Our World
in Data
Oxford Martin School logoUniversity of Oxford logoGlobal Change Data Lab logo
Data

Gender wage gap

ILO
See all data and research on:
Work & Employment

What you should know about this indicator

How is this data described by its producer - ILO?

With the aim of promoting international comparability, statistics presented on ILOSTAT are based on standard international definitions wherever feasible and may differ from official national figures. The gender wage gap is unadjusted and is calculated as the difference between average earnings of men and average earnings of women expressed as a percentage of average earnings of men. This indicator provides a measure of the relative difference between the earnings of men and those of women. Data disaggregated by occupation are provided according to the latest version of the International Standard Classification of Occupations (ISCO). Data may have been regrouped from the national classifications, which may not be strictly compatible with ISCO. For more information, refer to the Wages and Working Time Statistics (COND) database description.

Gender wage gap
ILO
Difference between average earnings of men and average earnings of women expressed as a percentage of average earnings of men.
Source
International Labour Organization (2026) – with major processing by Our World in Data
Last updated
February 3, 2026
Next expected update
February 2027
Date range
1969–2025
Unit
%

Related research and writing

A dark blue background with a lighter blue world map superimposed over it. Yellow text that says Economic Inequality by Gender by Our World in Data

Economic Inequality by Gender

Esteban Ortiz-Ospina, Joe Hasell, and Max Roser

Explore charts that include this data

Sources and processing

This data is based on the following sources

International Labour Organization – ILOSTAT

The ILO’s main online database, ILOSTAT, maintained by the Department of Statistics, is the world’s largest repository of labour market statistics. It covers all countries and regions and a wide range of labour-related topics, including employment, unemployment, wages, working time and labour productivity, to name a few. It includes time series going back as far as 1938; annual, quarterly and monthly labour statistics; country-level, regional and global estimates; and even projections of the main labour market indicators.

Retrieved on
February 11, 2026
Retrieved from
https://ilostat.ilo.org/
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.
International Labour Organization. (2026). ILO modelled estimates database, ILOSTAT [database]. Available from https://ilostat.ilo.org/data/.

The ILO’s main online database, ILOSTAT, maintained by the Department of Statistics, is the world’s largest repository of labour market statistics. It covers all countries and regions and a wide range of labour-related topics, including employment, unemployment, wages, working time and labour productivity, to name a few. It includes time series going back as far as 1938; annual, quarterly and monthly labour statistics; country-level, regional and global estimates; and even projections of the main labour market indicators.

Retrieved on
February 11, 2026
Retrieved from
https://ilostat.ilo.org/
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.
International Labour Organization. (2026). ILO modelled estimates database, ILOSTAT [database]. Available from https://ilostat.ilo.org/data/.

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline
Notes on our processing step for this indicator

We removed data points flagged as "unreliable" by the source (i.e. obs_status = "U" in the original data). These data points are likely to be inaccurate and misleading for analysis.

Reuse this work

  • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
  • All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Gender wage gap”, part of the following publication: Bertha Rohenkohl, Pablo Arriagada, and Esteban Ortiz-Ospina (2026) - “Work and Employment”. Data adapted from International Labour Organization. Retrieved from https://archive.ourworldindata.org/20260304-094028/grapher/gender-gap-in-average-wages-ilo.html [online resource] (archived on March 4, 2026).

How to cite this data

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

International Labour Organization (2026) – with major processing by Our World in Data

Full citation

International Labour Organization (2026) – with major processing by Our World in Data. “Gender wage gap – ILO” [dataset]. International Labour Organization, “ILOSTAT” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260304-094028/grapher/gender-gap-in-average-wages-ilo.html (archived on March 4, 2026).

Download

Quick download

Download the data shown in this chart as a ZIP file containing a CSV file, metadata in JSON format, and a README. The CSV file can be opened in Excel, Google Sheets, and other data analysis tools.

Download full data

Includes all entities and time points

Download displayed data

Includes only the entities and time points currently visible in the chart

Data API

Use these URLs to programmatically access this chart's data and configure your requests with the options below. Our documentation provides more information on how to use the API, and you can find a few code examples below.

Data URL (CSV format)
https://ourworldindata.org/grapher/gender-gap-in-average-wages-ilo.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/gender-gap-in-average-wages-ilo.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

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

Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/gender-gap-in-average-wages-ilo.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/gender-gap-in-average-wages-ilo.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/gender-gap-in-average-wages-ilo.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/gender-gap-in-average-wages-ilo.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/gender-gap-in-average-wages-ilo.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/gender-gap-in-average-wages-ilo.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear