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Data

Methodologies used for measuring literacy

About this data

Source
UNESCO Institute for Statistics (2017) – processed by Our World in Data
Last updated
February 20, 2018
Date range
1975–2016

Related research and writing

A dark blue background with a lighter blue world map superimposed over it. Yellow text that says Literacy by Our World in Data

Literacy

Max Roser and Esteban Ortiz-Ospina

How is literacy measured?

Esteban Ortiz-Ospina and Diana Beltekian

Sources and processing

This data is based on the following sources

UNESCO Institute for Statistics – UNESCO metadata on literacy

The UNESCO Institute for Statistics (UIS) collects cross-national education statistics. Data on literacy rates come from a variety of sources (such as national censuses and surveys) and reporting modes (self-reporting vs household declarations).

Data instruments used to estimate literacy are grouped under three main types: i) a census; ii) a survey; or iii) indirect estimates. Indirect estimates are extrapolated - based on educational attainment, or estimated based on the country's census.

Methodologies used for measuring literacy have been grouped into four categories: household declarations, self declarations, literacy tests, and national estimates. Both household and self-declared literacy are self-reports; in the former, it is the head of the household responding to the survey, and in the latter it is directly reported by the individuals themselves.

UNESCO categorised Chile's (2009) and the Philippines' (2003) mode of literacy reporting as 'Household/Self declaration'. We have categorised the two countries under the household declaration category.

Retrieved on
February 20, 2018
Retrieved from
http://data.uis.unesco.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.
UNESCO Institute for Statistics (UIS) (2017). UNESCO metadata on literacy.

The UNESCO Institute for Statistics (UIS) collects cross-national education statistics. Data on literacy rates come from a variety of sources (such as national censuses and surveys) and reporting modes (self-reporting vs household declarations).

Data instruments used to estimate literacy are grouped under three main types: i) a census; ii) a survey; or iii) indirect estimates. Indirect estimates are extrapolated - based on educational attainment, or estimated based on the country's census.

Methodologies used for measuring literacy have been grouped into four categories: household declarations, self declarations, literacy tests, and national estimates. Both household and self-declared literacy are self-reports; in the former, it is the head of the household responding to the survey, and in the latter it is directly reported by the individuals themselves.

UNESCO categorised Chile's (2009) and the Philippines' (2003) mode of literacy reporting as 'Household/Self declaration'. We have categorised the two countries under the household declaration category.

Retrieved on
February 20, 2018
Retrieved from
http://data.uis.unesco.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.
UNESCO Institute for Statistics (UIS) (2017). UNESCO metadata on literacy.

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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: Methodologies used for measuring literacy”. Our World in Data (2026). Data adapted from UNESCO Institute for Statistics. Retrieved from https://archive.ourworldindata.org/20260511-092124/grapher/mode-of-reporting-literacy-rates.html [online resource] (archived on May 11, 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 (2017) – processed by Our World in Data

Full citation

UNESCO Institute for Statistics (2017) – processed by Our World in Data. “Methodologies used for measuring literacy” [dataset]. UNESCO Institute for Statistics, “UNESCO metadata on literacy” [original data]. Retrieved May 14, 2026 from https://archive.ourworldindata.org/20260511-092124/grapher/mode-of-reporting-literacy-rates.html (archived on May 11, 2026).

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

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

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