placestats

Population 0-14 vs. Slum share of urban population

Fades after adjusting for income

Across 184 countries, Population 0-14 and Slum share of urban population rise and fall together at r = +0.76 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.34, which could easily be chance (q = 0.13, 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 population 0-14, 16.5% to 35.9% of population, slum share of urban population is worse by 35.5% of urban population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 22.7% of urban population.

Link before adjusting (r)+0.762p = 3.5e-36 · 184 countries
After adjusting for national income+0.342p = 2.1e-6 · likely range [+0.21, +0.46]
p, allowing for regions0.027standard error 0.117 · 7 regions
Check for luck (q)0.127across 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.

Population 0-14 · % of populationlink with income -0.86 · 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

Population 0-14 · % of population →Slum share of urban population · % of urban population →10.2%48.7%0%92.5%Aruba: 16.5% of population, 0% of urban populationAfghanistan: 42.6% of population, 71.6% of urban populationAngola: 44.1% of population, 62.7% of urban populationAlbania: 16.6% of population, 2.7% of urban populationAndorra: 11.6% of population, 0% of urban populationUnited Arab Emirates: 16% of population, 0.113% of urban populationArgentina: 21% of population, 14.5% of urban populationArmenia: 19% of population, 8.39% of urban populationAntigua and Barbuda: 17.5% of population, 2.65% of urban populationAustralia: 17.7% of population, 0.035% of urban populationAustria: 14.1% of population, 0.0466% of urban populationAzerbaijan: 21.2% of population, 26.9% of urban populationBurundi: 44% of population, 36.8% of urban populationBelgium: 15.7% of population, 0% of urban populationBenin: 41.3% of population, 64% of urban populationBurkina Faso: 41.2% of population, 87.9% of urban populationBangladesh: 27.6% of population, 51.5% of urban populationBulgaria: 14.4% of population, 0.197% of urban populationBahrain: 18.4% of population, 0% of urban populationBosnia and Herzegovina: 12.8% of population, 0.313% of urban populationBelarus: 15.9% of population, 0.004% of urban populationBelize: 26.3% of population, 15.7% of urban populationBermuda: 13.4% of population, 0.0776% of urban populationBolivia: 29.4% of population, 46.6% of urban populationBrazil: 19.4% of population, 14.9% of urban populationBrunei Darussalam: 20.6% of population, 21.6% of urban populationBhutan: 20.4% of population, 44.7% of urban populationBotswana: 32% of population, 39.6% of urban populationCentral African Republic: 48.7% of population, 68.9% of urban populationCanada: 15% of population, 1.1% of urban populationSwitzerland: 14.9% of population, 0% of urban populationChile: 16.5% of population, 7.32% of urban populationChina: 15.4% of population, 26.3% of urban populationCote d'Ivoire: 40.5% of population, 48.3% of urban populationCameroon: 41.1% of population, 32.7% of urban populationCongo, Dem. Rep.: 45.9% of population, 78.4% of urban populationCongo, Rep.: 39.9% of population, 75.3% of urban populationColombia: 20% of population, 9.7% of urban populationComoros: 36.9% of population, 48.5% of urban populationCabo Verde: 24.7% of population, 46.4% of urban populationCosta Rica: 18.3% of population, 3.55% of urban populationCayman Islands: 16.1% of population, 5.48% of urban populationCyprus: 16.1% of population, 0.272% of urban populationCzechia: 15.1% of population, 0.0273% of urban populationGermany: 13.9% of population, 0% of urban populationDjibouti: 28.8% of population, 48.7% of urban populationDenmark: 15.6% of population, 0% of urban populationDominican Republic: 26.2% of population, 11.2% of urban populationAlgeria: 29.9% of population, 13.2% of