placestats

Internet users vs. Slum share of urban population

Holds up after adjusting for income

Across 182 countries, Internet users and Slum share of urban population rise and fall together at r = -0.74 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.30 (q = 0.035, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a weak negative link remains.

Comparing countries across the middle half of internet users, 52.2% to 91.3% of population, slum share of urban population is better by 27.3% of urban population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 15% of urban population.

Link before adjusting (r)-0.742p = 3.9e-33 · 182 countries
After adjusting for national income-0.305p = 3.1e-5 · likely range [-0.43, -0.17]
p, allowing for regions0.003standard error 0.062 · 7 regions
Check for luck (q)0.035across 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.

Internet users · % of populationlink with income +0.85 · 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

Internet users · % of population →Slum share of urban population · % of urban population →8.6%100%0%92.5%Aruba: 97.2% of population, 0% of urban populationAfghanistan: 16.1% of population, 71.6% of urban populationAngola: 40.7% of population, 62.7% of urban populationAlbania: 85.9% of population, 2.7% of urban populationAndorra: 94.4% of population, 0% of urban populationUnited Arab Emirates: 100% of population, 0.113% of urban populationArgentina: 89.7% of population, 14.5% of urban populationArmenia: 81.3% of population, 8.39% of urban populationAntigua and Barbuda: 72.7% of population, 2.65% of urban populationAustralia: 96.1% of population, 0.035% of urban populationAustria: 91.9% of population, 0.0466% of urban populationAzerbaijan: 90.8% of population, 26.9% of urban populationBurundi: 8.6% of population, 36.8% of urban populationBelgium: 95.8% of population, 0% of urban populationBenin: 34% of population, 64% of urban populationBurkina Faso: 28.3% of population, 87.9% of urban populationBangladesh: 53.4% of population, 51.5% of urban populationBulgaria: 82.4% of population, 0.197% of urban populationBahrain: 100% of population, 0% of urban populationBosnia and Herzegovina: 86.1% of population, 0.313% of urban populationBelarus: 94.3% of population, 0.004% of urban populationBelize: 80% of population, 15.7% of urban populationBermuda: 98.4% of population, 0.0776% of urban populationBolivia: 79.7% of population, 46.6% of urban populationBrazil: 84.5% of population, 14.9% of urban populationBrunei Darussalam: 96.3% of population, 21.6% of urban populationBhutan: 91.3% of population, 44.7% of urban populationBotswana: 57.5% of population, 39.6% of urban populationCentral African Republic: 13.8% of population, 68.9% of urban populationCanada: 94.4% of population, 1.1% of urban populationSwitzerland: 97.3% of population, 0% of urban populationChile: 95.6% of population, 7.32% of urban populationChina: 91.6% of population, 26.3% of urban populationCote d'Ivoire: 41.4% of population, 48.3% of urban populationCameroon: 46.3% of population, 32.7% of urban populationCongo, Dem. Rep.: 19.7% of population, 78.4% of urban populationCongo, Rep.: 47.3% of population, 75.3% of urban populationColombia: 79.3% of population, 9.7% of urban populationComoros: 32.5% of population, 48.5% of urban populationCabo Verde: 74.7% of population, 46.4% of urban populationCosta Rica: 87.2% of population, 3.55% of urban populationCayman Islands: 81.1% of population, 5.48% of urban populationCyprus: 89.6% of population, 0.272% of urban populationCzechia: 87.7% of population, 0.0273% of urban populationGermany: 93.5% of population, 0% of urban populationDjibouti: 65.3% of population, 48.7% of urban populationDenmark: 99.8% of population, 0% of urban populationDominican Republic: 91% of