placestats

Fixed broadband vs. Slum share of urban population

Fades after adjusting for income

Across 182 countries, Fixed broadband and Slum share of urban population rise and fall together at r = -0.65 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.11, which could easily be chance (q = 0.39, 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 slum share of urban population, 0.561% to 44.5% of urban population, fixed broadband is worse by 18.4 per 100 people at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 2.72 per 100 people.

Link before adjusting (r)-0.651p = 2.5e-23 · 182 countries
After adjusting for national income-0.114p = 0.128 · likely range [-0.26, +0.03]
p, allowing for regions0.212standard error 0.081 · 7 regions
Check for luck (q)0.386across 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.

Fixed broadband · per 100 peoplelink with income +0.88 · 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

Fixed broadband · per 100 people →Slum share of urban population · % of urban population →056.40%92.5%Aruba: 17.5 per 100 people, 0% of urban populationAfghanistan: 0.0801 per 100 people, 71.6% of urban populationAngola: 0.363 per 100 people, 62.7% of urban populationAlbania: 24.9 per 100 people, 2.7% of urban populationAndorra: 52.4 per 100 people, 0% of urban populationUnited Arab Emirates: 40.8 per 100 people, 0.113% of urban populationArgentina: 26.1 per 100 people, 14.5% of urban populationArmenia: 20.3 per 100 people, 8.39% of urban populationAntigua and Barbuda: 10.7 per 100 people, 2.65% of urban populationAustralia: 36.5 per 100 people, 0.035% of urban populationAustria: 30.1 per 100 people, 0.0466% of urban populationAzerbaijan: 21.8 per 100 people, 26.9% of urban populationBurundi: 0.0203 per 100 people, 36.8% of urban populationBelgium: 44.2 per 100 people, 0% of urban populationBenin: 0.277 per 100 people, 64% of urban populationBurkina Faso: 0.851 per 100 people, 87.9% of urban populationBangladesh: 8.09 per 100 people, 51.5% of urban populationBulgaria: 37 per 100 people, 0.197% of urban populationBahrain: 17 per 100 people, 0% of urban populationBosnia and Herzegovina: 29.4 per 100 people, 0.313% of urban populationBelarus: 36.3 per 100 people, 0.004% of urban populationBelize: 9.75 per 100 people, 15.7% of urban populationBermuda: 51.3 per 100 people, 0.0776% of urban populationBolivia: 12.1 per 100 people, 46.6% of urban populationBrazil: 24.1 per 100 people, 14.9% of urban populationBrunei Darussalam: 21.2 per 100 people, 21.6% of urban populationBhutan: 2.92 per 100 people, 44.7% of urban populationBotswana: 7.44 per 100 people, 39.6% of urban populationCentral African Republic: 0.0161 per 100 people, 68.9% of urban populationCanada: 42.5 per 100 people, 1.1% of urban populationSwitzerland: 47 per 100 people, 0% of urban populationChile: 23.7 per 100 people, 7.32% of urban populationChina: 47.2 per 100 people, 26.3% of urban populationCote d'Ivoire: 1.6 per 100 people, 48.3% of urban populationCameroon: 2.13 per 100 people, 32.7% of urban populationCongo, Dem. Rep.: 0.032 per 100 people, 78.4% of urban populationCongo, Rep.: 1.27 per 100 people, 75.3% of urban populationColombia: 17.2 per 100 people, 9.7% of urban populationComoros: 0.388 per 100 people, 48.5% of urban populationCabo Verde: 8.31 per 100 people, 46.4% of urban populationCosta Rica: 23.3 per 100 people, 3.55% of urban populationCayman Islands: 56.4 per 100 people, 5.48% of urban populationCyprus: 37.6 per 100 people, 0.272% of urban populationCzechia: 38.9 per 100 people, 0.0273% of urban populationGermany: 45.6 per 100 people, 0% of urban populationDjibouti: 1.47 per 100 people, 48.7% of urban populationDenmark: 43.4 per 100 people, 0% of urban populationDominican Republic: 11.2 per 100 people, 11.2% of urban populationAlgeria: 