Data

Unemployment rate

ILO
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What you should know about this indicator

  • The unemployment rate measures the share of the that is without a job but actively looking for work and available to start soon. It is one of the most widely used indicators of labor market conditions across countries and over time.
  • When defining the labor force, the definition of “working age” varies across countries, depending on national laws and practices. In the ILO modeled estimates shown here, this is harmonized to refer to people aged 15 and older.
  • 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?

Unemployment refers to the share of the labor force that is without work but available for and seeking employment.

Aggregation method:

Weighted average

Statistical concept and methodology:

Methodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed.

The series is part of the "ILO modeled estimates database," including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/

Statistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.

Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).

The working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit).

Development relevance:

The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.

Given its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).

Limitations and exceptions:

While the unemployment rate may be considered the most informative labour market indicator, reflecting the general performance of the labour market and the economy as a whole, it should not be interpreted as a measure of economic hardship or of well-being. When based on the internationally-recommended standards, the unemployment rate simply reflects the proportion of the labour force that does not have a job but is available and actively looking for work. It says nothing about the economic resources of unemployed workers or their family members. Its use should, therefore, be limited to serving as a measurement of the utilization of labour and an indication of the failure to find work. Other measures, including income-related indicators, would be needed to evaluate economic hardship. An additional criticism of the aggregate unemployment measure is that it masks information on the composition of the jobless population and therefore misses out on the particularities of the education level, ethnic origin, socio-economic background, work experience, etc. of the unemployed. Moreover, the unemployment rate says nothing about the type of unemployment – whether it is cyclical and short-term or structural and long-term – which is a critical issue for policy makers in the development of their policy responses, especially given that structural unemployment cannot be addressed by boosting market demand only.

Other notes:

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

Unemployment rate
ILO
Share of the without work, but actively looking for a job and available to start soon.
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
1991–2025
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: external database, date accessed: January 17, 2026. Indicator SL.UEM.TOTL.ZS (https://data.worldbank.org/indicator/SL.UEM.TOTL.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: external database, date accessed: January 17, 2026. Indicator SL.UEM.TOTL.ZS (https://data.worldbank.org/indicator/SL.UEM.TOTL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-02-27.

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.

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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: Unemployment rate”, 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/unemployment-rate.html [online resource] (archived on May 12, 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:

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. “Unemployment rate – 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/unemployment-rate.html (archived on May 12, 2026).

Quick download

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

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

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