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Data

Average hourly earnings of employees

ILO
See all data and research on:
Work & Employment

What you should know about this indicator

  • This data includes remuneration in cash and as benefits in kind paid to employees at regular intervals. It excludes employers' contributions paid to social security and pensions schemes, the benefits received by employees under these schemes, as well as severance and termination pay.
  • Employees are defined as workers in paid employment jobs. Paid employment refers to all jobs where people hold an employment contract which gives them remuneration not dependent upon the revenue of the organization for which they work. This status in employment is provided according to the International Standard Classification of Status in Employment (ICSE-93).
  • This data is expressed in constant international dollars to adjust for inflation and differences in living costs between countries. Read more in our article, What are international dollars?
Learn more in the FAQs

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 earnings of employees relate to the gross remuneration in cash and in kind paid to employees, as a rule at regular intervals, for time worked or work done together with remuneration for time not worked, such as annual vacation, other type of paid leave or holidays. Earnings exclude employers' contributions in respect of their employees paid to social security and pension schemes and also the benefits received by employees under these schemes. Earnings also exclude severance and termination pay. Data are converted to U.S. dollars as the common currency, using exchange rates or using purchasing power parity (PPP) rates for private consumption expenditures. The latter series allows for international comparisons by taking account of the differences in relative prices between countries. For more information, refer to the Wages and Working Time Statistics (COND) database description.

Average hourly earnings of employees
ILO
Gross remuneration for time worked or work done, vacation, and other types of paid leave.
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
1990–2025
Unit
international-$ in 2021 prices

Frequently Asked Questions

What are international-$ and why are they used to measure incomes?

Much of the economic data we use to understand the world, such as the incomes people receive or the goods and services firms produce and people buy, is recorded in the local currencies of each country. That means the numbers start out in rupees, US dollars, yuan, and many others, and without adjusting for inflation over time. This is known as being in “current prices” or “nominal” terms.

Before these figures can be meaningfully compared, they need to be converted into common units. International dollars (int.-$) are a hypothetical currency that is used for this.

The idea is simple: one international dollar should buy the same quantity and quality of goods and services, no matter where or when it is spent. To achieve this, international dollars adjust for two things. First, they account for inflation within each country, so that values from different years can be compared (showing “constant” prices). Second, they account for differences in living costs across countries. This second adjustment uses purchasing power parity (PPP) rates, which reflect how much local currency is needed to buy what one US dollar would buy in the United States.

The United States is the benchmark, so that one 2021 int.-$ is defined as the value of goods and services that one US dollar would buy in the US in 2021. One 2011 int.-$ is defined in the same way, but for prices in 2011.

You can read more in our article, What are international dollars?

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.

We excluded observations where average hourly earnings were below 0.2 or above 100 international-$ in 2021 prices. Such values are highly unlikely as national averages and are therefore probably the result of miscalculation.

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: Average hourly earnings of employees”, 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/average-hourly-earnings.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. “Average hourly earnings of employees – ILO” [dataset]. International Labour Organization, “ILOSTAT” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260304-094028/grapher/average-hourly-earnings.html (archived on March 4, 2026).

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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/average-hourly-earnings.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/average-hourly-earnings.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/average-hourly-earnings.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/average-hourly-earnings.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/average-hourly-earnings.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

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

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