placestats

PISA science vs. Slum share of urban population

Fades after adjusting for income

Across 83 countries, PISA science and Slum share of urban population rise and fall together at r = -0.53 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.12, which could easily be chance (q = 0.31, 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 489 PISA points, slum share of urban population is better by 8.9% of urban population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 2.57% of urban population.

Link before adjusting (r)-0.527p = 3.2e-7 · 83 countries
After adjusting for national income-0.124p = 0.266 · likely range [-0.33, +0.10]
p, allowing for regions0.130standard error 0.065 · 5 regions
Check for luck (q)0.315across 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 science · PISA pointslink with income +0.78 · high
Slum share of urban population · % of urban populationlink 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

PISA science · PISA points →Slum share of urban population · % of urban population →3475610%45.1%Albania: 376 PISA points, 2.7% of urban populationUnited Arab Emirates: 432 PISA points, 0.113% of urban populationArgentina: 406 PISA points, 14.5% of urban populationAustralia: 507 PISA points, 0.035% of urban populationAustria: 491 PISA points, 0.0466% of urban populationBelgium: 491 PISA points, 0% of urban populationBulgaria: 421 PISA points, 0.197% of urban populationBosnia and Herzegovina: 398 PISA points, 0.313% of urban populationBelarus: 471 PISA points, 0.004% of urban populationBrazil: 403 PISA points, 14.9% of urban populationBrunei Darussalam: 446 PISA points, 21.6% of urban populationCanada: 515 PISA points, 1.1% of urban populationSwitzerland: 503 PISA points, 0% of urban populationChile: 444 PISA points, 7.32% of urban populationColombia: 411 PISA points, 9.7% of urban populationCosta Rica: 411 PISA points, 3.55% of urban populationCyprus: 411 PISA points, 0.272% of urban populationCzechia: 498 PISA points, 0.0273% of urban populationGermany: 492 PISA points, 0% of urban populationDenmark: 494 PISA points, 0% of urban populationDominican Republic: 360 PISA points, 11.2% of urban populationAlgeria: 376 PISA points, 13.2% of urban populationSpain: 485 PISA points, 0.046% of urban populationEstonia: 526 PISA points, 0.345% of urban populationFinland: 511 PISA points, 0% of urban populationFrance: 487 PISA points, 0% of urban populationUnited Kingdom: 500 PISA points, 0.163% of urban populationGeorgia: 384 PISA points, 7.08% of urban populationGreece: 441 PISA points, 0.05% of urban populationGuatemala: 373 PISA points, 37.6% of urban populationCroatia: 483 PISA points, 0.527% of urban populationHungary: 486 PISA points, 7.2% of urban populationIndonesia: 383 PISA points, 19.4% of urban populationIreland: 504 PISA points, 8.5% of urban populationIceland: 447 PISA points, 0% of urban populationItaly: 477 PISA points, 0.02% of urban populationJamaica: 403 PISA points, 0.883% of urban populationJordan: 375 PISA points, 23.4% of urban populationJapan: 547 PISA points, 2% of urban populationKazakhstan: 423 PISA points, 0.794% of urban populationCambodia: 347 PISA points, 42.3% of urban populationKorea, Rep.: 528 PISA points, 4.5% of urban populationLebanon: 384 PISA points, 4.53% of urban populationLithuania: 484 PISA points, 0.509% of urban populationLuxembourg: 477 PISA points, 0.0173% of urban populationLatvia: 494 PISA points, 0.593% of urban populationMorocco: 365 PISA points, 10.9% of urban populationMoldova: 417 PISA points, 6.5% of urban populationMexico: 410 PISA points, 17.6% of urban populationNorth Macedonia: 380 PISA points, 0.278% of urban populationMalta: 466 PISA points, 0.0375% of urban populationMontenegro: 403 PISA points, 8.77% of urban populationMongolia: 412 PISA points, 17.9% of urban populationMalaysia: 416 PISA points, 0.2% of urban populationNetherlands: 488 PISA points, 0% of urban populationNorway: 478 PISA points, 0% of urban populationNew Zealand: 504 PISA points, 0% of urban populationPanama: 388 PISA points, 16.3% of urban populationPeru: 408 PISA points, 45.1% of urban populationPhilippines: 356 PISA points, 35.9% of urban populationPoland: 499 PISA points, 4.22% of urban populationPortugal: 484 PISA points, 0.059% of urban populationParaguay: 368 PISA points, 15.1% of urban populationWest Bank and Gaza: 369 PISA points, 19.5% of urban populationQatar: 432 PISA points, 0% of urban populationRomania: 428 PISA points, 2.52% of urban populationRussian Federation: 478 PISA points, 2.62% of urban populationSaudi Arabia: 390 PISA points, 0.55% of urban populationSingapore: 561 PISA points, 0% of urban populationEl Salvador: 373 PISA points, 16.5% of urban populationSerbia: 447 PISA points, 1.42% of urban populationSlovak Republic: 462 PISA points, 0.066% of urban populationSlovenia: 500 PISA points, 0.527% of urban populationSweden: 494 PISA points, 0.336% of urban populationThailand: 409 PISA points, 2% of urban populationTrinidad and Tobago: 425 PISA points, 8.63% of urban populationTunisia: 386 PISA points, 7.64% of urban populationTurkiye: 476 PISA points, 14.1% of urban populationUkraine: 450 PISA points, 1.1% of urban populationUruguay: 435 PISA points, 1.3% of urban populationUnited States: 499 PISA points, 0.0932% of urban populationUzbekistan: 355 PISA points, 7.1% of urban populationViet Nam: 472 PISA points, 32.5% of urban populationALBESTGTMJPNKHMMKDPERPHLSGPUZBVNM

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.5273.2e-783
By rank (Spearman), not adjusted-0.6292.0e-1083
Standard (Pearson), adjusted for national income-0.1240.26683
By rank (Spearman), adjusted for national income-0.1480.18483

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 83 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: +0.402 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.198 (without VNM) to -0.084 (without BRN). 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 (-0.074).

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 & Pacific13-0.14
Europe & Central Asia43-0.11
Latin America & Caribbean15-0.20

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+0.01
Middle third27-0.26
Richest third28-0.13

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+0.34

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
Spain14-0.05
United Kingdom13-0.26

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 = -0.258 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 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 urban population		Years
Singapore SGP5610%2022
Japan JPN5472%2022
Korea, Rep. KOR5284.5%2022
Estonia EST5260.345%2022
Canada CAN5151.1%2022
Finland FIN5110%2022
Australia AUS5070.035%2022
New Zealand NZL5040%2022
Ireland IRL5048.5%2022
Switzerland CHE5030%2022
Slovenia SVN5000.527%2022
United Kingdom GBR5000.163%2022
United States USA4990.0932%2022
Poland POL4994.22%2022
Czechia CZE4980.0273%2022

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
World Bank, World Development Indicators

Sources

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

Slum share of urban population

Population living in slums (% of urban population)

UN-Habitat · CC BY 4.0 · updated quarterly

How this was computed

Data used: most recent value in 2010-2025; values come from 2012–2022. 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 science and Slum share of urban population

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

More links with PISA science or Slum share of urban population