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Data

Average height of men by year of birth

What you should know about this indicator

Additional information about this data

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.

Source
Height (Baten and Blum 2015); Baten and Blum (2015) – processed by Our World in Data
Last updated
September 9, 2017
Date range
1550–2000
Unit
cm
Links
https://datasets.socialhistory.org/dataset.xhtml?persistentId=hdl:10622/IAEKLA

Sources and processing

This data is based on the following sources

Height (Baten and Blum 2015)

Data published by

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)

Retrieved on
September 9, 2017
Retrieved from
https://datasets.socialhistory.org/dataset.xhtml?persistentId=hdl:10622/IAEKLA

Baten and Blum – Height Gini

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.

Retrieved on
September 9, 2017
Retrieved from
https://datasets.socialhistory.org/dataset.xhtml?persistentId=hdl:10622/HKZPR9
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.
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).

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.

Retrieved on
September 9, 2017
Retrieved from
https://datasets.socialhistory.org/dataset.xhtml?persistentId=hdl:10622/HKZPR9
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.
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.

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: Average height of men by year of birth”. Our World in Data (2026). Data adapted from Baten and Blum. Retrieved from https://feature-bespoke-viz-migratio.owid.pages.dev:8789/20260511-092124/grapher/average-height-of-men-by-year-of-birth.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:

Height (Baten and Blum 2015); Baten and Blum (2015) – processed by Our World in Data

Full citation

Height (Baten and Blum 2015); Baten and Blum (2015) – processed by Our World in Data. “Average height of men by year of birth” [dataset]. Baten and Blum, “Height Gini” [original data]. Retrieved May 11, 2026 from https://feature-bespoke-viz-migratio.owid.pages.dev:8789/20260511-092124/grapher/average-height-of-men-by-year-of-birth.html (archived on May 11, 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.

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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://feature-bespoke-viz-migratio.owid.pages.dev/grapher/average-height-of-men-by-year-of-birth.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://feature-bespoke-viz-migratio.owid.pages.dev/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://feature-bespoke-viz-migratio.owid.pages.dev/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://feature-bespoke-viz-migratio.owid.pages.dev/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://feature-bespoke-viz-migratio.owid.pages.dev/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://feature-bespoke-viz-migratio.owid.pages.dev/grapher/average-height-of-men-by-year-of-birth.csv?v=1&csvType=full&useColumnShortNames=false")

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