What you should know about this indicator
- The Gini coefficient is a measure of inequality that ranges between 0 and 1, where higher values indicate higher inequality.
- Depending on the country and year, the data relates to either disposable income or consumption per capita.
- Non-market sources of income, including food grown by subsistence farmers for their own consumption, are taken into account
Related research and writing
Frequently Asked Questions
Because there is no global survey of incomes, researchers need to rely on available national surveys. Such surveys are designed with cross-country comparability in mind, but because the surveys reflect the circumstances and priorities of individual countries at the time of the survey, there are some important differences. In collating this survey data the World Bank takes steps to harmonize it where possible, but comparability issues remain.
One important issue is that, whilst in most high-income countries the surveys capture people’s incomes, in poorer countries these surveys tend to capture people’s consumption.
Pooling the data available from different kinds of survey data is unavoidable if we want to get a global picture of poverty or inequality. But it’s important to bear in mind that, depending on the country or year, somewhat different things are being measured.
The two concepts are nevertheless closely related: the income of a household equals their consumption plus any saving, or minus any borrowing or spending out of savings.
One important difference is that, while zero consumption is not a feasible value – people must consume something to survive – a zero income is a feasible value. At the bottom end of the distribution, people’s consumption may be somewhat higher than their income. A common example here is retired people who are using their savings: they may have a very low, or even zero, income, but still have a high level of consumption.
Conversely, 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 these reasons, the distribution of consumption is generally more equal than the distribution of income.
There are a number of other ways in which comparability across surveys can be limited. In collating this survey data the World Bank takes a range of steps to harmonize it where possible, but comparability issues remain. 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 communicate this limitation of the data, the World Bank produces a companion indicator that groups data points within each individual country into ‘spells’. The surveys underlying the data within a given spell for a particular country are considered by World Bank researchers to be more comparable. The breaks between these comparable spells are shown in the chart below for the share of population living in extreme poverty. You can select to see these breaks for any indicator in our Data Explorer of the World Bank data. These spells are also indicated in our data download of the World Bank poverty and inequality data.
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
For a small number of country-year observations, the World Bank PIP data contains two estimates: one based on income data and one based on consumption data. In these cases we keep only the consumption estimate in order to obtain a single series for each country.
You can find the data with all available income and consumption data points, including these overlapping estimates, in this dataset.
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