placestats

Fixed broadband vs. Human capital (PWT)

Fades after adjusting for income

Across 142 countries, Fixed broadband and Human capital (PWT) rise and fall together at r = +0.80 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.34, 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 human capital (PWT), 2.2 to 3.31 points, fixed broadband is better by 20.5 per 100 people at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 8.72 per 100 people.

Link before adjusting (r)+0.796p = 2.3e-32 · 142 countries
After adjusting for national income+0.339p = 3.8e-5 · likely range [+0.18, +0.48]
p, allowing for regions0.001standard error 0.055 · 7 regions
Check for luck (q)0.120across 18,622 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
Human capital (PWT) · index (1 = no schooling)link with income +0.82 · proxy

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 →Human capital (PWT) · index (1 = no schooling) →0.0022748.91.263.99Angola: 0.363 per 100 people, 1.54 index (1 = no schooling)Albania: 24.9 per 100 people, 3.02 index (1 = no schooling)United Arab Emirates: 40.8 per 100 people, 3.3 index (1 = no schooling)Argentina: 26.1 per 100 people, 3.16 index (1 = no schooling)Armenia: 20.3 per 100 people, 3.16 index (1 = no schooling)Australia: 36.5 per 100 people, 3.61 index (1 = no schooling)Austria: 30.1 per 100 people, 3.45 index (1 = no schooling)Burundi: 0.0203 per 100 people, 1.48 index (1 = no schooling)Belgium: 44.2 per 100 people, 3.17 index (1 = no schooling)Benin: 0.277 per 100 people, 2.04 index (1 = no schooling)Burkina Faso: 0.851 per 100 people, 1.38 index (1 = no schooling)Bangladesh: 8.09 per 100 people, 2.15 index (1 = no schooling)Bulgaria: 37 per 100 people, 3.26 index (1 = no schooling)Bahrain: 17 per 100 people, 2.82 index (1 = no schooling)Belize: 9.75 per 100 people, 2.92 index (1 = no schooling)Bolivia: 12.1 per 100 people, 3.09 index (1 = no schooling)Brazil: 24.1 per 100 people, 3.36 index (1 = no schooling)Barbados: 37.5 per 100 people, 2.87 index (1 = no schooling)Brunei Darussalam: 21.2 per 100 people, 2.86 index (1 = no schooling)Botswana: 7.44 per 100 people, 3.05 index (1 = no schooling)Central African Republic: 0.0161 per 100 people, 1.6 index (1 = no schooling)Canada: 42.5 per 100 people, 3.77 index (1 = no schooling)Switzerland: 47 per 100 people, 3.75 index (1 = no schooling)Chile: 23.7 per 100 people, 3.24 index (1 = no schooling)China: 47.2 per 100 people, 2.75 index (1 = no schooling)Cote d'Ivoire: 1.6 per 100 people, 1.74 index (1 = no schooling)Cameroon: 2.13 per 100 people, 1.95 index (1 = no schooling)Congo, Dem. Rep.: 0.032 per 100 people, 1.83 index (1 = no schooling)Congo, Rep.: 1.27 per 100 people, 2.18 index (1 = no schooling)Colombia: 17.2 per 100 people, 2.68 index (1 = no schooling)Costa Rica: 23.3 per 100 people, 2.7 index (1 = no schooling)Cyprus: 37.6 per 100 people, 2.99 index (1 = no schooling)Czechia: 38.9 per 100 people, 3.78 index (1 = no schooling)Germany: 45.6 per 100 people, 3.71 index (1 = no schooling)Denmark: 43.4 per 100 people, 3.67 index (1 = no schooling)Dominican Republic: 11.2 per 100 people, 2.93 index (1 = no schooling)Algeria: 13.3 per 100 people, 2.51 