13.3 per 100 people, 13.2% of urban populationEcuador: 17.2 per 100 people, 57.8% of urban populationEgypt, Arab Rep.: 11.7 per 100 people, 3.84% of urban populationSpain: 39.2 per 100 people, 0.046% of urban populationEstonia: 35.4 per 100 people, 0.345% of urban populationEthiopia: 0.594 per 100 people, 64.3% of urban populationFinland: 36.5 per 100 people, 0% of urban populationFiji: 1.8 per 100 people, 9.4% of urban populationFrance: 48.9 per 100 people, 0% of urban populationGabon: 4.69 per 100 people, 38.8% of urban populationUnited Kingdom: 42.2 per 100 people, 0.163% of urban populationGeorgia: 30.4 per 100 people, 7.08% of urban populationGhana: 0.665 per 100 people, 33.5% of urban populationGuinea: 0.00628 per 100 people, 44% of urban populationGambia, The: 0.519 per 100 people, 37.1% of urban populationGuinea-Bissau: 0.386 per 100 people, 59% of urban populationEquatorial Guinea: 0.101 per 100 people, 64.7% of urban populationGreece: 44.7 per 100 people, 0.05% of urban populationGuatemala: 5.34 per 100 people, 37.6% of urban populationGuyana: 12.9 per 100 people, 11.3% of urban populationHonduras: 4.58 per 100 people, 31.5% of urban populationCroatia: 29.6 per 100 people, 0.527% of urban populationHaiti: 0.307 per 100 people, 51.1% of urban populationHungary: 37.5 per 100 people, 7.2% of urban populationIndonesia: 4.92 per 100 people, 19.4% of urban populationIndia: 3.15 per 100 people, 5.41% of urban populationIreland: 32.3 per 100 people, 8.5% of urban populationIran, Islamic Rep.: 12.1 per 100 people, 44.7% of urban populationIraq: 17.6 per 100 people, 49.3% of urban populationIceland: 37.5 per 100 people, 0% of urban populationItaly: 31.8 per 100 people, 0.02% of urban populationJamaica: 16 per 100 people, 0.883% of urban populationJordan: 7.02 per 100 people, 23.4% of urban populationJapan: 38.6 per 100 people, 2% of urban populationKazakhstan: 15.3 per 100 people, 0.794% of urban populationKenya: 3.04 per 100 people, 40.5% of urban populationKyrgyz Republic: 7.8 per 100 people, 2.44% of urban populationCambodia: 4.15 per 100 people, 42.3% of urban populationKiribati: 0.0349 per 100 people, 5.93% of urban populationKorea, Rep.: 47.8 per 100 people, 4.5% of urban populationKuwait: 1.01 per 100 people, 0% of urban populationLao PDR: 2.72 per 100 people, 54.8% of urban populationLebanon: 7.29 per 100 people, 4.53% of urban populationLiberia: 0.275 per 100 people, 60.5% of urban populationLibya: 4.52 per 100 people, 16.6% of urban populationSt. Lucia: 13.6 per 100 people, 0.441% of urban populationSri Lanka: 8.47 per 100 people, 44.7% of urban populationLesotho: 0.379 per 100 people, 25.6% of urban populationLithuania: 27.8 per 100 people, 0.509% of urban populationLuxembourg: 40.3 per 100 people, 0.0173% of urban populationLatvia: 25.8 per 100 people, 0.593% of urban populationMorocco: 7.3 per 100 people, 10.9% of urban populationMoldova: 29.6 per 100 people, 6.5% of urban populationMadagascar: 0.124 per 100 people, 65.7% of urban populationMaldives: 19.6 per 100 people, 34.8% of urban populationMexico: 21.7 per 100 people, 17.6% of urban populationMarshall Islands: 2.35 per 100 people, 2.38% of urban populationNorth Macedonia: 29.9 per 100 people, 0.278% of urban populationMali: 0.774 per 100 people, 92.5% of urban populationMalta: 44.5 per 100 people, 0.0375% of urban populationMyanmar: 2.87 per 100 people, 58.3% of urban populationMontenegro: 32.3 per 100 people, 8.77% of urban populationMongolia: 15.8 per 100 people, 17.9% of urban populationMozambique: 0.142 per 100 people, 55% of urban populationMauritania: 0.947 per 100 people, 58.6% of urban populationMauritius: 28 per 100 people, 48.7% of urban populationMalawi: 0.0864 per 100 people, 38% of urban populationMalaysia: 13.5 per 100 people, 0.2% of urban populationNamibia: 2.83 per 100 people, 41.4% of urban populationNiger: 0.111 per 100 people, 70.4% of urban populationNigeria: 0.0787 per 100 people, 48.5% of urban populationNicaragua: 5.78 per 100 people, 5.52% of urban populationNetherlands: 43 per 100 people, 0% of urban populationNorway: 45.2 per 100 people, 0% of urban populationNepal: 10.1 per 100 people, 40.1% of urban populationNaoero: 9.96 per 100 people, 0.597% of urban populationNew Zealand: 38 per 100 people, 0% of urban populationOman: 11.1 per 100 people, 0% of urban populationPakistan: 1.47 per 100 people, 56% of urban populationPanama: 18.1 per 100 people, 16.3% of urban populationPeru: 11.7 per 100 people, 45.1% of urban populationPhilippines: 7.14 per 100 people, 35.9% of urban populationPalau: 7.33 per 100 people, 0.618% of urban populationPapua New Guinea: 0.233 per 100 people, 22.3% of urban populationPoland: 25.4 per 100 people, 4.22% of urban populationPortugal: 45.2 per 100 people, 0.059% of urban populationParaguay: 12.9 per 100 people, 15.1% of urban populationWest Bank and Gaza: 8.16 per 100 people, 19.5% of urban populationQatar: 11.8 per 100 people, 0% of urban populationRomania: 35.8 per 100 people, 2.52% of urban populationRussian Federation: 27.1 per 100 people, 2.62% of urban populationRwanda: 0.596 per 100 people, 38.3% of urban populationSaudi Arabia: 44.1 per 100 people, 0.55% of urban populationSudan: 0.0604 per 100 people, 73.7% of urban populationSenegal: 4.04 per 100 people, 46.4% of urban populationSingapore: 27.8 per 100 people, 0% of urban populationSolomon Islands: 0.107 per 100 people, 1.95% of urban populationSierra Leone: 0.00227 per 100 people, 49.3% of urban populationEl Salvador: 12.5 per 100 people, 16.5% of urban populationSomalia, Fed. Rep.: 0.723 per 100 people, 48.7% of urban populationSerbia: 33.4 per 100 people, 1.42% of urban populationSao Tome and Principe: 2.99 per 100 people, 82.4% of urban populationSuriname: 18.8 per 100 people, 15.8% of urban populationSlovak Republic: 33.8 per 100 people, 0.066% of urban populationSlovenia: 34.2 per 100 people, 0.527% of urban populationSweden: 40.6 per 100 people, 0.336% of urban populationEswatini: 2.51 per 100 people, 17% of urban populationSeychelles: 31.6 per 100 people, 48.7% of urban populationSyrian Arab Republic: 6.86 per 100 people, 41.1% of urban populationChad: 0 per 100 people, 82% of urban populationTogo: 1.42 per 100 people, 38.5% of urban populationThailand: 14.9 per 100 people, 2% of urban populationTajikistan: 0.984 per 100 people, 0.05% of urban populationTurkmenistan: 5.22 per 100 people, 8.38% of urban populationTimor-Leste: 0.006 per 100 people, 33.9% of urban populationTonga: 8.17 per 100 people, 0.308% of urban populationTrinidad and Tobago: 27 per 100 people, 8.63% of urban populationTunisia: 15 per 100 people, 7.64% of urban populationTurkiye: 23.4 per 100 people, 14.1% of urban populationTuvalu: 4.52 per 100 people, 50.9% of urban populationTanzania: 0.246 per 100 people, 70.1% of urban populationUganda: 0.208 per 100 people, 52.7% of urban populationUkraine: 21.7 per 100 people, 1.1% of urban populationUruguay: 33 per 100 people, 1.3% of urban populationUnited States: 38.9 per 100 people, 0.0932% of urban populationUzbekistan: 34.7 per 100 people, 7.1% of urban populationSt. Vincent and the Grenadines: 29.5 per 100 people, 2.76% of urban populationVirgin Islands (U.S.): 10.1 per 100 people, 1.1% of urban populationViet Nam: 23.7 per 100 people, 32.5% of urban populationVanuatu: 1.16 per 100 people, 3.06% of urban populationSamoa: 0.878 per 100 people, 34.6% of urban populationSouth Africa: 5.35 per 100 people, 24.2% of urban populationZambia: 0.79 per 100 people, 48.3% of urban populationZimbabwe: 1.94 per 100 people, 54.9% of urban populationANDBFABMUCHNCYMKWTMLISLBSYCTCDTJK

