While the steep rise of inequality in the United States is well-known, long-run data on the incomes of the richest shows countries have followed a variety of trajectories. Here are four articles, written by Joe Hasell for Our World in Data, that explore a wide range of indicators on inequality and poverty and compare sources.
Thanks to a growing body of research on the incomes of the richest, we now have a much better understanding of the long-run evolution of income inequality.
This area of research has developed methods for estimating income inequality based on income tax records, or summary tables released by tax authorities. This is often available over much longer time horizons than household surveys, the most common data source for inequality researchers. For many countries, tax data can provide us with a view of the evolution of inequality over the past 100 years or more. It can also provide a more accurate view of the incomes of the richest, which are often poorly captured in survey data, as discussed further in a note at the end of this article. Read more here:
When interpreting data on inequality, it’s important to be clear about what is being measured: inequality of what?1
Usually, it is the inequality of incomes, but even here, there are different concepts to be aware of. The two definitions of income most commonly used are:
- Incomes counted before people have paid taxes and received any benefits from the government;
- Incomes counted after such transfers.
Unsurprisingly, the level of inequality when measured before and after tax can differ substantially. The difference reflects the extent of redistribution achieved through a country’s tax and benefits system.2 Read more here:
The Gini coefficient, or Gini index, is the most commonly used measure of inequality. It was developed by Italian statistician Corrado Gini (1884–1965) and is named after him.
It is typically used as a measure of income inequality, but it can be used to measure the inequality of any distribution – such as the distribution of wealth, or even life expectancy.1
It measures inequality on a scale from 0 to 1, where higher values indicate higher inequality. This can sometimes be shown as a percentage from 0 to 100%, this is then called the ‘Gini Index’.
At Our World in Data, we are building an extensive dataset of inequality and poverty indicators, pulling together multiple sources to provide as comprehensive a view as possible.
To make it easier to navigate this wide range of data, below we provide links to a set of Data Explorers that allow you to explore a very detailed range of indicators and compare data across sources. The explorers draw from three prominent sources, each offering a global perspective on poverty and inequality: the World Bank Poverty and Inequality Platform, the Luxembourg Income Study, and the World Inequality Database. Read more here:
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