index (1 = no schooling)Ecuador: 17.2 per 100 people, 2.83 index (1 = no schooling)Egypt, Arab Rep.: 11.7 per 100 people, 2.86 index (1 = no schooling)Spain: 39.2 per 100 people, 3.07 index (1 = no schooling)Estonia: 35.4 per 100 people, 3.58 index (1 = no schooling)Ethiopia: 0.594 per 100 people, 1.54 index (1 = no schooling)Finland: 36.5 per 100 people, 3.6 index (1 = no schooling)Fiji: 1.8 per 100 people, 2.74 index (1 = no schooling)France: 48.9 per 100 people, 3.31 index (1 = no schooling)Gabon: 4.69 per 100 people, 3.04 index (1 = no schooling)United Kingdom: 42.2 per 100 people, 3.85 index (1 = no schooling)Ghana: 0.665 per 100 people, 2.66 index (1 = no schooling)Gambia, The: 0.519 per 100 people, 1.49 index (1 = no schooling)Greece: 44.7 per 100 people, 3.24 index (1 = no schooling)Guatemala: 5.34 per 100 people, 2.04 index (1 = no schooling)Guyana: 12.9 per 100 people, 2.76 index (1 = no schooling)Hong Kong SAR, China: 40.3 per 100 people, 3.36 index (1 = no schooling)Honduras: 4.58 per 100 people, 2.58 index (1 = no schooling)Croatia: 29.6 per 100 people, 3.81 index (1 = no schooling)Haiti: 0.307 per 100 people, 1.8 index (1 = no schooling)Hungary: 37.5 per 100 people, 3.55 index (1 = no schooling)Indonesia: 4.92 per 100 people, 2.34 index (1 = no schooling)India: 3.15 per 100 people, 2.28 index (1 = no schooling)Ireland: 32.3 per 100 people, 3.27 index (1 = no schooling)Iran, Islamic Rep.: 12.1 per 100 people, 2.77 index (1 = no schooling)Iraq: 17.6 per 100 people, 2.39 index (1 = no schooling)Iceland: 37.5 per 100 people, 3.28 index (1 = no schooling)Israel: 29.2 per 100 people, 3.99 index (1 = no schooling)Italy: 31.8 per 100 people, 3.27 index (1 = no schooling)Jamaica: 16 per 100 people, 2.65 index (1 = no schooling)Jordan: 7.02 per 100 people, 2.88 index (1 = no schooling)Japan: 38.6 per 100 people, 3.66 index (1 = no schooling)Kazakhstan: 15.3 per 100 people, 3.39 index (1 = no schooling)Kenya: 3.04 per 100 people, 2.61 index (1 = no schooling)Kyrgyz Republic: 7.8 per 100 people, 3.57 index (1 = no schooling)Cambodia: 4.15 per 100 people, 2.06 index (1 = no schooling)Korea, Rep.: 47.8 per 100 people, 3.87 index (1 = no schooling)Kuwait: 1.01 per 100 people, 2.3 index (1 = no schooling)Lao PDR: 2.72 per 100 people, 2.01 index (1 = no schooling)Liberia: 0.275 per 100 people, 2.03 index (1 = no schooling)Sri Lanka: 8.47 per 100 people, 2.93 index (1 = no schooling)Lesotho: 0.379 per 100 people, 2.02 index (1 = no schooling)Lithuania: 27.8 per 100 people, 3.41 index (1 = no schooling)Luxembourg: 40.3 per 100 people, 3.47 index (1 = no schooling)Latvia: 25.8 per 100 people, 3.21 index (1 = no schooling)Macao SAR, China: 29.9 per 100 people, 3.05 index (1 = no schooling)Morocco: 7.3 per 100 people, 2.03 index (1 = no schooling)Moldova: 29.6 per 100 people, 3.18 index (1 = no schooling)Madagascar: 0.124 per 100 people, 1.72 index (1 = no schooling)Maldives: 19.6 per 100 people, 2.8 index (1 = no schooling)Mexico: 21.7 per 100 people, 2.85 index (1 = no schooling)Mali: 0.774 per 100 people, 1.48 index (1 = no schooling)Malta: 44.5 per 100 people, 3.48 index (1 = no schooling)Myanmar: 2.87 per 100 people, 1.94 index (1 = no