placestats

Labour score vs. PISA reading

Fades after adjusting for income

Across 86 countries, Labour score and PISA reading 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) shrinks it to +0.20, which could easily be chance (q = 0.44, 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 reading, 387 to 479 PISA points, labour score is better by 0.461 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.152 typical gaps.

Link before adjusting (r)+0.650p = 1.3e-11 · 86 countries
After adjusting for national income+0.199p = 0.069 · likely range [-0.02, +0.39]
p, allowing for regions0.119standard error 0.100 · 5 regions
Check for luck (q)0.442across 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 reading · PISA pointslink with income +0.77 · 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 reading · PISA points →-0.5781.36329543Albania: -0.39 typical gaps (standard deviations), 358 PISA pointsUnited Arab Emirates: 1.17 typical gaps (standard deviations), 417 PISA pointsArgentina: 0.34 typical gaps (standard deviations), 401 PISA pointsAustralia: 0.867 typical gaps (standard deviations), 498 PISA pointsAustria: 0.671 typical gaps (standard deviations), 480 PISA pointsBelgium: 0.682 typical gaps (standard deviations), 479 PISA pointsBulgaria: 0.954 typical gaps (standard deviations), 404 PISA pointsBosnia and Herzegovina: 0.126 typical gaps (standard deviations), 403 PISA pointsBelarus: 0.978 typical gaps (standard deviations), 474 PISA pointsBrazil: 0.16 typical gaps (standard deviations), 410 PISA pointsBrunei Darussalam: 0.857 typical gaps (standard deviations), 429 PISA pointsCanada: 0.581 typical gaps (standard deviations), 507 PISA pointsSwitzerland: 0.916 typical gaps (standard deviations), 483 PISA pointsChile: 0.344 typical gaps (standard deviations), 448 PISA pointsColombia: -0.377 typical gaps (standard deviations), 409 PISA pointsCosta Rica: 0.133 typical gaps (standard deviations), 415 PISA pointsCyprus: 0.812 typical gaps (standard deviations), 381 PISA pointsCzechia: 0.889 typical gaps (standard deviations), 489 PISA pointsGermany: 0.988 typical gaps (standard deviations), 480 PISA pointsDenmark: 0.819 typical gaps (standard deviations), 489 PISA pointsDominican Republic: -0.0819 typical gaps (standard deviations), 351 PISA pointsAlgeria: -0.0627 typical gaps (standard deviations), 350 PISA pointsSpain: 0.483 typical gaps (standard deviations), 474 PISA pointsEstonia: 0.558 typical gaps (standard deviations), 511 PISA pointsFinland: 0.419 typical gaps (standard deviations), 490 PISA pointsFrance: 0.595 typical gaps (standard deviations), 474 PISA pointsUnited Kingdom: 0.635 typical gaps (standard deviations), 494 PISA pointsGeorgia: -0.418 typical gaps (standard deviations), 374 PISA pointsGreece: 0.426 typical gaps (standard deviations), 438 PISA pointsGuatemala: 0.48 typical gaps (standard deviations), 374 PISA pointsHong Kong SAR, China: 1.11 typical gaps (standard deviations), 500 PISA pointsCroatia: 0.844 typical gaps (standard deviations), 475 PISA pointsHungary: 0.856 typical gaps (standard deviations), 473 PISA pointsIndonesia: 0.169 typical gaps (standard deviations), 359 PISA pointsIreland: 0.813 typical gaps (standard deviations), 516 PISA pointsIceland: 0.919 typical gaps (standard deviations), 436 PISA pointsIsrael: 0.943 typical gaps (standard deviations), 474 PISA pointsItaly: 0.579 typical gaps (standard deviations), 482 PISA pointsJamaica: 0.511 typical gaps (standard deviations), 410 PISA pointsJordan: -0.133 typical gaps (standard deviations), 342 PISA pointsJapan: 1.03 typical gaps (standard deviations), 516 PISA pointsKazakhstan: 0.516 typical gaps (standard deviations), 386 PISA pointsCambodia: 0.369 typical gaps (standard deviations), 329 PISA pointsKorea, Rep.: 0.822 typical gaps (standard deviations), 515 PISA pointsLebanon: 0.107 typical gaps (standard deviations), 353 PISA pointsLithuania: 0.626 typical gaps (standard deviations), 472 PISA pointsLuxembourg: 0.723 typical gaps (standard deviations), 470 PISA pointsLatvia: 0.673 typical gaps (standard deviations), 475 PISA pointsMacao SAR, China: 1.12 typical gaps (standard deviations), 510 PISA pointsMorocco: -0.0339 