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Data

Net enrollment rate in primary education

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Global Education

What you should know about this indicator

Net enrollment rate for primary school is calculated by dividing the number of students of official school age enrolled in primary education by the population of the age group which officially corresponds to primary education, and multiplying by 100.

How is this data described by its producer?

Total number of students of the official age group for primary education who are enrolled in any level of education, expressed as a percentage of the corresponding population. Divide the total number of students in the official school age range for primary education who are enrolled in any level of education by the population of the same age group and multiply the result by 100. The difference between the total NER and the adjusted NER provides a measure of the proportion of children in the official relevant school age group who are enrolled in levels of education below the one intended for their age. The difference between the total NER and the adjusted NER for primary education is due to enrolment in pre-primary education. The total NER should be based on total enrolment of the official relevant school age group in any level of education for all types of schools and education institutions, including public, private and all other institutions that provide organized educational programmes.

Net enrollment rate in primary education
Percentage of children of official primary school age who are enrolled in .
Source
UNESCO Institute for Statistics (2025); Lee and Lee (2016) – with major processing by Our World in Data
Last updated
July 17, 2023
Date range
1820–2024
Unit
%

Related research and writing

Featured image

Access to basic education: almost 60 million children of primary school age are not in school

Max Roser

Sources and processing

This data is based on the following sources

UNESCO Institute for Statistics – UNESCO Institute for Statistics (UIS) - Education

The UNESCO Institute for Statistics (UIS) is the official and trusted source of internationally-comparable data on education, science, culture and communication. As the official statistical agency of UNESCO, the UIS produces a wide range of state-of-the-art databases to fuel the policies and investments needed to transform lives and propel the world towards its development goals. The UIS provides free access to data for all UNESCO countries and regional groupings from 1970 to the most recent year available.

Retrieved on
May 1, 2025
Retrieved from
https://databrowser.uis.unesco.org/resources/bulk
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.
UNESCO Institute for Statistics (UIS), Education, https://uis.unesco.org/bdds, 2025

The UNESCO Institute for Statistics (UIS) is the official and trusted source of internationally-comparable data on education, science, culture and communication. As the official statistical agency of UNESCO, the UIS produces a wide range of state-of-the-art databases to fuel the policies and investments needed to transform lives and propel the world towards its development goals. The UIS provides free access to data for all UNESCO countries and regional groupings from 1970 to the most recent year available.

Retrieved on
May 1, 2025
Retrieved from
https://databrowser.uis.unesco.org/resources/bulk
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.
UNESCO Institute for Statistics (UIS), Education, https://uis.unesco.org/bdds, 2025

Lee and Lee – Human Capital in the Long Run

Datasets on estimated school enrollment ratios from 1820 to 2010 and estimated educational attainment for the total, female, and male populations from 1870 to 2010. The estimates are available in five-year intervals for 111 countries.

Datasets were last updated in 2021 September. The research provides insightful analysis on the progression and trends of educational attainment over a long historical period, offering a comprehensive understanding of educational developments globally.

Retrieved on
November 20, 2023
Retrieved from
https://barrolee.github.io/BarroLeeDataSet/DataLeeLee.html
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.
Lee, Jong-Wha and Hanol Lee, 2016, “Human Capital in the Long Run,” Journal of Development Economics, vol. 122, pp. 147-169.

Datasets on estimated school enrollment ratios from 1820 to 2010 and estimated educational attainment for the total, female, and male populations from 1870 to 2010. The estimates are available in five-year intervals for 111 countries.

Datasets were last updated in 2021 September. The research provides insightful analysis on the progression and trends of educational attainment over a long historical period, offering a comprehensive understanding of educational developments globally.

Retrieved on
November 20, 2023
Retrieved from
https://barrolee.github.io/BarroLeeDataSet/DataLeeLee.html
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.
Lee, Jong-Wha and Hanol Lee, 2016, “Human Capital in the Long Run,” Journal of Development Economics, vol. 122, pp. 147-169.

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

Historical data up to the year 1985 has been sourced from 'Human Capital in the Long Run' by Lee and Lee (2016). This historical data was then combined with recent estimates provided by the World Bank.

For the period before 1985, regional aggregates were computed by Our World in Data through yearly population-weighted averages, where annual values are proportionally adjusted to emphasize the influence of larger populations.

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: Net enrollment rate in primary education”, part of the following publication: Hannah Ritchie, Veronika Samborska, Esteban Ortiz-Ospina, and Max Roser (2023) - “Global Education”. Data adapted from UNESCO Institute for Statistics, Lee and Lee. Retrieved from https://archive.ourworldindata.org/20260304-094028/grapher/primary-enrollment-selected-countries.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:

UNESCO Institute for Statistics (2025); Lee and Lee (2016) – with major processing by Our World in Data

Full citation

UNESCO Institute for Statistics (2025); Lee and Lee (2016) – with major processing by Our World in Data. “Net enrollment rate in primary education” [dataset]. UNESCO Institute for Statistics, “UNESCO Institute for Statistics (UIS) - Education”; Lee and Lee, “Human Capital in the Long Run” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260304-094028/grapher/primary-enrollment-selected-countries.html (archived on March 4, 2026).

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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.

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

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

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