Tertiary education, academic staff (% female)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Angola Angola 21.5 -12.8% 55
Albania Albania 58.4 -0.883% 12
Andorra Andorra 46.7 +3.49% 28
United Arab Emirates United Arab Emirates 38.3 +2.94% 41
Armenia Armenia 61.8 +9.23% 7
Azerbaijan Azerbaijan 61 +4.42% 9
Burundi Burundi 12.7 -11.9% 58
Burkina Faso Burkina Faso 9.29 +2.33% 60
Bangladesh Bangladesh 27.8 -1.75% 50
Bahrain Bahrain 44.4 +1.31% 34
Bosnia & Herzegovina Bosnia & Herzegovina 46.8 +0.725% 27
Belarus Belarus 62.1 +0.577% 4
Belize Belize 54.4 +2.73% 19
Bermuda Bermuda 54.5 +16.2% 18
Brunei Brunei 49 +1.82% 26
Bhutan Bhutan 35.4 +10.7% 43
Botswana Botswana 41.5 +2.12% 37
Côte d’Ivoire Côte d’Ivoire 11.4 +1.55% 59
Cameroon Cameroon 22.8 -69.4% 54
Cuba Cuba 60.2 +1.87% 10
Algeria Algeria 46.5 +3.71% 29
Egypt Egypt 52.5 +3.76% 22
Georgia Georgia 58.4 +12.2% 13
Ghana Ghana 26.7 +8.59% 52
Guinea Guinea 9.27 +9.8% 61
Guatemala Guatemala 43.2 36
Indonesia Indonesia 44.6 +3.17% 33
India India 44.3 +1.88% 35
Jamaica Jamaica 80.7 +33.9% 1
Jordan Jordan 28 -5.84% 49
Kyrgyzstan Kyrgyzstan 65.9 +0.358% 3
Cambodia Cambodia 23.8 +10.4% 53
Macao SAR China Macao SAR China 38.7 +2.75% 40
Morocco Morocco 33.8 +2.39% 45
Monaco Monaco 56 +3.77% 15
Moldova Moldova 59.5 +2.16% 11
Madagascar Madagascar 34.1 +0.438% 44
Montenegro Montenegro 50.1 +2.41% 24
Mongolia Mongolia 61.3 +0.0268% 8
Malaysia Malaysia 56.4 +5.13% 14
Nicaragua Nicaragua 44.6 -3.64% 32
Nauru Nauru 15.4 57
Oman Oman 39.6 +14.4% 39
Philippines Philippines 51.2 -1.19% 23
Puerto Rico Puerto Rico 54.1 -2.22% 20
Palestinian Territories Palestinian Territories 31.2 +2.26% 46
Qatar Qatar 35.7 +2.4% 42
Rwanda Rwanda 20.2 +5.52% 56
El Salvador El Salvador 40 +1.38% 38
San Marino San Marino 30.9 -53.2% 47
Serbia Serbia 49.6 +0.754% 25
Sint Maarten Sint Maarten 61.9 -6% 5
Seychelles Seychelles 53.4 -15.1% 21
Thailand Thailand 55.2 -0.928% 16
Timor-Leste Timor-Leste 26.8 +6.31% 51
Tonga Tonga 55 +4.12% 17
Tanzania Tanzania 29.7 -0.403% 48
Ukraine Ukraine 61.9 +0.279% 6
Uzbekistan Uzbekistan 45.8 +2.69% 30
British Virgin Islands British Virgin Islands 66.7 -1.28% 2
Samoa Samoa 45 +19% 31 

                    
# 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.TER.TCHR.FE.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.TER.TCHR.FE.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))