New (2018) interactive graphics of the impact of inequality
Researchers have known for some time that high economic inequality has a detrimental effect on peoples’ lives. However, with the release of new data we can now compare all of the richest countries of the world alongside the states of the USA. The results are shocking.
The countries shown here are all the countries of the world for which data on the “income share of the best-off 1%” has been available since the early 1980s. This allows time-lag effects to be investigated. For example, life expectancy is influenced by events that occur over many decades prior to death. The source of the economic inequality data used here is the World Income and Wealth Database.
Each circle below is drawn with its area in proportion to its population. If you click on any circle you can see the name of the country or state of the USA that it represents. Circles are positioned so that the level of inequality back in 1983 is shown by their horizontal position (% taken by the best-off 1%), and the life expectancy of all people in that area 32 years later, in 2015, is shown by their vertical positions (years of life). The relationship is far stronger when all 70 of these countries and US states are compared than can be seen when just the countries on their own, or states on their own, are compared. Those subsets are truncated distributions of the full effect of inequality. The circles below are coloured by the Worldmapper regional colours and the graphics were kindly prepared by Benjamin Hennig.
To explore the relationship between inequality as measured at different points in time and upon other measures of social harm go to the interactive charts page of the book ‘Do we need economic inequality‘
The many mechanisms by which high economic inequality is associated with worse national and state health outcomes are likely to be a large, complex and interacting set. There are many possible and plausible pathways that can be investigated. What is far simpler is to clearly see that there is a strong overall relationship.
Just as with smoking tobacco and lung cancer, we should not wait until we know the precise reasons why something is harmful, and why it has a dose-response relationship with harm, before we begin to curtail our acceptance of it.
For more information on how comparable data for these countries was collected and what is known about the effects of inequality see the Book itself, published in 2018