placestats

Fixed broadband vs. Governance score

Fades after adjusting for income

Across 170 countries, Fixed broadband and Governance score rise and fall together at r = +0.54 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.27, which could easily be chance (q = 0.12, 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.

Comparing countries across the middle half of fixed broadband, 1.65 to 31.7 per 100 people, governance score is better by 0.897 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.688 typical gaps.

Link before adjusting (r)+0.543p = 2.1e-14 · 170 countries
After adjusting for national income+0.267p = 0.000 · likely range [+0.12, +0.40]
p, allowing for regions0.026standard error 0.091 · 7 regions
Check for luck (q)0.124across 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
Governance score · typical gaps (standard deviations)link with income +0.53 · moderate

“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 →Governance score · typical gaps (standard deviations) →052.4-1.41.47Afghanistan: 0.0801 per 100 people, -1.4 typical gaps (standard deviations)Angola: 0.363 per 100 people, -0.522 typical gaps (standard deviations)Albania: 24.9 per 100 people, 0.323 typical gaps (standard deviations)Andorra: 52.4 per 100 people, 0.558 typical gaps (standard deviations)United Arab Emirates: 40.8 per 100 people, -0.981 typical gaps (standard deviations)Argentina: 26.1 per 100 people, 0.815 typical gaps (standard deviations)Armenia: 20.3 per 100 people, 0.22 typical gaps (standard deviations)Australia: 36.5 per 100 people, 1.38 typical gaps (standard deviations)Austria: 30.1 per 100 people, 1.33 typical gaps (standard deviations)Azerbaijan: 21.8 per 100 people, -1.33 typical gaps (standard deviations)Burundi: 0.0203 per 100 people, -1.09 typical gaps (standard deviations)Belgium: 44.2 per 100 people, 1.41 typical gaps (standard deviations)Benin: 0.277 per 100 people, 0.285 typical gaps (standard deviations)Burkina Faso: 0.851 per 100 people, -0.784 typical gaps (standard deviations)Bangladesh: 8.09 per 100 people, -0.359 typical gaps (standard deviations)Bulgaria: 37 per 100 people, 0.74 typical gaps (standard deviations)Bahrain: 17 per 100 people, -1.05 typical gaps (standard deviations)Bosnia and Herzegovina: 29.4 per 100 people, 0.0899 typical gaps (standard deviations)Belarus: 36.3 per 100 people, -1.26 typical gaps (standard deviations)Belize: 9.75 per 100 people, 0.0883 typical gaps (standard deviations)Bolivia: 12.1 per 100 people, 0.552 typical gaps (standard deviations)Brazil: 24.1 per 100 people, 0.85 typical gaps (standard deviations)Barbados: 37.5 per 100 people, 1.24 typical gaps (standard deviations)Bhutan: 2.92 per 100 people, 0.61 typical gaps (standard deviations)Botswana: 7.44 per 100 people, 0.872 typical gaps (standard deviations)Central African Republic: 0.0161 per 100 people, -1.02 typical gaps (standard deviations)Canada: 42.5 per 100 people, 1.36 typical gaps (standard deviations)Switzerland: 47 per 100 people, 1.47 typical gaps (standard deviations)Chile: 23.7 per 100 people, 1.32 typical gaps (standard deviations)China: 47.2 per 100 people, -1.2 typical gaps (standard deviations)Cote d'Ivoire: 1.6 per 100 people, -0.116 typical gaps (standard deviations)Cameroon: 2.13 per 100 people, -0.834 typical gaps (standard deviations)Congo, Dem. Rep.: 0.032 per 100 people, -0.824 typical gaps (standard deviations)Congo, Rep.: 1.27 per 100 people, -0.925 typical gaps (standard deviations)Colombia: 17.2 per 100 people, 0.733 typical gaps (standard deviations)Comoros: 0.388 per 100 people, -0.464 typical gaps (standard deviations)Cabo Verde: 8.31 per 100 people, 1.15 typical gaps (standard deviations)Costa Rica: 23.3 per 100 people, 1.34 typical gaps (standard deviations)Cyprus: 37.6 per 100 people, 1.1 typical gaps (standard deviations)Czechia: 38.9 per 100 people, 1.31 typical gaps (standard deviations)Germany: 45.6 per 100 people, 1.36 typical gaps (standard deviations)Djibouti: 1.47 per 100 people, -0.784 typical gaps (standard deviations)Denmark: 43.4 per 100 people, 1.47 typical gaps (standard deviations)Dominican Republic: 11.2 per 100 people, 0.621 typical gaps (standard deviations)Algeria: 13.3 per 100 people, -0.695 typical gaps (standard deviations)Ecuador: 17.2 per 100 people, 0.447 typical gaps (standard deviations)Egypt, Arab Rep.: 11.7 per 100 people, -0.987 typical gaps (standard deviations)Spain: 