
Bangladesh Bangladesh 77 +3.13% 35
Bahrain Bahrain 100 0% 1
Bahamas Bahamas 89.8 +18.5% 20
Belarus Belarus 99.1 -0.0521% 3
Belize Belize 82.9 -0.0989% 29
Bolivia Bolivia 90.1 +1.39% 19
Barbados Barbados 66.8 -3.57% 42
Brunei Brunei 89.3 +1.02% 22
Côte d’Ivoire Côte d’Ivoire 100 0% 1
Congo - Kinshasa Congo - Kinshasa 11.7 -88.3% 58
Comoros Comoros 73.3 +34.5% 37
Cuba Cuba 100 0% 1
Cayman Islands Cayman Islands 94.7 +0.421% 13
Djibouti Djibouti 71.7 -28.3% 40
Dominica Dominica 62.3 +6.07% 44
Dominican Republic Dominican Republic 0 -100% 59
Algeria Algeria 90.9 -4.24% 17
Ecuador Ecuador 86.6 +1.02% 25
Fiji Fiji 97.1 +3.12% 9
Guyana Guyana 56.6 +6.63% 46
Hong Kong SAR China Hong Kong SAR China 92.9 -1.72% 15
Indonesia Indonesia 33.8 55
India India 94.3 +5.74% 14
Jamaica Jamaica 79.3 -16% 32
Jordan Jordan 100 0% 1
Kazakhstan Kazakhstan 100 0% 1
Kyrgyzstan Kyrgyzstan 96 -0.476% 12
Cambodia Cambodia 100 0% 1
Kiribati Kiribati 89.4 -5.51% 21
Laos Laos 51.6 -42.8% 48
Lebanon Lebanon 43.5 +14.6% 50
St. Lucia St. Lucia 53.8 -20.1% 47
Lesotho Lesotho 99.3 +1.72% 2
Macao SAR China Macao SAR China 98.7 +0.0465% 5
Morocco Morocco 100 0% 1
Monaco Monaco 78.9 +18.4% 33
Moldova Moldova 100 0% 1
Mali Mali 39 -23.3% 54
Mongolia Mongolia 96.6 -2.4% 11
Mauritius Mauritius 100 0% 1
Malaysia Malaysia 91.5 +1% 16
Niger Niger 96.7 +0.123% 10
Nicaragua Nicaragua 44.6 -18.9% 49
Nepal Nepal 98 -0.353% 7
Nauru Nauru 42.9 -57.1% 51
Oman Oman 100 0% 1
Peru Peru 14.9 57
Palau Palau 88.9 0% 23
Palestinian Territories Palestinian Territories 100 0% 1
Qatar Qatar 100 0% 1
Rwanda Rwanda 67.9 -9.27% 41
Senegal Senegal 77.1 +3.4% 34
Sierra Leone Sierra Leone 63.1 -10.4% 43
El Salvador El Salvador 98.4 +5.54% 6
San Marino San Marino 17.6 +50% 56
Seychelles Seychelles 72.1 -3.88% 39
Syria Syria 42.2 -51.2% 52
Turks & Caicos Islands Turks & Caicos Islands 82.4 -6.29% 31
Chad Chad 60.3 -2.24% 45
Togo Togo 83.2 +2.93% 28
Thailand Thailand 100 0% 1
Tonga Tonga 97.3 -0.453% 8
Trinidad & Tobago Trinidad & Tobago 84 +2.65% 27
Tunisia Tunisia 100 0% 1
Tuvalu Tuvalu 40 +20% 53
Ukraine Ukraine 90.2 -0.197% 18
Uzbekistan Uzbekistan 100 0% 1
St. Vincent & Grenadines St. Vincent & Grenadines 72.6 +1.12% 38
Venezuela Venezuela 88.3 24
Vietnam Vietnam 82.6 +10.6% 30 

                    
# 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.TCAQ.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.PRM.TCAQ.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))