placestats

Internet users vs. One-person households

Fades after adjusting for income

Across 148 countries, Internet users and One-person households rise and fall together at r = +0.57 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.03, which could easily be chance (q = 0.86, 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 one-person households, 6.6% to 22.6% of households, internet users is better by 23.8% of population at the top of that range than the bottom. Comparing only countries with similar national income, the gap reverses, to 0.785% of population the other way.

Link before adjusting (r)+0.573p = 2.7e-14 · 148 countries
After adjusting for national income-0.028p = 0.737 · likely range [-0.19, +0.13]
p, allowing for regions0.756standard error 0.086 · 7 regions
Check for luck (q)0.861across 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.

Internet users · % of populationlink with income +0.85 · proxy
One-person households · % of householdslink with income +0.70 · 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

Internet users · % of population →One-person households · % of households →8.6%99.1%0.19%41.1%Aruba: 97.2% of population, 21.4% of householdsAfghanistan: 16.1% of population, 0.19% of householdsAngola: 40.7% of population, 10.8% of householdsAlbania: 85.9% of population, 9.95% of householdsArgentina: 89.7% of population, 22.8% of householdsArmenia: 81.3% of population, 15.2% of householdsAustralia: 96.1% of population, 22.8% of householdsAustria: 91.9% of population, 36.3% of householdsBurundi: 8.6% of population, 6.04% of householdsBelgium: 95.8% of population, 34% of householdsBenin: 34% of population, 9.78% of householdsBurkina Faso: 28.3% of population, 6.64% of householdsBangladesh: 53.4% of population, 2.85% of householdsBulgaria: 82.4% of population, 30.8% of householdsBahamas, The: 92.5% of population, 17.7% of householdsBosnia and Herzegovina: 86.1% of population, 11.5% of householdsBelarus: 94.3% of population, 38% of householdsBelize: 80% of population, 17.2% of householdsBermuda: 98.4% of population, 34.1% of householdsBolivia: 79.7% of population, 20.5% of householdsBrazil: 84.5% of population, 12% of householdsBarbados: 70.4% of population, 26.3% of householdsBrunei Darussalam: 96.3% of population, 7.22% of householdsBhutan: 91.3% of population, 6.13% of householdsCentral African Republic: 13.8% of population, 6.2% of householdsCanada: 94.4% of population, 28.2% of householdsChile: 95.6% of population, 20.2% of householdsCote d'Ivoire: 41.4% of population, 17% of householdsCameroon: 46.3% of population, 15.3% of householdsCongo, Dem. Rep.: 19.7% of population, 7.55% of householdsCongo, Rep.: 47.3% of population, 13.8% of householdsColombia: 79.3% of population, 11.1% of householdsComoros: 32.5% of population, 4.91% of householdsCosta Rica: 87.2% of population, 12.1% of householdsCyprus: 89.6% of population, 20.8% of householdsCzechia: 87.7% of population, 32.5% of householdsGermany: 93.5% of population, 37.3% of householdsDominican Republic: 91% of population, 20.1% of householdsAlgeria: 77.4% of population, 1.61% of householdsEgypt, Arab Rep.: 74.6% of population, 6.48% of householdsSpain: 95.8% of population, 23.2% of householdsEthiopia: 21.9% of population, 6.36% of householdsFinland: 93.7% of population, 41% of householdsFiji: 74.7% of population, 5.94% of householdsFrance: 88.7% of population, 35.5% of householdsGabon: 68.7% of population, 22.4% of householdsUnited Kingdom: 95.5% of population, 30.6% of householdsGeorgia: 83.8% of population, 15% of householdsGhana: 72.2% of population, 21.5% of householdsGuinea: 33.3% of population, 5.07% of householdsGambia, The: 49.5% of population, 9.72% of householdsGuinea-Bissau: 29.8% of population, 4.14% of householdsGreece: 86.3% of population, 25.7% of householdsGuatemala: 60.2% of population, 4.33% of householdsGuyana: 83% of population, 15.8% of householdsHong Kong SAR, China: 95.8% of population, 18.3% of householdsHonduras: 58.6% of population, 10.4% of householdsCroatia: 83.6% of population, 24.6% of householdsHaiti: 47.9% of population, 9.43% of householdsHungary: 93.8% of population, 32.1% of householdsIndonesia: 72.8% of population, 7.1% of householdsIndia: 70% of population, 5.11% of householdsIreland: 97.2% of population, 22% of householdsIran, Islamic Rep.: 85.3% of population, 7.16% of householdsIraq: 81.5% of population, 1.01% of householdsItaly: 89.2% of population, 31.1% of householdsJamaica: 90.1% of population, 28.2% of householdsJordan: 95.6% of population, 4.36% of householdsJapan: 85.5% of population, 34.5% of householdsKazakhstan: 93.4% of population, 15.5% of householdsKenya: 35% of population, 19.4% of householdsKyrgyz Republic: 92% of population, 12.3% of householdsCambodia: 68.5% of population, 3.37% of householdsKiribati: 89.4% of population, 3.71% of householdsKorea, Rep.: 97.9% of population, 31.8% of householdsLao PDR: 65.6% of population, 3.18% of householdsLiberia: 32.2% of population, 12% of householdsSt. Lucia: 68.2% of population, 26.8% of householdsLesotho: 51.8% of population, 18% of householdsLithuania: 89.2% of population, 31.7% of householdsLuxembourg: 99.1% of population, 33.3% of householdsLatvia: 92.7% of population, 41.1% of householdsMacao SAR, China: 89.8% of population, 15.1% of householdsMorocco: 91.2% of population, 7.2% of householdsMoldova: 77.4% of population, 22.6% of householdsMadagascar: 18.7% of population, 7.11% of householdsMaldives: 85.2% of population, 4.49% of householdsMexico: 83.1% of population, 10.1% of householdsNorth Macedonia: 93.1% of population, 11.8% of householdsMali: 36.8% of population, 3.11% of householdsMalta: 93.9% of population, 22.6% of householdsMyanmar: 45.4% of population, 5.35% of householdsMontenegro: 88.9% of population, 15.4% of householdsMongolia: 85.1% of population, 8.96% of householdsMozambique: 20.5% of population, 8.41% of householdsMauritania: 45.8% of population, 3.97% of householdsMalawi: 19% of population, 7.2% of householdsNamibia: 64.9% of population, 17.4% of householdsNiger: 15.6% of population, 3.19% of householdsNigeria: 41.2% of population, 14.7% of householdsNorway: 99% of population, 39.6% of householdsNepal: 46.3% of population, 4.51% of householdsNew Zealand: 93.5% of population, 21.9% of householdsPakistan: 57.3% of population, 1.08% of householdsPanama: 72.8% of population, 13.4% of householdsPeru: 82% of population, 16.8% of householdsPhilippines: 67.3% of population, 9.18% of householdsPoland: 88.6% of population, 24% of householdsPuerto Rico (US): 87.3% of population, 23.1% of householdsPortugal: 88.5% of population, 21.4% of householdsParaguay: 81.6% of population, 10.4% of householdsWest Bank and Gaza: 86.6% of population, 4.31% of householdsQatar: 98.1% of population, 6.98% of householdsRomania: 91.3% of population, 26% of householdsRussian Federation: 94.4% of population, 25.7% of householdsRwanda: 31.7% of population, 7.69% of householdsSudan: 18.6% of population, 1.59% of householdsSenegal: 60.1% of population, 6.92% of householdsSingapore: 94.4% of population, 16.1% of householdsSierra Leone: 25.1% of population, 6.42% of householdsEl Salvador: 66.5% of population, 8.46% of householdsSomalia, Fed. Rep.: 27.9% of population, 1.53% of householdsSerbia: 87.7% of population, 21.8% of householdsSao Tome and Principe: 59.1% of population, 13.8% of householdsSuriname: 87.4% of population, 14% of householdsSlovak Republic: 89.8% of population, 25.3% of householdsSlovenia: 90.8% of population, 32.6% of householdsEswatini: 63.4% of population, 21.7% of householdsChad: 12.6% of population, 5.43% of householdsTogo: 39.5% of population, 15.7% of householdsThailand: 90.9% of population, 21.6% of householdsTajikistan: 55.8% of population, 2.88% of householdsTurkmenistan: 18% of population, 3.74% of householdsTimor-Leste: 35% of population, 6.41% of householdsTonga: 67.4% of population, 6.94% of householdsTunisia: 76.5% of population, 6.27% of householdsTuvalu: 35.2% of population, 6.92% of householdsTanzania: 31.2% of population, 9.63% of householdsUganda: 8.95% of population, 13.2% of householdsUkraine: 82.5% of population, 25% of householdsUruguay: 92% of population, 17.6% of householdsUnited States: 94.7% of population, 27.9% of householdsViet Nam: 84.2% of population, 10.5% of householdsSamoa: 21.2% of population, 4.4% of householdsKosovo: 89.4% of population, 3.32% of householdsSouth Africa: 78.4% of population, 23.8% of householdsZambia: 17.1% of population, 7.97% of householdsZimbabwe: 41.6% of population, 12.9% of householdsAFGFINJORKENLVANERNORQATSDNUGAXKX

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5732.7e-14148
By rank (Spearman), not adjusted+0.6111.7e-16148
Standard (Pearson), adjusted for national income-0.0280.737148
By rank (Spearman), adjusted for national income+0.0120.883148

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 148 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: +0.545 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.085 (without TKM) to -0.008 (without JOR). 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: TKM (-0.057).

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 & Pacific22+0.15
Europe & Central Asia40+0.27
Latin America & Caribbean25+0.32
Middle East, North Africa, Afghanistan & Pakistan12+0.07
Sub-Saharan Africa41+0.34

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 third50+0.01
Middle third49+0.03
Richest third49+0.18

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
Christians92+0.05
Muslims37+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
France22-0.26
Never a European colony15+0.27
Spain16+0.45
United Kingdom44-0.01

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 = -0.028 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 32years. 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 = +0.270, 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 internet users moved did not tend to see their one-person households move with it.

Across countries (adjusted)-0.028for comparison
Within countries+0.493each country vs itself
…and vs the same year elsewhere+0.270q = 0.064 · 49 countries
Year-on-year changes—q = —
Read with care.
  • 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.

345 country-years (one country in one year), 49 countries, 1990–2021, about 7 years each on average. Share of the variation that happens within countries over time: 12% for Internet users, 57% for One-person households. 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

0.40−0.4-100+7years One-person households trails Internet users →lag -10: r = 0.241 (n = 268)lag -9: r = 0.230 (n = 300)lag -8: r = 0.240 (n = 325)lag -7: r = 0.201 (n = 339)lag -6: r = 0.213 (n = 367)lag -5: r = 0.228 (n = 389)lag -4: r = 0.247 (n = 390)lag -3: r = 0.210 (n = 387)lag -2: r = 0.317 (n = 372)lag -1: r = 0.286 (n = 370)lag +0: r = 0.270 (n = 345)lag +1: r = 0.256 (n = 320)lag +2: r = 0.234 (n = 299)lag +3: r = 0.235 (n = 283)lag +4: r = 0.148 (n = 266)lag +5: r = 0.115 (n = 256)lag +6: r = 0.175 (n = 240)lag +7: r = 0.136 (n = 221)

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


% of population
% of households		Years
Luxembourg LUX99.1%33.3%2025, 2011
Norway NOR99%39.6%2024, 2011
Bermuda BMU98.4%34.1%2017, 2016
Qatar QAT98.1%6.98%2024, 2012
Korea, Rep. KOR97.9%31.8%2024, 2020
Ireland IRL97.2%22%2024, 2016
Aruba ABW97.2%21.4%2017, 2010
Brunei Darussalam BRN96.3%7.22%2024, 2011
Australia AUS96.1%22.8%2024, 2016
Belgium BEL95.8%34%2024, 2011
Spain ESP95.8%23.2%2024, 2011
Hong Kong SAR, China HKG95.8%18.3%2024, 2016
Jordan JOR95.6%4.36%2024, 2017
Chile CHL95.6%20.2%2024, 2017
United Kingdom GBR95.5%30.6%2024, 2011

World Bank, World Development Indicators
United Nations, Department of Economic and Social Affairs, Population Division (2022). Database on Household Size and Composition 2022.

Sources

Internet users

Individuals using the Internet (% of population)

International Telecommunication Union · CC BY 4.0 · updated quarterly

One-person households

Share of one-person households

United Nations Department of Economic and Social Affairs, Population Division · via Our World in Data · CC BY 3.0 IGO · updated irregular (2017, 2019 and 2022 editions)

Households of one person, from the same UN collection as the single-parent share and with the same limits: one to three readings per country, from sources that sort households differently, and few after 2019. It rises with age and with income, and falls with family size. That is three different things in one number. No "better" direction is set.

How this was computed

Data used: most recent value in 2010-2025; values come from 2010–2025. 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 Internet users and One-person households

This page shows that two measures go together. It does not show that Internet users changes One-person households, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with Internet users or One-person households