placestats

Fertility rate vs. PISA maths

Holds up after adjusting for income

Across 87 countries, Fertility rate and PISA maths rise and fall together at r = -0.63 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.34 (q = 0.023, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a weak negative link remains. Caution: correlation moves sharply once outliers are trimmed.

Comparing countries across the middle half of pisa maths, 389 to 482 PISA points, fertility rate is 0.555 births per woman lower at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.387 births per woman.

Link before adjusting (r)-0.627p = 8.4e-11 · 87 countries
After adjusting for national income-0.338p = 0.001 · likely range [-0.51, -0.14]
p, allowing for regions0.006standard error 0.063 · 5 regions
Check for luck (q)0.023across 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.

Fertility rate · births per womanlink with income -0.81 · proxy
PISA maths · PISA pointslink with income +0.79 · 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

Fertility rate · births per woman →PISA maths · PISA points →0.5823.49336575Albania: 1.34 births per woman, 368 PISA pointsUnited Arab Emirates: 1.21 births per woman, 431 PISA pointsArgentina: 1.5 births per woman, 378 PISA pointsAustralia: 1.48 births per woman, 487 PISA pointsAustria: 1.31 births per woman, 487 PISA pointsBelgium: 1.44 births per woman, 489 PISA pointsBulgaria: 1.72 births per woman, 417 PISA pointsBosnia and Herzegovina: 1.49 births per woman, 406 PISA pointsBelarus: 1.22 births per woman, 472 PISA pointsBrazil: 1.61 births per woman, 379 PISA pointsBrunei Darussalam: 1.73 births per woman, 442 PISA pointsCanada: 1.25 births per woman, 497 PISA pointsSwitzerland: 1.29 births per woman, 508 PISA pointsChile: 1.14 births per woman, 412 PISA pointsColombia: 1.63 births per woman, 383 PISA pointsCosta Rica: 1.32 births per woman, 385 PISA pointsCyprus: 1.38 births per woman, 418 PISA pointsCzechia: 1.36 births per woman, 487 PISA pointsGermany: 1.36 births per woman, 475 PISA pointsDenmark: 1.47 births per woman, 489 PISA pointsDominican Republic: 2.22 births per woman, 339 PISA pointsAlgeria: 2.72 births per woman, 360 PISA pointsSpain: 1.1 births per woman, 473 PISA pointsEstonia: 1.18 births per woman, 510 PISA pointsFinland: 1.25 births per woman, 484 PISA pointsFrance: 1.61 births per woman, 474 PISA pointsUnited Kingdom: 1.55 births per woman, 489 PISA pointsGeorgia: 1.8 births per woman, 390 PISA pointsGreece: 1.24 births per woman, 430 PISA pointsGuatemala: 2.29 births per woman, 344 PISA pointsHong Kong SAR, China: 0.841 births per woman, 540 PISA pointsCroatia: 1.46 births per woman, 463 PISA pointsHungary: 1.41 births per woman, 473 PISA pointsIndonesia: 2.12 births per woman, 366 PISA pointsIreland: 1.47 births per woman, 492 PISA pointsIceland: 1.56 births per woman, 459 PISA pointsIsrael: 2.87 births per woman, 458 PISA pointsItaly: 1.18 births per woman, 471 PISA pointsJamaica: 1.35 births per woman, 377 PISA pointsJordan: 2.6 births per woman, 361 PISA pointsJapan: 1.15 births per woman, 536 PISA pointsKazakhstan: 2.98 births per woman, 425 PISA pointsCambodia: 2.55 births per woman, 336 PISA pointsKorea, Rep.: 0.748 births per woman, 527 PISA pointsLebanon: 2.23 births per woman, 393 PISA pointsLithuania: 1.11 births per woman, 475 PISA pointsLuxembourg: 1.25 births per woman, 483 PISA pointsLatvia: 1.24 births per woman, 483 PISA pointsMacao SAR, China: 0.582 births per woman, 552 PISA pointsMorocco: 2.21 births per woman, 365 PISA pointsMoldova: 1.73 births per woman, 414 PISA pointsMexico: 1.89 births per woman, 395 PISA pointsNorth Macedonia: 1.4 births per woman, 389 PISA pointsMalta: 1.01 births per woman, 466 PISA pointsMontenegro: 1.8 births per woman, 406 PISA pointsMongolia: 2.5 births per woman, 425 PISA pointsMalaysia: 1.54 births per woman, 409 PISA pointsNetherlands: 1.43 births per woman, 493 PISA pointsNorway: 1.45 births per woman, 468 PISA pointsNew Zealand: 1.57 births per woman, 479 PISA pointsPanama: 2.11 births per woman, 357 PISA pointsPeru: 1.97 births per woman, 391 PISA pointsPhilippines: 1.89 births per woman, 355 PISA pointsPoland: 1.14 births per woman, 489 PISA pointsPortugal: 1.41 births per woman, 472 PISA pointsParaguay: 2.42 births per woman, 338 PISA pointsWest Bank and Gaza: 3.25 births per woman, 366 PISA pointsQatar: 1.72 births per woman, 414 PISA pointsRomania: 1.39 births per woman, 428 PISA pointsRussian Federation: 1.42 births per woman, 488 PISA pointsSaudi Arabia: 2.31 births per woman, 389 PISA pointsSingapore: 0.97 births per woman, 575 PISA pointsEl Salvador: 1.77 births per woman, 343 PISA pointsSerbia: 1.64 births per woman, 440 PISA pointsSlovak Republic: 1.46 births per woman, 464 PISA pointsSlovenia: 1.52 births per woman, 485 PISA pointsSweden: 1.43 births per woman, 482 PISA pointsThailand: 1.2 births per woman, 394 PISA pointsTrinidad and Tobago: 1.54 births per woman, 417 PISA pointsTunisia: 1.82 births per woman, 367 PISA pointsTurkiye: 1.48 births per woman, 453 PISA pointsUkraine: 0.988 births per woman, 441 PISA pointsUruguay: 1.4 births per woman, 409 PISA pointsUnited States: 1.63 births per woman, 465 PISA pointsUzbekistan: 3.49 births per woman, 364 PISA pointsViet Nam: 1.9 births per woman, 469 PISA pointsKosovo: 1.54 births per woman, 355 PISA pointsALBHKGISRJAMKAZKHMMACPSESGPUZBXKX

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.6278.4e-1187
By rank (Spearman), not adjusted-0.6481.2e-1187
Standard (Pearson), adjusted for national income-0.3380.00187
By rank (Spearman), adjusted for national income-0.4441.8e-587

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: +0.289 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.387 (without PSE) to -0.296 (without SAU). 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: PSE (-0.049).

Fragile: this link may not hold up.
  • 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.

nr
East Asia & Pacific15-0.35
Europe & Central Asia44-0.32
Latin America & Caribbean15-0.73

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 third29-0.26
Middle third29-0.45
Richest third29-0.75

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
Christians55-0.60
Muslims18-0.05

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
Spain14-0.70
United Kingdom14-0.20

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 = -0.490 after dropping 6 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 20years. 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 = -0.059, 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 fertility rate moved did not tend to see their pisa maths move with it.

Across countries (adjusted)-0.338for comparison
Within countries+0.105each country vs itself
…and vs the same year elsewhere-0.059q = 0.734 · 56 countries
Year-on-year changes—q = —
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.

367 country-years (one country in one year), 56 countries, 2003–2022, about 7 years each on average. Share of the variation that happens within countries over time: 5% for Fertility rate, 12% for PISA maths. 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

0.40−0.4-60+10years PISA maths trails Fertility rate →lag -6: r = 0.106 (n = 298)lag -5: r = 0.119 (n = 298)lag -4: r = 0.097 (n = 298)lag -3: r = 0.006 (n = 298)lag -2: r = -0.000 (n = 367)lag -1: r = -0.010 (n = 367)lag +0: r = -0.059 (n = 367)lag +1: r = -0.131 (n = 367)lag +2: r = -0.147 (n = 367)lag +3: r = -0.195 (n = 367)lag +4: r = -0.213 (n = 367)lag +5: r = -0.245 (n = 367)lag +6: r = -0.266 (n = 367)lag +7: r = -0.288 (n = 367)lag +8: r = -0.319 (n = 367)lag +9: r = -0.319 (n = 367)lag +10: r = -0.315 (n = 367)

Same year (lag 0): -0.059. 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 (-0.32 at lag +8) is higher than the same-year one by 0.26. That gap is not a finding. It is the largest of 17 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


births per woman
PISA points		Years
Uzbekistan UZB3.493642024, 2022
West Bank and Gaza PSE3.253662024, 2022
Kazakhstan KAZ2.984252024, 2022
Israel ISR2.874582024, 2022
Algeria DZA2.723602024, 2015
Jordan JOR2.63612024, 2022
Cambodia KHM2.553362024, 2022
Mongolia MNG2.54252024, 2022
Paraguay PRY2.423382024, 2022
Saudi Arabia SAU2.313892024, 2022
Guatemala GTM2.293442024, 2022
Lebanon LBN2.233932024, 2018
Dominican Republic DOM2.223392024, 2022
Morocco MAR2.213652024, 2022
Indonesia IDN2.123662024, 2022

World Bank, World Development Indicators
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

Fertility rate

Fertility rate, total (births per woman)

World Bank · CC BY 4.0 · updated quarterly

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 Fertility rate and PISA maths

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

More links with Fertility rate or PISA maths