Wealth share of the richest 1%
What you should know about this indicator
- Incomes are distributed very unequally, both between countries and within them. A large share of total income is concentrated among the very richest, making the top of the distribution important to understand. Read more in our topic page on Economic Inequality.
- This data shows the share of total income that goes to specific groups of the population. For example, if the richest decile receives 40% of total income, it means the richest 10% of people share 40% of all income between them.
- 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 comes from the World Inequality Database (WID), and it 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.
- For some countries and years, the underlying data sources are not available. In these cases, estimates are extrapolated from other years or modeled based on data from other countries, which introduces uncertainty. For more information, see the WID methodology documentation.
More Data on Economic Inequality
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 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.
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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: 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/20260501-095722/grapher/wealth-share-richest-1-percent.html [online resource] (archived on May 1, 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 DataFull 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 May 1, 2026 from https://archive.ourworldindata.org/20260501-095722/grapher/wealth-share-richest-1-percent.html (archived on May 1, 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/wealth-share-richest-1-percent.csv?v=1&csvType=full&useColumnShortNames=falseMetadata URL (JSON format)
https://ourworldindata.org/grapher/wealth-share-richest-1-percent.metadata.json?v=1&csvType=full&useColumnShortNames=falseExcel / 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