Child mortality rate: definition and caveats
What it measures
Long-run estimated share of newborns who die before reaching the age of five. Unit: deaths per 100 live births.
Key points from Our World in Data
- Child mortality, the death of children under the age of five, is still extremely common in our world today. The historical data makes clear that it doesn’t have to be this way: societies can protect their children and reduce child mortality to very low rates. For child mortality to reach low levels, many things have to go right at the same time: good healthcare, good nutrition, clean water and sanitation, maternal health, and high living standards. We can, therefore, think of child mortality as a proxy indicator of a country’s living conditions.
- It combines data from two sources: Gapminder and the UN Inter-agency Group for Child Mortality Estimation (UN IGME).
- Gapminder provides estimates of child mortality rates from 1800 to 2015. The full list of sources used can be found in their documentation.
- UN IGME provides estimates of child mortality rates for some countries from 1932 onward.
- For years where data from both sources is available, we prioritize the UN IGME data. See this page for more details on which source is used for each data point.
- This indicator is calculated as the number of children under the age of five who died in a given year, divided by the number of newborns in that year.
How Our World in Data processed it
This indicator is a combination of data from two sources:
- Gapminder, which provides estimates of child mortality rates for the years 1800 to 2015.
- The UN Inter-agency Group for Child Mortality Estimation (UN IGME) provides estimates of child mortality rates, for some countries from 1932 onward.
For years where data from both sources is available, we prioritize the UN IGME data. See this page for more details on which source is used for each data point.
In the Gapminder dataset we remove rows where the source is labeled as "Guesstimate" or "Model based on Life Expectancy" to try and ensure we use the best available data.
We remove data for Austria before 1830 from the Gapminder dataset, as there is a jump in 1830 that is likely an error.