Do Health score and Old-age spending go together across countries?

Across 42 countries the link before adjusting for anything is r = +0.42, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives +0.40.

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 Health score cause Old-age spending?

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.

Health score vs. Old-age spending

Holds up after adjusting for income

Across 42 countries, Health score and Old-age spending rise and fall together at r = +0.42 (on a scale from −1 to +1, where 0 means no link). It holds up almost unchanged when only countries with similar income (GDP per person) are compared (r = +0.38, q = 0.045, allowing for the many pairs tested). Caution: leave-one-out range spans 0.16 in r.

Comparing countries across the middle half of old-age spending, 5.23% to 9.69% of GDP, health score is better by 0.241 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.193 typical gaps.

Link before adjusting (r)+0.415p = 0.006 · 42 countries

After adjusting for national income+0.381p = 0.014 · likely range [+0.08, +0.62]

p, allowing for regions0.060standard error 0.146 · 5 regions

Check for luck (q)0.045across 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.

Health score · typical gaps (standard deviations)link with income +0.32 · moderate

Old-age spending · % of GDPlink with income +0.17 · independent

“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	+0.415	0.006	42
By rank (Spearman), not adjusted	+0.399	0.009	42
Standard (Pearson), adjusted for national income	+0.381	0.014	42
By rank (Spearman), adjusted for national income	+0.371	0.017	42

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 42 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: +0.034 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.287 (without IRL) to +0.448 (without KOR). 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: IRL (-0.095).

Fragile: this link may not hold up.

leave-one-out range spans 0.16 in r

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	+0.45

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	14	+0.13
Middle third	14	+0.18
Richest third	14	+0.51

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	+0.54

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 = +0.381 after dropping 0 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

	typical gaps (standard deviations)	% of GDP	Years
Japan JPN	0.971	8.29%	2023, 2022
Switzerland CHE	0.82	6.32%	2023, 2021
Norway NOR	0.746	8.43%	2023, 2021
France FRA	0.734	12.4%	2023, 2022
Austria AUT	0.731	13.2%	2023, 2021
Korea, Rep. KOR	0.724	3.62%	2024, 2022
Sweden SWE	0.696	10.1%	2023, 2021
Spain ESP	0.665	10.3%	2023, 2021
Canada CAN	0.62	5.59%	2023, 2022
Israel ISR	0.604	4.92%	2023
Finland FIN	0.577	13%	2023, 2021
Denmark DNK	0.495	9.27%	2024, 2021
Netherlands NLD	0.491	7.19%	2024, 2021
Italy ITA	0.463	13.7%	2024, 2021
Slovak Republic SVK	0.319	7.15%	2023, 2021

wdi; who_gho
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

Sources

Health score

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

The average of 23 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: Anaemia in women, HIV prevalence, Under-5 mortality, Premature NCD mortality, Basic water access, DTP3 immunisation, Physicians, Tobacco use, Basic sanitation, Skilled birth attendance, Maternal mortality, Child stunting, Suicide rate, Road traffic deaths, Tuberculosis incidence, Out-of-pocket health spending, Undernourishment, Food insecurity, Adolescent fertility, Infant mortality, Life expectancy, Adult obesity, Health coverage index.

Old-age spending

Public spending on old-age benefits

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

Public pensions and other benefits for older people. It mostly reflects how old a country is and how its pensions are set up. That is why its strongest link is with the share of people aged 65 and over.

How this was computed

Data used: most recent value in 2010-2025; values come from 2021–2024. 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 Health score and Old-age spending

This page shows that two measures go together. It does not show that Health score changes Old-age spending, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.