urban populationEcuador: 23.9% of population, 57.8% of urban populationEgypt, Arab Rep.: 31.6% of population, 3.84% of urban populationSpain: 12.6% of population, 0.046% of urban populationEstonia: 15.3% of population, 0.345% of urban populationEthiopia: 38.8% of population, 64.3% of urban populationFinland: 14.3% of population, 0% of urban populationFiji: 26.8% of population, 9.4% of urban populationFrance: 16.2% of population, 0% of urban populationGabon: 36.2% of population, 38.8% of urban populationUnited Kingdom: 17% of population, 0.163% of urban populationGeorgia: 20.5% of population, 7.08% of urban populationGhana: 35.4% of population, 33.5% of urban populationGuinea: 40.5% of population, 44% of urban populationGambia, The: 39.7% of population, 37.1% of urban populationGuinea-Bissau: 38.3% of population, 59% of urban populationEquatorial Guinea: 37.1% of population, 64.7% of urban populationGreece: 12.9% of population, 0.05% of urban populationGuatemala: 31% of population, 37.6% of urban populationGuyana: 28.9% of population, 11.3% of urban populationHonduras: 30.3% of population, 31.5% of urban populationCroatia: 13.7% of population, 0.527% of urban populationHaiti: 30.8% of population, 51.1% of urban populationHungary: 14.4% of population, 7.2% of urban populationIndonesia: 24.2% of population, 19.4% of urban populationIndia: 24.2% of population, 5.41% of urban populationIreland: 18.1% of population, 8.5% of urban populationIran, Islamic Rep.: 22% of population, 44.7% of urban populationIraq: 36% of population, 49.3% of urban populationIceland: 17.5% of population, 0% of urban populationItaly: 11.7% of population, 0.02% of urban populationJamaica: 18.3% of population, 0.883% of urban populationJordan: 30.2% of population, 23.4% of urban populationJapan: 11.2% of population, 2% of urban populationKazakhstan: 29.1% of population, 0.794% of urban populationKenya: 36.3% of population, 40.5% of urban populationKyrgyz Republic: 31.9% of population, 2.44% of urban populationCambodia: 29.5% of population, 42.3% of urban populationKiribati: 34.4% of population, 5.93% of urban populationKorea, Rep.: 10.2% of population, 4.5% of urban populationKuwait: 18% of population, 0% of urban populationLao PDR: 29.8% of population, 54.8% of urban populationLebanon: 25.6% of population, 4.53% of urban populationLiberia: 39.1% of population, 60.5% of urban populationLibya: 26.8% of population, 16.6% of urban populationSt. Lucia: 17.2% of population, 0.441% of urban populationSri Lanka: 21.7% of population, 44.7% of urban populationLesotho: 34.1% of population, 25.6% of urban populationLithuania: 14.5% of population, 0.509% of urban populationLuxembourg: 15.7% of population, 0.0173% of urban populationLatvia: 15.2% of population, 0.593% of urban populationMorocco: 25.2% of population, 10.9% of urban populationMoldova: 19.5% of population, 6.5% of urban populationMadagascar: 38.9% of population, 65.7% of urban populationMaldives: 19% of population, 34.8% of urban populationMexico: 24.1% of population, 17.6% of urban populationMarshall Islands: 33.9% of population, 2.38% of urban populationNorth Macedonia: 16.5% of population, 0.278% of urban populationMali: 45.8% of population, 92.5% of urban populationMalta: 12.9% of population, 0.0375% of urban populationMyanmar: 24.1% of population, 58.3% of urban populationMontenegro: 17.9% of population, 8.77% of urban populationMongolia: 31.6% of population, 17.9% of urban populationMozambique: 44.2% of population, 55% of urban populationMauritania: 42.4% of population, 58.6% of urban populationMauritius: 14.5% of population, 48.7% of urban populationMalawi: 40.1% of population, 38% of urban populationMalaysia: 21.3% of population, 0.2% of urban populationNamibia: 36.7% of population, 41.4% of urban populationNiger: 46.2% of population, 70.4% of urban