Country

Indicator

Value type

Values present

Part time employment, total (% of total employment)

Source: worldbank.org, 07.12.2025

Year: 2023

Country	Value	Value change, %	Rank
Albania	17.5	-6.33%	61
United Arab Emirates	3.24	-17.8%	79
Argentina	40.6	-1.29%	14
Austria	49.9	+1.28%	3
Belgium	43.3	+3.99%	10
Bulgaria	13.5	+12.9%	67
Bosnia & Herzegovina	5.94	+7.03%	77
Belarus	14.1	-7.42%	65
Bolivia	38.5	+1.77%	18
Brazil	25.6	-0.931%	44
Brunei	9	+13.2%	72
Botswana	24.1	-2.11%	46
Canada	40.1	-3.51%	15
Switzerland	34.4	+2.2%	25
Chile	28	+2.75%	38
Colombia	21.5	+3.66%	51
Costa Rica	21.3	-8.04%	52
Cyprus	29.2	-12.7%	35
Czechia	30.7	+2.57%	31
Germany	43.7	+2.01%	9
Denmark	44	-0.159%	8
Dominican Republic	29.6	+4.6%	34
Egypt	13.5	+10.4%	66
Spain	35.6	-2.36%	22
Estonia	33.2	-0.658%	27
Finland	46.2	-0.0433%	7
France	39.1	+1.85%	17
United Kingdom	40.9	+0.912%	13
Gambia	39.5	+267%	16
Greece	22.4	-13%	50
Grenada	11.5	-3.59%	69
Guatemala	30.3	+3.2%	32
Honduras	28.2	-16%	37
Croatia	26.4	-6.69%	41
Hungary	26.6	+15.6%	39
Indonesia	38.2	-2.43%	20
India	24	+15.1%	47
Ireland	41.8	+8.18%	12
Iceland	50	-0.497%	2
Israel	34.8	+2.6%	23
Italy	34.6	+1.32%	24
Jamaica	7.19	-15.9%	75
Jordan	7.65	-11.9%	74
South Korea	25	-16.6%	45
St. Lucia	8.55	-12.8%	73
Lithuania	22.6	-6.9%	49
Luxembourg	37.2	+2.88%	21
Latvia	18.5	-0.431%	58
Moldova	11.7	+29.7%	68
Mexico	25.9	+1.81%	43
North Macedonia	20.2	+19.1%	55
Malta	38.3	+2.79%	19
Mongolia	5.83	-6.72%	78
Mauritius	26.5	+98.4%	40
Netherlands	61.8	+2.37%	1
Norway	49.7	+2.2%	4
Panama	31.5	-16.4%	29
Peru	28.4	-0.281%	36
Poland	20.6	-0.242%	54
Portugal	32.4	+4.89%	28
Paraguay	34	-1.34%	26
Romania	11.3	+11.8%	70
Russia	7.06	-6.12%	76
Rwanda	49.3	-0.785%	5
El Salvador	20.7	+4.65%	53
Serbia	17.6	+11.8%	59
Slovakia	30.2	+6.5%	33
Slovenia	31.1	+4.43%	30
Sweden	47.5	+1.41%	6
Seychelles	17.6	+60.1%	60
Thailand	19.2	+1.59%	57
Trinidad & Tobago	9.17	-2.76%	71
Turkey	23.7	+3.59%	48
Uruguay	42.5	+12.3%	11
United States	26.3	-0.718%	42
Vietnam	20	-4.86%	56
South Africa	15.7	+4.17%	63
Zambia	16.6	-0.837%	62
Zimbabwe	14.3	-7.83%	64

Part-time employment percentage is a crucial economic indicator that reflects the portion of total employment that consists of individuals working in part-time roles. This figure is essential for understanding labor market dynamics, particularly the balance between full-time and part-time work options available to the workforce. In 2023, the global median value for part-time employment stands at 26.49% of total employment, indicative of the varying degree of part-time work being utilized across different regions.

The importance of part-time employment cannot be understated. It is a critical aspect of labor flexibility, allowing individuals to find work-life balance, pursue education, or manage personal responsibilities. For employers, part-time roles can serve as a cost-efficient alternative to full-time positions, enabling businesses to adjust their workforce in accordance with fluctuating demand without the additional costs associated with full-time employees, such as benefits and extended hours.

Analyzing the top five areas for part-time employment reveals significant disparities across the globe. The Netherlands leads with an impressive 61.84%, followed by Iceland at 50.04%, Austria at 49.93%, Norway at 49.7%, and Rwanda at 49.29%. The high rates in these countries may be attributed to several factors including robust labor laws that promote part-time employment, cultural acceptance of non-traditional work arrangements, and social welfare systems that enable individuals to choose part-time work while maintaining their quality of life. For instance, the Netherlands is known for its progressive labor regulations and has a well-established system supporting various employment arrangements, making part-time work a viable choice for many citizens.

On the other end of the spectrum, the bottom five areas for part-time employment include the United Arab Emirates at 3.24%, Mongolia at 5.83%, Bosnia & Herzegovina at 5.94%, Russia at 7.06%, and Jamaica at 7.19%. These lower figures can generally be ascribed to economic structures that emphasize full-time employment or cultural norms that do not widely accommodate part-time roles. In the UAE, the focus on a highly competitive and fast-paced labor market means that part-time positions are less common, as companies often prefer full-time employees to maintain productivity and commitment. Similarly, in the case of Russia and other regions, economic instability and labor market restrictions may additionally hinder the growth of part-time opportunities.

Part-time employment is related intricately to various other economic and social indicators. For instance, unemployment rates may be mitigated by the presence of more part-time jobs, allowing individuals who may not be available for full-time work to still participate in the labor force. Furthermore, it connects to underemployment rates, as individuals working part-time may desire more hours yet are unable to secure them due to market conditions. Similarly, women's participation in the workforce is often heavily reliant on part-time employment opportunities, making this indicator a useful gauge for gender equality in labor markets.

Several key factors influence the rate of part-time employment, including economic conditions, demographic shifts, labor market regulations, and cultural attitudes toward work. In times of economic downturn, businesses may lean more towards hiring part-time workers to cut costs, while a thriving economy may see an increase in full-time roles. Demographically speaking, an aging population may also correlate with higher part-time employment as older individuals seek less demanding work roles.

Employers can adopt strategies to utilize part-time employment effectively. Offering flexible work schedules and promoting a culture of work-life balance can make part-time roles more appealing. Moreover, integrating part-time positions within staff development pathways can encourage qualified individuals to work less than full-time while still aiming for career advancement. Additionally, implementing policies that address the needs of part-time workers—like pro-rated benefits, equal pay, and career training—can elevate the naivety of available jobs.

Despite its advantages, the part-time employment model is not without flaws. Workers in part-time positions often face lower wages compared to their full-time counterparts and may lack access to necessary benefits, such as health insurance and retirement savings plans. Furthermore, part-time workers can experience job insecurity, as they're often the first to be laid off in volatile economic conditions. Therefore, while part-time employment presents many opportunities, it is essential to address these issues to improve workers' overall welfare.

In summary, part-time employment is a critical metric that showcases a diverse range of labor market trends and cultural attitudes toward work. The 2023 data highlights both the opportunities and challenges presented by varying levels of part-time employment around the globe. By understanding and addressing the underlying factors influencing this trend, policy-makers and employers alike can create a labor market that benefits both businesses and workers.

Python
R

                    
# 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.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.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))

Compare

Part time employment, total (% of total employment)

Year: 2023

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