Share of young people not in education, employment or training
What you should know about this indicator
- This indicator, also known as the NEET rate, reflects how well young people are integrating into education and the labor market. High NEET rates indicate that many young people are missing opportunities to build skills, gain work experience, and earn income, with potentially long-term consequences for their employment prospects, earnings, and well-being.
- This data comes from national sources, typically labor force surveys, household surveys, or population censuses. Each country uses its own definitions and methods, so this data may not be directly comparable across countries and over time.
- This data follows the standards of the 13th International Classification of Labour Statisticians (ICLS). 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 19th ICLS, 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.
Sources and processing
This data is based on the following sources
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.
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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: Share of young people not in education, employment or training”. Our World in Data (2026). Data adapted from International Labour Organization. Retrieved from https://archive.ourworldindata.org/20260304-094028/grapher/youth-not-in-education-employment-training.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 – with minor processing by Our World in DataFull citation
International Labour Organization – with minor processing by Our World in Data. “Share of young people not in education, employment or training” [dataset]. International Labour Organization, “Data from multiple sources” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260304-094028/grapher/youth-not-in-education-employment-training.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.
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/youth-not-in-education-employment-training.csv?v=1&csvType=full&useColumnShortNames=falseMetadata URL (JSON format)
https://ourworldindata.org/grapher/youth-not-in-education-employment-training.metadata.json?v=1&csvType=full&useColumnShortNames=falseExcel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/youth-not-in-education-employment-training.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/youth-not-in-education-employment-training.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/youth-not-in-education-employment-training.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()R
library(jsonlite)
# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/youth-not-in-education-employment-training.csv?v=1&csvType=full&useColumnShortNames=false")
# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/youth-not-in-education-employment-training.metadata.json?v=1&csvType=full&useColumnShortNames=false")Stata
import delimited "https://ourworldindata.org/grapher/youth-not-in-education-employment-training.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear