Do Road traffic deaths and Wellbeing score go together across countries?

Across 103 countries the link before adjusting for anything is r = -0.61, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.64.

Does the link hold within countries over time, not just across countries?

There are not enough years of data within countries to check this over time.

Does Road traffic deaths cause Wellbeing score?

This analysis cannot answer that. It shows only that the two go together. A link between two country measures, adjusted or not, does not show that either one changes the other. Country figures also say nothing about individual people.

Road traffic deaths vs. Wellbeing score

Fades after adjusting for income

Across 103 countries, Road traffic deaths and Wellbeing score rise and fall together at r = -0.61 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.15, which could easily be chance (q = 0.46, allowing for the many pairs tested, and allowing for countries in the same region being alike). So the first figure mostly shows richer countries differing from poorer ones, not a link between these two things. Caution: direction is not consistent across regions.

Comparing countries across the middle half of road traffic deaths, 5.8 to 17 per 100,000 population, wellbeing score is worse by 0.638 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.158 typical gaps.

Link before adjusting (r)-0.610p = 8.1e-12 · 103 countries

After adjusting for national income-0.152p = 0.128 · likely range [-0.34, +0.04]

p, allowing for regions0.258standard error 0.121 · 7 regions

Check for luck (q)0.464across 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.

Road traffic deaths · per 100,000 populationlink with income -0.71 · high

Wellbeing score · typical gaps (standard deviations)link with income +0.80 · 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	-0.610	8.1e-12	103
By rank (Spearman), not adjusted	-0.643	2.4e-13	103
Standard (Pearson), adjusted for national income	-0.152	0.128	103
By rank (Spearman), adjusted for national income	-0.023	0.816	103

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 103 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: +0.458 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.191 (without VNM) to -0.073 (without ZWE). 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 (+0.079).

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	13	+0.09
Europe & Central Asia	48	-0.34
Latin America & Caribbean	13	+0.21
Middle East, North Africa, Afghanistan & Pakistan	14	-0.12

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	35	-0.30
Middle third	34	+0.29
Richest third	34	-0.34

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	61	-0.23
Muslims	28	-0.07

By last european coloniser

	n	r
Never a European colony	15	-0.13
Spain	12	+0.16
United Kingdom	25	-0.28

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 = -0.073 after dropping 1 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 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

	per 100,000 population	typical gaps (standard deviations)	Years
Zimbabwe ZWE	41.2	-1.98	2019, 2022
Thailand THA	32.2	0.256	2019, 2022
Burkina Faso BFA	31	-0.476	2019, 2010
Viet Nam VNM	30.6	0.469	2019, 2022
Rwanda RWA	29.4	-0.867	2019, 2014
Kenya KEN	28.3	-0.678	2019, 2022
Ethiopia ETH	28.2	-0.726	2019, 2022
Iraq IRQ	27.3	-0.726	2019, 2022
Ghana GHA	25.7	-0.73	2019, 2014
Guatemala GTM	22.9	0.352	2019, 2022
Mali MLI	22.7	-0.328	2019, 2010
Malaysia MYS	22.5	0.215	2019, 2022
South Africa ZAF	22.2	-0.515	2019, 2014
Iran, Islamic Rep. IRN	21.5	-0.684	2019, 2022
Libya LBY	21.3	-0.158	2019, 2022

World Bank, World Development Indicators
ivs; whr

Sources

Road traffic deaths

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.

Wellbeing score

PlaceStats · via PlaceStats · Derived work; each member keeps its own licence · updated recomputed with the panel

The average of 3 measures from 2 publishers. Each measure is first put on a common scale: 0 is the average country in an average year, and 1 is a typical gap between countries (a standard deviation). Each is turned so that higher is better. Measures are averaged within each publisher first, so a publisher with many measures does not count for more. Members: Self-reported happiness, Interpersonal trust, Life satisfaction.

How this was computed

Data used: most recent value in 2010-2025; values come from 2010–2022. 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 Road traffic deaths and Wellbeing score

This page shows that two measures go together. It does not show that Road traffic deaths changes Wellbeing score, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.