Data

Ratio of female to male labor force participation rates

ILO
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What you should know about this indicator

  • This data comes from the ILO Modelled Estimates series. combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the Modelled Estimates documentation.
  • This data follows the standards of the . Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the , data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in this explainer by the ILO.

How is this data described by its producer - ILO?

Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period.

Aggregation method:

Weighted average

Statistical concept and methodology:

Methodology: Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100. The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed.

Labor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework.

Population censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.

Statistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.

Development relevance:

Estimates of women in the labor force and employment are generally lower than those of men and are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic. In many low-income countries women often work on farms or in other family enterprises without pay, and others work in or near their homes, mixing work and family activities during the day. In many high-income economies, women have been increasingly acquiring higher education that has led to better-compensated, longer-term careers rather than lower-skilled, shorter-term jobs. However, access to good- paying occupations for women remains unequal in many occupations and countries around the world. Labor force statistics by gender is important to monitor gender disparities in employment and unemployment patterns.

Limitations and exceptions:

Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.

The reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.

Differing definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates.

Other notes:

National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

Ratio of female to male labor force participation rates
ILO
This ratio is calculated by dividing the labor force participation rate among women by the corresponding rate for men. The labor force participation rate is the share of the working-age population (ages 15 and over) who are economically active (employed or unemployed).
Source
ILO Modelled Estimates, via World Bank (2026)processed by Our World in Data
Last updated
February 27, 2026
Next expected update
February 2027
Date range
1990–2025
Unit
%

Sources and processing

ILO Modelled Estimates, via World Bank – World Development Indicators

The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades.WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability.The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization.WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally.Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.

Retrieved on
February 27, 2026
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database, date accessed:  January 17, 2026. Indicator SL.TLF.CACT.FM.ZS (https://data.worldbank.org/indicator/SL.TLF.CACT.FM.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-02-27.

The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades.WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability.The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization.WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally.Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.

Retrieved on
February 27, 2026
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database, date accessed:  January 17, 2026. Indicator SL.TLF.CACT.FM.ZS (https://data.worldbank.org/indicator/SL.TLF.CACT.FM.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-02-27.

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“Data Page: Ratio of female to male labor force participation rates”, part of the following publication: Bertha Rohenkohl, Pablo Arriagada, and Esteban Ortiz-Ospina (2026) - “Work and Employment”. Data adapted from ILO Modelled Estimates, via World Bank. Retrieved from https://archive.ourworldindata.org/20260512-185716/grapher/ratio-of-female-to-male-labor-force-participation-rates-ilo-wdi.html [online resource] (archived on May 12, 2026).

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In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

ILO Modelled Estimates, via World Bank (2026) – processed by Our World in Data

Full citation

ILO Modelled Estimates, via World Bank (2026) – processed by Our World in Data. “Ratio of female to male labor force participation rates – ILO” [dataset]. ILO Modelled Estimates, via World Bank, “World Development Indicators 125” [original data]. Retrieved May 13, 2026 from https://archive.ourworldindata.org/20260512-185716/grapher/ratio-of-female-to-male-labor-force-participation-rates-ilo-wdi.html (archived on May 12, 2026).

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Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/ratio-of-female-to-male-labor-force-participation-rates-ilo-wdi.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/ratio-of-female-to-male-labor-force-participation-rates-ilo-wdi.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://ourworldindata.org/grapher/ratio-of-female-to-male-labor-force-participation-rates-ilo-wdi.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/ratio-of-female-to-male-labor-force-participation-rates-ilo-wdi.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/ratio-of-female-to-male-labor-force-participation-rates-ilo-wdi.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/ratio-of-female-to-male-labor-force-participation-rates-ilo-wdi.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear