Across 183 countries, Infant mortality and Road traffic deaths rise and fall together at r = +0.67 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.32 (q = 0.021, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a weak positive link remains. Caution: direction is not consistent across regions.
Comparing countries across the middle half of infant mortality, 4.7 to 29.1 per 1,000 live births, road traffic deaths is worse by 10.3 per 100,000 population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 6.33 per 100,000 population.
Link before adjusting (r)+0.669p = 3.9e-25 · 183 countries
After adjusting for national income+0.320p = 1.1e-5 · likely range [+0.18, +0.44]
p, allowing for regions0.001standard error 0.054 · 7 regions
Check for luck (q)0.021across 12,497 pairs
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.
Infant mortality · per 1,000 live birthslink with income -0.86 · proxy
Road traffic deaths · per 100,000 populationlink with income -0.71 · high
Both of these rise and fall closely with national income, so a strong link between them before adjusting was close to certain. Read the adjusted figure, not the first one.
“Link with income” is how closely each measure tracks GDP per person across countries, from −1 to +1.
Every country as a dot
best-fit line line after adjusting
Four ways to measure the link
Method
r
p
Countries
Standard (Pearson), not adjusted
+0.669
3.9e-25
183
By rank (Spearman), not adjusted
+0.725
3.9e-31
183
Standard (Pearson), adjusted for national income
+0.320
1.1e-5
183
By rank (Spearman), adjusted for national income
+0.317
1.3e-5
183
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 183 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: +0.349 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from +0.291 (without ZWE) to +0.342 (without TON). The bar above shows that range on a scale from −1 to +1, with the solid line at the full result. The single country that moves r most: ZWE (-0.029).
Fragile: this link may not hold up.
direction is not consistent across regions
Within each region
Countries are not separate, unrelated cases, and neighbors share a lot. A link that holds inside each region is far more believable than one that shows up only when comparing regions.
n
r
East Asia & Pacific
27
-0.36
Europe & Central Asia
50
+0.13
Latin America & Caribbean
30
+0.37
Middle East, North Africa, Afghanistan & Pakistan
22
+0.04
Sub-Saharan Africa
46
+0.22
By income third
If the link shows up only in the richest third of countries, it is a feature of rich countries, not a general pattern.
n
r
Poorest third
61
+0.36
Middle third
61
+0.37
Richest third
61
+0.43
By largest religious group
Countries grouped by a label they share, such as main religion, rather than by a number. Groups of fewer than twelve countries are not shown. These groups differ in income, region and history all at once. So a gap between them is a place to start asking questions, not an answer.
n
r
Christians
108
+0.47
Muslims
49
+0.29
By last european coloniser
Countries grouped by a label they share, such as main religion, rather than by a number. Groups of fewer than twelve countries are not shown. These groups differ in income, region and history all at once. So a gap between them is a place to start asking questions, not an answer.
n
r
France
25
+0.38
Never a European colony
21
+0.24
Spain
20
+0.51
United Kingdom
60
+0.30
Without outliers (countries far outside the middle half of countries on either measure, by more than one and a half times the width of that middle half): r = +0.321 after dropping 2 countries.
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.
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Over time, within countries
Everything above compares countries with each other at one moment. This section compares each country only with itself over 20years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.
Within countries over time, there is no link that is clearly more than chance (r = -0.134, comparing each country only with itself over the years, and each year only with the same year elsewhere). Whatever the cross-country picture shows, countries whose infant mortality moved did not tend to see their road traffic deaths move with it.
Across countries (adjusted)+0.320for comparison
Within countries+0.065each country vs itself
…and vs the same year elsewhere-0.134q = 0.068 · 183 countries
Year-on-year changes+0.003q = 0.847
3,660 country-years (one country in one year), 183 countries, 2000–2019, about 20 years each on average. Share of the variation that happens within countries over time: 11% for Infant mortality, 13% for Road traffic deaths. The margins of error allow for each country appearing many times, and for each year’s value being close to the year before.
Year-to-year changes are a cautious test, not a final answer. Looking only at changes from one year to the next removes each country’s own trends. But it also makes measurement mistakes louder, which pulls the results toward zero. A near-zero result here is weak evidence of no link, not proof.
With one measure years behind the other
Same year (lag 0): -0.134. A peak away from zero does not show which comes first. These series change slowly — this year’s value is close to last year’s. That makes the curve smooth and nearly symmetric, whatever produced the data. Even a sharp peak at one lag (a gap of some years) would not prove that one measure changes the other.
The largest value on this curve (-0.19 at lag +5) is higher than the same-year one by 0.05. That gap is not a finding. It is the largest of 21 closely related estimates, and picking the biggest of many tests always makes it look bigger. That is exactly why nothing on this site is ranked or chosen by lag.
The data
per 1,000 live births
per 100,000 population
Years
Nigeria NGA
69.8
20.7
2024, 2019
Niger NER
65.6
25.5
2024, 2019
Somalia, Fed. Rep. SOM
65.4
27.4
2024, 2019
Liberia LBR
63.2
38.9
2024, 2019
Zimbabwe ZWE
62.4
41.2
2024, 2019
Guinea GIN
59.8
29.7
2024, 2019
Central African Republic CAF
58.8
37.7
2024, 2019
Congo, Dem. Rep. COD
56.4
34.9
2024, 2019
Chad TCD
56.3
32.4
2024, 2019
Lesotho LSO
56.3
31.9
2024, 2019
Sierra Leone SLE
54.6
33
2024, 2019
Afghanistan AFG
48.3
15.9
2024, 2019
Pakistan PAK
48.2
13
2024, 2019
Equatorial Guinea GNQ
47.6
27.2
2024, 2019
Mali MLI
47.5
22.7
2024, 2019
World Bank, World Development Indicators World Bank, World Development Indicators
Mortality from road traffic injury (per 100,000 population)
World Health Organization · CC BY 4.0 · updated quarterly
The World Bank's copy stops at 2019.
How this was computed
Data used: most recent value in 2010-2025; values come from 2013–2024. 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 12,497 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Infant mortality and Road traffic deaths
This page shows that two measures go together. It does not show that Infant mortality changes Road traffic deaths, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.
More links with Infant mortality or Road traffic deaths