placestats

Education score vs. Slum share of urban population

Holds up after adjusting for income

Across 180 countries, Education score and Slum share of urban population rise and fall together at r = -0.78 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.46 (q = 0.021, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a moderate negative link remains. Caution: direction is not consistent across regions.

Comparing countries across the middle half of slum share of urban population, 0.544% to 44.1% of urban population, education score is worse by 0.882 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.447 typical gaps.

Link before adjusting (r)-0.779p = 5.7e-38 · 180 countries
After adjusting for national income-0.457p = 1.2e-10 · likely range [-0.57, -0.33]
p, allowing for regions0.003standard error 0.094 · 7 regions
Check for luck (q)0.021across 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.

Education score · typical gaps (standard deviations)link with income +0.81 · proxy
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

Education score · typical gaps (standard deviations) →Slum share of urban population · % of urban population →-2.11.440%92.5%Aruba: 0.697 typical gaps (standard deviations), 0% of urban populationAfghanistan: -0.557 typical gaps (standard deviations), 71.6% of urban populationAngola: -0.482 typical gaps (standard deviations), 62.7% of urban populationAlbania: 0.923 typical gaps (standard deviations), 2.7% of urban populationAndorra: 0.892 typical gaps (standard deviations), 0% of urban populationUnited Arab Emirates: 0.911 typical gaps (standard deviations), 0.113% of urban populationArgentina: 1.03 typical gaps (standard deviations), 14.5% of urban populationArmenia: 0.65 typical gaps (standard deviations), 8.39% of urban populationAntigua and Barbuda: 0.912 typical gaps (standard deviations), 2.65% of urban populationAustralia: 1.03 typical gaps (standard deviations), 0.035% of urban populationAustria: 1 typical gaps (standard deviations), 0.0466% of urban populationAzerbaijan: 0.629 typical gaps (standard deviations), 26.9% of urban populationBurundi: -0.587 typical gaps (standard deviations), 36.8% of urban populationBelgium: 1.25 typical gaps (standard deviations), 0% of urban populationBenin: -0.586 typical gaps (standard deviations), 64% of urban populationBurkina Faso: -0.582 typical gaps (standard deviations), 87.9% of urban populationBangladesh: 0.335 typical gaps (standard deviations), 51.5% of urban populationBulgaria: 0.794 typical gaps (standard deviations), 0.197% of urban populationBahrain: 0.537 typical gaps (standard deviations), 0% of urban populationBosnia and Herzegovina: 0.607 typical gaps (standard deviations), 0.313% of urban populationBelarus: 0.722 typical gaps (standard deviations), 0.004% of urban populationBelize: 0.166 typical gaps (standard deviations), 15.7% of urban populationBermuda: 0.693 typical gaps (standard deviations), 0.0776% of urban populationBolivia: 0.634 typical gaps (standard deviations), 46.6% of urban populationBrazil: 0.731 typical gaps (standard deviations), 14.9% of urban populationBrunei Darussalam: 0.499 typical gaps (standard deviations), 21.6% of urban populationBhutan: 0.277 typical gaps (standard deviations), 44.7% of urban populationBotswana: 0.469 typical gaps (standard deviations), 39.6% of urban populationCentral African Republic: -1.65 typical gaps (standard deviations), 68.9% of urban populationCanada: 1.06 typical gaps (standard deviations), 1.1% of urban populationSwitzerland: 0.92 typical gaps (standard deviations), 0% of urban populationChile: 0.979 typical gaps (standard deviations), 7.32% of urban populationChina: 0.674 typical gaps (standard deviations), 26.3% of urban populationCote d'Ivoire: -0.0434 typical gaps (standard deviations), 48.3% of urban populationCameroon: -0.125 typical gaps (standard deviations), 32.7% of urban populationCongo, Dem. Rep.: -0.251 typical gaps (standard deviations), 78.4% of urban populationCongo, Rep.: 0.119 typical gaps (standard deviations), 75.3% of urban populationColombia: 0.63 typical gaps (standard deviations), 9.7% of urban populationComoros: 0.00771 typical gaps (standard deviations), 48.5% of urban populationCabo Verde: -0.216 typical gaps (standard deviations), 46.4% of urban populationCosta Rica: 0.629 typical gaps (standard deviations), 3.55% of urban populationCyprus: 1.02 typical gaps (standard deviations), 0.272% of urban populationCzechia: 1.08 typical gaps (standard deviations), 0.0273% of urban populationGermany: 0.938 typical gaps (standard deviations), 0% of urban populationDjibouti: -0.825 typical gaps (standard deviations), 48.7% of urban populationDenmark: 0.941 typical gaps (standard deviations), 0% of urban populationDominican Republic: 0.651 typical gaps (standard deviations), 11.2% of urban populationAlgeria: 0.663 typical gaps (standard deviations), 13.2% of urban populationEcuador: 0.722 typical gaps (standard deviations), 57.8% of urban populationEgypt, Arab Rep.: 0.489 typical gaps (standard deviations), 