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Gender Inequality Index

This index captures gender-based disadvantage in reproductive health, empowerment and the labour market; ranges from 0 (full equality) to 1 (maximum inequality), representing the share of potential human development lost because of gender gaps.

Data source

UNDP

Last updated
2025-05-07
Next expected update
2026-07-12
Managed by
Lucas Rodés-Guirao and Veronika Samborska

  • The Gender Inequality Index (GII) reflects gender-based disadvantage in three dimensions— reproductive health, empowerment and the labour market—for as many countries as data of reasonable quality allow. It shows the loss in potential human development due to inequality between female and male achievements in these dimensions.

  • It ranges from 0, where women and men fare equally, to 1, where one gender fares as poorly as possible in all measured dimensions.

  • The GII highlights how unequal opportunities for women and men translate into foregone national human‑development potential.

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  • It combines five indicators: maternal mortality ratio, adolescent birth rate, women’s parliamentary seats, secondary‑education attainment, and labour‑force participation (all sex‑disaggregated).

  • Data is originally sourced from WHO/UNICEF/UNFPA/World Bank, UNDESA, IPU, UNESCO UIS, Barro-Lee, DHS/MICS and ILO datasets.

Data sources

UNDP, Human Development Report – Human Development Report

Artificial intelligence (AI) has broken into a dizzying gallop. While AI feats grab headlines, they privilege technology in a make-believe vacuum, obscuring what really matters: people's choices.

The choices that people have and can realize, within ever expanding freedoms, are essential to human development, whose goal is for people to live lives they value and have reason to value. A world with AI is flush with choices the exercise of which is both a matter of human development and a means to advance it.

Going forward, development depends less on what AI can do—not on how human-like it is perceived to be—and more on mobilizing people's imaginations to reshape economies and societies to make the most of it. Instead of trying vainly to predict what will happen, the 2025's Human Development Report asks what choices can be made so that new development pathways for all countries dot the horizon, helping everyone have a shot at thriving in a world with AI.

For more details, refer to https://hdr.undp.org/data-center/documentation-and-downloads

Retrieved on
May 7, 2025
Retrieved from
https://hdr.undp.org/
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. 
UNDP (United Nations Development Programme). 2025. Human Development Report 2025: A matter of choice: People and possibilities in the age of AI. New York.

Citations

How should I cite this data in a news article?

If 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 Data

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UNDP, Human Development Report (2025) – with minor processing by Our World in Data. “Gender Inequality Index – UNDP” [dataset]. UNDP, Human Development Report, “Human Development Report” [original data]. Retrieved June 12, 2026 from https://datapage-v2.owid.pages.dev/grapher/gender-inequality-index-from-the-human-development-report

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Data URL (CSV format)
https://datapage-v2.owid.pages.dev/grapher/gender-inequality-index-from-the-human-development-report.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://datapage-v2.owid.pages.dev/grapher/gender-inequality-index-from-the-human-development-report.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://datapage-v2.owid.pages.dev/grapher/gender-inequality-index-from-the-human-development-report.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://datapage-v2.owid.pages.dev/grapher/gender-inequality-index-from-the-human-development-report.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://datapage-v2.owid.pages.dev/grapher/gender-inequality-index-from-the-human-development-report.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://datapage-v2.owid.pages.dev/grapher/gender-inequality-index-from-the-human-development-report.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://datapage-v2.owid.pages.dev/grapher/gender-inequality-index-from-the-human-development-report.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://datapage-v2.owid.pages.dev/grapher/gender-inequality-index-from-the-human-development-report.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear