placestats

Basic sanitation vs. Labour score

Fades after adjusting for income

Across 184 countries, Basic sanitation and Labour score rise and fall together at r = +0.61 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.09, which could easily be chance (q = 0.46, 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 basic sanitation, 66.8% to 99% of population, labour score is better by 0.504 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.082 typical gaps.

Link before adjusting (r)+0.613p = 2.1e-20 · 184 countries
After adjusting for national income+0.087p = 0.241 · likely range [-0.06, +0.23]
p, allowing for regions0.257standard error 0.070 · 7 regions
Check for luck (q)0.463across 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.

Basic sanitation · % of populationlink with income +0.79 · high
Labour score · typical gaps (standard deviations)link with income +0.79 · 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

Basic sanitation · % of population →Labour score · typical gaps (standard deviations) →10.4%100%-1.971.36Afghanistan: 54.5% of population, -1.26 typical gaps (standard deviations)Angola: 50.3% of population, -0.939 typical gaps (standard deviations)Albania: 99.3% of population, -0.39 typical gaps (standard deviations)United Arab Emirates: 99.1% of population, 1.17 typical gaps (standard deviations)Argentina: 94.8% of population, 0.34 typical gaps (standard deviations)Armenia: 93.7% of population, -0.302 typical gaps (standard deviations)Antigua and Barbuda: 99.6% of population, 0.235 typical gaps (standard deviations)Australia: 100% of population, 0.867 typical gaps (standard deviations)Austria: 100% of population, 0.671 typical gaps (standard deviations)Azerbaijan: 99% of population, -0.0868 typical gaps (standard deviations)Burundi: 44.6% of population, -0.275 typical gaps (standard deviations)Belgium: 99.5% of population, 0.682 typical gaps (standard deviations)Benin: 22.2% of population, -0.233 typical gaps (standard deviations)Burkina Faso: 31.8% of population, -0.52 typical gaps (standard deviations)Bangladesh: 67.6% of population, -0.0147 typical gaps (standard deviations)Bulgaria: 86.1% of population, 0.954 typical gaps (standard deviations)Bahrain: 100% of population, 1.27 typical gaps (standard deviations)Bahamas, The: 93.7% of population, 0.318 typical gaps (standard deviations)Bosnia and Herzegovina: 95.4% of population, 0.126 typical gaps (standard deviations)Belarus: 99.7% of population, 0.978 typical gaps (standard deviations)Belize: 84.3% of population, 0.0851 typical gaps (standard deviations)Bolivia: 71.6% of population, -0.0485 typical gaps (standard deviations)Brazil: 92.4% of population, 0.16 typical gaps (standard deviations)Barbados: 98.1% of population, 0.485 typical gaps (standard deviations)Brunei Darussalam: 100% of population, 0.857 typical gaps (standard deviations)Bhutan: 86.8% of population, -0.0349 typical gaps (standard deviations)Botswana: 72.1% of population, -1.12 typical gaps (standard deviations)Central African Republic: 14% of population, -0.633 typical gaps (standard deviations)Canada: 98.6% of population, 0.581 typical gaps (standard deviations)Switzerland: 99.9% of population, 0.916 typical gaps (standard deviations)Chile: 100% of population, 0.344 typical gaps (standard deviations)China: 97.3% of population, 0.367 typical gaps (standard deviations)Cote d'Ivoire: 40.4% of population, -0.122 typical gaps (standard deviations)Cameroon: 47.1% of population, -0.192 typical gaps (standard deviations)Congo, Dem. Rep.: 16.3% of population, -0.523 typical gaps (standard deviations)Congo, Rep.: 20.6% of population, -1.69 typical gaps (standard deviations)Colombia: 96.6% of population, -0.377 typical gaps (standard deviations)Comoros: 57.2% of population, -0.00862 typical gaps (standard deviations)Cabo Verde: 83.1% of population, -0.252 typical gaps (standard deviations)Costa Rica: 98.8% of population, 0.133 typical gaps (standard deviations)Cyprus: 99.5% of population, 0.812 typical gaps (standard deviations)Czechia: 99.1% of population, 0.889 typical gaps (standard deviations)Germany: 99.2% of population, 0.988 typical gaps (standard deviations)Djibouti: 67% of population, -1.82 typical gaps (standard deviations)Denmark: 99.6% of population, 0.819 typical gaps (standard deviations)Dominican Republic: 90.1% of population, -0.0819 typical gaps (standard deviations)Algeria: 85.9% of population, -0.0627 typical gaps (standard deviations)Ecuador: 91.2% of population, -0.398 typical gaps (standard deviations)Egypt, Arab Rep.: 97.1% of population, 0.367 typical gaps (standard deviations)Spain: 99.9% of population, 0.483 typical gaps (standard deviations)Estonia: 99% of population, 0.558 typical gaps (standard deviations)Ethiopia: 10.4% of population, -0.481 typical gaps (standard deviations)Finland: 99.4% of population, 0.419 typical gaps (standard deviations)Fiji: 92.8% of population, 0.318 typical gaps (standard deviations)France: 98.6% of population, 0.595 typical gaps (standard deviations)Gabon: 57.4% of population, -0.844 typical gaps (standard deviations)United Kingdom: 99.1% of population, 0.635 typical gaps (standard deviations)Georgia: 90.7% of population, -0.418 typical gaps (standard deviations)Ghana: 31.6% of population, 0.00187 typical gaps (standard deviations)Guinea: 32.6% of population, -0.539 typical gaps (standard deviations)Gambia, The: 48.2% of population, -0.899 typical gaps (standard deviations)Guinea-Bissau: 28.9% of population, -0.255 typical gaps (standard deviations)Equatorial Guinea: 66.3% of population, 0.255 typical gaps (standard deviations)Greece: 99% of population, 0.426 typical gaps (standard deviations)Grenada: 92.6% of population, -0.263 typical gaps (standard deviations)Guatemala: 71.2% of population, 0.48 typical gaps (standard deviations)Guyana: 91.3% of population, -0.0857 typical gaps (standard deviations)Hong Kong SAR, China: 94.8% of population, 1.11 typical gaps (standard deviations)Honduras: 87.7% of population, -0.175 typical gaps (standard deviations)Croatia: 95.8% of population, 0.844 typical gaps (standard deviations)Haiti: 29.1% of population, -1.28 typical gaps (standard deviations)Hungary: 98% of population, 0.856 typical gaps (standard deviations)Indonesia: 88.2% of population, 0.169 typical gaps (standard deviations)India: 83.4% of population, -0.486 typical gaps (standard deviations)Ireland: 89.7% of population, 0.813 typical gaps (standard deviations)Iran, Islamic Rep.: 90.5% of population, -0.0514 typical gaps (standard deviations)Iraq: 98.5% of population, -0.409 typical gaps (standard deviations)Iceland: 98.8% of population, 0.919 typical gaps (standard deviations)Israel: 99.9% of population, 0.943 typical gaps (standard deviations)Italy: 99.9% of population, 0.579 typical gaps (standard deviations)Jamaica: 91.6% of population, 0.511 typical gaps (standard deviations)Jordan: 96.4% of population, -0.133 typical gaps (standard deviations)Japan: 99.9% of population, 1.03 typical gaps (standard deviations)Kazakhstan: 97.9% of population, 0.516 typical gaps (standard deviations)Kenya: 40.9% of population, -0.274 typical gaps (standard deviations)Kyrgyz Republic: 98.5% of population, 0.54 typical gaps (standard deviations)Cambodia: 83.3% of population, 0.369 typical gaps (standard deviations)Kiribati: 45.2% of population, -1.29 typical gaps (standard deviations)Korea, Rep.: 99.7% of population, 0.822 typical gaps (standard deviations)Kuwait: 100% of population, 1.15 typical gaps (standard deviations)Lao PDR: 88.7% of population, -0.16 typical gaps (standard deviations)Lebanon: 99.2% of population, 0.107 typical gaps (standard deviations)Liberia: 23.3% of population, -0.308 typical gaps (standard deviations)Libya: 88.5% of population, -0.574 typical gaps (standard deviations)St. Lucia: 83.4% of population, -0.149 typical gaps (standard deviations)Sri Lanka: 95.4% of population, 0.307 typical gaps (standard deviations)Lesotho: 47.9% of population, -0.581 typical gaps (standard deviations)Lithuania: 96.4% of population, 0.626 typical gaps (standard deviations)Luxembourg: 97.6% of population, 0.723 typical gaps (standard deviations)Latvia: 92.5% of population, 0.673 typical gaps (standard deviations)Macao SAR, China: 100% of population, 1.12 typical gaps (standard deviations)Morocco: 87.8% of population, -0.0339 typical gaps (standard deviations)Moldova: 86.9% of population, 0.412 typical gaps (standard deviations)Madagascar: 15.4% of population, -0.463 typical gaps (standard deviations)Maldives: 99.7% of population, 0.686 typical gaps (standard deviations)Mexico: 93% of population, 0.513 typical gaps (standard deviations)Marshall Islands: 79% of population, -1.09 typical gaps (standard deviations)North Macedonia: 99% of population, 0.0322 typical gaps (standard deviations)Mali: 47.8% of population, -0.283 typical gaps (standard deviations)Malta: 100% of population, 0.947 typical gaps (standard deviations)Myanmar: 74.2% of population, -0.0589 typical gaps (standard deviations)Montenegro: 97.8% of population, -0.0101 typical gaps (standard deviations)Mongolia: 74.3% of population, 0.223 typical gaps (standard deviations)Mozambique: 38.6% of population, -0.745 typical gaps (standard deviations)Mauritania: 58.7% of population, -0.477 typical gaps (standard deviations)Mauritius: 97.4% of population, 0.632 typical gaps (standard deviations)Malawi: 49.2% of population, -0.169 typical gaps (standard deviations)Malaysia: 96% of population, 0.654 typical gaps (standard deviations)Namibia: 38.7% of population, -0.775 typical gaps (standard deviations)Niger: 16.3% of population, -0.233 typical gaps (standard deviations)Nigeria: 47.9% of population, -0.128 typical gaps (standard deviations)Netherlands: 97.7% of population, 0.804 typical gaps (standard deviations)Norway: 98% of population, 0.915 typical gaps (standard deviations)Nepal: 86% of population, -0.934 typical gaps (standard deviations)Naoero: 71.3% of population, -1.38 typical gaps (standard deviations)New Zealand: 100% of population, 0.721 typical gaps (standard deviations)Oman: 99.3% of population, 1.04 typical gaps (standard deviations)Pakistan: 71.9% of population, -0.988 typical gaps (standard deviations)Panama: 89.1% of population, 0.0819 typical gaps (standard deviations)Peru: 78.8% of population, -0.454 typical gaps (standard deviations)Philippines: 87.2% of population, 0.559 typical gaps (standard deviations)Palau: 99% of population, 0.825 typical gaps (standard deviations)Papua New Guinea: 23.6% of population, -0.175 typical gaps (standard deviations)Poland: 99% of population, 0.842 typical gaps (standard deviations)Puerto Rico (US): 100% of population, 0.688 typical gaps (standard deviations)Portugal: 99.7% of population, 0.843 typical gaps (standard deviations)Paraguay: 95.9% of population, -0.242 typical gaps (standard deviations)West Bank and Gaza: 99.4% of population, -0.578 typical gaps (standard deviations)Qatar: 99.9% of population, 1.36 typical gaps (standard deviations)Romania: 90.5% of population, 0.637 typical gaps (standard deviations)Russian Federation: 92.2% of population, 1.14 typical gaps (standard deviations)Rwanda: 81.3% of population, -0.108 typical gaps (standard deviations)Saudi Arabia: 97.6% of population, 0.782 typical gaps (standard deviations)Sudan: 65.7% of population, -1.35 typical gaps (standard deviations)Senegal: 62.5% of population, -0.0349 typical gaps (standard deviations)Singapore: 100% of population, 1.05 typical gaps (standard deviations)Solomon Islands: 34.6% of population, -0.0344 typical gaps (standard deviations)Sierra Leone: 24.9% of population, -0.503 typical gaps (standard deviations)El Salvador: 88% of population, 0.523 typical gaps (standard deviations)Somalia, Fed. Rep.: 44.5% of population, -1.05 typical gaps (standard deviations)Serbia: 97.9% of population, 0.409 typical gaps (standard deviations)Sao Tome and Principe: 48.7% of population, -0.0345 typical gaps (standard deviations)Suriname: 90% of population, 0.385 typical gaps (standard deviations)Slovak Republic: 97.5% of population, 0.713 typical gaps (standard deviations)Slovenia: 98.3% of population, 0.914 typical gaps (standard deviations)Sweden: 98.7% of population, 0.531 typical gaps (standard deviations)Eswatini: 64.5% of population, -1.97 typical gaps (standard deviations)Seychelles: 99.7% of population, 0.418 typical gaps (standard deviations)Syrian Arab Republic: 95.6% of population, -0.472 typical gaps (standard deviations)Chad: 13.1% of population, -0.409 typical gaps (standard deviations)Togo: 25.2% of population, -0.201 typical gaps (standard deviations)Thailand: 99.4% of population, 0.466 typical gaps (standard deviations)Tajikistan: 96% of population, 0.278 typical gaps (standard deviations)Turkmenistan: 99.7% of population, 0.492 typical gaps (standard deviations)Timor-Leste: 72.9% of population, 0.141 typical gaps (standard deviations)Tonga: 93.9% of population, 0.576 typical gaps (standard deviations)Trinidad and Tobago: 94.2% of population, 0.701 typical gaps (standard deviations)Tunisia: 98.5% of population, -0.205 typical gaps (standard deviations)Turkiye: 99.3% of population, 0.224 typical gaps (standard deviations)Tuvalu: 89.7% of population, -0.6 typical gaps (standard deviations)Tanzania: 37.2% of population, -0.328 typical gaps (standard deviations)Uganda: 23.9% of population, -0.25 typical gaps (standard deviations)Ukraine: 97.7% of population, 0.208 typical gaps (standard deviations)Uruguay: 99.2% of population, 0.298 typical gaps (standard deviations)United States: 99.6% of population, 0.862 typical gaps (standard deviations)Uzbekistan: 96.5% of population, 0.368 typical gaps (standard deviations)St. Vincent and the Grenadines: 89.7% of population, -0.519 typical gaps (standard deviations)Virgin Islands (U.S.): 99.3% of population, 0.331 typical gaps (standard deviations)Viet Nam: 95.4% of population, 0.283 typical gaps (standard deviations)Vanuatu: 47.8% of population, -0.495 typical gaps (standard deviations)Samoa: 97.1% of population, 0.181 typical gaps (standard deviations)South Africa: 77.4% of population, -1.51 typical gaps (standard deviations)Zambia: 37.5% of population, -0.432 typical gaps (standard deviations)Zimbabwe: 34.6% of population, -0.673 typical gaps (standard deviations)BHRCOGDJIETHHTIKWTNERQATSWZTCDZAF

