African ancestry vs. Infant mortality

Holds up after adjusting for income

Across 156 countries, African ancestry and Infant mortality rise and fall together at r = +0.81 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.56 (q = 0.015, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a moderate positive link remains.

Comparing countries across the middle half of african ancestry, 0% to 96.4% of population, infant mortality is worse by 31.3 per 1,000 live births at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 16.7 per 1,000 live births.

How to read these.r runs from −1 (one goes up as the other goes down) to +1 (they rise together), and 0 means no link. Adjusting for national income means comparing countries with the same GDP per person, so differences in wealth can’t explain the link. Income is used in percentage terms, since an extra $1,000 means more to a poor country than to a rich one. p is how likely a link this strong would be if there were really none. Below 0.05 means it is unlikely to be chance. Very small values are written in short form: 1.1e-32 means a decimal point followed by 31 zeros and then 11, so very close to zero. The likely range is where the true figure probably lies, and a range that crosses zero means the link could easily be chance. Countries in the same region tend to resemble each other, so the third figure allows for that, which gives a fairer p. The standard error is the typical size of the error in r. Finally, testing this many pairs means some will look strong by luck alone. A q below 0.05 means this link is unlikely to be one of those.

Every country as a dot

Four ways to measure the link

The rank version uses only each country’s place in line, from lowest to highest, so a few extreme countries count for less. The adjusted and unadjusted versions use the same 156 countries: those that report both measures and GDP per person. So the gap between them comes from the adjustment, not from a change in which countries are counted. Drop in the size of r after adjusting: +0.246 (a minus sign means it grew).

How solid is it?

Two series, one country

Both measures over the years for one country you pick. The two charts are stacked so they share only the years along the bottom.

Loading…

Over time, within countries

Everything above compares countries with each other at one moment. This section compares each country only with itself over 0years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

Not enough history inside each country to look at this over time. That needs countries measured for several years in a row, and at least one of these measures is measured too rarely.

The data

Sources

How this was computed

Data used: most recent value in 2010-2025; values come from 2000–2024. To adjust for income, each measure is first predicted from GDP per person (in percentage terms, on a log scale) with a straight-line fit. Only what is left over is then compared. This is called a partial correlation. The line is drawn the standard way (ordinary least squares), which picks the line that comes closest to all the countries at once. The error margins allow for countries in the same region resembling each other, using 7 World Bank regions. The q-value corrects for testing all 13,797 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore African ancestry and Infant mortality

This page shows that two measures go together. It does not show that African ancestry changes Infant mortality, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with African ancestry or Infant mortality

• African ancestry vs Basic sanitation before -0.86 → after adjusting -0.68
• Infant mortality vs Maternal mortality before +0.85 → after adjusting +0.68
• Infant mortality vs Life expectancy before -0.89 → after adjusting -0.66
• African ancestry vs Market-income Gini before +0.69 → after adjusting +0.61
• African ancestry vs Electricity access before -0.82 → after adjusting -0.60
• HDI vs Infant mortality before -0.87 → after adjusting -0.58
• African ancestry vs Under-5 mortality before +0.81 → after adjusting +0.57
• Birth rate vs Infant mortality before +0.86 → after adjusting +0.56