placestats

Labour score vs. PISA science

Fades after adjusting for income

Across 86 countries, Labour score and PISA science rise and fall together at r = +0.67 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.24, which could easily be chance (q = 0.42, allowing for the many pairs tested, and allowing for countries in the same region being alike). So the first figure mostly shows richer countries differing from poorer ones, not a link between these two things.

Comparing countries across the middle half of pisa science, 403 to 491 PISA points, labour score is better by 0.462 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.177 typical gaps.

Link before adjusting (r)+0.666p = 2.6e-12 · 86 countries
After adjusting for national income+0.236p = 0.030 · likely range [+0.02, +0.43]
p, allowing for regions0.099standard error 0.110 · 5 regions
Check for luck (q)0.421across 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.

Labour score · typical gaps (standard deviations)link with income +0.79 · high
PISA science · PISA pointslink with income +0.78 · 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

Labour score · typical gaps (standard deviations) →PISA science · PISA points →-0.5781.36347561Albania: -0.39 typical gaps (standard deviations), 376 PISA pointsUnited Arab Emirates: 1.17 typical gaps (standard deviations), 432 PISA pointsArgentina: 0.34 typical gaps (standard deviations), 406 PISA pointsAustralia: 0.867 typical gaps (standard deviations), 507 PISA pointsAustria: 0.671 typical gaps (standard deviations), 491 PISA pointsBelgium: 0.682 typical gaps (standard deviations), 491 PISA pointsBulgaria: 0.954 typical gaps (standard deviations), 421 PISA pointsBosnia and Herzegovina: 0.126 typical gaps (standard deviations), 398 PISA pointsBelarus: 0.978 typical gaps (standard deviations), 471 PISA pointsBrazil: 0.16 typical gaps (standard deviations), 403 PISA pointsBrunei Darussalam: 0.857 typical gaps (standard deviations), 446 PISA pointsCanada: 0.581 typical gaps (standard deviations), 515 PISA pointsSwitzerland: 0.916 typical gaps (standard deviations), 503 PISA pointsChile: 0.344 typical gaps (standard deviations), 444 PISA pointsColombia: -0.377 typical gaps (standard deviations), 411 PISA pointsCosta Rica: 0.133 typical gaps (standard deviations), 411 PISA pointsCyprus: 0.812 typical gaps (standard deviations), 411 PISA pointsCzechia: 0.889 typical gaps (standard deviations), 498 PISA pointsGermany: 0.988 typical gaps (standard deviations), 492 PISA pointsDenmark: 0.819 typical gaps (standard deviations), 494 PISA pointsDominican Republic: -0.0819 typical gaps (standard deviations), 360 PISA pointsAlgeria: -0.0627 typical gaps (standard deviations), 376 PISA pointsSpain: 0.483 typical gaps (standard deviations), 485 PISA pointsEstonia: 0.558 typical gaps (standard deviations), 526 PISA pointsFinland: 0.419 typical gaps (standard deviations), 511 PISA pointsFrance: 0.595 typical gaps (standard deviations), 487 PISA pointsUnited Kingdom: 0.635 typical gaps (standard deviations), 500 PISA pointsGeorgia: -0.418 typical gaps (standard deviations), 384 PISA pointsGreece: 0.426 typical gaps (standard deviations), 441 PISA pointsGuatemala: 0.48 typical gaps (standard deviations), 373 PISA pointsHong Kong SAR, China: 1.11 typical gaps (standard deviations), 520 PISA pointsCroatia: 0.844 typical gaps (standard deviations), 483 PISA pointsHungary: 0.856 typical gaps (standard deviations), 486 PISA pointsIndonesia: 0.169 typical gaps (standard deviations), 383 PISA pointsIreland: 0.813 typical gaps (standard deviations), 504 PISA pointsIceland: 0.919 typical gaps (standard deviations), 447 PISA pointsIsrael: 0.943 typical gaps (standard deviations), 465 PISA pointsItaly: 0.579 typical gaps (standard deviations), 477 PISA pointsJamaica: 0.511 typical gaps (standard deviations), 403 PISA pointsJordan: -0.133 typical gaps (standard deviations), 375 PISA pointsJapan: 1.03 typical gaps (standard deviations), 547 PISA pointsKazakhstan: 0.516 typical gaps (standard deviations), 423 PISA pointsCambodia: 0.369 typical gaps (standard deviations), 347 PISA pointsKorea, Rep.: 0.822 typical gaps (standard deviations), 528 PISA pointsLebanon: 0.107 typical gaps (standard deviations), 384 PISA pointsLithuania: 0.626 typical gaps (standard deviations), 484 PISA pointsLuxembourg: 0.723 typical gaps (standard deviations), 477 PISA pointsLatvia: 0.673 typical gaps (standard deviations), 494 PISA pointsMacao SAR, China: 1.12 typical gaps (standard deviations), 543 PISA pointsMorocco: -0.0339 typical gaps (standard deviations), 365 PISA pointsMoldova: 0.412 typical gaps (standard deviations), 417 PISA pointsMexico: 0.513 typical gaps (standard deviations), 410 PISA pointsNorth Macedonia: 0.0322 typical gaps (standard deviations), 380 PISA pointsMalta: 0.947 typical gaps (standard deviations), 466 PISA pointsMontenegro: -0.0101 typical gaps (standard deviations), 403 PISA pointsMongolia: 0.223 typical gaps (standard deviations), 412 PISA pointsMalaysia: 0.654 typical gaps (standard deviations), 416 PISA pointsNetherlands: 0.804 typical gaps (standard deviations), 488 PISA pointsNorway: 0.915 typical gaps (standard deviations), 478 PISA pointsNew Zealand: 0.721 typical gaps (standard deviations), 504 PISA pointsPanama: 0.0819 typical gaps (standard deviations), 388 PISA pointsPeru: -0.454 typical gaps (standard deviations), 408 PISA pointsPhilippines: 0.559 typical gaps (standard deviations), 356 PISA pointsPoland: 0.842 typical gaps (standard deviations), 499 PISA pointsPortugal: 0.843 typical gaps (standard deviations), 484 PISA pointsParaguay: -0.242 typical gaps (standard deviations), 368 PISA pointsWest Bank and Gaza: -0.578 typical gaps (standard deviations), 369 PISA pointsQatar: 1.36 typical gaps (standard deviations), 432 PISA pointsRomania: 0.637 typical gaps (standard deviations), 428 PISA pointsRussian Federation: 1.14 typical gaps (standard deviations), 478 PISA pointsSaudi Arabia: 0.782 typical gaps (standard deviations), 390 PISA pointsSingapore: 1.05 typical gaps (standard deviations), 561 PISA pointsEl Salvador: 0.523 typical gaps (standard deviations), 373 PISA pointsSerbia: 0.409 typical gaps (standard deviations), 447 PISA pointsSlovak Republic: 0.713 typical gaps (standard deviations), 462 PISA pointsSlovenia: 0.914 typical gaps (standard deviations), 500 PISA pointsSweden: 0.531 typical gaps (standard deviations), 494 PISA pointsThailand: 0.466 typical gaps (standard deviations), 409 PISA pointsTrinidad and Tobago: 0.701 typical gaps (standard deviations), 425 PISA pointsTunisia: -0.205 typical gaps (standard deviations), 386 PISA pointsTurkiye: 0.224 typical gaps (standard deviations), 476 PISA pointsUkraine: 0.208 typical gaps (standard deviations), 450 PISA pointsUruguay: 0.298 typical gaps (standard deviations), 435 PISA pointsUnited States: 0.862 typical gaps (standard deviations), 499 PISA pointsUzbekistan: 0.368 typical gaps (standard deviations), 355 PISA pointsViet Nam: 0.283 typical gaps (standard deviations), 472 PISA pointsALBARECOLFINGEOJPNMACPERPHLPSEQATSGP

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.6662.6e-1286
By rank (Spearman), not adjusted+0.6731.3e-1286
Standard (Pearson), adjusted for national income+0.2360.03086
By rank (Spearman), adjusted for national income+0.1950.07386

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 86 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: +0.430 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.206 (without GEO) to +0.282 (without QAT). 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 (+0.046).

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+0.17
Europe & Central Asia43+0.39
Latin America & Caribbean15+0.22

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+0.17
Middle third28+0.34
Richest third29-0.27

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+0.35
Muslims17-0.17

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+0.03
United Kingdom14-0.40

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 = +0.236 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
Qatar QAT1.364322025, 2022
United Arab Emirates ARE1.174322025, 2022
Russian Federation RUS1.144782025, 2018
Macao SAR, China MAC1.125432025, 2022
Hong Kong SAR, China HKG1.115202025, 2022
Singapore SGP1.055612025, 2022
Japan JPN1.035472025, 2022
Germany DEU0.9884922025, 2022
Belarus BLR0.9784712025, 2018
Bulgaria BGR0.9544212025, 2022
Malta MLT0.9474662025, 2022
Israel ISR0.9434652025, 2022
Iceland ISL0.9194472025, 2022
Switzerland CHE0.9165032025, 2022
Norway NOR0.9154782025, 2022

un_sdg; wdi
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

Labour score

Labour score

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

The average of 4 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: Informal employment, Vulnerable employment, Youth not in work or study, Unemployment.

PISA science

PISA mean science score

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

How this was computed

Data used: most recent value in 2010-2025; values come from 2015–2025. 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 Labour score and PISA science

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

More links with Labour score or PISA science