population, 11.2% of urban populationAlgeria: 77.4% of population, 13.2% of urban populationEcuador: 77.2% of population, 57.8% of urban populationEgypt, Arab Rep.: 74.6% of population, 3.84% of urban populationSpain: 95.8% of population, 0.046% of urban populationEstonia: 92.2% of population, 0.345% of urban populationEthiopia: 21.9% of population, 64.3% of urban populationFinland: 93.7% of population, 0% of urban populationFiji: 74.7% of population, 9.4% of urban populationFrance: 88.7% of population, 0% of urban populationGabon: 68.7% of population, 38.8% of urban populationUnited Kingdom: 95.5% of population, 0.163% of urban populationGeorgia: 83.8% of population, 7.08% of urban populationGhana: 72.2% of population, 33.5% of urban populationGuinea: 33.3% of population, 44% of urban populationGambia, The: 49.5% of population, 37.1% of urban populationGuinea-Bissau: 29.8% of population, 59% of urban populationEquatorial Guinea: 63.3% of population, 64.7% of urban populationGreece: 86.3% of population, 0.05% of urban populationGuatemala: 60.2% of population, 37.6% of urban populationGuyana: 83% of population, 11.3% of urban populationHonduras: 58.6% of population, 31.5% of urban populationCroatia: 83.6% of population, 0.527% of urban populationHaiti: 47.9% of population, 51.1% of urban populationHungary: 93.8% of population, 7.2% of urban populationIndonesia: 72.8% of population, 19.4% of urban populationIndia: 70% of population, 5.41% of urban populationIreland: 97.2% of population, 8.5% of urban populationIran, Islamic Rep.: 85.3% of population, 44.7% of urban populationIraq: 81.5% of population, 49.3% of urban populationIceland: 98.2% of population, 0% of urban populationItaly: 89.2% of population, 0.02% of urban populationJamaica: 90.1% of population, 0.883% of urban populationJordan: 95.6% of population, 23.4% of urban populationJapan: 85.5% of population, 2% of urban populationKazakhstan: 93.4% of population, 0.794% of urban populationKenya: 35% of population, 40.5% of urban populationKyrgyz Republic: 92% of population, 2.44% of urban populationCambodia: 68.5% of population, 42.3% of urban populationKiribati: 89.4% of population, 5.93% of urban populationKorea, Rep.: 97.9% of population, 4.5% of urban populationKuwait: 100% of population, 0% of urban populationLao PDR: 65.6% of population, 54.8% of urban populationLebanon: 80.6% of population, 4.53% of urban populationLiberia: 32.2% of population, 60.5% of urban populationLibya: 82% of population, 16.6% of urban populationSt. Lucia: 68.2% of population, 0.441% of urban populationSri Lanka: 54.6% of population, 44.7% of urban populationLesotho: 51.8% of population, 25.6% of urban populationLithuania: 89.2% of population, 0.509% of urban populationLuxembourg: 99.1% of population, 0.0173% of urban populationLatvia: 92.7% of population, 0.593% of urban populationMorocco: 91.2% of population, 10.9% of urban populationMoldova: 77.4% of population, 6.5% of urban populationMadagascar: 18.7% of population, 65.7% of urban populationMaldives: 85.2% of population, 34.8% of urban populationMexico: 83.1% of population, 17.6% of urban populationMarshall Islands: 65.9% of population, 2.38% of urban populationNorth Macedonia: 93.1% of population, 0.278% of urban populationMali: 36.8% of population, 92.5% of urban populationMalta: 93.9% of population, 0.0375% of urban populationMyanmar: 45.4% of population, 58.3% of urban populationMontenegro: 88.9% of population, 8.77% of urban populationMongolia: 85.1% of population, 17.9% of urban populationMozambique: 20.5% of population, 55% of urban populationMauritania: 45.8% of population, 58.6% of urban populationMauritius: 73.3% of population, 48.7% of urban populationMalawi: 19% of population, 38% of urban populationMalaysia: 98% of population, 0.2% of