placestats

Fixed broadband vs. Hospital beds

Holds up after adjusting for income

Across 171 countries, Fixed broadband and Hospital beds rise and fall together at r = +0.60 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.38 (q = 0.045, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a weak positive link remains.

Comparing countries across the middle half of hospital beds, 1.18 to 3.82 per 1,000 people, fixed broadband is better by 11.2 per 100 people at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 4.76 per 100 people.

Link before adjusting (r)+0.605p = 1.9e-18 · 171 countries
After adjusting for national income+0.376p = 4.4e-7 · likely range [+0.24, +0.50]
p, allowing for regions0.001standard error 0.063 · 7 regions
Check for luck (q)0.045across 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
Hospital beds · per 1,000 peoplelink with income +0.63 · 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 →Hospital beds · per 1,000 people →048.90.1512.8Afghanistan: 0.0801 per 100 people, 0.35 per 1,000 peopleAngola: 0.363 per 100 people, 0.75 per 1,000 peopleAlbania: 24.9 per 100 people, 2.9 per 1,000 peopleUnited Arab Emirates: 40.8 per 100 people, 1.87 per 1,000 peopleArgentina: 26.1 per 100 people, 3.36 per 1,000 peopleArmenia: 20.3 per 100 people, 4.24 per 1,000 peopleAntigua and Barbuda: 10.7 per 100 people, 3.42 per 1,000 peopleAustralia: 36.5 per 100 people, 3.82 per 1,000 peopleAustria: 30.1 per 100 people, 6.7 per 1,000 peopleAzerbaijan: 21.8 per 100 people, 3.68 per 1,000 peopleBurundi: 0.0203 per 100 people, 0.69 per 1,000 peopleBelgium: 44.2 per 100 people, 5.42 per 1,000 peopleBenin: 0.277 per 100 people, 0.43 per 1,000 peopleBurkina Faso: 0.851 per 100 people, 0.2 per 1,000 peopleBangladesh: 8.09 per 100 people, 0.92 per 1,000 peopleBulgaria: 37 per 100 people, 8.2 per 1,000 peopleBahrain: 17 per 100 people, 1.84 per 1,000 peopleBahamas, The: 25.3 per 100 people, 2.17 per 1,000 peopleBosnia and Herzegovina: 29.4 per 100 people, 2.35 per 1,000 peopleBelarus: 36.3 per 100 people, 9.77 per 1,000 peopleBelize: 9.75 per 100 people, 1 per 1,000 peopleBolivia: 12.1 per 100 people, 1.49 per 1,000 peopleBrazil: 24.1 per 100 people, 2.52 per 1,000 peopleBarbados: 37.5 per 100 people, 6.4 per 1,000 peopleBrunei Darussalam: 21.2 per 100 people, 3.58 per 1,000 peopleBhutan: 2.92 per 100 people, 2.16 per 1,000 peopleBotswana: 7.44 per 100 people, 2.36 per 1,000 peopleCentral African Republic: 0.0161 per 100 people, 1 per 1,000 peopleCanada: 42.5 per 100 people, 2.54 per 1,000 peopleSwitzerland: 47 per 100 people, 4.38 per 1,000 peopleChile: 23.7 per 100 people, 1.94 per 1,000 peopleChina: 47.2 per 100 people, 5.63 per 1,000 peopleCameroon: 2.13 per 100 people, 2.65 per 1,000 peopleColombia: 17.2 per 100 people, 1.7 per 1,000 peopleComoros: 0.388 per 100 people, 2.16 per 1,000 peopleCabo Verde: 8.31 per 100 people, 2.21 per 1,000 peopleCosta Rica: 23.3 per 100 people, 1.14 per 1,000 peopleCyprus: 37.6 per 100 people, 2.15 per 1,000 peopleCzechia: 38.9 per 100 people, 6.54 per 1,000 peopleGermany: 45.6 per 100 people, 7.55 per 1,000 peopleDjibouti: 1.47 per 100 people, 1.4 per 1,000 peopleDominica: 20.9 per 100 people, 3.97 per 1,000 peopleDenmark: 43.4 per 100 people, 2.43 per 1,000 peopleDominican Republic: 11.2 per 100 people, 0.75 per 1,000 peopleAlgeria: 13.3 per 100 people, 1.61 per 1,000 peopleEcuador: 17.2 per 100 people, 1.32 per 1,000 peopleEgypt, Arab Rep.: 11.7 per 100 people, 1.06 per 1,000 peopleSpain: 39.2 per 100 people, 2.91 per 1,000 peopleEstonia: 35.4 per 100 people, 4.13 per 1,000 peopleEthiopia: 0.594 per 100 people, 0.33 per 1,000 peopleFinland: 36.5 per 100 people, 2.61 per 1,000 peopleFiji: 1.8 per 100 people, 1.89 per 1,000 peopleFrance: 48.9 per 100 people, 5.65 per 1,000 peopleGabon: 4.69 per 100 people, 1.95 per 1,000 peopleUnited Kingdom: 42.2 per 100 people, 2.42 per 1,000 peopleGeorgia: 30.4 per 100 people, 4.04 per 1,000 peopleGhana: 0.665 per 100 people, 0.69 per 1,000 peopleGuinea: 0.00628 per 100 people, 0.3 per 1,000 peopleGambia, The: 0.519 per 100 people, 1.11 per 1,000 peopleEquatorial Guinea: 0.101 per 100 people, 2.1 per 1,000 peopleGreece: 44.7 per 100 people, 4.27 per 1,000 peopleGrenada: 29.8 per 100 people, 3.01 per 1,000 peopleGuatemala: 5.34 per 100 people, 0.45 per 1,000 peopleGuyana: 12.9 per 100 people, 3.24 per 1,000 peopleHonduras: 4.58 per 100 people, 0.53 per 1,000 peopleCroatia: 29.6 per 100 people, 5.6 per 1,000 peopleHaiti: 0.307 per 100 people, 4.9 per 1,000 peopleHungary: 37.5 per 100 people, 6.63 per 1,000 peopleIndonesia: 4.92 per 100 people, 1.37 per 1,000 peopleIndia: 3.15 per 100 people, 1.59 per 1,000 peopleIreland: 32.3 per 100 people, 2.96 per 1,000 peopleIran, Islamic Rep.: 12.1 per 100 people, 1.85 per 1,000 peopleIraq: 17.6 per 100 people, 1.06 per 1,000 peopleIceland: 37.5 per 100 people, 2.83 per 1,000 peopleIsrael: 29.2 per 100 people, 3.14 per 1,000 peopleItaly: 31.8 per 100 people, 3.06 per 1,000 peopleJamaica: 16 per 100 people, 1.65 per 1,000 peopleJordan: 7.02 per 100 people, 1.41 per 1,000 peopleJapan: 38.6 per 100 people, 12.6 per 1,000 peopleKazakhstan: 15.3 per 100 people, 6.54 per 1,000 peopleKenya: 3.04 per 100 people, 1.33 per 1,000 peopleKyrgyz Republic: 7.8 per 100 people, 3.73 per 1,000 peopleCambodia: 4.15 per 100 people, 0.73 per 1,000 peopleKiribati: 0.0349 per 100 people, 1.86 per 1,000 peopleSt. Kitts and Nevis: 46.7 per 100 people, 3.98 per 1,000 peopleKorea, Rep.: 47.8 per 100 people, 12.8 per 1,000 peopleKuwait: 1.01 per 100 people, 2.33 per 1,000 peopleLao PDR: 2.72 per 100 people, 1.71 per 1,000 peopleLebanon: 7.29 per 100 people, 2.73 per 1,000 peopleLiberia: 0.275 per 100 people, 1.59 per 1,000 peopleLibya: 4.52 per 100 people, 3.5 per 1,000 peopleSt. Lucia: 13.6 per 100 people, 1.09 per 1,000 peopleSri Lanka: 8.47 per 100 people, 3.93 per 1,000 peopleLithuania: 27.8 per 100 people, 5.71 per 1,000 peopleLuxembourg: 40.3 per 100 people, 3.95 per 1,000 peopleLatvia: 25.8 per 100 people, 4.95 per 1,000 peopleMorocco: 7.3 per 100 people, 0.73 per 1,000 peopleMoldova: 29.6 per 100 people, 5.54 per 1,000 peopleMadagascar: 0.124 per 100 people, 0.32 per 1,000 peopleMaldives: 19.6 per 100 people, 4.56 per 1,000 peopleMexico: 21.7 per 100 people, 1.01 per 1,000 peopleMarshall Islands: 2.35 per 100 people, 2.8 per 1,000 peopleNorth Macedonia: 29.9 per 100 people, 4.74 per 1,000 peopleMali: 0.774 per 100 people, 0.25 per 1,000 peopleMalta: 44.5 per 100 people, 4.09 per 1,000 peopleMyanmar: 2.87 per 100 people, 1.07 per 1,000 peopleMontenegro: 32.3 per 100 people, 3.82 per 1,000 peopleMongolia: 15.8 per 100 people, 8.58 per 1,000 peopleMozambique: 0.142 per 100 people, 