Do Government spending per capita and Road traffic deaths go together across countries?

Across 43 countries the link before adjusting for anything is r = -0.67, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.73.

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

Comparing each country only with itself across 2007-2019, and setting aside what happened worldwide in each year, the link is +0.065 (43 countries, 554 country-years). Using year-to-year changes, it is +0.038.

Does Government spending per capita cause Road traffic deaths?

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.

Government spending per capita vs. Road traffic deaths

Fades after adjusting for income

Across 43 countries, Government spending per capita 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 -0.08, which could easily be chance (q = 0.64, 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: sign flips when a single country is removed.

Comparing countries across the middle half of government spending per capita, $21,202 to $34,936, road traffic deaths is better by 2.81 per 100,000 population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.454 per 100,000 population.

Link before adjusting (r)-0.669p = 9.3e-7 · 43 countries

After adjusting for national income-0.082p = 0.605 · likely range [-0.38, +0.23]

p, allowing for regions0.451standard error 0.099 · 5 regions

Check for luck (q)0.642across 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.

Government spending per capita · current international dollarslink with income +0.92 · 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	9.3e-7	43
By rank (Spearman), not adjusted	-0.726	3.7e-8	43
Standard (Pearson), adjusted for national income	-0.082	0.605	43
By rank (Spearman), adjusted for national income	-0.170	0.281	43

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 43 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: +0.587 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.263 (without LUX) to +0.000 (without IRL). 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: LUX (-0.181).

Fragile: this link may not hold up.

sign flips when a single country is removed
leave-one-out range spans 0.26 in r
correlation moves sharply once outliers are trimmed

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
Europe & Central Asia	30	-0.03

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	15	-0.14
Middle third	14	-0.33
Richest third	14	-0.07

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	35	-0.11

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 = -0.263 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 13years. 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 = +0.065, 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 government spending per capita moved did not tend to see their road traffic deaths move with it.

Across countries (adjusted)-0.082for comparison

Within countries-0.537each country vs itself

…and vs the same year elsewhere+0.065q = 0.513 · 43 countries

Year-on-year changes+0.038q = 0.486

The direction flips. Comparing each country only with itself gives -0.54. Also taking out what happened to the whole world each year gives +0.07. So the first figure was picking up a trend both measures share with worldwide progress, not a link between them.

Read with care.

Almost all of this indicator's variation is between countries, not within them, so the within-country estimate rests on a small part of the data.
Much of the within-country association disappears once shared global year effects are removed, which points to a common worldwide trend rather than a country-level relationship.

554 country-years (one country in one year), 43 countries, 2007–2019, about 13 years each on average. Share of the variation that happens within countries over time: 7% for Government spending per capita, 18% 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): +0.065. 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 (-0.15 at lag +6) is higher than the same-year one by 0.08. That gap is not a finding. It is the largest of 19 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

	current international dollars	per 100,000 population	Years
Luxembourg LUX	$72,957	4.1	2024, 2019
Norway NOR	$50,329	2.1	2024, 2019
Austria AUT	$41,817	4.9	2024, 2019
Belgium BEL	$40,194	5.8	2024, 2019
Iceland ISL	$39,385	2	2024, 2019
Denmark DNK	$38,309	3.7	2024, 2019
Netherlands NLD	$37,878	4	2024, 2019
Finland FIN	$37,614	3.9	2024, 2019
Germany DEU	$36,555	3.8	2024, 2019
Sweden SWE	$35,854	3.1	2024, 2019
France FRA	$35,798	5.1	2024, 2019
United States USA	$34,073	12.7	2024, 2019
Switzerland CHE	$32,409	2.2	2024, 2019
Ireland IRL	$31,581	3.1	2024, 2019
Italy ITA	$31,377	5.3	2024, 2019

OECD databases as named on each indicator: PISA, Social Expenditure Database (SOCX), Income Distribution Database, Development Assistance Committee statistics, Family Database, Government at a Glance, Corporate Tax Statistics, employment and environment statistics
World Bank, World Development Indicators

Sources

Government spending per capita

General government expenditure per capita

OECD · via Our World in Data · CC BY 4.0 (OECD terms and conditions) · updated annual, except the snapshots noted per indicator

All levels of government, in current dollars adjusted for what money buys in each country (purchasing power). So it grows over time with inflation as well as differing across countries. Where it is used for adjusting, it is measured in percentage terms (on a log scale), like income. Compared with national income, this measure answers a question nobody is asking. It is money per person set against money per person. So it moves with GDP per capita at about +0.86. Its development loading, which shows how much a measure just reflects how developed a country is, is +0.92. Rich countries spend far more per person because they have far more per person, and almost all of that figure comes from dividing by people. To ask whether government is a larger part of what a country has, you need a share of GDP, not an amount per head. Three measures here do that: government consumption, government expense and tax revenue. Against income those run +0.12, +0.29 and +0.32. That is a much smaller answer, and the real one.

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.

How this was computed

Data used: most recent value in 2010-2025; values come from 2019–2024. 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 5 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 Government spending per capita and Road traffic deaths

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