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Data

Wealth share of the richest 1%

WID
See all data and research on:
Economic Inequality

What you should know about this indicator

  • This measure is related to net national wealth, which is the total value of non-financial (housing, land) and financial assets (deposits, bonds, equities) held by households, minus their debts.
  • The data is constructed using the Mixed Income Capitalization-Survey (MICS) method, which combines capitalized income flows from tax data with survey-based estimates for assets that do not generate taxable income. This is done because wealth distributions are usually not directly available in administrative data, and because wealth surveys coverage is even more limited than for income.
  • Even with those considerations, the underlying data is not always available. For some countries, observations are extrapolated from data relating to other years, or are sometimes modeled based on data observed in other countries. For more information on this methodology, see this related technical note.
Wealth share of the richest 1%
WID
The share of wealth owned by the richest 1% of the population. Wealth is defined as the total value of non-financial and financial assets (housing, land, deposits, bonds, equities, etc.) held by households, minus their debts.
Source
World Inequality Database (WID.world) (2026) – with major processing by Our World in Data
Last updated
February 10, 2026
Next expected update
February 2027
Date range
1800–2024
Unit
%

Sources and processing

This data is based on the following sources

World Inequality Database (WID.world) – World Inequality Database (WID)

The World Inequality Database (WID.world) aims to provide open and convenient access to the most extensive available database on the historical evolution of the world distribution of income and wealth, both within countries and between countries.

Retrieved on
March 17, 2026
Retrieved from
https://wid.world
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 Inequality Database (WID), https://wid.world

The World Inequality Database (WID.world) aims to provide open and convenient access to the most extensive available database on the historical evolution of the world distribution of income and wealth, both within countries and between countries.

Retrieved on
March 17, 2026
Retrieved from
https://wid.world
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 Inequality Database (WID), https://wid.world

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
Notes on our processing step for this indicator

We extract estimations of Gini, mean, percentile thresholds, averages, and shares via the wid Stata command. We calculate threshold and share ratios by dividing different thresholds and shares, respectively.

Interpolations and extrapolations are excluded by using the option exclude in the Stata command.

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: Wealth share of the richest 1%”, part of the following publication: Joe Hasell, Bertha Rohenkohl, Pablo Arriagada, Esteban Ortiz-Ospina, and Max Roser (2023) - “Economic Inequality”. Data adapted from World Inequality Database (WID.world). Retrieved from https://archive.ourworldindata.org/20260317-123427/grapher/wealth-share-richest-1-percent.html [online resource] (archived on March 17, 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 Inequality Database (WID.world) (2026) – with major processing by Our World in Data

Full citation

World Inequality Database (WID.world) (2026) – with major processing by Our World in Data. “Wealth share of the richest 1% – WID” [dataset]. World Inequality Database (WID.world), “World Inequality Database (WID)” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260317-123427/grapher/wealth-share-richest-1-percent.html (archived on March 17, 2026).

Download

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

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

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