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Data

Childhood stunting rates

See all data and research on:
Child & Infant MortalityHunger & Undernourishment

What you should know about this indicator

  • Stunting rates are an important indicator of child health and nutrition. High rates can reflect poor nutrition and frequent exposure to disease or illness, which increase a child’s nutrient requirements and affect their ability to retain nutrients. This can hinder physical and cognitive development, and can persist throughout someone’s life.
  • Children are considered stunted if their height-for-age is more than two standard deviations below the median of the World Health Organization (WHO) child growth standards.
  • This data comes from an article by Scheider et al. (2026) that compiles historical studies and the UN Joint Malnutrition Estimates (JME) database to provide a long-term perspective on child stunting rates.
  • Schneider et al. compile stunting rates among children between 2 and 10 years old. This data is therefore not directly comparable to more recent datasets (such as the UN JME database) which provide stunting rates for children under 5 years old.
  • This chart includes only data points from high-quality studies e.g. studies that are nationally representative and have larger sample sizes. The full dataset includes additional data points and is available via the original paper here
  • The year shown is the birth decade of the children in each study, plotted at the midpoint of that decade. So the 1955 data point includes children born between 1950 and 1959.
Childhood stunting rates
Share of children between 2 and 10 years old who are , so shorter than would be expected for their age. Stunting is a sign of long-term malnutrition or poor health in early childhood.
Source
Schneider et al. (2026) – with minor processing by Our World in Data
Last updated
March 27, 2026
Date range
1895–2015
Unit
%

Sources and processing

This data is based on the following sources

Schneider et al. – Worldwide Historical Child Stunting Dataset

This dataset presents child stunting rates from the systematic review of historical child growth studies performed by Schneider et al. (2026). "The Decline of Child Stunting in 122 Countries: A Systematic Review of Child Growth Studies Since the Nineteenth Century".

The article is a systematic review of 923 child growth studies covering 122 countries from 1814 to 2016, reconstructing historical rates of child stunting before 1990.

Retrieved on
March 27, 2026
Retrieved from
https://zenodo.org/records/18262234
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.
Schneider EB, Jaramillo Echeverri J, Purcell M, A'Hearn B, Arthi V, Blum M, et al.
The decline of child stunting in 122 countries: a systematic review of child growth studies
since the 19th century. BMJ Global Health. 2026;11:e018607.
https://doi.org/10.1136/bmjgh-2024-018607, Data available at https://zenodo.org/records/18262234

This dataset presents child stunting rates from the systematic review of historical child growth studies performed by Schneider et al. (2026). "The Decline of Child Stunting in 122 Countries: A Systematic Review of Child Growth Studies Since the Nineteenth Century".

The article is a systematic review of 923 child growth studies covering 122 countries from 1814 to 2016, reconstructing historical rates of child stunting before 1990.

Retrieved on
March 27, 2026
Retrieved from
https://zenodo.org/records/18262234
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.
Schneider EB, Jaramillo Echeverri J, Purcell M, A'Hearn B, Arthi V, Blum M, et al.
The decline of child stunting in 122 countries: a systematic review of child growth studies
since the 19th century. BMJ Global Health. 2026;11:e018607.
https://doi.org/10.1136/bmjgh-2024-018607, Data available at https://zenodo.org/records/18262234

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

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: Childhood stunting rates”, part of the following publication: Saloni Dattani, Fiona Spooner, Hannah Ritchie, and Max Roser (2023) - “Child and Infant Mortality”. Data adapted from Schneider et al.. Retrieved from https://archive.ourworldindata.org/20260430-112638/grapher/long-run-childhood-stunting-rates.html [online resource] (archived on April 30, 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:

Schneider et al. (2026) – with minor processing by Our World in Data

Full citation

Schneider et al. (2026) – with minor processing by Our World in Data. “Childhood stunting rates” [dataset]. Schneider et al., “Worldwide Historical Child Stunting Dataset” [original data]. Retrieved April 30, 2026 from https://archive.ourworldindata.org/20260430-112638/grapher/long-run-childhood-stunting-rates.html (archived on April 30, 2026).

Download

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

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

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