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Data

Average height of men by year of birth

About this data

Average height of men by year of birth
Heights by birth decade and country, both before and after 1800.
Source
Various sources (2015) – processed by Our World in Data
Last updated
September 9, 2017
Date range
1550–2000
Unit
cm

Sources and processing

This data is based on the following sources

Various sources – Clio-Infra - Biological Standards of Living

Clio-Infra is a collaborative project that provides historical data on global economic development. The Biological Standards of Living theme brings together datasets on human heights and anthropometric inequality, reconstructed by birth decade and country from a variety of historical and scholarly sources.

This snapshot is an Our World in Data compilation of two Clio-Infra datasets curated by Baten and Blum (2015) on biological standards of living:

Additional information: Methodologies used for data collection and processing. Reconstruction of heights by birth decade using a variety of different sources. Please see link for the complete list.

Additional information: Methodologies used for data collection and processing. Reconstruction of height ginis by birth decade using a variety of different sources. The height gini is a transformation of the coefficient of height inequality, based on Moradi and Baten's formula: g htgini=-33.5 + 20.5*cv.

Data quality: As good as possible, but counter-checking and improvement welcome. Interpretations on individual country level should be done with careful checking. An important caveat is many individual countries cannot be measured without a substantial amount of measurement error. The tendencies of anthropometric inequality by world region (in which country-specific measurement error tends to average out) is probably informative.

Publisher source: Data collated from a host of academic papers. Please refer to the link for the complete list.

Retrieved on
September 9, 2017
Retrieved from
https://clio-infra.eu/
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.
Clio-Infra. (2015). Biological Standards of Living datasets curated by Baten and Blum (2015). International Institute of Social History. Underlying publications:
For the post-1800 period: Baten, Joerg and Matthias Blum, "Growing Taller, but Unequal: Biological Well-Being in World Regions and Its Determinants, 1810-1989." Economic History of Developing Regions (2012).
For the pre-1800 period: Koepke, Nikola and Baten, Joerg "The Biological Standard of Living in Europe During the Last Two Millennia," in European Review of Economic History 9-1 (2005).
Jörg Baten and Matthias Blum, "Anthropometric within-country Inequality and the Estimation of Skill Premia with Anthropometric Indicators", Review of Economics -- Jahrbuch fuer Wirtschaftswissenschaften (2011).

Clio-Infra is a collaborative project that provides historical data on global economic development. The Biological Standards of Living theme brings together datasets on human heights and anthropometric inequality, reconstructed by birth decade and country from a variety of historical and scholarly sources.

This snapshot is an Our World in Data compilation of two Clio-Infra datasets curated by Baten and Blum (2015) on biological standards of living:

Additional information: Methodologies used for data collection and processing. Reconstruction of heights by birth decade using a variety of different sources. Please see link for the complete list.

Additional information: Methodologies used for data collection and processing. Reconstruction of height ginis by birth decade using a variety of different sources. The height gini is a transformation of the coefficient of height inequality, based on Moradi and Baten's formula: g htgini=-33.5 + 20.5*cv.

Data quality: As good as possible, but counter-checking and improvement welcome. Interpretations on individual country level should be done with careful checking. An important caveat is many individual countries cannot be measured without a substantial amount of measurement error. The tendencies of anthropometric inequality by world region (in which country-specific measurement error tends to average out) is probably informative.

Publisher source: Data collated from a host of academic papers. Please refer to the link for the complete list.

Retrieved on
September 9, 2017
Retrieved from
https://clio-infra.eu/
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.
Clio-Infra. (2015). Biological Standards of Living datasets curated by Baten and Blum (2015). International Institute of Social History. Underlying publications:
For the post-1800 period: Baten, Joerg and Matthias Blum, "Growing Taller, but Unequal: Biological Well-Being in World Regions and Its Determinants, 1810-1989." Economic History of Developing Regions (2012).
For the pre-1800 period: Koepke, Nikola and Baten, Joerg "The Biological Standard of Living in Europe During the Last Two Millennia," in European Review of Economic History 9-1 (2005).
Jörg Baten and Matthias Blum, "Anthropometric within-country Inequality and the Estimation of Skill Premia with Anthropometric Indicators", Review of Economics -- Jahrbuch fuer Wirtschaftswissenschaften (2011).

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: Average height of men by year of birth”. Our World in Data (2026). Data adapted from Various sources. Retrieved from https://archive.ourworldindata.org/20260512-000143/grapher/average-height-of-men-by-year-of-birth.html [online resource] (archived on May 12, 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:

Various sources (2015) – processed by Our World in Data

Full citation

Various sources (2015) – processed by Our World in Data. “Average height of men by year of birth” [dataset]. Various sources, “Clio-Infra - Biological Standards of Living” [original data]. Retrieved May 12, 2026 from https://archive.ourworldindata.org/20260512-000143/grapher/average-height-of-men-by-year-of-birth.html (archived on May 12, 2026).

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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/average-height-of-men-by-year-of-birth.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/average-height-of-men-by-year-of-birth.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/average-height-of-men-by-year-of-birth.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/average-height-of-men-by-year-of-birth.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/average-height-of-men-by-year-of-birth.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/average-height-of-men-by-year-of-birth.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/average-height-of-men-by-year-of-birth.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/average-height-of-men-by-year-of-birth.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear