Income inequality: Gini coefficient

The measures inequality on a scale from 0 to 1. Higher values indicate higher inequality.

Data source

World Bank

Last updated
2026-03-24
Next expected update
2026-09-20
Managed by
Pablo Rosado
  • Incomes are distributed very unequally, both between countries and within them. The Gini coefficient is a common measure of inequality, which summarizes the distribution and expresses it in terms of a number from 0 to 1. Higher values indicate higher inequality. We explain how it works in our article Measuring inequality: what is the Gini coefficient?, and discuss income inequality more broadly on our page on economic inequality.

  • Depending on the country and year, the data refers either to income (after taxes and benefits) or to consumption, . These are not perfectly comparable — consumption tends to be more evenly distributed than income. For most countries, we have only one option available. But when there is a mix of consumption and income data points, we process the data to keep one observation per country and year.

  • Many people, today and in the past, have no formal monetary income. This data accounts for that by including the estimated value of non-market income, such as food grown by subsistence farmers for their own use.

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  • The data comes from the World Bank's Poverty and Inequality Platform (PIP), which draws on national household surveys. Regional and global estimates are extrapolated to the year of the data release using GDP growth estimates and forecasts. For more details about the methodology, please refer to the World Bank PIP documentation.

Data sources

World Bank Poverty and Inequality Platform – World Bank Poverty and Inequality Platform (PIP)

The Poverty and Inequality Platform (PIP) is an interactive computational tool that offers users quick access to the World Bank’s estimates of poverty, inequality, and shared prosperity. PIP provides a comprehensive view of global, regional, and country-level trends for over 170 economies around the world.

Retrieved on
March 24, 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.
World Bank (2026). Poverty and Inequality Platform (version 20260324_2021 and 20260324_2017) [Data set]. World Bank Group. https://pip.worldbank.org/.

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:

World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data

How should I cite this in an academic article or report?

World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data. “Income inequality: Gini coefficient – World Bank” [dataset]. World Bank Poverty and Inequality Platform, “World Bank Poverty and Inequality Platform (PIP) 20260324_2021, 20260324_2017” [original data]. Retrieved June 12, 2026 from https://datapage-v2.owid.pages.dev/grapher/economic-inequality-gini-index

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What are international-$ and why are they used to measure incomes?

Much of the economic data we use to understand the world, such as the incomes people receive or the goods and services firms produce and people buy, is recorded in the local currencies of each country. That means the numbers start out in rupees, US dollars, yuan, and many others, and without adjusting for inflation over time. This is known as being in “current prices” or “nominal” terms.

Before these figures can be meaningfully compared, they need to be converted into common units. International dollars (int.-$) are a hypothetical currency that is used for this.

The idea is simple: one international dollar should buy the same quantity and quality of goods and services, no matter where or when it is spent. To achieve this, international dollars adjust for two things. First, they account for inflation within each country, so that values from different years can be compared (showing “constant” prices). Second, they account for differences in living costs across countries. This second adjustment uses purchasing power parity (PPP) rates, which reflect how much local currency is needed to buy what one US dollar would buy in the United States.

The United States is the benchmark, so that one 2021 int.-$ is defined as the value of goods and services that one US dollar would buy in the US in 2021. One 2011 int.-$ is defined in the same way, but for prices in 2011.

You can read more in our article, What are international dollars?

How comparable is the World Bank data on household incomes across time or between countries?

There is no single global survey of incomes. What we have instead are national surveys, each designed by a different statistical agency, using different methodologies. The World Bank collects and harmonizes this data, but important comparability issues remain.

One key issue is that high-income countries typically measure people's incomes, while lower-income countries more often measure consumption expenditure — what households spend on goods and services. Pooling both types of survey is unavoidable if we want a global picture of inequality, but it means that somewhat different things are being measured depending on the country or year.

The two concepts are closely related: the income of a household equals its consumption plus savings.

At the bottom end of the income distribution, people’s consumption may be somewhat higher than their income. While zero consumption is not a feasible value — people must consume something to survive — a zero income is a feasible value. A common example is retired people drawing down their savings: they may have a very low, or even zero, income, but still have a high level of consumption.

At the top end of the distribution, consumption is typically lower than income. The gap rises with income, with households generally saving a higher share of their income the richer they are.

For both reasons, the distribution of consumption is generally more equal than the distribution of income. This means that inequality estimates tend to be somewhat lower when based on consumption surveys.

There are other comparability issues too — differences in survey design, coverage, and methodology. The PIP Methodology Handbook provides a good summary of the comparability and data quality issues affecting this data and how it tries to address them.

To help readers see where comparisons may be less reliable, the World Bank groups data points within each country into "spells" — periods where the underlying surveys are considered more comparable. Where available, you can reveal these breaks in our charts using the "breaks in data" option.

How does the World Bank produce global and regional estimates of poverty and inequality from national data?

For its poverty and inequality data, the World Bank relies on household surveys that are conducted nationally. In order to produce global or regional estimates, the survey data from different countries is “lined up” and aggregated. For each year, the World Bank finds the most recent survey for each country and projects the data forward (or backward) to the year being estimated. This is necessary, particularly since surveys are less frequently available in poorer countries and for earlier decades.

These projections are generally based on the assumption that incomes or expenditure grow in line with the growth rates observed in national accounts data. You can read more about the interpolation methods used by the World Bank in Chapter 5 of the Poverty and Inequality Platform Methodology Handbook.

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://datapage-v2.owid.pages.dev/grapher/economic-inequality-gini-index.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://datapage-v2.owid.pages.dev/grapher/economic-inequality-gini-index.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/economic-inequality-gini-index.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/economic-inequality-gini-index.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/economic-inequality-gini-index.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/economic-inequality-gini-index.csv?v=1&csvType=full&useColumnShortNames=false")

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