schooling)Mongolia: 15.8 per 100 people, 2.76 index (1 = no schooling)Mozambique: 0.142 per 100 people, 1.29 index (1 = no schooling)Mauritania: 0.947 per 100 people, 1.85 index (1 = no schooling)Mauritius: 28 per 100 people, 2.7 index (1 = no schooling)Malawi: 0.0864 per 100 people, 2.14 index (1 = no schooling)Malaysia: 13.5 per 100 people, 3.21 index (1 = no schooling)Namibia: 2.83 per 100 people, 2.31 index (1 = no schooling)Niger: 0.111 per 100 people, 1.26 index (1 = no schooling)Nigeria: 0.0787 per 100 people, 2.13 index (1 = no schooling)Nicaragua: 5.78 per 100 people, 2.41 index (1 = no schooling)Netherlands: 43 per 100 people, 3.46 index (1 = no schooling)Norway: 45.2 per 100 people, 3.75 index (1 = no schooling)Nepal: 10.1 per 100 people, 1.93 index (1 = no schooling)New Zealand: 38 per 100 people, 3.38 index (1 = no schooling)Pakistan: 1.47 per 100 people, 1.76 index (1 = no schooling)Panama: 18.1 per 100 people, 2.97 index (1 = no schooling)Peru: 11.7 per 100 people, 2.79 index (1 = no schooling)Philippines: 7.14 per 100 people, 2.96 index (1 = no schooling)Poland: 25.4 per 100 people, 3.49 index (1 = no schooling)Portugal: 45.2 per 100 people, 2.61 index (1 = no schooling)Paraguay: 12.9 per 100 people, 2.73 index (1 = no schooling)Qatar: 11.8 per 100 people, 3.14 index (1 = no schooling)Romania: 35.8 per 100 people, 3.35 index (1 = no schooling)Russian Federation: 27.1 per 100 people, 3.34 index (1 = no schooling)Rwanda: 0.596 per 100 people, 1.88 index (1 = no schooling)Saudi Arabia: 44.1 per 100 people, 2.99 index (1 = no schooling)Sudan: 0.0604 per 100 people, 1.65 index (1 = no schooling)Senegal: 4.04 per 100 people, 1.72 index (1 = no schooling)Singapore: 27.8 per 100 people, 3.4 index (1 = no schooling)Sierra Leone: 0.00227 per 100 people, 1.73 index (1 = no schooling)El Salvador: 12.5 per 100 people, 2.38 index (1 = no schooling)Serbia: 33.4 per 100 people, 3.53 index (1 = no schooling)Slovak Republic: 33.8 per 100 people, 3.89 index (1 = no schooling)Slovenia: 34.2 per 100 people, 3.66 index (1 = no schooling)Sweden: 40.6 per 100 people, 3.5 index (1 = no schooling)Eswatini: 2.51 per 100 people, 1.91 index (1 = no schooling)Syrian Arab Republic: 6.86 per 100 people, 2.7 index (1 = no schooling)Togo: 1.42 per 100 people, 2.13 index (1 = no schooling)Thailand: 14.9 per 100 people, 2.86 index (1 = no schooling)Tajikistan: 0.984 per 100 people, 3.11 index (1 = no schooling)Trinidad and Tobago: 27 per 100 people, 3.13 index (1 = no schooling)Tunisia: 15 per 100 people, 2.84 index (1 = no schooling)Turkiye: 23.4 per 100 people, 2.62 index (1 = no schooling)Tanzania: 0.246 per 100 people, 1.77 index (1 = no schooling)Uganda: 0.208 per 100 people, 2.69 index (1 = no schooling)Ukraine: 21.7 per 100 people, 3.32 index (1 = no schooling)Uruguay: 33 per 100 people, 3.01 index (1 = no schooling)United States: 38.9 per 100 people, 3.83 index (1 = no schooling)Viet Nam: 23.7 per 100 people, 2.74 index (1 = no schooling)South Africa: 5.35 per 100 people, 2.9 index (1 = no schooling)Zambia: 0.79 per 100 people, 2.77 index (1 = no schooling)Zimbabwe: 1.94 per 100 people, 2.82 index (1 = no schooling)BFACHECHNGABKGZKORMOZNERNORPRTSAUTJK