39.2 per 100 people, 1.32 typical gaps (standard deviations)Estonia: 35.4 per 100 people, 1.41 typical gaps (standard deviations)Ethiopia: 0.594 per 100 people, -0.741 typical gaps (standard deviations)Finland: 36.5 per 100 people, 1.38 typical gaps (standard deviations)Fiji: 1.8 per 100 people, 0.57 typical gaps (standard deviations)France: 48.9 per 100 people, 1.24 typical gaps (standard deviations)Gabon: 4.69 per 100 people, -0.529 typical gaps (standard deviations)United Kingdom: 42.2 per 100 people, 1.27 typical gaps (standard deviations)Georgia: 30.4 per 100 people, 0.0179 typical gaps (standard deviations)Ghana: 0.665 per 100 people, 0.845 typical gaps (standard deviations)Guinea: 0.00628 per 100 people, -0.676 typical gaps (standard deviations)Gambia, The: 0.519 per 100 people, 0.362 typical gaps (standard deviations)Guinea-Bissau: 0.386 per 100 people, -0.465 typical gaps (standard deviations)Equatorial Guinea: 0.101 per 100 people, -1.32 typical gaps (standard deviations)Greece: 44.7 per 100 people, 0.914 typical gaps (standard deviations)Guatemala: 5.34 per 100 people, 0.291 typical gaps (standard deviations)Guyana: 12.9 per 100 people, 0.538 typical gaps (standard deviations)Hong Kong SAR, China: 40.3 per 100 people, -0.536 typical gaps (standard deviations)Honduras: 4.58 per 100 people, 0.146 typical gaps (standard deviations)Croatia: 29.6 per 100 people, 0.865 typical gaps (standard deviations)Haiti: 0.307 per 100 people, -0.66 typical gaps (standard deviations)Hungary: 37.5 per 100 people, 0.225 typical gaps (standard deviations)Indonesia: 4.92 per 100 people, 0.125 typical gaps (standard deviations)India: 3.15 per 100 people, 0.146 typical gaps (standard deviations)Ireland: 32.3 per 100 people, 1.41 typical gaps (standard deviations)Iran, Islamic Rep.: 12.1 per 100 people, -1.26 typical gaps (standard deviations)Iraq: 17.6 per 100 people, -0.488 typical gaps (standard deviations)Iceland: 37.5 per 100 people, 1.32 typical gaps (standard deviations)Israel: 29.2 per 100 people, 0.783 typical gaps (standard deviations)Italy: 31.8 per 100 people, 1 typical gaps (standard deviations)Jamaica: 16 per 100 people, 1.01 typical gaps (standard deviations)Jordan: 7.02 per 100 people, -0.271 typical gaps (standard deviations)Japan: 38.6 per 100 people, 1.28 typical gaps (standard deviations)Kazakhstan: 15.3 per 100 people, -0.796 typical gaps (standard deviations)Kenya: 3.04 per 100 people, 0.217 typical gaps (standard deviations)Kyrgyz Republic: 7.8 per 100 people, -0.725 typical gaps (standard deviations)Cambodia: 4.15 per 100 people, -1.01 typical gaps (standard deviations)Korea, Rep.: 47.8 per 100 people, 1.05 typical gaps (standard deviations)Kuwait: 1.01 per 100 people, -0.696 typical gaps (standard deviations)Lao PDR: 2.72 per 100 people, -1.14 typical gaps (standard deviations)Lebanon: 7.29 per 100 people, -0.112 typical gaps (standard deviations)Liberia: 0.275 per 100 people, 0.416 typical gaps (standard deviations)Libya: 4.52 per 100 people, -0.885 typical gaps (standard deviations)St. Lucia: 13.6 per 100 people, 0.552 typical gaps (standard deviations)Sri Lanka: 8.47 per 100 people, 0.602 typical gaps (standard deviations)Lesotho: 0.379 per 100 people, 0.671 typical gaps (standard deviations)Lithuania: 27.8 per 100 people, 1.14 typical gaps (standard deviations)Luxembourg: 40.3 per 100 people, 1.38 typical gaps (standard deviations)Latvia: 25.8 per 100 people, 1.22 typical gaps (standard deviations)Morocco: 7.3 per 100 people, -0.351 typical gaps (standard deviations)Moldova: 29.6 per 100 people, 0.5 typical gaps (standard deviations)Madagascar: 0.124 per 100 people, -0.193 typical gaps (standard deviations)Maldives: 19.6 per 100 people, 0.0802 typical gaps (standard deviations)Mexico: 21.7 per 100 people, 0.134 typical gaps (standard deviations)North Macedonia: 29.9 per 100 people, 0.352 typical gaps (standard deviations)Mali: 0.774 per 100 people, -0.753 typical gaps (standard deviations)Malta: 44.5 per 100 people, 1.12 typical gaps (standard deviations)Myanmar: 2.87 per 100 people, -1.39 typical gaps (standard deviations)Montenegro: 32.3 per 100 people, 0.55 typical gaps (standard deviations)Mongolia: 15.8 per 100 people, 0.716 typical gaps (standard deviations)Mozambique: 0.142 per 100 people, -0.298 typical gaps (standard deviations)Mauritania: 0.947 per 100 people, -0.421 typical gaps (standard deviations)Mauritius: 28 per 