placestats

Labour score vs. One-person households

Fades after adjusting for income

Across 145 countries, Labour score and One-person households rise and fall together at r = +0.53 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.07, which could easily be chance (q = 0.69, 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 one-person households, 6.64% to 22.6% of households, labour score is better by 0.531 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.0659 typical gaps.

Link before adjusting (r)+0.531p = 6.7e-12 · 145 countries
After adjusting for national income+0.072p = 0.389 · likely range [-0.09, +0.23]
p, allowing for regions0.598standard error 0.130 · 7 regions
Check for luck (q)0.686across 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.

Labour score · typical gaps (standard deviations)link with income +0.79 · high
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

Labour score · typical gaps (standard deviations) →One-person households · % of households →-1.971.360.19%41.1%Afghanistan: -1.26 typical gaps (standard deviations), 0.19% of householdsAngola: -0.939 typical gaps (standard deviations), 10.8% of householdsAlbania: -0.39 typical gaps (standard deviations), 9.95% of householdsArgentina: 0.34 typical gaps (standard deviations), 22.8% of householdsArmenia: -0.302 typical gaps (standard deviations), 15.2% of householdsAustralia: 0.867 typical gaps (standard deviations), 22.8% of householdsAustria: 0.671 typical gaps (standard deviations), 36.3% of householdsBurundi: -0.275 typical gaps (standard deviations), 6.04% of householdsBelgium: 0.682 typical gaps (standard deviations), 34% of householdsBenin: -0.233 typical gaps (standard deviations), 9.78% of householdsBurkina Faso: -0.52 typical gaps (standard deviations), 6.64% of householdsBangladesh: -0.0147 typical gaps (standard deviations), 2.85% of householdsBulgaria: 0.954 typical gaps (standard deviations), 30.8% of householdsBahamas, The: 0.318 typical gaps (standard deviations), 17.7% of householdsBosnia and Herzegovina: 0.126 typical gaps (standard deviations), 11.5% of householdsBelarus: 0.978 typical gaps (standard deviations), 38% of householdsBelize: 0.0851 typical gaps (standard deviations), 17.2% of householdsBolivia: -0.0485 typical gaps (standard deviations), 20.5% of householdsBrazil: 0.16 typical gaps (standard deviations), 12% of householdsBarbados: 0.485 typical gaps (standard deviations), 26.3% of householdsBrunei Darussalam: 0.857 typical gaps (standard deviations), 7.22% of householdsBhutan: -0.0349 typical gaps (standard deviations), 6.13% of householdsCentral African Republic: -0.633 typical gaps (standard deviations), 6.2% of householdsCanada: 0.581 typical gaps (standard deviations), 28.2% of householdsChile: 0.344 typical gaps (standard deviations), 20.2% of householdsCote d'Ivoire: -0.122 typical gaps (standard deviations), 17% of householdsCameroon: -0.192 typical gaps (standard deviations), 15.3% of householdsCongo, Dem. Rep.: -0.523 typical gaps (standard deviations), 7.55% of householdsCongo, Rep.: -1.69 typical gaps (standard deviations), 13.8% of householdsColombia: -0.377 typical gaps (standard deviations), 11.1% of householdsComoros: -0.00862 typical gaps (standard deviations), 4.91% of householdsCosta Rica: 0.133 typical gaps (standard deviations), 12.1% of householdsCyprus: 0.812 typical gaps (standard deviations), 20.8% of householdsCzechia: 0.889 typical gaps (standard deviations), 32.5% of householdsGermany: 0.988 typical gaps (standard deviations), 37.3% of householdsDominican Republic: -0.0819 typical gaps (standard deviations), 20.1% of householdsAlgeria: -0.0627 typical gaps (standard deviations), 1.61% of householdsEgypt, Arab Rep.: 0.367 typical gaps (standard deviations), 6.48% of householdsSpain: 0.483 typical gaps (standard deviations), 23.2% of householdsEthiopia: -0.481 typical gaps (standard deviations), 6.36% of householdsFinland: 0.419 typical gaps (standard deviations), 41% of householdsFiji: 0.318 typical gaps (standard deviations), 5.94% of householdsFrance: 0.595 typical gaps (standard deviations), 35.5% of householdsGabon: -0.844 typical gaps (standard deviations), 22.4% of householdsUnited Kingdom: 0.635 typical gaps (standard deviations), 30.6% of householdsGeorgia: -0.418 typical gaps (standard deviations), 15% of householdsGhana: 0.00187 typical gaps (standard deviations), 21.5% of householdsGuinea: -0.539 typical gaps (standard deviations), 5.07% of householdsGambia, The: -0.899 typical gaps (standard deviations), 9.72% of householdsGuinea-Bissau: -0.255 typical gaps (standard deviations), 4.14% of householdsGreece: 0.426 typical gaps (standard deviations), 25.7% of householdsGuatemala: 0.48 typical gaps (standard deviations), 4.33% of householdsGuyana: -0.0857 typical gaps (standard deviations), 15.8% of householdsHong Kong SAR, China: 1.11 typical gaps (standard deviations), 18.3% of householdsHonduras: -0.175 typical gaps (standard deviations), 10.4% of householdsCroatia: 0.844 typical gaps (standard deviations), 24.6% of householdsHaiti: -1.28 typical gaps (standard deviations), 9.43% of householdsHungary: 0.856 typical gaps (standard deviations), 32.1% of householdsIndonesia: 0.169 typical gaps (standard deviations), 7.1% of householdsIndia: -0.486 typical gaps (standard deviations), 5.11% of householdsIreland: 0.813 typical gaps (standard deviations), 22% of householdsIran, Islamic Rep.: -0.0514 typical gaps (standard deviations), 7.16% of householdsIraq: -0.409 typical gaps (standard deviations), 1.01% of householdsItaly: 0.579 typical gaps (standard deviations), 31.1% of householdsJamaica: 0.511 typical gaps (standard deviations), 28.2% of householdsJordan: -0.133 typical gaps (standard deviations), 4.36% of householdsJapan: 1.03 typical gaps (standard deviations), 34.5% of householdsKazakhstan: 0.516 typical gaps (standard deviations), 15.5% of householdsKenya: -0.274 typical gaps (standard deviations), 19.4% of householdsKyrgyz Republic: 0.54 typical gaps (standard deviations), 12.3% of householdsCambodia: 0.369 typical gaps (standard deviations), 3.37% of householdsKiribati: -1.29 typical gaps (standard deviations), 3.71% of householdsKorea, Rep.: 0.822 typical gaps (standard deviations), 31.8% of householdsLao PDR: -0.16 typical gaps (standard deviations), 3.18% of householdsLiberia: -0.308 typical gaps (standard deviations), 12% of householdsSt. Lucia: -0.149 typical gaps (standard deviations), 26.8% of householdsLesotho: -0.581 typical gaps (standard deviations), 18% of householdsLithuania: 0.626 typical gaps (standard deviations), 31.7% of householdsLuxembourg: 0.723 typical gaps (standard deviations), 33.3% of householdsLatvia: 0.673 typical gaps (standard deviations), 41.1% of householdsMacao SAR, China: 1.12 typical gaps (standard deviations), 15.1% of householdsMorocco: -0.0339 typical gaps (standard deviations), 7.2% of householdsMoldova: 0.412 typical gaps (standard deviations), 22.6% of householdsMadagascar: -0.463 typical gaps (standard deviations), 7.11% of householdsMaldives: 0.686 typical gaps (standard deviations), 4.49% of householdsMexico: 0.513 typical gaps (standard deviations), 10.1% of householdsNorth Macedonia: 0.0322 typical gaps (standard deviations), 11.8% of householdsMali: -0.283 typical gaps (standard deviations), 3.11% of householdsMalta: 0.947 typical gaps (standard deviations), 22.6% of householdsMyanmar: -0.0589 typical gaps (standard deviations), 5.35% of householdsMontenegro: -0.0101 typical gaps (standard deviations), 15.4% of householdsMongolia: 0.223 typical gaps (standard deviations), 8.96% of householdsMozambique: -0.745 typical gaps (standard deviations), 8.41% of householdsMauritania: -0.477 typical gaps (standard deviations), 3.97% of householdsMalawi: -0.169 typical gaps (standard deviations), 7.2% of householdsNamibia: -0.775 typical gaps (standard deviations), 17.4% of householdsNiger: -0.233 typical gaps (standard deviations), 3.19% of householdsNigeria: -0.128 typical gaps (standard deviations), 14.7% of householdsNorway: 0.915 typical gaps (standard deviations), 39.6% of householdsNepal: -0.934 typical gaps (standard deviations), 4.51% of householdsNew Zealand: 0.721 typical gaps (standard deviations), 21.9% of householdsPakistan: -0.988 typical gaps (standard deviations), 1.08% of householdsPanama: 0.0819 typical gaps (standard deviations), 13.4% of householdsPeru: -0.454 typical gaps (standard deviations), 16.8% of householdsPhilippines: 0.559 typical gaps (standard deviations), 9.18% of householdsPoland: 0.842 typical gaps (standard deviations), 24% of householdsPuerto Rico (US): 0.688 typical gaps (standard deviations), 23.1% of householdsPortugal: 0.843 typical gaps (standard deviations), 21.4% of householdsParaguay: -0.242 typical gaps (standard deviations), 10.4% of householdsWest Bank and Gaza: -0.578 typical gaps (standard deviations), 4.31% of householdsQatar: 1.36 typical gaps (standard deviations), 6.98% of householdsRomania: 0.637 typical gaps (standard deviations), 26% of householdsRussian Federation: 1.14 typical gaps (standard deviations), 25.7% of householdsRwanda: -0.108 typical gaps (standard deviations), 7.69% of householdsSudan: -1.35 typical gaps (standard deviations), 1.59% of householdsSenegal: -0.0349 typical gaps (standard deviations), 6.92% of householdsSingapore: 1.05 typical gaps (standard deviations), 16.1% of householdsSierra Leone: -0.503 typical gaps (standard deviations), 6.42% of householdsEl Salvador: 0.523 typical gaps (standard deviations), 8.46% of householdsSomalia, Fed. Rep.: -1.05 typical gaps (standard deviations), 1.53% of householdsSerbia: 0.409 typical gaps (standard deviations), 21.8% of householdsSao Tome and Principe: -0.0345 typical gaps (standard deviations), 13.8% of householdsSuriname: 0.385 typical gaps (standard deviations), 14% of householdsSlovak Republic: 0.713 typical gaps (standard deviations), 25.3% of householdsSlovenia: 0.914 typical gaps (standard deviations), 32.6% of householdsEswatini: -1.97 typical gaps (standard deviations), 21.7% of householdsChad: -0.409 typical gaps (standard deviations), 5.43% of householdsTogo: -0.201 typical gaps (standard deviations), 15.7% of householdsThailand: 0.466 typical gaps (standard deviations), 21.6% of householdsTajikistan: 0.278 typical gaps (standard deviations), 2.88% of householdsTurkmenistan: 0.492 typical gaps (standard deviations), 3.74% of householdsTimor-Leste: 0.141 typical gaps (standard deviations), 6.41% of householdsTonga: 0.576 typical gaps (standard deviations), 6.94% of householdsTunisia: -0.205 typical gaps (standard deviations), 6.27% of householdsTuvalu: -0.6 typical gaps (standard deviations), 6.92% of householdsTanzania: -0.328 typical gaps (standard deviations), 9.63% of householdsUganda: -0.25 typical gaps (standard deviations), 13.2% of householdsUkraine: 0.208 typical gaps (standard deviations), 25% of householdsUruguay: 0.298 typical gaps (standard deviations), 17.6% of householdsUnited States: 0.862 typical gaps (standard deviations), 27.9% of householdsViet Nam: 0.283 typical gaps (standard deviations), 10.5% of householdsSamoa: 0.181 typical gaps (standard deviations), 4.4% of householdsSouth Africa: -1.51 typical gaps (standard deviations), 23.8% of householdsZambia: -0.432 typical gaps (standard deviations), 7.97% of householdsZimbabwe: -0.673 typical gaps (standard deviations), 12.9% of householdsAFGBLRBRNCOGDEUKIRMDVNORQATSDNSWZZAF