0.74 per 1,000 peopleMauritius: 28 per 100 people, 3.8 per 1,000 peopleMalawi: 0.0864 per 100 people, 1.3 per 1,000 peopleMalaysia: 13.5 per 100 people, 1.96 per 1,000 peopleNiger: 0.111 per 100 people, 0.28 per 1,000 peopleNicaragua: 5.78 per 100 people, 0.97 per 1,000 peopleNetherlands: 43 per 100 people, 2.42 per 1,000 peopleNorway: 45.2 per 100 people, 3.3 per 1,000 peopleNepal: 10.1 per 100 people, 0.54 per 1,000 peopleNaoero: 9.96 per 100 people, 4.97 per 1,000 peopleNew Zealand: 38 per 100 people, 2.51 per 1,000 peopleOman: 11.1 per 100 people, 0.99 per 1,000 peoplePakistan: 1.47 per 100 people, 0.63 per 1,000 peoplePanama: 18.1 per 100 people, 1.95 per 1,000 peoplePeru: 11.7 per 100 people, 1.56 per 1,000 peoplePhilippines: 7.14 per 100 people, 0.97 per 1,000 peoplePalau: 7.33 per 100 people, 4.34 per 1,000 peoplePapua New Guinea: 0.233 per 100 people, 0.62 per 1,000 peoplePoland: 25.4 per 100 people, 6.04 per 1,000 peoplePortugal: 45.2 per 100 people, 3.48 per 1,000 peopleParaguay: 12.9 per 100 people, 1.04 per 1,000 peopleWest Bank and Gaza: 8.16 per 100 people, 1.3 per 1,000 peopleQatar: 11.8 per 100 people, 1.25 per 1,000 peopleRomania: 35.8 per 100 people, 7.23 per 1,000 peopleRussian Federation: 27.1 per 100 people, 6.81 per 1,000 peopleRwanda: 0.596 per 100 people, 0.75 per 1,000 peopleSaudi Arabia: 44.1 per 100 people, 2.41 per 1,000 peopleSudan: 0.0604 per 100 people, 0.66 per 1,000 peopleSenegal: 4.04 per 100 people, 0.71 per 1,000 peopleSingapore: 27.8 per 100 people, 2.81 per 1,000 peopleEl Salvador: 12.5 per 100 people, 1.21 per 1,000 peopleSan Marino: 35.8 per 100 people, 3.35 per 1,000 peopleSomalia, Fed. Rep.: 0.723 per 100 people, 0.87 per 1,000 peopleSerbia: 33.4 per 100 people, 5.78 per 1,000 peopleSao Tome and Principe: 2.99 per 100 people, 2.91 per 1,000 peopleSuriname: 18.8 per 100 people, 2.87 per 1,000 peopleSlovak Republic: 33.8 per 100 people, 5.57 per 1,000 peopleSlovenia: 34.2 per 100 people, 4.11 per 1,000 peopleSweden: 40.6 per 100 people, 1.9 per 1,000 peopleEswatini: 2.51 per 100 people, 2.02 per 1,000 peopleSeychelles: 31.6 per 100 people, 2.63 per 1,000 peopleSyrian Arab Republic: 6.86 per 100 people, 1.43 per 1,000 peopleChad: 0 per 100 people, 0.15 per 1,000 peopleTogo: 1.42 per 100 people, 0.33 per 1,000 peopleThailand: 14.9 per 100 people, 2.39 per 1,000 peopleTajikistan: 0.984 per 100 people, 4.04 per 1,000 peopleTurkmenistan: 5.22 per 100 people, 3.6 per 1,000 peopleTimor-Leste: 0.006 per 100 people, 0.38 per 1,000 peopleTonga: 8.17 per 100 people, 2.48 per 1,000 peopleTrinidad and Tobago: 27 per 100 people, 2.37 per 1,000 peopleTunisia: 15 per 100 people, 1.82 per 1,000 peopleTurkiye: 23.4 per 100 people, 3.05 per 1,000 peopleTanzania: 0.246 per 100 people, 0.84 per 1,000 peopleUganda: 0.208 per 100 people, 0.5 per 1,000 peopleUkraine: 21.7 per 100 people, 6.14 per 1,000 peopleUruguay: 33 per 100 people, 2.44 per 1,000 peopleUnited States: 38.9 per 100 people, 2.68 per 1,000 peopleUzbekistan: 34.7 per 100 people, 4.89 per 1,000 peopleSt. Vincent and the Grenadines: 29.5 per 100 people, 3.21 per 1,000 peopleViet Nam: 23.7 per 100 people, 2.52 per 1,000 peopleSouth Africa: 5.35 per 100 people, 2.25 per 1,000 peopleZambia: 0.79 per 100 people, 2 per 1,000 peopleZimbabwe: 1.94 per 100 people, 1.95 per 1,000 peopleAREBLRGINJPNKORMNGNERSAUSWETCD

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.6051.9e-18171
By rank (Spearman), not adjusted+0.6911.2e-25171
Standard (Pearson), adjusted for national income+0.3764.4e-7171
By rank (Spearman), adjusted for national income+0.3697.6e-7171

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 171 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: +0.229 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.353 (without KOR) to +0.393 (without MNG). 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: KOR (-0.023).

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 & Pacific24+0.54
Europe & Central Asia49+0.09
Latin America & Caribbean31+0.51
Middle East, North Africa, Afghanistan & Pakistan22+0.30
Sub-Saharan Africa37+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 third57+0.03
Middle third57+0.43
Richest third57+0.30

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+0.43
Muslims44+0.19

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
France22+0.09
Never a European colony21+0.38
Spain19+0.26
United Kingdom56+0.32

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 = +0.352 after dropping 5 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 26years. 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 = -0.121, 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 fixed broadband moved did not tend to see their hospital beds move with it.

Across countries (adjusted)+0.376for comparison
Within countries-0.278each country vs itself
…and vs the same year elsewhere-0.121q = 0.289 · 151 countries
Year-on-year changes-0.033q = 0.515
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.

2,651 country-years (one country in one year), 151 countries, 1998–2023, about 18 years each on average. Share of the variation that happens within countries over time: 5% for Fixed broadband, 40% for Hospital beds. 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 Hospital beds trails Fixed broadband →lag -10: r = -0.139 (n = 2,265)lag -9: r = -0.122 (n = 2,341)lag -8: r = -0.108 (n = 2,411)lag -7: r = -0.087 (n = 2,456)lag -6: r = -0.084 (n = 2,526)lag -5: r = -0.071 (n = 2,590)lag -4: r = -0.055 (n = 2,638)lag -3: r = -0.049 (n = 2,675)lag -2: r = -0.081 (n = 2,727)lag -1: r = -0.095 (n = 2,727)lag +0: r = -0.121 (n = 2,651)lag +1: r = -0.138 (n = 2,555)lag +2: r = -0.148 (n = 2,444)lag +3: r = -0.148 (n = 2,328)lag +4: r = -0.147 (n = 2,214)lag +5: r = -0.145 (n = 2,107)lag +6: r = -0.143 (n = 1,997)lag +7: r = -0.137 (n = 1,877)lag +8: r = -0.153 (n = 1,747)lag +9: r = -0.179 (n = 1,604)lag +10: r = -0.207 (n = 1,470)

Same year (lag 0): -0.121. 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.

This curve peaks at the edge of the window (lag +10), so it is still drifting where the search stops. That pattern usually reflects a slow trend left over in the data, not a link with real timing. The peak value should not be read as the “true” link at the right lag.

The data


per 100 people
per 1,000 people		Years
France FRA48.95.652024, 2022
Korea, Rep. KOR47.812.82024, 2022
China CHN47.25.632024, 2023
Switzerland CHE474.382024, 2023
St. Kitts and Nevis KNA46.73.982022, 2023
Germany DEU45.67.552024, 2023
Portugal PRT45.23.482024, 2022
Norway NOR45.23.32024, 2023
Greece GRC44.74.272024, 2022
Malta MLT44.54.092024, 2022
Belgium BEL44.25.422024, 2023
Saudi Arabia SAU44.12.412024, 2023
Denmark DNK43.42.432024, 2023
Netherlands NLD432.422024, 2022
Canada CAN42.52.542024, 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.

Hospital beds

Hospital beds (per 1,000 people)

World Bank · CC BY 4.0 · updated quarterly

How this was computed

Data used: most recent value in 2010-2025; values come from 2010–2024. 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 Hospital beds

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

More links with Fixed broadband or Hospital beds