100 people, 1.07 typical gaps (standard deviations)Malawi: 0.0864 per 100 people, 0.576 typical gaps (standard deviations)Malaysia: 13.5 per 100 people, 0.169 typical gaps (standard deviations)Namibia: 2.83 per 100 people, 0.69 typical gaps (standard deviations)Niger: 0.111 per 100 people, -0.548 typical gaps (standard deviations)Nigeria: 0.0787 per 100 people, -0.0132 typical gaps (standard deviations)Nicaragua: 5.78 per 100 people, -1.24 typical gaps (standard deviations)Netherlands: 43 per 100 people, 1.32 typical gaps (standard deviations)Norway: 45.2 per 100 people, 1.43 typical gaps (standard deviations)Nepal: 10.1 per 100 people, 0.441 typical gaps (standard deviations)New Zealand: 38 per 100 people, 1.32 typical gaps (standard deviations)Oman: 11.1 per 100 people, -0.665 typical gaps (standard deviations)Pakistan: 1.47 per 100 people, -0.437 typical gaps (standard deviations)Panama: 18.1 per 100 people, 0.84 typical gaps (standard deviations)Peru: 11.7 per 100 people, 0.522 typical gaps (standard deviations)Philippines: 7.14 per 100 people, 0.0585 typical gaps (standard deviations)Papua New Guinea: 0.233 per 100 people, 0.227 typical gaps (standard deviations)Poland: 25.4 per 100 people, 0.996 typical gaps (standard deviations)Portugal: 45.2 per 100 people, 1.26 typical gaps (standard deviations)Paraguay: 12.9 per 100 people, 0.349 typical gaps (standard deviations)Qatar: 11.8 per 100 people, -0.908 typical gaps (standard deviations)Romania: 35.8 per 100 people, 0.749 typical gaps (standard deviations)Russian Federation: 27.1 per 100 people, -1.14 typical gaps (standard deviations)Rwanda: 0.596 per 100 people, -0.868 typical gaps (standard deviations)Saudi Arabia: 44.1 per 100 people, -1.14 typical gaps (standard deviations)Sudan: 0.0604 per 100 people, -1.22 typical gaps (standard deviations)Senegal: 4.04 per 100 people, 0.683 typical gaps (standard deviations)Singapore: 27.8 per 100 people, 0.0449 typical gaps (standard deviations)Solomon Islands: 0.107 per 100 people, 0.697 typical gaps (standard deviations)Sierra Leone: 0.00227 per 100 people, 0.299 typical gaps (standard deviations)El Salvador: 12.5 per 100 people, -0.352 typical gaps (standard deviations)Somalia, Fed. Rep.: 0.723 per 100 people, -1.01 typical gaps (standard deviations)Serbia: 33.4 per 100 people, -0.0738 typical gaps (standard deviations)Sao Tome and Principe: 2.99 per 100 people, 0.8 typical gaps (standard deviations)Suriname: 18.8 per 100 people, 0.996 typical gaps (standard deviations)Slovak Republic: 33.8 per 100 people, 1.02 typical gaps (standard deviations)Slovenia: 34.2 per 100 people, 1.19 typical gaps (standard deviations)Sweden: 40.6 per 100 people, 1.42 typical gaps (standard deviations)Eswatini: 2.51 per 100 people, -1.05 typical gaps (standard deviations)Seychelles: 31.6 per 100 people, 0.975 typical gaps (standard deviations)Syrian Arab Republic: 6.86 per 100 people, -1.05 typical gaps (standard deviations)Chad: 0 per 100 people, -0.999 typical gaps (standard deviations)Togo: 1.42 per 100 people, -0.371 typical gaps (standard deviations)Thailand: 14.9 per 100 people, -0.383 typical gaps (standard deviations)Tajikistan: 0.984 per 100 people, -1.34 typical gaps (standard deviations)Turkmenistan: 5.22 per 100 people, -1.39 typical gaps (standard deviations)Timor-Leste: 0.006 per 100 people, 0.779 typical gaps (standard deviations)Trinidad and Tobago: 27 per 100 people, 1.03 typical gaps (standard deviations)Tunisia: 15 per 100 people, -0.257 typical gaps (standard deviations)Turkiye: 23.4 per 100 people, -0.691 typical gaps (standard deviations)Tanzania: 0.246 per 100 people, -0.315 typical gaps (standard deviations)Uganda: 0.208 per 100 people, -0.646 typical gaps (standard deviations)Ukraine: 21.7 per 100 people, -0.0525 typical gaps (standard deviations)Uruguay: 33 per 100 people, 1.39 typical gaps (standard deviations)United States: 38.9 per 100 people, 0.998 typical gaps (standard deviations)Uzbekistan: 34.7 per 100 people, -1.04 typical gaps (standard deviations)Viet Nam: 23.7 per 100 people, -0.933 typical gaps (standard deviations)Vanuatu: 1.16 per 100 people, 0.885 typical gaps (standard deviations)South Africa: 5.35 per 100 people, 0.883 typical gaps (standard deviations)Zambia: 0.79 per 100 people, 0.212 typical gaps (standard deviations)Zimbabwe: 1.94 per 100 people, -0.609 typical gaps (standard deviations)AFGBLRCHECHNFRAGHAGNQNORSAUTJKTLSVUT

