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Comparison of labor estimates: modeled vs. national data

ILO
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Comparison of labor estimates: modeled vs. national data

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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.
  • 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. Definitions of labor force and unemployment differ by country.

Limitations and exceptions:

The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.

There may be also persons not currently in the labour market who want to work but do not actively "seek" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the "hidden unemployed" or "discouraged workers") is a criterion that will affect the unemployment count of both women and men.

However, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment.

Statistical concept and methodology:

The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or who have voluntarily left work. Persons who did not look for work but have an arrangements for a future job are also counted as unemployed.

Some unemployment is unavoidable. At any time some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. It is the labour force or the economically active portion of the population that serves as the base for this indicator, not the total population.

Unemployment rates: modeled vs. national estimates
ILO
Share of the without work, but actively looking for a job and available to start soon.
Source
Labour Force Statistics, via World Bank (2026)processed by Our World in Data
Last updated
February 27, 2026
Next expected update
February 2027
Date range
1960–2025
Unit
%

Sources and processing

Labour Force Statistics, 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.
Labour Force Statistics database (LFS), 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.NE.ZS (https://data.worldbank.org/indicator/SL.UEM.TOTL.NE.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.
Labour Force Statistics database (LFS), 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.NE.ZS (https://data.worldbank.org/indicator/SL.UEM.TOTL.NE.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.

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

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: Unemployment rates: modeled vs. national estimates”, part of the following publication: Bertha Rohenkohl, Pablo Arriagada, and Esteban Ortiz-Ospina (2026) - “Work and Employment”. Data adapted from Labour Force Statistics, via World Bank. Retrieved from https://data-childlabor-ilounicef.owid.pages.dev:8789/20260304-094028/grapher/ilo-national-vs-modeled-data.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:

Labour Force Statistics, via World Bank (2026) – processed by Our World in Data

Full citation

Labour Force Statistics, via World Bank (2026) – processed by Our World in Data. “Unemployment rates: modeled vs. national estimates – ILO” [dataset]. Labour Force Statistics, via World Bank, “World Development Indicators 125” [original data]. Retrieved April 14, 2026 from https://data-childlabor-ilounicef.owid.pages.dev:8789/20260304-094028/grapher/ilo-national-vs-modeled-data.html (archived on March 4, 2026).

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.

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://data-childlabor-ilounicef.owid.pages.dev/grapher/ilo-national-vs-modeled-data.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://data-childlabor-ilounicef.owid.pages.dev/grapher/ilo-national-vs-modeled-data.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://data-childlabor-ilounicef.owid.pages.dev/grapher/ilo-national-vs-modeled-data.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://data-childlabor-ilounicef.owid.pages.dev/grapher/ilo-national-vs-modeled-data.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://data-childlabor-ilounicef.owid.pages.dev/grapher/ilo-national-vs-modeled-data.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://data-childlabor-ilounicef.owid.pages.dev/grapher/ilo-national-vs-modeled-data.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://data-childlabor-ilounicef.owid.pages.dev/grapher/ilo-national-vs-modeled-data.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://data-childlabor-ilounicef.owid.pages.dev/grapher/ilo-national-vs-modeled-data.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear