placestats

Health score vs. Interpersonal trust

Holds up after adjusting for income

Across 103 countries, Health score and Interpersonal trust rise and fall together at r = +0.48 (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 = +0.43, q = 0.049, allowing for the many pairs tested).

Comparing countries across the middle half of interpersonal trust, 12.9% to 30.2% of respondents, health score is better by 0.2 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.206 typical gaps.

Link before adjusting (r)+0.484p = 2.2e-7 · 103 countries
After adjusting for national income+0.426p = 8.2e-6 · likely range [+0.25, +0.57]
p, allowing for regions0.014standard error 0.124 · 7 regions
Check for luck (q)0.049across 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
Interpersonal trust · % of respondentslink with income +0.66 · high

“Link with income” is how closely each measure tracks GDP per person across countries, from −1 to +1.

Every country as a dot

Health score · typical gaps (standard deviations) →Interpersonal trust · % of respondents →-0.8880.9712.14%73.9%Albania: 0.0262 typical gaps (standard deviations), 2.77% of respondentsAndorra: 0.584 typical gaps (standard deviations), 25.5% of respondentsArgentina: 0.0904 typical gaps (standard deviations), 19.2% of respondentsArmenia: -0.0378 typical gaps (standard deviations), 17.5% of respondentsAustralia: -0.15 typical gaps (standard deviations), 48.5% of respondentsAustria: 0.731 typical gaps (standard deviations), 49.8% of respondentsAzerbaijan: -0.252 typical gaps (standard deviations), 26.3% of respondentsBelgium: 0.207 typical gaps (standard deviations), 35.7% of respondentsBurkina Faso: 0.237 typical gaps (standard deviations), 13.8% of respondentsBangladesh: 0.494 typical gaps (standard deviations), 12.9% of respondentsBulgaria: 0.0679 typical gaps (standard deviations), 17.1% of respondentsBosnia and Herzegovina: 0.154 typical gaps (standard deviations), 9.64% of respondentsBelarus: 0.529 typical gaps (standard deviations), 40% of respondentsBolivia: -0.427 typical gaps (standard deviations), 8.51% of respondentsBrazil: -0.257 typical gaps (standard deviations), 6.52% of respondentsCanada: 0.62 typical gaps (standard deviations), 46.7% of respondentsSwitzerland: 0.82 typical gaps (standard deviations), 58.5% of respondentsChile: -0.672 typical gaps (standard deviations), 12.9% of respondentsChina: 0.798 typical gaps (standard deviations), 63.5% of respondentsColombia: -0.0327 typical gaps (standard deviations), 4.54% of respondentsCyprus: 0.44 typical gaps (standard deviations), 6.6% of respondentsCzechia: -0.0446 typical gaps (standard deviations), 27.3% of respondentsGermany: 0.212 typical gaps (standard deviations), 41.6% of respondentsDenmark: 0.495 typical gaps (standard deviations), 73.9% of respondentsAlgeria: -0.0347 typical gaps (standard deviations), 17.2% of respondentsEcuador: -0.298 typical gaps (standard deviations), 5.75% of respondentsEgypt, Arab Rep.: -0.888 typical gaps (standard deviations), 7.33% of respondentsSpain: 0.665 typical gaps (standard deviations), 41% of respondentsEstonia: 0.166 typical gaps (standard deviations), 33.9% of respondentsEthiopia: 0.399 typical gaps (standard deviations), 11.9% of respondentsFinland: 0.577 typical gaps (standard deviations), 68.4% of respondentsFrance: 0.734 typical gaps (standard deviations), 26.3% of respondentsUnited Kingdom: -0.013 typical gaps (standard deviations), 43.3% of respondentsGeorgia: -0.551 typical gaps (standard deviations), 9.02% of respondentsGhana: 0.171 typical gaps (standard deviations), 5.03% of respondentsGreece: -0.0978 typical gaps (standard deviations), 8.45% of respondentsGuatemala: -0.385 typical gaps (standard deviations), 18% of respondentsCroatia: 0.22 typical gaps (standard deviations), 13.6% of respondentsHaiti: 0.124 typical gaps (standard deviations), 21.3% of respondentsHungary: 0.238 typical gaps (standard deviations), 27.2% of respondentsIndonesia: 0.231 typical gaps (standard deviations), 4.64% of respondentsIndia: 0.476 typical gaps (standard deviations), 16.9% of respondentsIreland: -0.0749 typical gaps (standard deviations), 37.3% of respondentsIran, Islamic Rep.: 0.331 typical gaps (standard deviations), 14.8% of respondentsIraq: -0.707 typical gaps (standard deviations), 11% of respondentsIceland: 0.196 typical gaps (standard deviations), 62.3% of respondentsItaly: 0.463 typical gaps (standard deviations), 26.6% of respondentsJordan: -0.495 typical gaps (standard deviations), 15.9% of respondentsJapan: 0.971 typical gaps (standard deviations), 33.7% of respondentsKazakhstan: 0.134 typical gaps (standard deviations), 22.8% of respondentsKenya: 0.167 typical gaps (standard deviations), 9.48% of respondentsKyrgyz Republic: -0.00976 typical gaps (standard deviations), 12.8% of respondentsKorea, Rep.: 0.724 typical gaps (standard deviations), 32.9% of respondentsKuwait: -0.571 typical gaps (standard deviations), 28.5% of respondentsLebanon: -0.489 typical gaps (standard deviations), 9.92% of respondentsLibya: -0.455 typical gaps (standard deviations), 9.11% of respondentsLithuania: 0.085 typical gaps (standard deviations), 31.7% of respondentsLuxembourg: 0.284 typical gaps (standard deviations), 31.4% of respondentsLatvia: 0.0586 typical gaps (standard deviations), 22.2% of respondentsMorocco: 0.0638 typical gaps (standard deviations), 16.5% of respondentsMoldova: -0.0414 typical gaps (standard deviations), 14.1% of respondentsMaldives: 0.274 typical gaps (standard deviations), 21.1% of respondentsMexico: -0.619 typical gaps (standard deviations), 10.5% of respondentsNorth Macedonia: -0.158 typical gaps (standard deviations), 15.1% of respondentsMali: 0.0623 typical gaps (standard deviations), 14.9% of respondentsMalta: -0.207 typical gaps (standard deviations), 21.3% of respondentsMyanmar: 0.201 typical gaps (standard deviations), 15.1% of respondentsMontenegro: 0.227 typical gaps (standard deviations), 21.7% of respondentsMongolia: -0.241 typical gaps (standard deviations), 26% of respondentsMalaysia: -0.00499 typical gaps (standard deviations), 19.6% of respondentsNigeria: -0.0754 typical gaps (standard deviations), 13% of respondentsNicaragua: -0.0661 typical gaps (standard deviations), 4.25% of respondentsNetherlands: 0.491 typical gaps (standard deviations), 57% of respondentsNorway: 0.746 typical gaps (standard deviations), 72.1% of respondentsNew Zealand: -0.304 typical gaps (standard deviations), 56.6% of respondentsPakistan: -0.326 typical gaps (standard deviations), 23.3% of respondentsPeru: -0.287 typical gaps (standard deviations), 4.16% of respondentsPhilippines: 0.279 typical gaps (standard deviations), 5.33% of respondentsPoland: 0.151 typical gaps (standard deviations), 24.1% of respondentsPortugal: 0.271 typical gaps (standard deviations), 16.9% of respondentsWest Bank and Gaza: -0.253 typical gaps (standard deviations), 15.8% of respondentsQatar: -0.632 typical gaps (standard deviations), 21.4% of respondentsRomania: -0.422 typical gaps (standard deviations), 12.1% of respondentsRussian Federation: 0.507 typical gaps (standard deviations), 22.9% of respondentsRwanda: 0.513 typical gaps (standard deviations), 16.6% of respondentsSingapore: 0.403 typical gaps (standard deviations), 34.4% of respondentsSerbia: 0.0396 typical gaps (standard deviations), 16.3% of respondentsSlovak Republic: 0.319 typical gaps (standard deviations), 21.6% of respondentsSlovenia: 0.242 typical gaps (standard deviations), 25.3% of respondentsSweden: 0.696 typical gaps (standard deviations), 62.8% of respondentsThailand: 0.284 typical gaps (standard deviations), 28.9% of respondentsTajikistan: 0.097 typical gaps (standard deviations), 20.6% of respondentsTrinidad and Tobago: 0.0941 typical gaps (standard deviations), 3.2% of respondentsTunisia: -0.0807 typical gaps (standard deviations), 13.8% of respondentsTurkiye: 0.232 typical gaps (standard deviations), 14% of respondentsUkraine: 0.358 typical gaps (standard deviations), 28.3% of respondentsUruguay: 0.275 typical gaps (standard deviations), 14.5% of respondentsUnited States: 0.258 typical gaps (standard deviations), 37% of respondentsUzbekistan: 0.137 typical gaps (standard deviations), 33.6% of respondentsViet Nam: 0.794 typical gaps (standard deviations), 27.7% of respondentsSouth Africa: -0.831 typical gaps (standard deviations), 23.3% of respondentsZambia: -0.0273 typical gaps (standard deviations), 10.8% of respondentsZimbabwe: -0.159 typical gaps (standard deviations), 2.14% of respondentsAUSBGDCHNCYPDNKEGYIRQJPNMEXNORNZLZAF

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.4842.2e-7103
By rank (Spearman), not adjusted+0.4724.8e-7103
Standard (Pearson), adjusted for national income+0.4268.2e-6103
By rank (Spearman), adjusted for national income+0.3540.000103

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: +0.058 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.397 (without CHN) to +0.461 (without NZL). 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: NZL (+0.035).

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.

nr
East Asia & Pacific13+0.03
Europe & Central Asia48+0.48
Latin America & Caribbean13+0.12
Middle East, North Africa, Afghanistan & Pakistan14+0.26

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.

nr
Poorest third35+0.21
Middle third34+0.57
Richest third34+0.31

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.

nr
Christians61+0.50
Muslims28+0.04

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.

nr
Never a European colony15+0.54
Spain12+0.09
United Kingdom25+0.04

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 = +0.377 after dropping 11 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 respondents		Years
Japan JPN0.97133.7%2023, 2022
Switzerland CHE0.8258.5%2023, 2022
China CHN0.79863.5%2023, 2022
Viet Nam VNM0.79427.7%2024, 2022
Norway NOR0.74672.1%2023, 2022
France FRA0.73426.3%2023, 2022
Austria AUT0.73149.8%2023, 2022
Korea, Rep. KOR0.72432.9%2024, 2022
Sweden SWE0.69662.8%2023, 2022
Spain ESP0.66541%2023, 2022
Canada CAN0.6246.7%2023, 2022
Andorra AND0.58425.5%2023, 2022
Finland FIN0.57768.4%2023, 2022
Belarus BLR0.52940%2023, 2022
Rwanda RWA0.51316.6%2024, 2014

wdi; who_gho
Haerpfer, C. et al., World Values Survey; EVS, European Values Study

Sources

Health score

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.

Interpersonal trust

Share agreeing that most people can be trusted

World Values Survey Association and European Values Study · via Our World in Data · Free for non-commercial use with attribution · updated irregular (survey waves, roughly every 5 years)

The share who answer "most people can be trusted" rather than "you can't be too careful". Trust in other people is the classic measure of social ties (social capital). It is only loosely linked to national income. That makes it one of the few measures here where adjusting for GDP is a real test, not a foregone result. The survey runs in waves, not every year. So data for each country are sparse and uneven, and two readings for a country may be five or more years apart.

How this was computed

Data used: most recent value in 2010-2025; values come from 2010–2024. 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 Health score and Interpersonal trust

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

More links with Health score or Interpersonal trust