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Data

Historical Gender Equality Index

About this data

Source
How Was Life? (2014) – processed by Our World in Data
Last updated
March 21, 2018
Date range
1950–2000

Sources and processing

This data is based on the following sources

How Was Life? – Historical Gender Equality Index

The HGI is constructed by following Hausmann et al. (2012) who created the Global Gender Gap index (GGG). The composite index includes the gender differences in four dimensions, health, socio-economic resources, household and politics. Health is measured by life expectancy and sex ratios whereas socio-economic resources are captured by average years of education and labour force participation. The gender disparities in the household are captured by the marriage ages and the data on distribution of parliamentary seats between men and women is used as an indication of gender disparities in the politics. Each of these variables is presented in female/male ratio. Before creating the composite index, values above 1 were truncated to be 1 except for sex ratio where the equality benchmark is set to be 0.944. For health and socio-economic resources, we have two indicators capturing these dimensions. We have given a weight to each of these indicators, so that the variable with higher standard deviation would not get a higher weight in the sub-index. Thus we normalize the variables in each sub-index by first determining what a 1% point change would translate into in the standard deviations (calculated by dividing .01 by the standard deviation of each variable), then determining the weight to each variable. As a final step, the total of the four sub-indexes was taken, divided by four and multiplied by 100 for the ease of interpretation. A higher score in our index thus highlights less gender inequality in favour of women. A more detailed discussion of the composite index is provided in Dilli et al. (2014).

Note: this is an expanded version compared to the one released in 2014. In line with the procedure required for the How was life report (Carmichael et al. 2014), only decennial averages for the 25 clio-infra countries were reported. This dataset contains all our observations.

Retrieved on
March 21, 2018
Retrieved from
https://dspace.library.uu.nl/bitstream/handle/1874/306236/3014041ec016.pdf?sequence=1
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.
How Was Life? - Gender inequality since 1820 (2014).

The HGI is constructed by following Hausmann et al. (2012) who created the Global Gender Gap index (GGG). The composite index includes the gender differences in four dimensions, health, socio-economic resources, household and politics. Health is measured by life expectancy and sex ratios whereas socio-economic resources are captured by average years of education and labour force participation. The gender disparities in the household are captured by the marriage ages and the data on distribution of parliamentary seats between men and women is used as an indication of gender disparities in the politics. Each of these variables is presented in female/male ratio. Before creating the composite index, values above 1 were truncated to be 1 except for sex ratio where the equality benchmark is set to be 0.944. For health and socio-economic resources, we have two indicators capturing these dimensions. We have given a weight to each of these indicators, so that the variable with higher standard deviation would not get a higher weight in the sub-index. Thus we normalize the variables in each sub-index by first determining what a 1% point change would translate into in the standard deviations (calculated by dividing .01 by the standard deviation of each variable), then determining the weight to each variable. As a final step, the total of the four sub-indexes was taken, divided by four and multiplied by 100 for the ease of interpretation. A higher score in our index thus highlights less gender inequality in favour of women. A more detailed discussion of the composite index is provided in Dilli et al. (2014).

Note: this is an expanded version compared to the one released in 2014. In line with the procedure required for the How was life report (Carmichael et al. 2014), only decennial averages for the 25 clio-infra countries were reported. This dataset contains all our observations.

Retrieved on
March 21, 2018
Retrieved from
https://dspace.library.uu.nl/bitstream/handle/1874/306236/3014041ec016.pdf?sequence=1
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.
How Was Life? - Gender inequality since 1820 (2014).

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: Historical Gender Equality Index”. Our World in Data (2026). Data adapted from How Was Life?. Retrieved from https://archive.ourworldindata.org/20260511-092124/grapher/regional-averages-of-the-composite-gender-equality-index.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:

How Was Life? (2014) – processed by Our World in Data

Full citation

How Was Life? (2014) – processed by Our World in Data. “Historical Gender Equality Index” [dataset]. How Was Life?, “Historical Gender Equality Index” [original data]. Retrieved May 13, 2026 from https://archive.ourworldindata.org/20260511-092124/grapher/regional-averages-of-the-composite-gender-equality-index.html (archived on May 11, 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/regional-averages-of-the-composite-gender-equality-index.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/regional-averages-of-the-composite-gender-equality-index.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/regional-averages-of-the-composite-gender-equality-index.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/regional-averages-of-the-composite-gender-equality-index.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/regional-averages-of-the-composite-gender-equality-index.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/regional-averages-of-the-composite-gender-equality-index.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/regional-averages-of-the-composite-gender-equality-index.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/regional-averages-of-the-composite-gender-equality-index.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear