Gender Development Index

What you should know about this indicator
- Compares female and male achievements in health, education and command over economic resources by taking the ratio of the female HDI to the male HDI; a value of 1 denotes gender parity, while values below (above) 1 indicate disadvantage for women (men).
- It highlights how much human development potential is lost to gender disparities and guides gender‑responsive budgeting.
- The Gender Development Index (GDI) measures gender inequalities in achievement in three basic dimensions of human development: health, measured by female and male life expectancy at birth; education, measured by female and male expected years of schooling for children and female and male mean years of schooling for adults ages 25 years and older; and command over economic resources, measured by female and male estimated earned income.
- Values below 1 indicate higher human development for men than women, while values above 1 indicate the opposite. Values close to 1 therefore indicate higher gender equality.
- Interpretable scale: an absolute deviation of, say, 5% means women's HDI is 95 % of men's (or vice-versa).
- Data is originally sourced from UNDESA life tables, DHS/MICS & UIS education surveys, ILO labour data, IMF & World Bank income statistics and HDRO wage-ratio estimates.
Related research and writing
Sources and processing
This data is based on the following sources
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.
Notes on our processing step for this indicator
We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area.
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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: Gender Development Index”. Our World in Data (2026). Data adapted from UNDP, Human Development Report. Retrieved from https://archive.ourworldindata.org/20260325-171315/grapher/gender-development-index.html [online resource] (archived on March 25, 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:
UNDP, Human Development Report (2025) – with minor processing by Our World in DataFull citation
UNDP, Human Development Report (2025) – with minor processing by Our World in Data. “Gender Development Index – UNDP” [dataset]. UNDP, Human Development Report, “Human Development Report” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260325-171315/grapher/gender-development-index.html (archived on March 25, 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.
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/gender-development-index.csv?v=1&csvType=full&useColumnShortNames=falseMetadata URL (JSON format)
https://ourworldindata.org/grapher/gender-development-index.metadata.json?v=1&csvType=full&useColumnShortNames=falseExcel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/gender-development-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/gender-development-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/gender-development-index.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()R
library(jsonlite)
# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/gender-development-index.csv?v=1&csvType=full&useColumnShortNames=false")
# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/gender-development-index.metadata.json?v=1&csvType=full&useColumnShortNames=false")Stata
import delimited "https://ourworldindata.org/grapher/gender-development-index.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear