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.7962.3e-32142
By rank (Spearman), not adjusted+0.8294.4e-37142
Standard (Pearson), adjusted for national income+0.3393.8e-5142
By rank (Spearman), adjusted for national income+0.3941.4e-6142

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 142 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: +0.457 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.297 (without KWT) to +0.369 (without PRT). 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: KWT (-0.042).

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 & Pacific19+0.48
Europe & Central Asia40+0.04
Latin America & Caribbean23+0.45
Middle East, North Africa, Afghanistan & Pakistan16+0.50
Sub-Saharan Africa37+0.04

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 third48+0.24
Middle third47+0.13
Richest third47+0.51

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
Christians87+0.28
Muslims34+0.21

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
France20+0.36
Never a European colony15-0.10
Spain18+0.28
United Kingdom45+0.42

By official ideology

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
Nationalist20+0.25
No strong official ideology97+0.37

By party system

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
A few parties banned20+0.23
No parties banned103+0.29

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 = +0.339 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.

Loading…

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 = -0.076, 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 human capital (PWT) move with it.

Across countries (adjusted)+0.339for comparison
Within countries+0.635each country vs itself
…and vs the same year elsewhere-0.076q = 0.440 · 143 countries
Year-on-year changes+0.031q = 0.768
The direction flips. Comparing each country only with itself gives +0.63. Also taking out what happened to the whole world each year gives -0.08. 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.
  • 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.

3,037 country-years (one country in one year), 143 countries, 1998–2023, about 21 years each on average. Share of the variation that happens within countries over time: 5% for Fixed broadband, 38% for Human capital (PWT). 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

0.20−0.2-100+10years Human capital (PWT) trails Fixed broadband →lag -10: r = -0.026 (n = 3,135)lag -9: r = -0.044 (n = 3,149)lag -8: r = -0.060 (n = 3,159)lag -7: r = -0.067 (n = 3,159)lag -6: r = -0.071 (n = 3,159)lag -5: r = -0.075 (n = 3,159)lag -4: r = -0.076 (n = 3,159)lag -3: r = -0.075 (n = 3,159)lag -2: r = -0.073 (n = 3,159)lag -1: r = -0.071 (n = 3,159)lag +0: r = -0.076 (n = 3,037)lag +1: r = -0.085 (n = 2,904)lag +2: r = -0.096 (n = 2,762)lag +3: r = -0.105 (n = 2,620)lag +4: r = -0.108 (n = 2,479)lag +5: r = -0.115 (n = 2,337)lag +6: r = -0.125 (n = 2,202)lag +7: r = -0.138 (n = 2,057)lag +8: r = -0.147 (n = 1,909)lag +9: r = -0.155 (n = 1,771)lag +10: r = -0.161 (n = 1,631)

Same year (lag 0): -0.076. 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

index (1 = no schooling)
Years
France FRA48.93.312024, 2023
Korea, Rep. KOR47.83.872024, 2023
China CHN47.22.752024, 2023
Switzerland CHE473.752024, 2023
Germany DEU45.63.712024, 2023
Portugal PRT45.22.612024, 2023
Norway NOR45.23.752024, 2023
Greece GRC44.73.242024, 2023
Malta MLT44.53.482024, 2023
Belgium BEL44.23.172024, 2023
Saudi Arabia SAU44.12.992024, 2023
Denmark DNK43.43.672024, 2023
Netherlands NLD433.462024, 2023
Canada CAN42.53.772024, 2023
United Kingdom GBR42.23.852024, 2023

World Bank, World Development Indicators
Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table", American Economic Review, 105(10), 3150-3182, www.ggdc.net/pwt. Data: Penn World Table version 11.0, Groningen Growth and Development Centre, doi:10.34894/FABVLR.

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.

Human capital (PWT)

Human capital index, from years of schooling (Penn World Table)

Groningen Growth and Development Centre, University of Groningen · via DataverseNL · CC BY 4.0 · updated irregular (new versions every few years)

"Human capital" means the skills and knowledge workers bring to their jobs. This index measures it from schooling alone. It starts from how many years adults spent in school, then counts the early years for more than the later ones, following studies of how much higher pay tends to be with each extra year. A score of 1 means no schooling; the US is about 3.8. It uses the same schooling figures as mean years of schooling, so the two are close by construction. For that reason it is left out of the education score, which already counts years of schooling.

How this was computed

Data used: most recent value in 2010-2025; values come from 2021–2024. 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 18,622 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated V-Dem v16; V-Party v2. How the country data works · explore Fixed broadband and Human capital (PWT)

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

More links with Fixed broadband or Human capital (PWT)