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5316.7e-12145
By rank (Spearman), not adjusted+0.5707.4e-14145
Standard (Pearson), adjusted for national income+0.0720.389145
By rank (Spearman), adjusted for national income+0.0380.647145

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 145 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: +0.458 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.050 (without BLR) to +0.113 (without SWZ). 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: SWZ (+0.041).

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 & Pacific22+0.13
Europe & Central Asia39+0.41
Latin America & Caribbean24+0.12
Middle East, North Africa, Afghanistan & Pakistan12+0.31
Sub-Saharan Africa41-0.07

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 third49+0.11
Middle third48-0.28
Richest third48+0.24

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
Christians91+0.11
Muslims36+0.16

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
France22-0.39
Never a European colony15+0.34
Spain16-0.27
United Kingdom44-0.13

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 = +0.130 after dropping 2 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


typical gaps (standard deviations)
% of households		Years
Qatar QAT1.366.98%2025, 2012
Russian Federation RUS1.1425.7%2025, 2010
Macao SAR, China MAC1.1215.1%2025, 2016
Hong Kong SAR, China HKG1.1118.3%2025, 2016
Singapore SGP1.0516.1%2025, 2020
Japan JPN1.0334.5%2025, 2015
Germany DEU0.98837.3%2025, 2011
Belarus BLR0.97838%2025, 2019
Bulgaria BGR0.95430.8%2025, 2011
Malta MLT0.94722.6%2025, 2011
Norway NOR0.91539.6%2025, 2011
Slovenia SVN0.91432.6%2025, 2015
Czechia CZE0.88932.5%2025, 2011
Australia AUS0.86722.8%2025, 2016
United States USA0.86227.9%2025, 2015

un_sdg; wdi
United Nations, Department of Economic and Social Affairs, Population Division (2022). Database on Household Size and Composition 2022.

Sources

Labour score

Labour score

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

The average of 4 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: Informal employment, Vulnerable employment, Youth not in work or study, Unemployment.

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 Labour score and One-person households

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

More links with Labour score or One-person households