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5432.1e-14170
By rank (Spearman), not adjusted+0.5498.7e-15170
Standard (Pearson), adjusted for national income+0.2670.000170
By rank (Spearman), adjusted for national income+0.2030.008170

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 170 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: +0.275 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.233 (without QAT) to +0.311 (without CHN). 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: CHN (+0.044).

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 & Pacific21+0.03
Europe & Central Asia49+0.33
Latin America & Caribbean25+0.59
Middle East, North Africa, Afghanistan & Pakistan21+0.29
Sub-Saharan Africa46+0.31

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 third57-0.08
Middle third56-0.07
Richest third57+0.52

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
Christians101+0.20
Muslims47-0.08

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
France25-0.11
Never a European colony21+0.21
Spain19+0.50
United Kingdom51+0.40

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 = +0.267 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 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 AND52.40.5582024, 2022
France FRA48.91.242024, 2025
Korea, Rep. KOR47.81.052024, 2025
China CHN47.2-1.22024, 2025
Switzerland CHE471.472024, 2025
Germany DEU45.61.362024, 2025
Portugal PRT45.21.262024, 2025
Norway NOR45.21.432024, 2025
Greece GRC44.70.9142024, 2025
Malta MLT44.51.122024, 2025
Belgium BEL44.21.412024, 2025
Saudi Arabia SAU44.1-1.142024, 2025
Denmark DNK43.41.472024, 2025
Netherlands NLD431.322024, 2025
Canada CAN42.51.362024, 2025

World Bank, World Development Indicators
fh; un_sdg; vdem; wdi; wgi

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.

Governance score

Governance score

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

The average of 19 measures from 5 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: Civil liberties (FH), Political rights (FH), Pre-trial detainee share, Civil society participation, Political corruption, Electoral democracy, Freedom of association, Freedom of expression, Free and fair elections, Liberal democracy, Participatory democracy, Political civil liberties, Birth registration, Control of corruption, Government effectiveness, Political stability, Rule of law, Regulatory quality, Voice and accountability.

How this was computed

Data used: most recent value in 2010-2025; values come from 2011–2025. 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 Fixed broadband and Governance score

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

More links with Fixed broadband or Governance score