3.84% of urban populationSpain: 0.983 typical gaps (standard deviations), 0.046% of urban populationEstonia: 0.841 typical gaps (standard deviations), 0.345% of urban populationEthiopia: -0.777 typical gaps (standard deviations), 64.3% of urban populationFinland: 1.02 typical gaps (standard deviations), 0% of urban populationFiji: 0.887 typical gaps (standard deviations), 9.4% of urban populationFrance: 0.907 typical gaps (standard deviations), 0% of urban populationGabon: -0.00264 typical gaps (standard deviations), 38.8% of urban populationUnited Kingdom: 1.19 typical gaps (standard deviations), 0.163% of urban populationGeorgia: 0.697 typical gaps (standard deviations), 7.08% of urban populationGhana: 0.168 typical gaps (standard deviations), 33.5% of urban populationGuinea: -0.598 typical gaps (standard deviations), 44% of urban populationGambia, The: -0.345 typical gaps (standard deviations), 37.1% of urban populationGuinea-Bissau: -1.3 typical gaps (standard deviations), 59% of urban populationEquatorial Guinea: -0.759 typical gaps (standard deviations), 64.7% of urban populationGreece: 1.31 typical gaps (standard deviations), 0.05% of urban populationGuatemala: 0.22 typical gaps (standard deviations), 37.6% of urban populationGuyana: 0.39 typical gaps (standard deviations), 11.3% of urban populationHonduras: 0.0516 typical gaps (standard deviations), 31.5% of urban populationCroatia: 0.985 typical gaps (standard deviations), 0.527% of urban populationHungary: 0.83 typical gaps (standard deviations), 7.2% of urban populationIndonesia: 0.591 typical gaps (standard deviations), 19.4% of urban populationIndia: 0.605 typical gaps (standard deviations), 5.41% of urban populationIreland: 1.18 typical gaps (standard deviations), 8.5% of urban populationIran, Islamic Rep.: 0.667 typical gaps (standard deviations), 44.7% of urban populationIraq: -0.133 typical gaps (standard deviations), 49.3% of urban populationIceland: 1.26 typical gaps (standard deviations), 0% of urban populationItaly: 0.94 typical gaps (standard deviations), 0.02% of urban populationJamaica: 0.446 typical gaps (standard deviations), 0.883% of urban populationJordan: 0.68 typical gaps (standard deviations), 23.4% of urban populationJapan: 1.04 typical gaps (standard deviations), 2% of urban populationKazakhstan: 0.74 typical gaps (standard deviations), 0.794% of urban populationKenya: 0.0734 typical gaps (standard deviations), 40.5% of urban populationKyrgyz Republic: 0.764 typical gaps (standard deviations), 2.44% of urban populationCambodia: 0.174 typical gaps (standard deviations), 42.3% of urban populationKiribati: 0.376 typical gaps (standard deviations), 5.93% of urban populationKorea, Rep.: 1.03 typical gaps (standard deviations), 4.5% of urban populationKuwait: 0.25 typical gaps (standard deviations), 0% of urban populationLao PDR: -0.35 typical gaps (standard deviations), 54.8% of urban populationLebanon: 0.088 typical gaps (standard deviations), 4.53% of urban populationLiberia: -1.22 typical gaps (standard deviations), 60.5% of urban populationSt. Lucia: 0.462 typical gaps (standard deviations), 0.441% of urban populationSri Lanka: 0.421 typical gaps (standard deviations), 44.7% of urban populationLesotho: -0.337 typical gaps (standard deviations), 25.6% of urban populationLithuania: 0.928 typical gaps (standard deviations), 0.509% of urban populationLuxembourg: 0.66 typical gaps (standard deviations), 0.0173% of urban populationLatvia: 0.923 typical gaps (standard deviations), 0.593% of urban populationMorocco: 0.345 typical gaps (standard deviations), 10.9% of urban populationMoldova: 0.734 typical gaps (standard deviations), 6.5% of urban populationMadagascar: -0.569 typical gaps (standard deviations), 65.7% of urban populationMaldives: 0.427 typical gaps (standard deviations), 34.8% of urban populationMexico: 0.636 typical gaps (standard deviations), 17.6% of urban populationMarshall Islands: 0.934 typical gaps (standard deviations), 2.38% of urban populationNorth Macedonia: 0.658 typical gaps (standard deviations), 0.278% of urban populationMali: -1.24 typical gaps (standard deviations), 92.5% of urban populationMalta: 0.945 typical gaps (standard deviations), 0.0375% of urban populationMyanmar: -0.0676 typical gaps (standard deviations), 58.3% of urban populationMontenegro: 0.943 typical gaps (standard deviations), 8.77% of urban populationMongolia: 0.749 typical gaps (standard deviations), 17.9% of urban populationMozambique: -0.56 typical gaps (standard deviations), 55% of urban populationMauritania: -0.911 typical gaps (standard deviations), 58.6% of urban populationMauritius: 0.621 typical gaps (standard deviations), 48.7% of urban populationMalawi: -0.546 typical gaps (standard deviations), 38% of urban populationMalaysia: 0.529 typical gaps (standard deviations), 0.2% of urban populationNamibia: 0.228 typical gaps (standard deviations), 41.4% of urban populationNiger: -1.04 typical gaps (standard deviations), 70.4% of urban populationNigeria: -0.241 typical gaps (standard deviations), 48.5% of urban populationNicaragua: 0.168 typical gaps (standard deviations), 5.52% of urban populationNetherlands: 1.27 typical gaps (standard deviations), 0% of urban populationNorway: 1 typical gaps (standard deviations), 0% of urban populationNepal: 0.452 typical gaps (standard deviations), 40.1% of urban populationNaoero: 0.496 typical gaps (standard deviations), 0.597% of urban populationNew Zealand: 1.44 typical gaps (standard deviations), 0% of urban populationOman: 0.293 typical gaps (standard deviations), 0% of urban populationPakistan: -0.556 typical gaps (standard deviations), 56% of urban populationPanama: 0.393 typical gaps (standard deviations), 16.3% of urban populationPeru: 0.773 typical gaps (standard deviations), 45.1% of urban populationPhilippines: 0.548 typical gaps (standard deviations), 35.9% of urban populationPalau: 0.84 typical gaps (standard deviations), 0.618% of urban populationPapua New Guinea: -0.297 typical gaps (standard deviations), 22.3% of urban populationPoland: 0.952 typical gaps (standard deviations), 4.22% of urban populationPortugal: 1.02 typical gaps (standard deviations), 0.059% of urban populationParaguay: 0.452 typical gaps (standard deviations), 15.1% of urban populationWest Bank and Gaza: 0.564 typical gaps (standard deviations), 19.5% of urban populationQatar: 0.734 typical gaps (standard deviations), 0% of urban populationRomania: 0.584 typical gaps (standard deviations), 2.52% of urban populationRussian Federation: 0.85 typical gaps (standard deviations), 2.62% of urban populationRwanda: -0.127 typical gaps (standard deviations), 38.3% of urban populationSaudi Arabia: 0.8 typical gaps (standard deviations), 0.55% of urban populationSudan: -0.831 typical gaps (standard deviations), 73.7% of urban populationSenegal: -0.815 typical gaps (standard deviations), 46.4% of urban populationSingapore: 1.26 typical gaps (standard deviations), 0% of urban populationSolomon Islands: -0.0603 typical gaps (standard deviations), 1.95% of urban populationSierra Leone: -0.279 typical gaps (standard deviations), 49.3% of urban populationEl Salvador: 0.37 typical gaps (standard deviations), 16.5% of urban populationSomalia, Fed. Rep.: -2.1 typical gaps (standard deviations), 48.7% of urban populationSerbia: 0.761 typical gaps (standard deviations), 1.42% of urban populationSao Tome and Principe: 0.172 typical gaps (standard deviations), 82.4% of urban populationSuriname: -0.157 typical gaps (standard deviations), 15.8% of urban populationSlovak Republic: 0.801 typical gaps (standard deviations), 0.066% of urban populationSlovenia: 1.18 typical gaps (standard deviations), 0.527% of urban populationSweden: 0.968 typical gaps (standard deviations), 0.336% of urban populationEswatini: 0.404 typical gaps (standard deviations), 17% of urban populationSint Maarten (Dutch part): 0.541 typical gaps (standard deviations), 2.89% of urban populationSeychelles: 0.782 typical gaps (standard deviations), 48.7% of urban populationSyrian Arab Republic: -0.332 typical gaps (standard deviations), 41.1% of urban populationTurks and Caicos Islands: 0.306 typical gaps (standard deviations), 0.631% of urban populationChad: -1.2 typical gaps (standard deviations), 82% of urban populationTogo: 0.075 typical gaps (standard deviations), 38.5% of urban populationThailand: 0.764 typical gaps (standard deviations), 2% of urban populationTajikistan: 0.525 typical gaps (standard deviations), 0.05% of urban populationTurkmenistan: 0.598 typical gaps (standard deviations), 8.38% of urban populationTimor-Leste: 0.12 typical gaps (standard deviations), 33.9% of urban populationTonga: 0.921 typical gaps (standard deviations), 0.308% of urban populationTrinidad and Tobago: 0.581 typical gaps (standard deviations), 8.63% of urban populationTunisia: 0.496 typical gaps (standard deviations), 7.64% of urban populationTurkiye: 1.03 typical gaps (standard deviations), 14.1% of urban populationTuvalu: 0.577 typical gaps (standard deviations), 50.9% of urban populationTanzania: -0.351 typical gaps (standard deviations), 70.1% of urban populationUganda: -0.832 typical gaps (standard deviations), 52.7% of urban populationUkraine: 0.784 typical gaps (standard deviations), 1.1% of urban populationUruguay: 0.9 typical gaps (standard deviations), 1.3% of urban populationUnited States: 1.11 typical gaps (standard deviations), 0.0932% of urban populationUzbekistan: 0.848 typical gaps (standard deviations), 7.1% of urban populationSt. Vincent and the Grenadines: 0.91 typical gaps (standard deviations), 2.76% of urban populationViet Nam: 0.537 typical gaps (standard deviations), 32.5% of urban populationVanuatu: 0.198 typical gaps (standard deviations), 3.06% of urban populationSamoa: 0.515 typical gaps (standard deviations), 34.6% of urban populationSouth Africa: 0.665 typical gaps (standard deviations), 24.2% of urban populationZambia: -0.123 typical gaps (standard deviations), 48.3% of urban populationZimbabwe: 0.181 typical gaps (standard deviations), 54.9% of urban populationCAFCOGECUGRCMLINLDNZLSLBSOMSTP

