Female share of employment in senior and middle management (%)

Source: worldbank.org, 19.12.2024

Year: 2023

Flag Country Value Value change, % Rank
Argentina Argentina 38 +0.19% 25
Australia Australia 38.9 +2.32% 21
Austria Austria 33.8 +7.19% 33
Burkina Faso Burkina Faso 29.2 +101% 40
Bosnia & Herzegovina Bosnia & Herzegovina 27.9 +15.8% 42
Belarus Belarus 41.6 -1.9% 13
Bolivia Bolivia 28 -6.55% 41
Brazil Brazil 38.9 +1.61% 20
Brunei Brunei 39 +19.9% 18
Bhutan Bhutan 17.9 +7.61% 46
Botswana Botswana 52.7 -11% 2
Switzerland Switzerland 31.9 +5.82% 37
Colombia Colombia 42 +0.696% 10
Costa Rica Costa Rica 47.2 +2.78% 5
Dominican Republic Dominican Republic 54 -8.1% 1
Ecuador Ecuador 42.4 +6.27% 9
France France 38.7 -1.71% 22
United Kingdom United Kingdom 39.5 +0.98% 16
Gambia Gambia 31.4 -3.97% 38
Greece Greece 31.4 -7.04% 39
Guatemala Guatemala 42.9 +13.8% 8
Honduras Honduras 41.4 +48.5% 14
Indonesia Indonesia 24.8 +27.8% 43
India India 12.7 -21.5% 47
Israel Israel 32.2 -3.63% 36
Mexico Mexico 38.9 -0.381% 19
North Macedonia North Macedonia 32.4 +8.74% 35
Mongolia Mongolia 48.9 +26.3% 4
Mauritius Mauritius 35.4 +30.9% 30
Panama Panama 47 +7.96% 6
Peru Peru 38.6 +17.1% 23
Poland Poland 41.8 +5.02% 12
Portugal Portugal 37.8 -1.3% 26
Romania Romania 34 +4.63% 32
Russia Russia 41.9 +1.81% 11
Rwanda Rwanda 38.4 +53.6% 24
Singapore Singapore 39.8 -2.06% 15
El Salvador El Salvador 39 -8.71% 17
Serbia Serbia 37.1 -7.07% 27
Seychelles Seychelles 50.3 +25.6% 3
Thailand Thailand 34.7 +4.73% 31
Turkey Turkey 20.5 +4.59% 45
Uruguay Uruguay 35.5 -1.06% 29
United States United States 44.4 +3.58% 7
Vietnam Vietnam 21.8 +27.8% 44
South Africa South Africa 36 -1.37% 28
Zimbabwe Zimbabwe 33.4 +14% 34 

                    
# 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 = 'SL.EMP.SMGT.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 <- 'SL.EMP.SMGT.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))