urban populationNamibia: 64.9% of population, 41.4% of urban populationNiger: 15.6% of population, 70.4% of urban populationNigeria: 41.2% of population, 48.5% of urban populationNicaragua: 61.4% of population, 5.52% of urban populationNetherlands: 97% of population, 0% of urban populationNorway: 99% of population, 0% of urban populationNepal: 46.3% of population, 40.1% of urban populationNaoero: 83% of population, 0.597% of urban populationNew Zealand: 93.5% of population, 0% of urban populationOman: 95.3% of population, 0% of urban populationPakistan: 57.3% of population, 56% of urban populationPanama: 72.8% of population, 16.3% of urban populationPeru: 82% of population, 45.1% of urban populationPhilippines: 67.3% of population, 35.9% of urban populationPalau: 83.9% of population, 0.618% of urban populationPapua New Guinea: 18.8% of population, 22.3% of urban populationPoland: 88.6% of population, 4.22% of urban populationPortugal: 88.5% of population, 0.059% of urban populationParaguay: 81.6% of population, 15.1% of urban populationWest Bank and Gaza: 86.6% of population, 19.5% of urban populationQatar: 98.1% of population, 0% of urban populationRomania: 91.3% of population, 2.52% of urban populationRussian Federation: 94.4% of population, 2.62% of urban populationRwanda: 31.7% of population, 38.3% of urban populationSaudi Arabia: 100% of population, 0.55% of urban populationSudan: 18.6% of population, 73.7% of urban populationSenegal: 60.1% of population, 46.4% of urban populationSingapore: 94.4% of population, 0% of urban populationSolomon Islands: 29% of population, 1.95% of urban populationSierra Leone: 25.1% of population, 49.3% of urban populationEl Salvador: 66.5% of population, 16.5% of urban populationSomalia, Fed. Rep.: 27.9% of population, 48.7% of urban populationSerbia: 87.7% of population, 1.42% of urban populationSao Tome and Principe: 59.1% of population, 82.4% of urban populationSuriname: 87.4% of population, 15.8% of urban populationSlovak Republic: 89.8% of population, 0.066% of urban populationSlovenia: 90.8% of population, 0.527% of urban populationSweden: 95.8% of population, 0.336% of urban populationEswatini: 63.4% of population, 17% of urban populationSeychelles: 87.8% of population, 48.7% of urban populationSyrian Arab Republic: 33.8% of population, 41.1% of urban populationChad: 12.6% of population, 82% of urban populationTogo: 39.5% of population, 38.5% of urban populationThailand: 90.9% of population, 2% of urban populationTajikistan: 55.8% of population, 0.05% of urban populationTurkmenistan: 18% of population, 8.38% of urban populationTimor-Leste: 35% of population, 33.9% of urban populationTonga: 67.4% of population, 0.308% of urban populationTrinidad and Tobago: 82.2% of population, 8.63% of urban populationTunisia: 76.5% of population, 7.64% of urban populationTurkiye: 89.8% of population, 14.1% of urban populationTuvalu: 35.2% of population, 50.9% of urban populationTanzania: 31.2% of population, 70.1% of urban populationUganda: 8.95% of population, 52.7% of urban populationUkraine: 82.5% of population, 1.1% of urban populationUruguay: 92% of population, 1.3% of urban populationUnited States: 94.7% of population, 0.0932% of urban populationUzbekistan: 89.5% of population, 7.1% of urban populationSt. Vincent and the Grenadines: 76.2% of population, 2.76% of urban populationVirgin Islands (U.S.): 64.4% of population, 1.1% of urban populationViet Nam: 84.2% of population, 32.5% of urban populationVanuatu: 22.4% of population, 3.06% of urban populationSamoa: 21.2% of population, 34.6% of urban populationSouth Africa: 78.4% of population, 24.2% of urban populationZambia: 17.1% of population, 48.3% of urban populationZimbabwe: 41.6% of population, 54.9% of urban populationAREBFABHRBTNCODKWTSLBSTPSYCTCDTKMVUT

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.7423.9e-33182
By rank (Spearman), not adjusted-0.7732.4e-37182
Standard (Pearson), adjusted for national income-0.3053.1e-5182
By rank (Spearman), adjusted for national income-0.3013.8e-5182

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 182 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: +0.438 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.338 (without TKM) to -0.269 (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: KIR (+0.036).

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 & Pacific28-0.22
Europe & Central Asia49-0.06
Latin America & Caribbean29-0.09
Middle East, North Africa, Afghanistan & Pakistan21-0.44
Sub-Saharan Africa46-0.26

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 third61-0.36
Middle third60-0.20
Richest third61-0.15

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
Christians106-0.24
Muslims49-0.42

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
France25-0.36
Never a European colony21-0.24
Spain19-0.29
United Kingdom57-0.37

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 = -0.305 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 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 = +0.053, 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 internet users moved did not tend to see their slum share of urban population move with it.

Across countries (adjusted)-0.305for comparison
Within countries-0.435each country vs itself
…and vs the same year elsewhere+0.053q = 0.627 · 169 countries
Year-on-year changes—q = —
The direction flips. Comparing each country only with itself gives -0.44. Also taking out what happened to the whole world each year gives +0.05. So the first figure was picking up a trend both measures share with worldwide progress, not a link between them.
Read with care.
  • Almost all of the second indicator's variation is between countries, 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.

1,874 country-years (one country in one year), 169 countries, 2000–2022, about 11 years each on average. Share of the variation that happens within countries over time: 42% for Internet users, 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 Internet users →lag -10: r = -0.163 (n = 1,179)lag -9: r = -0.135 (n = 1,216)lag -8: r = -0.152 (n = 1,367)lag -7: r = -0.126 (n = 1,394)lag -6: r = -0.136 (n = 1,543)lag -5: r = -0.104 (n = 1,563)lag -4: r = -0.100 (n = 1,698)lag -3: r = -0.063 (n = 1,720)lag -2: r = -0.040 (n = 1,868)lag -1: r = 0.005 (n = 1,883)lag +0: r = 0.053 (n = 1,874)lag +1: r = 0.095 (n = 1,891)lag +2: r = 0.139 (n = 1,882)lag +3: r = 0.179 (n = 1,883)lag +4: r = 0.209 (n = 1,858)lag +5: r = 0.231 (n = 1,831)lag +6: r = 0.249 (n = 1,784)lag +7: r = 0.279 (n = 1,733)lag +8: r = 0.286 (n = 1,664)lag +9: r = 0.294 (n = 1,591)lag +10: r = 0.286 (n = 1,657)

Same year (lag 0): +0.053. 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 largest value on this curve (+0.29 at lag +9) is higher than the same-year one by 0.24. That gap is not a finding. It is the largest of 21 closely related estimates, and picking the biggest of many tests always makes it look bigger. That is exactly why nothing on this site is ranked or chosen by lag.

The data


% of population
% of urban population		Years
Saudi Arabia SAU100%0.55%2024, 2022
Kuwait KWT100%0%2025, 2022
Bahrain BHR100%0%2024, 2018
United Arab Emirates ARE100%0.113%2024, 2022
Denmark DNK99.8%0%2024, 2022
Luxembourg LUX99.1%0.0173%2025, 2022
Norway NOR99%0%2024, 2022
Bermuda BMU98.4%0.0776%2017, 2022
Iceland ISL98.2%0%2024, 2022
Qatar QAT98.1%0%2024, 2022
Malaysia MYS98%0.2%2024, 2020
Korea, Rep. KOR97.9%4.5%2024, 2022
Switzerland CHE97.3%0%2025, 2022
Ireland IRL97.2%8.5%2024, 2022
Aruba ABW97.2%0%2017, 2022

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

Sources

Internet users

Individuals using the Internet (% of population)

International Telecommunication Union · CC BY 4.0 · updated quarterly

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 Internet users and Slum share of urban population

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

More links with Internet users or Slum share of urban population