Part time employment, female (% of total female employment)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Albania Albania 22 -7.89% 56
United Arab Emirates United Arab Emirates 5.57 -5.75% 78
Argentina Argentina 55.3 -0.7% 7
Austria Austria 64.5 +0.0775% 2
Belgium Belgium 56.1 +1.35% 6
Bulgaria Bulgaria 15.8 +12.2% 65
Bosnia & Herzegovina Bosnia & Herzegovina 6.68 +6.03% 77
Belarus Belarus 20.6 -6.42% 58
Bolivia Bolivia 49.1 +1.03% 17
Brazil Brazil 33.3 -1.25% 40
Brunei Brunei 11 +10.8% 70
Botswana Botswana 27.8 -2.93% 50
Canada Canada 48.8 -3.35% 18
Switzerland Switzerland 49.7 +1.08% 16
Chile Chile 35.8 +0.647% 36
Colombia Colombia 30.2 +4.98% 48
Costa Rica Costa Rica 30.4 -9.55% 46
Cyprus Cyprus 32.8 -12.6% 41
Czechia Czechia 37.7 +0.0265% 30
Germany Germany 59.9 +1.3% 3
Denmark Denmark 53.9 -0.0742% 9
Dominican Republic Dominican Republic 36.5 +4.71% 34
Egypt Egypt 18.1 -6.85% 62
Spain Spain 43.9 -2.94% 27
Estonia Estonia 38.9 -2.6% 29
Finland Finland 52.3 -0.324% 13
France France 47.3 +0.233% 20
United Kingdom United Kingdom 53.3 -0.206% 12
Gambia Gambia 47.2 +259% 21
Greece Greece 30.2 -11.9% 47
Grenada Grenada 11.4 -4.67% 69
Guatemala Guatemala 44.7 +4.83% 25
Honduras Honduras 36.5 -14.2% 34
Croatia Croatia 29.3 -8.46% 49
Hungary Hungary 32.3 +11.5% 43
Indonesia Indonesia 47.5 -0.42% 19
India India 45.9 +12.9% 22
Ireland Ireland 54.1 +7.98% 8
Iceland Iceland 59.8 -0.482% 4
Israel Israel 44.1 -0.429% 26
Italy Italy 49.8 +0.667% 15
Jamaica Jamaica 8.06 -17.2% 74
Jordan Jordan 8.1 -25.6% 73
South Korea South Korea 33.8 -12.9% 39
St. Lucia St. Lucia 7.8 -19.2% 75
Lithuania Lithuania 27.2 -5.99% 51
Luxembourg Luxembourg 45.6 -0.719% 23
Latvia Latvia 22.5 -6.4% 55
Moldova Moldova 12.3 +27.2% 68
Mexico Mexico 35.5 +1.69% 37
North Macedonia North Macedonia 22.9 +22.6% 54
Malta Malta 45 +3.4% 24
Mongolia Mongolia 7.5 -5.54% 76
Mauritius Mauritius 31.2 +69.3% 45
Netherlands Netherlands 77.2 +0.889% 1
Norway Norway 57.8 +1.49% 5
Panama Panama 34.9 -13.9% 38
Peru Peru 37.4 -1.6% 31
Poland Poland 26.4 -0.151% 53
Portugal Portugal 36.4 +6.59% 35
Paraguay Paraguay 43.2 -1.75% 28
Romania Romania 13.1 +9.23% 67
Russia Russia 9.05 -8.4% 72
Rwanda Rwanda 51.3 +0.766% 14
El Salvador El Salvador 26.5 +5.5% 52
Serbia Serbia 20.2 +9.78% 60
Slovakia Slovakia 36.9 +5.16% 32
Slovenia Slovenia 36.7 +4.14% 33
Sweden Sweden 53.8 +0.693% 10
Seychelles Seychelles 17.3 +73.7% 63
Thailand Thailand 19.6 +3.16% 61
Trinidad & Tobago Trinidad & Tobago 9.64 +1.47% 71
Turkey Turkey 31.6 +0.0317% 44
Uruguay Uruguay 53.5 +7.12% 11
United States United States 32.6 -0.183% 42
Vietnam Vietnam 21.4 -5.47% 57
South Africa South Africa 20.2 +4.98% 59
Zambia Zambia 17.2 -2.93% 64
Zimbabwe Zimbabwe 15.1 -6.95% 66 

                    
# 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.TLF.PART.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.TLF.PART.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))