populationNigeria: 40.5% of population, 48.5% of urban populationNicaragua: 28.3% of population, 5.52% of urban populationNetherlands: 14.9% of population, 0% of urban populationNorway: 15.9% of population, 0% of urban populationNepal: 28.1% of population, 40.1% of urban populationNaoero: 37.2% of population, 0.597% of urban populationNew Zealand: 17.9% of population, 0% of urban populationOman: 24.3% of population, 0% of urban populationPakistan: 36.2% of population, 56% of urban populationPanama: 24.5% of population, 16.3% of urban populationPeru: 23.6% of population, 45.1% of urban populationPhilippines: 27.1% of population, 35.9% of urban populationPalau: 17.9% of population, 0.618% of urban populationPapua New Guinea: 33.1% of population, 22.3% of urban populationPoland: 14.5% of population, 4.22% of urban populationPortugal: 12.7% of population, 0.059% of urban populationParaguay: 28.4% of population, 15.1% of urban populationWest Bank and Gaza: 37.6% of population, 19.5% of urban populationQatar: 15% of population, 0% of urban populationRomania: 15.6% of population, 2.52% of urban populationRussian Federation: 17% of population, 2.62% of urban populationRwanda: 37% of population, 38.3% of urban populationSaudi Arabia: 23.6% of population, 0.55% of urban populationSudan: 40.2% of population, 73.7% of urban populationSenegal: 37.7% of population, 46.4% of urban populationSingapore: 11.7% of population, 0% of urban populationSolomon Islands: 36.6% of population, 1.95% of urban populationSierra Leone: 37.7% of population, 49.3% of urban populationEl Salvador: 24.4% of population, 16.5% of urban populationSomalia, Fed. Rep.: 46.6% of population, 48.7% of urban populationSerbia: 14.3% of population, 1.42% of urban populationSao Tome and Principe: 37.5% of population, 82.4% of urban populationSuriname: 25.3% of population, 15.8% of urban populationSlovak Republic: 15.6% of population, 0.066% of urban populationSlovenia: 14.5% of population, 0.527% of urban populationSweden: 16.7% of population, 0.336% of urban populationEswatini: 33% of population, 17% of urban populationSint Maarten (Dutch part): 14.8% of population, 2.89% of urban populationSeychelles: 19.8% of population, 48.7% of urban populationSyrian Arab Republic: 28.4% of population, 41.1% of urban populationTurks and Caicos Islands: 16% of population, 0.631% of urban populationChad: 46% of population, 82% of urban populationTogo: 40.4% of population, 38.5% of urban populationThailand: 14.4% of population, 2% of urban populationTajikistan: 35.9% of population, 0.05% of urban populationTurkmenistan: 31.2% of population, 8.38% of urban populationTimor-Leste: 33.1% of population, 33.9% of urban populationTonga: 34.9% of population, 0.308% of urban populationTrinidad and Tobago: 17.5% of population, 8.63% of urban populationTunisia: 23.6% of population, 7.64% of urban populationTurkiye: 21% of population, 14.1% of urban populationTuvalu: 33% of population, 50.9% of urban populationTanzania: 42.3% of population, 70.1% of urban populationUganda: 43.1% of population, 52.7% of urban populationUkraine: 13.7% of population, 1.1% of urban populationUruguay: 17.9% of population, 1.3% of urban populationUnited States: 17.1% of population, 0.0932% of urban populationUzbekistan: 31.3% of population, 7.1% of urban populationSt. Vincent and the Grenadines: 20.9% of population, 2.76% of urban populationVirgin Islands (U.S.): 16.5% of population, 1.1% of urban populationViet Nam: 22.9% of population, 32.5% of urban populationVanuatu: 38% of population, 3.06% of urban populationSamoa: 38.1% of population, 34.6% of urban populationSouth Africa: 25.7% of population, 24.2% of urban populationZambia: 41% of population, 48.3% of urban populationZimbabwe: 40.3% of population, 54.9% of urban populationANDCODITAMLIMMRMUSNRUSGPSYCTCDTJKVUT

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.7623.5e-36184
By rank (Spearman), not adjusted+0.7711.6e-37184
Standard (Pearson), adjusted for national income+0.3422.1e-6184
By rank (Spearman), adjusted for national income+0.2916.5e-5184

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 184 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: +0.420 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.323 (without GNQ) to +0.365 (without MUS). 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: MUS (+0.023).

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 & Pacific28-0.14
Europe & Central Asia49+0.21
Latin America & Caribbean31+0.47
Middle East, North Africa, Afghanistan & Pakistan21+0.17
Sub-Saharan Africa46+0.30

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 third62+0.37
Middle third61+0.28
Richest third61+0.41

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
Christians106+0.54
Muslims49+0.29

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
France25+0.48
Never a European colony21-0.20
Spain19+0.54
United Kingdom57+0.18

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 = +0.342 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.

Loading…

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.

Within countries over time, there is no link that is clearly more than chance (r = +0.096, comparing each country only with itself over the years, and each year only with the same year elsewhere). Whatever the cross-country picture shows, countries whose population 0-14 moved did not tend to see their slum share of urban population move with it.

Across countries (adjusted)+0.342for comparison
Within countries+0.416each country vs itself
…and vs the same year elsewhere+0.096q = 0.249 · 174 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.

1,988 country-years (one country in one year), 174 countries, 2000–2022, about 11 years each on average. Share of the variation that happens within countries over time: 4% for Population 0-14, 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

0.20−0.2-100+10years Slum share of urban population trails Population 0-14 →lag -10: r = 0.034 (n = 1,309)lag -9: r = 0.056 (n = 1,477)lag -8: r = 0.043 (n = 1,477)lag -7: r = 0.064 (n = 1,647)lag -6: r = 0.057 (n = 1,647)lag -5: r = 0.074 (n = 1,810)lag -4: r = 0.072 (n = 1,810)lag -3: r = 0.086 (n = 1,988)lag -2: r = 0.088 (n = 1,988)lag -1: r = 0.092 (n = 1,988)lag +0: r = 0.096 (n = 1,988)lag +1: r = 0.102 (n = 1,988)lag +2: r = 0.108 (n = 1,988)lag +3: r = 0.114 (n = 1,988)lag +4: r = 0.118 (n = 1,988)lag +5: r = 0.121 (n = 1,988)lag +6: r = 0.122 (n = 1,988)lag +7: r = 0.121 (n = 1,988)lag +8: r = 0.119 (n = 1,988)lag +9: r = 0.117 (n = 1,988)lag +10: r = 0.112 (n = 1,988)

Same year (lag 0): +0.096. 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 population
% of urban population		Years
Central African Republic CAF48.7%68.9%2025, 2022
Somalia, Fed. Rep. SOM46.6%48.7%2025, 2022
Niger NER46.2%70.4%2025, 2022
Chad TCD46%82%2025, 2022
Congo, Dem. Rep. COD45.9%78.4%2025, 2022
Mali MLI45.8%92.5%2025, 2022
Mozambique MOZ44.2%55%2025, 2022
Angola AGO44.1%62.7%2025, 2022
Burundi BDI44%36.8%2025, 2022
Uganda UGA43.1%52.7%2025, 2022
Afghanistan AFG42.6%71.6%2025, 2022
Mauritania MRT42.4%58.6%2025, 2022
Tanzania TZA42.3%70.1%2025, 2022
Benin BEN41.3%64%2025, 2022
Burkina Faso BFA41.2%87.9%2025, 2022

World Bank, World Development Indicators
World Bank, World Development Indicators

Sources

Population 0-14

Population ages 0-14 (% of total)

World Bank · CC BY 4.0 · updated quarterly

The two age shares and the median age are treated as measuring the same thing. Each is a reading of the same age pyramid. So the young share against the old share, at -0.86, is not a finding about anything.

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 Population 0-14 and Slum share of urban population

This page shows that two measures go together. It does not show that Population 0-14 changes Slum share of urban population, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with Population 0-14 or Slum share of urban population