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.6512.5e-23182
By rank (Spearman), not adjusted-0.7337.0e-32182
Standard (Pearson), adjusted for national income-0.1140.128182
By rank (Spearman), adjusted for national income-0.1300.081182

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: +0.537 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.127 (without CHN) to -0.093 (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: GNQ (+0.021).

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 & Pacific28-0.03
Europe & Central Asia49-0.25
Latin America & Caribbean29-0.06
Middle East, North Africa, Afghanistan & Pakistan21+0.04
Sub-Saharan Africa46+0.07

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 third61-0.22
Middle third60-0.20
Richest third61-0.12

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
Christians106-0.26
Muslims49-0.10

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
France25-0.31
Never a European colony21+0.19
Spain19-0.36
United Kingdom57-0.03

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 = -0.114 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.

The link shows up within countries over time (r = +0.270) 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)-0.114for comparison
Within countries-0.174each country vs itself
…and vs the same year elsewhere+0.270q = 0.001 · 169 countries
Year-on-year changes—q = —
The direction flips. Comparing each country only with itself gives -0.17. Also taking out what happened to the whole world each year gives +0.27. 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 this indicator's variation is between countries, not within them, so the within-country estimate rests on a small part of the data.

1,604 country-years (one country in one year), 169 countries, 2000–2022, about 10 years each on average. Share of the variation that happens within countries over time: 7% for Fixed broadband, 32% 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 Fixed broadband →lag -10: r = 0.077 (n = 1,186)lag -9: r = 0.117 (n = 1,231)lag -8: r = 0.126 (n = 1,358)lag -7: r = 0.133 (n = 1,381)lag -6: r = 0.157 (n = 1,491)lag -5: r = 0.183 (n = 1,514)lag -4: r = 0.208 (n = 1,598)lag -3: r = 0.273 (n = 1,610)lag -2: r = 0.270 (n = 1,694)lag -1: r = 0.285 (n = 1,678)lag +0: r = 0.270 (n = 1,604)lag +1: r = 0.264 (n = 1,530)lag +2: r = 0.286 (n = 1,422)lag +3: r = 0.278 (n = 1,330)lag +4: r = 0.281 (n = 1,247)lag +5: r = 0.258 (n = 1,166)lag +6: r = 0.258 (n = 1,077)lag +7: r = 0.232 (n = 967)lag +8: r = 0.236 (n = 862)lag +9: r = 0.215 (n = 756)lag +10: r = 0.166 (n = 616)

Same year (lag 0): +0.270. 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


per 100 people
% of urban population		Years
Cayman Islands CYM56.45.48%2022
Andorra AND52.40%2024, 2022
Bermuda BMU51.30.0776%2024, 2022
France FRA48.90%2024, 2022
Korea, Rep. KOR47.84.5%2024, 2022
China CHN47.226.3%2024, 2022
Switzerland CHE470%2024, 2022
Germany DEU45.60%2024, 2022
Portugal PRT45.20.059%2024, 2022
Norway NOR45.20%2024, 2022
Greece GRC44.70.05%2024, 2012
Malta MLT44.50.0375%2024, 2022
Belgium BEL44.20%2024, 2022
Saudi Arabia SAU44.10.55%2024, 2022
Denmark DNK43.40%2024, 2022

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

Sources

Fixed broadband

Fixed broadband subscriptions (per 100 people)

International Telecommunication Union · CC BY 4.0 · updated quarterly

Subscriptions, not people. A household line counts once, however many people use it. A country that went straight to mobile data can be well connected with few of these lines. Internet users, already on the site, is the measure of people.

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 2012–2024. 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 Fixed broadband and Slum share of urban population

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

More links with Fixed broadband or Slum share of urban population