What you should know about this indicator

How is this data described by its producer?

Definition: Percentage of firms with females as the top manager.

Aggregation method: Unweighted average

General comments: Statistical concept(s): The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.

To achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.

The Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.

Enterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.

Stratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, “rest of manufacturing”; retail and “rest of services” provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.

Share of firms with a female top manager
“Top manager” refers to the highest-ranking manager or chief executive officer of the organization. This may be the owner if she works as the manager of the firm.
Source
World Bank – processed by Our World in Data
Last updated
November 6, 2026
Next expected update
November 2027
Date range
2007–2025
Unit
% of firms

Sources and processing

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
November 6, 2026
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, 2026. 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
November 6, 2026
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, 2026. Licence: CC BY 4.0.

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

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 firms with a female top manager”, 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/20261106-103225/grapher/share-firms-top-female-manager.html [online resource] (archived on November 6, 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:

World Bank – processed by Our World in Data

Full citation

World Bank – processed by Our World in Data. “Share of firms with a female top manager” [dataset]. World Bank Gender Statistics, “World Bank Gender Statistics” [original data]. Retrieved November 15, 2026 from https://archive.ourworldindata.org/20261106-103225/grapher/share-firms-top-female-manager.html (archived on November 6, 2026).

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/share-firms-top-female-manager.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/share-firms-top-female-manager.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/share-firms-top-female-manager.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/share-firms-top-female-manager.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/share-firms-top-female-manager.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/share-firms-top-female-manager.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/share-firms-top-female-manager.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/share-firms-top-female-manager.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear