placestats

Primary completion vs. Slum share of urban population

Fades after adjusting for income

Across 159 countries, Primary completion and Slum share of urban population rise and fall together at r = -0.69 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.29, which could easily be chance (q = 0.21, 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. Caution: direction is not consistent across regions.

Comparing countries across the middle half of primary completion, 80.9% to 99.5% of children, slum share of urban population is better by 18.7% of urban population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 7.6% of urban population.

Link before adjusting (r)-0.695p = 3.1e-24 · 159 countries
After adjusting for national income-0.286p = 0.000 · likely range [-0.42, -0.14]
p, allowing for regions0.062standard error 0.125 · 7 regions
Check for luck (q)0.206across 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.

Primary completion · % of childrenlink with income +0.83 · 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

Primary completion · % of children →Slum share of urban population · % of urban population →28.8%100%0%92.5%Afghanistan: 55.4% of children, 71.6% of urban populationAngola: 65.2% of children, 62.7% of urban populationAlbania: 97.4% of children, 2.7% of urban populationArgentina: 96.7% of children, 14.5% of urban populationArmenia: 99.5% of children, 8.39% of urban populationAustralia: 99.5% of children, 0.035% of urban populationAustria: 100% of children, 0.0466% of urban populationAzerbaijan: 98.4% of children, 26.9% of urban populationBurundi: 60.5% of children, 36.8% of urban populationBelgium: 98.9% of children, 0% of urban populationBenin: 70.1% of children, 64% of urban populationBurkina Faso: 72.2% of children, 87.9% of urban populationBangladesh: 90.8% of children, 51.5% of urban populationBulgaria: 99.1% of children, 0.197% of urban populationBosnia and Herzegovina: 99.8% of children, 0.313% of urban populationBelarus: 99.8% of children, 0.004% of urban populationBelize: 90.2% of children, 15.7% of urban populationBolivia: 98.5% of children, 46.6% of urban populationBrazil: 93% of children, 14.9% of urban populationBhutan: 93.6% of children, 44.7% of urban populationBotswana: 99% of children, 39.6% of urban populationCentral African Republic: 30.8% of children, 68.9% of urban populationCanada: 99.9% of children, 1.1% of urban populationSwitzerland: 99.8% of children, 0% of urban populationChile: 97.4% of children, 7.32% of urban populationChina: 99% of children, 26.3% of urban populationCote d'Ivoire: 78.8% of children, 48.3% of urban populationCameroon: 77.8% of children, 32.7% of urban populationCongo, Dem. Rep.: 68.4% of children, 78.4% of urban populationCongo, Rep.: 93.9% of children, 75.3% of urban populationColombia: 95.5% of children, 9.7% of urban populationComoros: 86.1% of children, 48.5% of urban populationCosta Rica: 98.9% of children, 3.55% of urban populationCyprus: 99% of children, 0.272% of urban populationCzechia: 99.9% of children, 0.0273% of urban populationGermany: 100% of children, 0% of urban populationDjibouti: 89.4% of children, 48.7% of urban populationDenmark: 99.9% of children, 0% of urban populationDominican Republic: 96.1% of children, 11.2% of urban populationAlgeria: 96.5% of children, 13.2% of urban populationEcuador: 99.5% of children, 57.8% of urban populationEgypt, Arab Rep.: 96.3% of children, 3.84% of urban populationSpain: 98.6% of children, 0.046% of urban populationEstonia: 99.5% of children, 0.345% of urban populationEthiopia: 64.6% of children, 64.3% of urban populationFinland: 99.7% of children, 0% of urban populationFiji: 98.9% of children, 9.4% of urban populationFrance: 99.6% of children, 0% of urban populationGabon: 74.3% of children, 38.8% of urban populationUnited Kingdom: 99.9% of children, 0.163% of urban populationGeorgia: 99.8% of children, 7.08% of urban populationGhana: 79.1% of children, 33.5% of urban populationGuinea: 61% of children, 44% of urban populationGambia, The: 74.8% of children, 37.1% of urban populationGuinea-Bissau: 30.5% of children, 59% of urban populationEquatorial Guinea: 73.6% of children, 64.7% of urban populationGreece: 99.3% of children, 0.05% of urban populationGuatemala: 87% of children, 37.6% of urban populationGuyana: 99.2% of children, 11.3% of urban populationHonduras: 89.6% of children, 31.5% of urban populationCroatia: 99.8% of children, 0.527% of urban populationHaiti: 50.5% of children, 51.1% of urban populationHungary: 99.2% of children, 7.2% of urban populationIndonesia: 98.9% of children, 19.4% of urban populationIndia: 96.5% of children, 5.41% of urban populationIreland: 99.8% of children, 8.5% of urban populationIran, Islamic Rep.: 96.7% of children, 44.7% of urban populationIraq: 83.3% of children, 49.3% of urban populationIceland: 97.4% of children, 0% of urban populationItaly: 99.9% of children, 0.02% of urban populationJamaica: 99.4% of children, 0.883% of urban populationJordan: 98.5% of children, 23.4% of urban populationJapan: 100% of children, 2% of urban populationKazakhstan: 99.7% of children, 0.794% of urban populationKenya: 88.4% of children, 40.5% of urban populationKyrgyz Republic: 99.4% of children, 2.44% of urban populationCambodia: 82.7% of children, 42.3% of urban populationKiribati: 94.4% of children, 5.93% of urban populationKorea, Rep.: 100% of children, 4.5% of urban populationLao PDR: 70.1% of children, 54.8% of urban populationLiberia: 29.8% of children, 60.5% of urban populationSt. Lucia: 99.6% of children, 0.441% of urban populationLesotho: 79.8% of children, 25.6% of urban populationLithuania: 99.7% of children, 0.509% of urban populationLuxembourg: 98.6% of children, 0.0173% of urban populationLatvia: 99.6% of children, 0.593% of urban populationMorocco: 84.6% of children, 10.9% of urban populationMoldova: 98.8% of children, 6.5% of urban populationMadagascar: 54% of children, 65.7% of urban populationMaldives: 99.4% of children, 34.8% of urban populationMexico: 98.2% of children, 17.6% of urban populationNorth Macedonia: 99.5% of children, 0.278% of urban populationMali: 50% of children, 92.5% of urban populationMalta: 98.1% of children, 0.0375% of urban populationMyanmar: 89.8% of children, 58.3% of urban populationMontenegro: 98.9% of children, 8.77% of urban populationMongolia: 99.7% of children, 17.9% of urban populationMozambique: 53.5% of children, 55% of urban populationMauritania: 51.8% of children, 58.6% of urban populationMauritius: 99.9% of children, 48.7% of urban populationMalawi: 57.7% of children, 38% of urban populationMalaysia: 99.8% of children, 0.2% of urban populationNamibia: 86.3% of children, 41.4% of urban populationNiger: 77.5% of children, 70.4% of urban populationNigeria: 76.8% of children, 48.5% of urban populationNicaragua: 77.7% of children, 5.52% of urban populationNetherlands: 99.8% of children, 0% of urban populationNorway: 99.8% of children, 0% of urban populationNepal: 90.8% of children, 40.1% of urban populationNaoero: 89.2% of children, 0.597% of urban populationPakistan: 59.5% of children, 56% of urban populationPanama: 95.1% of children, 16.3% of urban populationPeru: 98.1% of children, 45.1% of urban populationPhilippines: 96.2% of children, 35.9% of urban populationPapua New Guinea: 66% of children, 22.3% of urban populationPoland: 99.7% of children, 4.22% of urban populationPortugal: 99.5% of children, 0.059% of urban populationParaguay: 94.9% of children, 15.1% of urban populationWest Bank and Gaza: 99.6% of children, 19.5% of urban populationQatar: 100% of children, 0% of urban populationRomania: 99.1% of children, 2.52% of urban populationRussian Federation: 99.7% of children, 2.62% of urban populationRwanda: 71.9% of children, 38.3% of urban populationSudan: 75.6% of children, 73.7% of urban populationSenegal: 65.7% of children, 46.4% of urban populationSierra Leone: 68.8% of children, 49.3% of urban populationEl Salvador: 93.7% of children, 16.5% of urban populationSomalia, Fed. Rep.: 28.8% of children, 48.7% of urban populationSerbia: 99.7% of children, 1.42% of urban populationSao Tome and Principe: 93.7% of children, 82.4% of urban populationSuriname: 91.3% of children, 15.8% of urban populationSlovak Republic: 99.8% of children, 0.066% of urban populationSlovenia: 99.5% of children, 0.527% of urban populationSweden: 100% of children, 0.336% of urban populationEswatini: 82.5% of children, 17% of urban populationSyrian Arab Republic: 98.1% of children, 41.1% of urban populationChad: 37.8% of children, 82% of urban populationTogo: 87.8% of children, 38.5% of urban populationThailand: 99.2% of children, 2% of urban populationTajikistan: 98.8% of children, 0.05% of urban populationTurkmenistan: 99.8% of children, 8.38% of urban populationTimor-Leste: 82.1% of children, 33.9% of urban populationTonga: 98.4% of children, 0.308% of urban populationTrinidad and Tobago: 98% of children, 8.63% of urban populationTunisia: 96.9% of children, 7.64% of urban populationTurkiye: 98% of children, 14.1% of urban populationTuvalu: 98.8% of children, 50.9% of urban populationTanzania: 75.7% of children, 70.1% of urban populationUganda: 30.4% of children, 52.7% of urban populationUkraine: 99.6% of children, 1.1% of urban populationUruguay: 97.9% of children, 1.3% of urban populationUnited States: 100% of children, 0.0932% of urban populationUzbekistan: 99.8% of children, 7.1% of urban populationViet Nam: 98.6% of children, 32.5% of urban populationVanuatu: 79.8% of children, 3.06% of urban populationSamoa: 98.5% of children, 34.6% of urban populationSouth Africa: 97.1% of children, 24.2% of urban populationZambia: 67.4% of children, 48.3% of urban populationZimbabwe: 86.9% of children, 54.9% of urban populationAUTCAFCOGDEUECUGNBMLIQATSOMSTPTCDUGA

