Secondary education, teachers, female

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Albania Albania 16,317 +0.153% 35
Andorra Andorra 417 +3.99% 64
United Arab Emirates United Arab Emirates 67,895 +32.9% 16
Armenia Armenia 20,929 +0.644% 32
Azerbaijan Azerbaijan 90,024 -0.0677% 15
Burkina Faso Burkina Faso 12,205 +2.48% 38
Bangladesh Bangladesh 149,371 -0.0856% 12
Bahrain Bahrain 5,313 +2.25% 49
Bahamas Bahamas 2,015 +21.8% 56
Bosnia & Herzegovina Bosnia & Herzegovina 17,594 -1.68% 33
Belarus Belarus 58,527 -1.01% 18
Belize Belize 1,505 -5.94% 58
Bolivia Bolivia 37,022 +1.49% 27
Barbados Barbados 813 +2.01% 60
Brunei Brunei 3,512 -0.987% 53
China China 4,290,848 +4.46% 1
Côte d’Ivoire Côte d’Ivoire 15,879 +6.79% 37
Cameroon Cameroon 48,324 +3.01% 24
Congo - Kinshasa Congo - Kinshasa 109,250 +19.9% 13
Congo - Brazzaville Congo - Brazzaville 4,503 +74.9% 51
Cuba Cuba 54,784 -2.65% 20
Cayman Islands Cayman Islands 259 +6.15% 67
Djibouti Djibouti 987 +10.2% 59
Dominica Dominica 357 -1.92% 66
Dominican Republic Dominican Republic 51,517 +11.4% 21
Ecuador Ecuador 54,922 +1.67% 19
Egypt Egypt 238,790 -2.88% 5
Gibraltar Gibraltar 130 -12.2% 71
Guatemala Guatemala 49,815 -18.6% 23
Guyana Guyana 3,731 -1.37% 52
Hong Kong SAR China Hong Kong SAR China 17,438 -1.23% 34
Indonesia Indonesia 634,553 -19.8% 4
India India 3,232,800 +2.46% 2
Jamaica Jamaica 9,005 -1.24% 43
Jordan Jordan 41,997 +10.3% 25
Kazakhstan Kazakhstan 185,745 -8.83% 8
St. Lucia St. Lucia 775 +1.04% 61
Lesotho Lesotho 3,133 +2.22% 54
Macao SAR China Macao SAR China 1,648 +2.04% 57
Morocco Morocco 66,496 +8.04% 17
Monaco Monaco 258 -2.64% 68
Moldova Moldova 16,160 -1.4% 36
Mali Mali 10,669 +36.8% 40
Montenegro Montenegro 5,693 48
Mauritius Mauritius 6,014 -2.35% 46
Malaysia Malaysia 161,285 +2% 10
Niger Niger 6,714 +2.57% 45
Nicaragua Nicaragua 7,967 -11% 44
Nepal Nepal 28,606 -8.65% 29
Nauru Nauru 13 -18.8% 74
Oman Oman 29,761 +8.26% 28
Peru Peru 99,773 +1.79% 14
Palau Palau 99 +3.13% 72
Palestinian Territories Palestinian Territories 28,457 +3.2% 30
Qatar Qatar 5,959 +8.76% 47
Russia Russia 1,105,635 +0.236% 3
Rwanda Rwanda 11,462 +19.8% 39
Senegal Senegal 10,170 +1.48% 42
El Salvador El Salvador 10,382 +0.28% 41
San Marino San Marino 211 -1.4% 69
Seychelles Seychelles 389 +4.01% 65
Syria Syria 37,939 +13.4% 26
Turks & Caicos Islands Turks & Caicos Islands 176 +10.7% 70
Chad Chad 3,009 +9.1% 55
Togo Togo 23,389 +901% 31
Thailand Thailand 150,700 -2.96% 11
Trinidad & Tobago Trinidad & Tobago 5,300 -2.56% 50
Tunisia Tunisia 50,754 +2.14% 22
Tuvalu Tuvalu 69 -50% 73
Ukraine Ukraine 235,103 -9.14% 6
Uzbekistan Uzbekistan 213,756 +4.26% 7
St. Vincent & Grenadines St. Vincent & Grenadines 560 -0.885% 63
Venezuela Venezuela 184,250 +1,069% 9
Vanuatu Vanuatu 634 +15.9% 62 

                    
# 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.TCHR.FE'

# 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.TCHR.FE'

# 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))