placestats

Health score vs. PISA maths

Holds up after adjusting for income

Across 84 countries, Health score and PISA maths rise and fall together at r = +0.58 (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 = +0.51, q = 0.020, allowing for the many pairs tested).

Comparing countries across the middle half of pisa maths, 389 to 480 PISA points, health score is better by 0.383 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.487 typical gaps.

Link before adjusting (r)+0.579p = 8.0e-9 · 84 countries
After adjusting for national income+0.508p = 9.3e-7 · likely range [+0.33, +0.65]
p, allowing for regions0.002standard error 0.067 · 5 regions
Check for luck (q)0.020across 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
PISA maths · PISA pointslink with income +0.79 · 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) →PISA maths · PISA points →-0.6720.971336575Albania: 0.0262 typical gaps (standard deviations), 368 PISA pointsUnited Arab Emirates: 0.386 typical gaps (standard deviations), 431 PISA pointsArgentina: 0.0904 typical gaps (standard deviations), 378 PISA pointsAustralia: -0.15 typical gaps (standard deviations), 487 PISA pointsAustria: 0.731 typical gaps (standard deviations), 487 PISA pointsBelgium: 0.207 typical gaps (standard deviations), 489 PISA pointsBulgaria: 0.0679 typical gaps (standard deviations), 417 PISA pointsBosnia and Herzegovina: 0.154 typical gaps (standard deviations), 406 PISA pointsBelarus: 0.529 typical gaps (standard deviations), 472 PISA pointsBrazil: -0.257 typical gaps (standard deviations), 379 PISA pointsBrunei Darussalam: 0.279 typical gaps (standard deviations), 442 PISA pointsCanada: 0.62 typical gaps (standard deviations), 497 PISA pointsSwitzerland: 0.82 typical gaps (standard deviations), 508 PISA pointsChile: -0.672 typical gaps (standard deviations), 412 PISA pointsColombia: -0.0327 typical gaps (standard deviations), 383 PISA pointsCosta Rica: -0.275 typical gaps (standard deviations), 385 PISA pointsCyprus: 0.44 typical gaps (standard deviations), 418 PISA pointsCzechia: -0.0446 typical gaps (standard deviations), 487 PISA pointsGermany: 0.212 typical gaps (standard deviations), 475 PISA pointsDenmark: 0.495 typical gaps (standard deviations), 489 PISA pointsDominican Republic: 0.0534 typical gaps (standard deviations), 339 PISA pointsAlgeria: -0.0347 typical gaps (standard deviations), 360 PISA pointsSpain: 0.665 typical gaps (standard deviations), 473 PISA pointsEstonia: 0.166 typical gaps (standard deviations), 510 PISA pointsFinland: 0.577 typical gaps (standard deviations), 484 PISA pointsFrance: 0.734 typical gaps (standard deviations), 474 PISA pointsUnited Kingdom: -0.013 typical gaps (standard deviations), 489 PISA pointsGeorgia: -0.551 typical gaps (standard deviations), 390 PISA pointsGreece: -0.0978 typical gaps (standard deviations), 430 PISA pointsGuatemala: -0.385 typical gaps (standard deviations), 344 PISA pointsCroatia: 0.22 typical gaps (standard deviations), 463 PISA pointsHungary: 0.238 typical gaps (standard deviations), 473 PISA pointsIndonesia: 0.231 typical gaps (standard deviations), 366 PISA pointsIreland: -0.0749 typical gaps (standard deviations), 492 PISA pointsIceland: 0.196 typical gaps (standard deviations), 459 PISA pointsIsrael: 0.604 typical gaps (standard deviations), 458 PISA pointsItaly: 0.463 typical gaps (standard deviations), 471 PISA pointsJamaica: -0.486 typical gaps (standard deviations), 377 PISA pointsJordan: -0.495 typical gaps (standard deviations), 361 PISA pointsJapan: 0.971 typical gaps (standard deviations), 536 PISA pointsKazakhstan: 0.134 typical gaps (standard deviations), 425 PISA pointsCambodia: 0.55 typical gaps (standard deviations), 336 PISA pointsKorea, Rep.: 0.724 typical gaps (standard deviations), 527 PISA pointsLebanon: -0.489 typical gaps (standard deviations), 393 PISA pointsLithuania: 0.085 typical gaps (standard deviations), 475 PISA pointsLuxembourg: 0.284 typical gaps (standard deviations), 483 PISA pointsLatvia: 0.0586 typical gaps (standard deviations), 483 PISA pointsMorocco: 0.0638 typical gaps (standard deviations), 365 PISA pointsMoldova: -0.0414 typical gaps (standard deviations), 414 PISA pointsMexico: -0.619 typical gaps (standard deviations), 395 PISA pointsNorth Macedonia: -0.158 typical gaps (standard deviations), 389 PISA pointsMalta: -0.207 typical gaps (standard deviations), 466 PISA pointsMontenegro: 0.227 typical gaps (standard deviations), 406 PISA pointsMongolia: -0.241 typical gaps (standard deviations), 425 PISA pointsMalaysia: -0.00499 typical gaps (standard deviations), 409 PISA pointsNetherlands: 0.491 typical gaps (standard deviations), 493 PISA pointsNorway: 0.746 typical gaps (standard deviations), 468 PISA pointsNew Zealand: -0.304 typical gaps (standard deviations), 479 PISA pointsPanama: -0.616 typical gaps (standard deviations), 357 PISA pointsPeru: -0.287 typical gaps (standard deviations), 391 PISA pointsPhilippines: 0.279 typical gaps (standard deviations), 355 PISA pointsPoland: 0.151 typical gaps (standard deviations), 489 PISA pointsPortugal: 0.271 typical gaps (standard deviations), 472 PISA pointsParaguay: -0.463 typical gaps (standard deviations), 338 PISA pointsWest Bank and Gaza: -0.253 typical gaps (standard deviations), 366 PISA pointsQatar: -0.632 typical gaps (standard deviations), 414 PISA pointsRomania: -0.422 typical gaps (standard deviations), 428 PISA pointsRussian Federation: 0.507 typical gaps (standard deviations), 488 PISA pointsSaudi Arabia: -0.597 typical gaps (standard deviations), 389 PISA pointsSingapore: 0.403 typical gaps (standard deviations), 575 PISA pointsEl Salvador: -0.377 typical gaps (standard deviations), 343 PISA pointsSerbia: 0.0396 typical gaps (standard deviations), 440 PISA pointsSlovak Republic: 0.319 typical gaps (standard deviations), 464 PISA pointsSlovenia: 0.242 typical gaps (standard deviations), 485 PISA pointsSweden: 0.696 typical gaps (standard deviations), 482 PISA pointsThailand: 0.284 typical gaps (standard deviations), 394 PISA pointsTrinidad and Tobago: 0.0941 typical gaps (standard deviations), 417 PISA pointsTunisia: -0.0807 typical gaps (standard deviations), 367 PISA pointsTurkiye: 0.232 typical gaps (standard deviations), 453 PISA pointsUkraine: 0.358 typical gaps (standard deviations), 441 PISA pointsUruguay: 0.275 typical gaps (standard deviations), 409 PISA pointsUnited States: 0.258 typical gaps (standard deviations), 465 PISA pointsUzbekistan: 0.137 typical gaps (standard deviations), 364 PISA pointsViet Nam: 0.794 typical gaps (standard deviations), 469 PISA pointsAUSDOMGTMJPNKHMKORNZLPANPHLPRYSGP

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5798.0e-984
By rank (Spearman), not adjusted+0.5477.2e-884
Standard (Pearson), adjusted for national income+0.5089.3e-784
By rank (Spearman), adjusted for national income+0.3940.00084

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 84 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: +0.071 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.463 (without QAT) to +0.528 (without KHM). 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: QAT (-0.045).

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+0.53
Europe & Central Asia43+0.32
Latin America & Caribbean15+0.09

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 third28+0.31
Middle third28+0.60
Richest third28+0.52

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
Christians55+0.45
Muslims17+0.38

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
Spain14+0.09
United Kingdom14+0.31

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 = +0.508 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)
PISA points		Years
Japan JPN0.9715362023, 2022
Switzerland CHE0.825082023, 2022
Viet Nam VNM0.7944692024, 2022
Norway NOR0.7464682023, 2022
France FRA0.7344742023, 2022
Austria AUT0.7314872023, 2022
Korea, Rep. KOR0.7245272024, 2022
Sweden SWE0.6964822023, 2022
Spain ESP0.6654732023, 2022
Canada CAN0.624972023, 2022
Israel ISR0.6044582023, 2022
Finland FIN0.5774842023, 2022
Cambodia KHM0.553362024, 2022
Belarus BLR0.5294722023, 2018
Russian Federation RUS0.5074882023, 2018

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

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.

PISA maths

PISA mean mathematics score

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

The average score of 15-year-olds on the same test in every country taking part. It is scaled so the OECD average in the starting year was 500, with a standard deviation (a typical gap between students) of 100. Held every three years. The 2021 round was held in 2022, and the OECD says it can still be compared with earlier rounds despite the pandemic. The three subjects are treated as measuring the same thing. Their links with each other are above 0.95, and that close match is a fact about the test, not about schooling.

How this was computed

Data used: most recent value in 2010-2025; values come from 2015–2024. 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 PISA maths

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

More links with Health score or PISA maths