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.6953.1e-24159
By rank (Spearman), not adjusted-0.7711.3e-32159
Standard (Pearson), adjusted for national income-0.2860.000159
By rank (Spearman), adjusted for national income-0.3233.5e-5159

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 159 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: +0.409 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.318 (without STP) to -0.262 (without KIR). 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: STP (-0.032).

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 & Pacific22-0.12
Europe & Central Asia48-0.28
Latin America & Caribbean24+0.06
Middle East, North Africa, Afghanistan & Pakistan14-0.56
Sub-Saharan Africa44-0.12

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 third53-0.30
Middle third53-0.39
Richest third53-0.20

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
Christians100-0.21
Muslims41-0.44

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
France24-0.30
Never a European colony19-0.57
Spain19+0.02
United Kingdom45-0.16

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 = -0.284 after dropping 9 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 23years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

The link shows up within countries over time (r = -0.251) but mostly disappears in year-to-year changes (r = —). That points to slow trends particular to each country, not the two measures moving together from year to year.

Across countries (adjusted)-0.286for comparison
Within countries-0.489each country vs itself
…and vs the same year elsewhere-0.251q = 0.003 · 151 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.
  • Much of the within-country association disappears once shared global year effects are removed, which points to a common worldwide trend rather than a country-level relationship.

1,722 country-years (one country in one year), 151 countries, 2000–2022, about 11 years each on average. Share of the variation that happens within countries over time: 7% for Primary completion, 8% for Slum share of urban population. 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

0.30−0.3-100+10years Slum share of urban population trails Primary completion →lag -10: r = -0.206 (n = 1,136)lag -9: r = -0.223 (n = 1,282)lag -8: r = -0.227 (n = 1,282)lag -7: r = -0.243 (n = 1,426)lag -6: r = -0.248 (n = 1,426)lag -5: r = -0.260 (n = 1,567)lag -4: r = -0.265 (n = 1,567)lag -3: r = -0.243 (n = 1,722)lag -2: r = -0.246 (n = 1,722)lag -1: r = -0.248 (n = 1,722)lag +0: r = -0.251 (n = 1,722)lag +1: r = -0.253 (n = 1,722)lag +2: r = -0.254 (n = 1,722)lag +3: r = -0.255 (n = 1,722)lag +4: r = -0.255 (n = 1,722)lag +5: r = -0.254 (n = 1,722)lag +6: r = -0.253 (n = 1,722)lag +7: r = -0.251 (n = 1,722)lag +8: r = -0.248 (n = 1,722)lag +9: r = -0.244 (n = 1,722)lag +10: r = -0.238 (n = 1,722)

Same year (lag 0): -0.251. 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 data


% of children
% of urban population		Years
Qatar QAT100%0%2025, 2022
Korea, Rep. KOR100%4.5%2025, 2022
Japan JPN100%2%2025, 2022
Germany DEU100%0%2025, 2022
United States USA100%0.0932%2025, 2022
Austria AUT100%0.0466%2025, 2022
Sweden SWE100%0.336%2025, 2022
Italy ITA99.9%0.02%2025, 2022
Denmark DNK99.9%0%2025, 2022
Czechia CZE99.9%0.0273%2025, 2022
Canada CAN99.9%1.1%2025, 2022
Mauritius MUS99.9%48.7%2025, 2022
United Kingdom GBR99.9%0.163%2025, 2022
Malaysia MYS99.8%0.2%2025, 2020
Turkmenistan TKM99.8%8.38%2025, 2022

UNESCO Institute for Statistics, SDG indicator 4.1.2 modelled estimates
World Bank, World Development Indicators

Sources

Primary completion

Primary completion rate, modelled estimate

UNESCO Institute for Statistics · via Our World in Data · CC BY 3.0 IGO · updated annual

The share of children who finish primary school. UNESCO estimates it from household surveys and uses a statistical model to fill in across years. It is not the World Bank's primary completion rate, which is a ratio built from school enrolment counts of children entering the last grade. This one asks whether the child actually finished.

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 2012–2025. 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 Primary completion and Slum share of urban population

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

More links with Primary completion or Slum share of urban population