placestats

Fixed broadband vs. Wellbeing score

Fades after adjusting for income

Across 106 countries, Fixed broadband and Wellbeing score rise and fall together at r = +0.68 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.20, which could easily be chance (q = 0.085, 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 fixed broadband, 9.04 to 36.9 per 100 people, wellbeing score is better by 0.938 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.327 typical gaps.

Link before adjusting (r)+0.675p = 2.1e-15 · 106 countries
After adjusting for national income+0.200p = 0.041 · likely range [+0.01, +0.38]
p, allowing for regions0.013standard error 0.057 · 7 regions
Check for luck (q)0.085across 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
Wellbeing score · typical gaps (standard deviations)link with income +0.80 · 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 →Wellbeing score · typical gaps (standard deviations) →0.078752.4-1.981.89Albania: 24.9 per 100 people, -0.617 typical gaps (standard deviations)Andorra: 52.4 per 100 people, 0.512 typical gaps (standard deviations)Argentina: 26.1 per 100 people, 0.216 typical gaps (standard deviations)Armenia: 20.3 per 100 people, -0.118 typical gaps (standard deviations)Australia: 36.5 per 100 people, 1.25 typical gaps (standard deviations)Austria: 30.1 per 100 people, 1.26 typical gaps (standard deviations)Azerbaijan: 21.8 per 100 people, -0.0603 typical gaps (standard deviations)Belgium: 44.2 per 100 people, 0.818 typical gaps (standard deviations)Burkina Faso: 0.851 per 100 people, -0.476 typical gaps (standard deviations)Bangladesh: 8.09 per 100 people, -0.561 typical gaps (standard deviations)Bulgaria: 37 per 100 people, -0.572 typical gaps (standard deviations)Bosnia and Herzegovina: 29.4 per 100 people, -0.087 typical gaps (standard deviations)Belarus: 36.3 per 100 people, 0.732 typical gaps (standard deviations)Bolivia: 12.1 per 100 people, -0.361 typical gaps (standard deviations)Brazil: 24.1 per 100 people, 0.123 typical gaps (standard deviations)Canada: 42.5 per 100 people, 1.06 typical gaps (standard deviations)Switzerland: 47 per 100 people, 1.55 typical gaps (standard deviations)Chile: 23.7 per 100 people, 0.19 typical gaps (standard deviations)China: 47.2 per 100 people, 0.913 typical gaps (standard deviations)Colombia: 17.2 per 100 people, -0.125 typical gaps (standard deviations)Cyprus: 37.6 per 100 people, -0.0176 typical gaps (standard deviations)Czechia: 38.9 per 100 people, 0.726 typical gaps (standard deviations)Germany: 45.6 per 100 people, 0.981 typical gaps (standard deviations)Denmark: 43.4 per 100 people, 1.89 typical gaps (standard deviations)Algeria: 13.3 per 100 people, -0.205 typical gaps (standard deviations)Ecuador: 17.2 per 100 people, -0.164 typical gaps (standard deviations)Egypt, Arab Rep.: 11.7 per 100 people, -1.08 typical gaps (standard deviations)Spain: 39.2 per 100 people, 0.816 typical gaps (standard deviations)Estonia: 35.4 per 100 people, 0.622 typical gaps (standard deviations)Ethiopia: 0.594 per 100 people, -0.726 typical gaps (standard deviations)Finland: 36.5 per 100 people, 1.87 typical gaps (standard deviations)France: 48.9 per 100 people, 0.717 typical gaps (standard deviations)United Kingdom: 42.2 per 100 people, 1.07 typical gaps (standard deviations)Georgia: 30.4 per 100 people, -0.493 typical gaps (standard deviations)Ghana: 0.665 per 100 people, -0.73 typical gaps (standard deviations)Greece: 44.7 per 100 people, -0.284 typical gaps (standard deviations)Guatemala: 5.34 per 100 people, 0.352 typical gaps (standard deviations)Hong Kong SAR, China: 40.3 per 100 people, 0.0778 typical gaps (standard deviations)Croatia: 29.6 per 100 people, 0.0883 typical gaps (standard deviations)Haiti: 0.307 per 100 people, -0.461 typical gaps (standard deviations)Hungary: 37.5 per 100 people, 0.266 typical gaps (standard deviations)Indonesia: 4.92 per 100 people, -0.192 typical gaps (standard deviations)India: 3.15 per 100 people, -0.736 typical gaps (standard deviations)Ireland: 32.3 per 100 people, 0.894 typical gaps (standard deviations)Iran, Islamic Rep.: 12.1 per 100 people, -0.684 typical gaps (standard deviations)Iraq: 17.6 per 100 people, -0.726 typical gaps (standard deviations)Iceland: 37.5 per 100 people, 1.78 typical gaps (standard deviations)Italy: 31.8 per 100 people, 0.438 typical gaps (standard deviations)Jordan: 7.02 per 100 people, -0.666 typical gaps (standard deviations)Japan: 38.6 per 100 people, 0.552 typical gaps (standard deviations)Kazakhstan: 15.3 per 100 people, 0.304 typical gaps (standard deviations)Kenya: 3.04 per 100 people, -0.678 typical gaps (standard deviations)Kyrgyz Republic: 7.8 per 100 people, 0.225 typical gaps (standard deviations)Korea, Rep.: 47.8 per 100 people, 0.475 typical gaps (standard deviations)Kuwait: 1.01 per 100 people, 0.607 typical gaps (standard deviations)Lebanon: 7.29 per 100 people, -1.54 typical gaps (standard deviations)Libya: 4.52 per 100 people, -0.158 typical gaps (standard deviations)Lithuania: 27.8 per 100 people, 0.51 typical gaps (standard deviations)Luxembourg: 40.3 per 100 people, 0.652 typical gaps (standard deviations)Latvia: 25.8 per 100 people, 0.212 typical gaps (standard deviations)Macao SAR, China: 29.9 per 100 people, 0.875 typical gaps (standard deviations)Morocco: 7.3 per 100 people, -0.382 typical gaps (standard deviations)Moldova: 29.6 per 100 people, -1.29 typical gaps (standard deviations)Maldives: 19.6 per 100 people, 0.0845 typical gaps (standard deviations)Mexico: 21.7 per 100 people, 0.335 typical gaps (standard deviations)North Macedonia: 29.9 per 100 people, -0.171 typical gaps (standard deviations)Mali: 0.774 per 100 people, -0.328 typical gaps (standard deviations)Malta: 44.5 per 100 people, 0.15 typical gaps (standard deviations)Myanmar: 2.87 per 100 people, -0.547 typical gaps (standard deviations)Montenegro: 32.3 per 100 people, 0.239 typical gaps (standard deviations)Mongolia: 15.8 per 100 people, 0.000193 typical gaps (standard deviations)Malaysia: 13.5 per 100 people, 0.215 typical gaps (standard deviations)Nigeria: 0.0787 per 100 people, -0.611 typical gaps (standard deviations)Nicaragua: 5.78 per 100 people, 0.0736 typical gaps (standard deviations)Netherlands: 43 per 100 people, 1.54 typical gaps (standard deviations)Norway: 45.2 per 100 people, 1.82 typical gaps (standard deviations)New Zealand: 38 per 100 people, 1.46 typical gaps (standard deviations)Pakistan: 1.47 per 100 people, -0.299 typical gaps (standard deviations)Peru: 11.7 per 100 people, -0.348 typical gaps (standard deviations)Philippines: 7.14 per 100 people, -0.119 typical gaps (standard deviations)Poland: 25.4 per 100 people, 0.505 typical gaps (standard deviations)Puerto Rico (US): 28 per 100 people, 0.077 typical gaps (standard deviations)Portugal: 45.2 per 100 people, 0.195 typical gaps (standard deviations)West Bank and Gaza: 8.16 per 100 people, -0.644 typical gaps (standard deviations)Qatar: 11.8 per 100 people, 0.756 typical gaps (standard deviations)Romania: 35.8 per 100 people, 0.197 typical gaps (standard deviations)Russian Federation: 27.1 per 100 people, -0.00878 typical gaps (standard deviations)Rwanda: 0.596 per 100 people, -0.867 typical gaps (standard deviations)Singapore: 27.8 per 100 people, 0.779 typical gaps (standard deviations)Serbia: 33.4 per 100 people, 0.105 typical gaps (standard deviations)Slovak Republic: 33.8 per 100 people, 0.318 typical gaps (standard deviations)Slovenia: 34.2 per 100 people, 0.55 typical gaps (standard deviations)Sweden: 40.6 per 100 people, 1.7 typical gaps (standard deviations)Thailand: 14.9 per 100 people, 0.256 typical gaps (standard deviations)Tajikistan: 0.984 per 100 people, 0.104 typical gaps (standard deviations)Trinidad and Tobago: 27 per 100 people, 0.0637 typical gaps (standard deviations)Tunisia: 15 per 100 people, -0.73 typical gaps (standard deviations)Turkiye: 23.4 per 100 people, -0.508 typical gaps (standard deviations)Ukraine: 21.7 per 100 people, -0.244 typical gaps (standard deviations)Uruguay: 33 per 100 people, 0.449 typical gaps (standard deviations)United States: 38.9 per 100 people, 0.943 typical gaps (standard deviations)Uzbekistan: 34.7 per 100 people, 0.617 typical gaps (standard deviations)Viet Nam: 23.7 per 100 people, 0.469 typical gaps (standard deviations)South Africa: 5.35 per 100 people, -0.515 typical gaps (standard deviations)Zambia: 0.79 per 100 people, -1.67 typical gaps (standard deviations)Zimbabwe: 1.94 per 100 people, -1.98 typical gaps (standard deviations)BGRCHEDNKGRCGTMKWTLBNMDANORQATZMBZWE

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.6752.1e-15106
By rank (Spearman), not adjusted+0.7044.0e-17106
Standard (Pearson), adjusted for national income+0.2000.041106
By rank (Spearman), adjusted for national income+0.1370.163106

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 106 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: +0.476 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.167 (without UZB) to +0.235 (without MDA). 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: MDA (+0.035).

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 & Pacific15+0.49
Europe & Central Asia48+0.07
Latin America & Caribbean14+0.03
Middle East, North Africa, Afghanistan & Pakistan14-0.30

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 third36+0.33
Middle third35+0.10
Richest third35+0.21

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
Christians62-0.02
Muslims28-0.03

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
Never a European colony15+0.53
Spain12-0.05
United Kingdom25+0.25

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 = +0.198 after dropping 1 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 0years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

Not enough history inside each country to look at this over time. That needs countries measured for several years in a row, and at least one of these measures is measured too rarely.

The data


per 100 people

typical gaps (standard deviations)
Years
Andorra AND52.40.5122024, 2022
France FRA48.90.7172024, 2022
Korea, Rep. KOR47.80.4752024, 2022
China CHN47.20.9132024, 2022
Switzerland CHE471.552024, 2022
Germany DEU45.60.9812024, 2022
Portugal PRT45.20.1952024, 2022
Norway NOR45.21.822024, 2022
Greece GRC44.7-0.2842024, 2022
Malta MLT44.50.152024, 2010
Belgium BEL44.20.8182024, 2010
Denmark DNK43.41.892024, 2022
Netherlands NLD431.542024, 2022
Canada CAN42.51.062024, 2022
United Kingdom GBR42.21.072024, 2022

World Bank, World Development Indicators
ivs; whr

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.

Wellbeing score

Wellbeing score

PlaceStats · via PlaceStats · Derived work; each member keeps its own licence · updated recomputed with the panel

The average of 3 measures from 2 publishers. Each measure is first put on a common scale: 0 is the average country in an average year, and 1 is a typical gap between countries (a standard deviation). Each is turned so that higher is better. Measures are averaged within each publisher first, so a publisher with many measures does not count for more. Members: Self-reported happiness, Interpersonal trust, Life satisfaction.

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 Wellbeing score

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

More links with Fixed broadband or Wellbeing score