typical gaps (standard deviations), 339 PISA pointsMoldova: 0.412 typical gaps (standard deviations), 411 PISA pointsMexico: 0.513 typical gaps (standard deviations), 415 PISA pointsNorth Macedonia: 0.0322 typical gaps (standard deviations), 359 PISA pointsMalta: 0.947 typical gaps (standard deviations), 445 PISA pointsMontenegro: -0.0101 typical gaps (standard deviations), 405 PISA pointsMongolia: 0.223 typical gaps (standard deviations), 378 PISA pointsMalaysia: 0.654 typical gaps (standard deviations), 388 PISA pointsNetherlands: 0.804 typical gaps (standard deviations), 459 PISA pointsNorway: 0.915 typical gaps (standard deviations), 477 PISA pointsNew Zealand: 0.721 typical gaps (standard deviations), 501 PISA pointsPanama: 0.0819 typical gaps (standard deviations), 392 PISA pointsPeru: -0.454 typical gaps (standard deviations), 408 PISA pointsPhilippines: 0.559 typical gaps (standard deviations), 347 PISA pointsPoland: 0.842 typical gaps (standard deviations), 489 PISA pointsPortugal: 0.843 typical gaps (standard deviations), 477 PISA pointsParaguay: -0.242 typical gaps (standard deviations), 373 PISA pointsWest Bank and Gaza: -0.578 typical gaps (standard deviations), 349 PISA pointsQatar: 1.36 typical gaps (standard deviations), 419 PISA pointsRomania: 0.637 typical gaps (standard deviations), 428 PISA pointsRussian Federation: 1.14 typical gaps (standard deviations), 479 PISA pointsSaudi Arabia: 0.782 typical gaps (standard deviations), 383 PISA pointsSingapore: 1.05 typical gaps (standard deviations), 543 PISA pointsEl Salvador: 0.523 typical gaps (standard deviations), 365 PISA pointsSerbia: 0.409 typical gaps (standard deviations), 440 PISA pointsSlovak Republic: 0.713 typical gaps (standard deviations), 447 PISA pointsSlovenia: 0.914 typical gaps (standard deviations), 469 PISA pointsSweden: 0.531 typical gaps (standard deviations), 487 PISA pointsThailand: 0.466 typical gaps (standard deviations), 379 PISA pointsTrinidad and Tobago: 0.701 typical gaps (standard deviations), 427 PISA pointsTunisia: -0.205 typical gaps (standard deviations), 361 PISA pointsTurkiye: 0.224 typical gaps (standard deviations), 456 PISA pointsUkraine: 0.208 typical gaps (standard deviations), 428 PISA pointsUruguay: 0.298 typical gaps (standard deviations), 430 PISA pointsUnited States: 0.862 typical gaps (standard deviations), 504 PISA pointsUzbekistan: 0.368 typical gaps (standard deviations), 336 PISA pointsViet Nam: 0.283 typical gaps (standard deviations), 462 PISA pointsALBARECOLESTGEOJPNMACPERPHLPSEQATSGP

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.6501.3e-1186
By rank (Spearman), not adjusted+0.6576.2e-1286
Standard (Pearson), adjusted for national income+0.1990.06986
By rank (Spearman), adjusted for national income+0.1770.10686

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: +0.451 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.164 (without ALB) to +0.244 (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 (+0.045).

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 & Pacific15+0.19
Europe & Central Asia43+0.37
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+0.20
Middle third28+0.31
Richest third29-0.35

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+0.30
Muslims17-0.21

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 Kingdom14-0.21

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 = +0.199 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 QAT1.364192025, 2022
United Arab Emirates ARE1.174172025, 2022
Russian Federation RUS1.144792025, 2018
Macao SAR, China MAC1.125102025, 2022
Hong Kong SAR, China HKG1.115002025, 2022
Singapore SGP1.055432025, 2022
Japan JPN1.035162025, 2022
Germany DEU0.9884802025, 2022
Belarus BLR0.9784742025, 2018
Bulgaria BGR0.9544042025, 2022
Malta MLT0.9474452025, 2022
Israel ISR0.9434742025, 2022
Iceland ISL0.9194362025, 2022
Switzerland CHE0.9164832025, 2022
Norway NOR0.9154772025, 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 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

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 reading

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

More links with Labour score or PISA reading