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.6132.1e-20184
By rank (Spearman), not adjusted+0.7403.5e-33184
Standard (Pearson), adjusted for national income+0.0870.241184
By rank (Spearman), adjusted for national income+0.2520.001184

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 184 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: +0.526 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.043 (without COG) to +0.100 (without NPL). 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: COG (-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 & Pacific30+0.20
Europe & Central Asia48-0.03
Latin America & Caribbean30+0.40
Middle East, North Africa, Afghanistan & Pakistan22+0.53
Sub-Saharan Africa46+0.09

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 third62+0.13
Middle third61+0.47
Richest third61+0.16

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
Christians108+0.25
Muslims49-0.15

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+0.40
Never a European colony21+0.06
Spain18-0.14
United Kingdom61+0.09

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 = +0.134 after dropping 8 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


% of population
typical gaps (standard deviations)		Years
Singapore SGP100%1.052024, 2025
Puerto Rico (US) PRI100%0.6882024, 2025
New Zealand NZL100%0.7212024, 2025
Malta MLT100%0.9472024, 2025
Macao SAR, China MAC100%1.122024, 2025
Kuwait KWT100%1.152024, 2025
Brunei Darussalam BRN100%0.8572024, 2025
Bahrain BHR100%1.272024, 2025
Australia AUS100%0.8672024, 2025
Chile CHL100%0.3442024, 2025
Austria AUT100%0.6712024, 2025
Japan JPN99.9%1.032024, 2025
Qatar QAT99.9%1.362024, 2025
Israel ISR99.9%0.9432024, 2025
Spain ESP99.9%0.4832024, 2025

World Bank, World Development Indicators
un_sdg; wdi

Sources

Basic sanitation

People using at least basic sanitation services (% of population)

World Bank · CC BY 4.0 · updated quarterly

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.

How this was computed

Data used: most recent value in 2010-2025; values come from 2016–2025. 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 Basic sanitation and Labour score

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

More links with Basic sanitation or Labour score