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Share of married women making their own health care decisions

See all data and research on:
Women's RightsGlobal Education

What you should know about this indicator

  • This measures the percentage of married women aged 15–49 who decide on their own health care.
  • Women are counted if they report being the main decision-maker.
  • Health care autonomy is important for women’s well-being and reproductive rights.
  • Greater decision-making power suggests stronger independence and bargaining power.
  • Limited autonomy may reflect cultural or household barriers to women’s control over health.

How is this data described by its producer?

Definition: Decision maker about women own health care: mainly wife is Percentage of currently married women aged 15-49 for whom the decision maker for their own health care is mainly the respondent

Share of married women making their own health care decisions
Share of married women aged 15–49 reporting that they themselves are the main decision-makers for their health care.
Source
DHS via World Bank – processed by Our World in Data
Last updated
September 8, 2025
Next expected update
September 2026
Date range
1999–2022
Unit
%

Sources and processing

This data is based on the following sources

World Bank Gender Statistics

The World Bank Gender Statistics dataset provides a comprehensive range of gender-related indicators grouped by various topics. These indicators are categorized under different themes such as education, employment and time use, entrepreneurship, environment, health, leadership, norms and decision-making, technology, violence, and contextual information. Each category contains numerous specific indicators, covering a wide range of issues such as literacy rates, employment by sector, legal rights, health statistics, and more. This dataset offers detailed information and insights into various aspects of gender disparity and equality across different regions and countries.

Retrieved on
September 8, 2025
Retrieved from
https://genderdata.worldbank.org/en/home
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.
World Bank Gender Statistics, World Bank, 2025. Licence: CC BY 4.0.

The World Bank Gender Statistics dataset provides a comprehensive range of gender-related indicators grouped by various topics. These indicators are categorized under different themes such as education, employment and time use, entrepreneurship, environment, health, leadership, norms and decision-making, technology, violence, and contextual information. Each category contains numerous specific indicators, covering a wide range of issues such as literacy rates, employment by sector, legal rights, health statistics, and more. This dataset offers detailed information and insights into various aspects of gender disparity and equality across different regions and countries.

Retrieved on
September 8, 2025
Retrieved from
https://genderdata.worldbank.org/en/home
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.
World Bank Gender Statistics, World Bank, 2025. Licence: CC BY 4.0.

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: Share of married women making their own health care decisions”, part of the following publication: Bastian Herre, Veronika Samborska, Pablo Arriagada, and Hannah Ritchie (2023) - “Women’s Rights”. Data adapted from World Bank Gender Statistics. Retrieved from https://archive.ourworldindata.org/20260304-094028/grapher/proportion-of-women-who-make-their-own-informed-health-care-decisions.html [online resource] (archived on March 4, 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:

DHS via World Bank – processed by Our World in Data

Full citation

DHS via World Bank – processed by Our World in Data. “Share of married women making their own health care decisions” [dataset]. World Bank Gender Statistics, “World Bank Gender Statistics” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260304-094028/grapher/proportion-of-women-who-make-their-own-informed-health-care-decisions.html (archived on March 4, 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/proportion-of-women-who-make-their-own-informed-health-care-decisions.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/proportion-of-women-who-make-their-own-informed-health-care-decisions.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/proportion-of-women-who-make-their-own-informed-health-care-decisions.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/proportion-of-women-who-make-their-own-informed-health-care-decisions.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/proportion-of-women-who-make-their-own-informed-health-care-decisions.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/proportion-of-women-who-make-their-own-informed-health-care-decisions.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/proportion-of-women-who-make-their-own-informed-health-care-decisions.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/proportion-of-women-who-make-their-own-informed-health-care-decisions.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear