placestats

PISA reading vs. Religiously unaffiliated

Fades after adjusting for income

A country is not a person. Countries where more people are counted as belonging to this group also tend to differ on the other measure, and that is all. It does not follow that the people in the group are the people with the different score. Making that leap is called the ecological fallacy: reading a pattern across places as a fact about individual people. It is the most common misuse of this kind of data. Religion also varies strongly with region, history and national income, so much of what looks religious here is really those. That is what the adjusted figures are for. The religion shares are estimates for two years only, and the survey questions are asked in rounds that can be ten years apart.

Across 87 countries, PISA reading and Religiously unaffiliated rise and fall together at r = +0.65 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) leaves r = +0.57, which could easily be chance (q = 0.15, allowing for the many pairs tested, and allowing for countries in the same region being alike).

Comparing countries across the middle half of pisa reading, 384 to 479 PISA points, religiously unaffiliated is 21.2% of population higher at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 27.3% of population.

Link before adjusting (r)+0.645p = 1.5e-11 · 87 countries
After adjusting for national income+0.573p = 8.2e-9 · likely range [+0.41, +0.70]
p, allowing for regions0.034standard error 0.182 · 5 regions
Check for luck (q)0.146across 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.

PISA reading · PISA pointslink with income +0.77 · high
Religiously unaffiliated · % of populationlink with income +0.51 · moderate

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

Every country as a dot

PISA reading · PISA points →Religiously unaffiliated · % of population →3295430%72.8%Albania: 358 PISA points, 7.65% of populationUnited Arab Emirates: 417 PISA points, 0.336% of populationArgentina: 401 PISA points, 9.23% of populationAustralia: 498 PISA points, 42.3% of populationAustria: 480 PISA points, 22.4% of populationBelgium: 479 PISA points, 39% of populationBulgaria: 404 PISA points, 10% of populationBosnia and Herzegovina: 403 PISA points, 1.05% of populationBelarus: 474 PISA points, 13.8% of populationBrazil: 410 PISA points, 13.5% of populationBrunei Darussalam: 429 PISA points, 0.156% of populationCanada: 507 PISA points, 34.6% of populationSwitzerland: 483 PISA points, 30.8% of populationChile: 448 PISA points, 30.3% of populationColombia: 409 PISA points, 9.94% of populationCosta Rica: 415 PISA points, 10.1% of populationCyprus: 381 PISA points, 6.57% of populationCzechia: 489 PISA points, 72.8% of populationGermany: 480 PISA points, 36.1% of populationDenmark: 489 PISA points, 16.6% of populationDominican Republic: 351 PISA points, 19.6% of populationAlgeria: 350 PISA points, 1.27% of populationSpain: 474 PISA points, 26.4% of populationEstonia: 511 PISA points, 43.6% of populationFinland: 490 PISA points, 25% of populationFrance: 474 PISA points, 42.6% of populationUnited Kingdom: 494 PISA points, 40.2% of populationGeorgia: 374 PISA points, 0.988% of populationGreece: 438 PISA points, 4.74% of populationGuatemala: 374 PISA points, 6.02% of populationHong Kong SAR, China: 500 PISA points, 71.4% of populationCroatia: 475 PISA points, 6.66% of populationHungary: 473 PISA points, 27% of populationIndonesia: 359 PISA points, 0.092% of populationIreland: 516 PISA points, 15.6% of populationIceland: 436 PISA points, 19.8% of populationIsrael: 474 PISA points, 4.37% of populationItaly: 482 PISA points, 13.3% of populationJamaica: 410 PISA points, 23.7% of populationJordan: 342 PISA points, 0.0474% of populationJapan: 516 PISA points, 57.5% of populationKazakhstan: 386 PISA points, 2.53% of populationCambodia: 329 PISA points, 0.079% of populationKorea, Rep.: 515 PISA points, 48.3% of populationLebanon: 353 PISA points, 0.0188% of populationLithuania: 472 PISA points, 7.08% of populationLuxembourg: 470 PISA points, 25.3% of populationLatvia: 475 PISA points, 17% of populationMacao SAR, China: 510 PISA points, 67.6% of populationMorocco: 339 PISA points, 0.131% of populationMoldova: 411 PISA points, 0.234% of populationMexico: 415 PISA points, 10.6% of populationNorth Macedonia: 359 PISA points, 0.516% of populationMalta: 445 PISA points, 5.24% of populationMontenegro: 405 PISA points, 1.14% of populationMongolia: 378 PISA points, 40.8% of populationMalaysia: 388 PISA points, 0.845% of populationNetherlands: 459 PISA points, 54.1% of populationNorway: 477 PISA points, 22.5% of populationNew Zealand: 501 PISA points, 51.4% of populationPanama: 392 PISA points, 6.21% of populationPeru: 408 PISA points, 5.11% of populationPhilippines: 347 PISA points, 0.0411% of populationPoland: 489 PISA points, 8.65% of populationPortugal: 477 PISA points, 13.8% of populationParaguay: 373 PISA points, 4.25% of populationWest Bank and Gaza: 349 PISA points, 0% of populationQatar: 419 PISA points, 0.274% of populationRomania: 428 PISA points, 0.94% of populationRussian Federation: 479 PISA points, 20.2% of populationSaudi Arabia: 383 PISA points, 0.112% of populationSingapore: 543 PISA points, 19.9% of populationEl Salvador: 365 PISA points, 11.9% of populationSerbia: 440 PISA points, 4.01% of populationSlovak Republic: 447 PISA points, 25.3% of populationSlovenia: 469 PISA points, 32.3% of populationSweden: 487 PISA points, 28.9% of populationThailand: 379 PISA points, 0.0126% of populationTrinidad and Tobago: 427 PISA points, 2.45% of populationTunisia: 361 PISA points, 0.441% of populationTurkiye: 456 PISA points, 2.53% of populationUkraine: 428 PISA points, 15.1% of populationUruguay: 430 PISA points, 52.4% of populationUnited States: 504 PISA points, 29.7% of populationUzbekistan: 336 PISA points, 0.552% of populationViet Nam: 462 PISA points, 67.7% of populationKosovo: 342 PISA points, 0.139% of populationCZEHKGIRLKHMMACMARMNGPOLSGPUZBVNM

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.6451.5e-1187
By rank (Spearman), not adjusted+0.7461.1e-1687
Standard (Pearson), adjusted for national income+0.5738.2e-987
By rank (Spearman), adjusted for national income+0.6481.5e-1187

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 87 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: +0.072 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.537 (without VNM) to +0.592 (without MNG). 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: VNM (-0.036).

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 & Pacific15+0.79
Europe & Central Asia44+0.40
Latin America & Caribbean15+0.32

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 third29+0.60
Middle third29+0.46
Richest third29+0.64

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+0.50
Muslims18+0.00

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+0.30
United Kingdom14+0.81

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 = +0.576 after dropping 2 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


PISA points
% of population		Years
Singapore SGP54319.9%2022, 2020
Ireland IRL51615.6%2022, 2020
Japan JPN51657.5%2022, 2020
Korea, Rep. KOR51548.3%2022, 2020
Estonia EST51143.6%2022, 2020
Macao SAR, China MAC51067.6%2022, 2020
Canada CAN50734.6%2022, 2020
United States USA50429.7%2022, 2020
New Zealand NZL50151.4%2022, 2020
Hong Kong SAR, China HKG50071.4%2022, 2020
Australia AUS49842.3%2022, 2020
United Kingdom GBR49440.2%2022, 2020
Finland FIN49025%2022, 2020
Denmark DNK48916.6%2022, 2020
Poland POL4898.65%2022, 2020

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
Pew Research Center, Religious Composition by Country, 2010-2020. Pew Research Center bears no responsibility for the analyses or interpretations of the data presented here.
One of these measures is published under a licence that forbids changes. Values are shown exactly as published and must not be republished in a changed or rescaled form.

Sources

PISA reading

PISA mean reading score

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

Religiously unaffiliated

Share of the population religiously unaffiliated

Pew Research Center · via Our World in Data · Pew Research Center terms of use, excerpts with attribution · updated decennial · comparable from 2010 only

People who belong to no religion: atheists, agnostics and those who say "nothing in particular". Pew estimated it from censuses and surveys for 2010 and 2020. Belonging is a third thing, beside the attendance and importance measures already here. A country can be almost all affiliated and rarely attend, as much of Europe is. With only two years, nothing here shows a trend. No "better" direction is set.

How this was computed

Data used: most recent value in 2010-2025; values come from 2015–2022. 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 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 PISA reading and Religiously unaffiliated

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

More links with PISA reading or Religiously unaffiliated