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.7795.7e-38180
By rank (Spearman), not adjusted-0.7656.8e-36180
Standard (Pearson), adjusted for national income-0.4571.2e-10180
By rank (Spearman), adjusted for national income-0.3462.1e-6180

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 180 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: +0.322 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.493 (without SOM) to -0.436 (without GNQ). 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: SOM (-0.036).

Fragile: this link may not hold up.
  • direction is not consistent across regions

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 & Pacific28-0.50
Europe & Central Asia49-0.13
Latin America & Caribbean28+0.10
Middle East, North Africa, Afghanistan & Pakistan20-0.49
Sub-Saharan Africa46-0.34

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 third60-0.55
Middle third60-0.42
Richest third60-0.18

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
Christians104-0.41
Muslims48-0.52

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
France24-0.42
Never a European colony21-0.50
Spain19-0.29
United Kingdom56-0.34

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 = -0.469 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.

Loading…

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)
% of urban population		Years
New Zealand NZL1.440%2023, 2022
Greece GRC1.310.05%2023, 2012
Netherlands NLD1.270%2023, 2022
Iceland ISL1.260%2023, 2022
Singapore SGP1.260%2023, 2022
Belgium BEL1.250%2023, 2022
United Kingdom GBR1.190.163%2023, 2022
Ireland IRL1.188.5%2022
Slovenia SVN1.180.527%2023, 2022
United States USA1.110.0932%2022
Czechia CZE1.080.0273%2023, 2022
Canada CAN1.061.1%2023, 2022
Japan JPN1.042%2023, 2022
Argentina ARG1.0314.5%2023, 2022
Australia AUS1.030.035%2024, 2022

uis; undp_hdr; wdi
World Bank, World Development Indicators

Sources

Education score

Education score

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

The average of 10 measures from 3 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: Primary completion, Expected schooling, Mean years of schooling, Adult literacy, Pupil-teacher ratio, Primary enrollment, Out of school, primary, Secondary enrollment, Tertiary enrollment, Researchers.

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 2010–2025. 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 7 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 Education score and Slum share of urban population

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

More links with Education score or Slum share of urban population