placestats

Basic sanitation vs. Women's age at first marriage (census-based)

Fades after adjusting for income

Across 175 countries, Basic sanitation and Women's age at first marriage (census-based) 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 -0.09, which could easily be chance (q = 0.57, 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 women's age at first marriage (census-based), 21.9 to 27.9 years, basic sanitation is better by 20.7% of population at the top of that range than the bottom. Comparing only countries with similar national income, the gap reverses, to 3.13% of population the other way.

Link before adjusting (r)+0.530p = 4.6e-14 · 175 countries
After adjusting for national income-0.090p = 0.238 · likely range [-0.24, +0.06]
p, allowing for regions0.508standard error 0.128 · 7 regions
Check for luck (q)0.570across 14,952 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
Women's age at first marriage (census-based) · yearslink with income +0.75 · 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 →Women's age at first marriage (census-based) · years →10.4%100%1734Aruba: 98.8% of population, 28.5 yearsAfghanistan: 54.5% of population, 21.5 yearsAngola: 50.3% of population, 21.5 yearsAlbania: 99.3% of population, 24.2 yearsArgentina: 94.8% of population, 24.6 yearsArmenia: 93.7% of population, 24.2 yearsAustralia: 100% of population, 29.9 yearsAustria: 100% of population, 27.1 yearsAzerbaijan: 99% of population, 24.2 yearsBurundi: 44.6% of population, 22.8 yearsBelgium: 99.5% of population, 26.3 yearsBenin: 22.2% of population, 20.7 yearsBurkina Faso: 31.8% of population, 20 yearsBangladesh: 67.6% of population, 18.8 yearsBulgaria: 86.1% of population, 32.6 yearsBahrain: 100% of population, 26.9 yearsBahamas, The: 93.7% of population, 27.9 yearsBosnia and Herzegovina: 95.4% of population, 25.7 yearsBelarus: 99.7% of population, 22.5 yearsBelize: 84.3% of population, 20.5 yearsBermuda: 99.9% of population, 31.9 yearsBolivia: 71.6% of population, 24 yearsBrazil: 92.4% of population, 23.9 yearsBarbados: 98.1% of population, 17 yearsBrunei Darussalam: 100% of population, 26.1 yearsBhutan: 86.8% of population, 23.8 yearsBotswana: 72.1% of population, 22.9 yearsCentral African Republic: 14% of population, 17.3 yearsCanada: 98.6% of population, 27.2 yearsSwitzerland: 99.9% of population, 30.9 yearsChile: 100% of population, 27.4 yearsChina: 97.3% of population, 25.4 yearsCote d'Ivoire: 40.4% of population, 23 yearsCameroon: 47.1% of population, 22.2 yearsCongo, Dem. Rep.: 16.3% of population, 20.9 yearsCongo, Rep.: 20.6% of population, 21.9 yearsColombia: 96.6% of population, 22.7 yearsComoros: 57.2% of population, 22.6 yearsCosta Rica: 98.8% of population, 26.4 yearsCayman Islands: 83.2% of population, 28 yearsCyprus: 99.5% of population, 26.4 yearsCzechia: 99.1% of population, 32.4 yearsGermany: 99.2% of population, 31.7 yearsDjibouti: 67% of population, 29.9 yearsDenmark: 99.6% of population, 31.9 yearsDominican Republic: 90.1% of population, 21.5 yearsAlgeria: 85.9% of population, 28.2 yearsEcuador: 91.2% of population, 21.8 yearsEgypt, Arab Rep.: 97.1% of population, 22.3 yearsSpain: 99.9% of population, 27.7 yearsEstonia: 99% of population, 33.6 yearsEthiopia: 10.4% of population, 21.3 yearsFinland: 99.4% of population, 30.7 yearsFrance: 98.6% of population, 32 yearsFaroe Islands: 90.9% of population, 31 yearsMicronesia, Fed. Sts.: 90.5% of population, 25.3 yearsGabon: 57.4% of population, 22.9 yearsUnited Kingdom: 99.1% of population, 27 yearsGeorgia: 90.7% of population, 22.8 yearsGhana: 31.6% of population, 23.7 yearsGuinea: 32.6% of population, 19.8 yearsGambia, The: 48.2% of population, 21.5 yearsGuinea-Bissau: 28.9% of population, 24.1 yearsEquatorial Guinea: 66.3% of population, 20.5 yearsGreece: 99% of population, 29.2 yearsGreenland: 62.5% of population, 32.9 yearsGuatemala: 71.2% of population, 22.7 yearsGuyana: 91.3% of population, 19.2 yearsHong Kong SAR, China: 94.8% of population, 29.9 yearsHonduras: 87.7% of population, 21.2 yearsCroatia: 95.8% of population, 27.4 yearsHaiti: 29.1% of population, 24.9 yearsHungary: 98% of population, 32.9 yearsIndonesia: 88.2% of population, 22.4 yearsIndia: 83.4% of population, 21.4 yearsIreland: 89.7% of population, 32.4 yearsIran, Islamic Rep.: 90.5% of population, 23.1 yearsIraq: 98.5% of population, 22.7 yearsIceland: 98.8% of population, 32.4 yearsIsrael: 99.9% of population, 26.2 yearsItaly: 99.9% of population, 32.2 yearsJamaica: 91.6% of population, 25.3 yearsJordan: 96.4% of population, 24.6 yearsJapan: 99.9% of population, 29.2 yearsKazakhstan: 97.9% of population, 22.4 yearsKenya: 40.9% of population, 21.9 yearsKyrgyz Republic: 98.5% of population, 21.2 yearsCambodia: 83.3% of population, 21.6 yearsKiribati: 45.2% of population, 21.6 yearsKorea, Rep.: 99.7% of population, 31.5 yearsKuwait: 100% of population, 25.7 yearsLao PDR: 88.7% of population, 20.7 yearsLiberia: 23.3% of population, 22.6 yearsSt. Lucia: 83.4% of population, 21.9 yearsSri Lanka: 95.4% of population, 23.9 yearsLesotho: 47.9% of population, 22.2 yearsLithuania: 96.4% of population, 32.7 yearsLuxembourg: 97.6% of population, 30.7 yearsLatvia: 92.5% of population, 30.4 yearsMacao SAR, China: 100% of population, 30.3 yearsMorocco: 87.8% of population, 24.4 yearsMoldova: 86.9% of population, 25 yearsMaldives: 99.7% of population, 22.5 yearsMexico: 93% of population, 23.2 yearsMarshall Islands: 79% of population, 24.2 yearsNorth Macedonia: 99% of population, 25.5 yearsMali: 47.8% of population, 18.8 yearsMalta: 100% of population, 28 yearsMyanmar: 74.2% of population, 22.9 yearsMontenegro: 97.8% of population, 26.3 yearsMongolia: 74.3% of population, 23 yearsMozambique: 38.6% of population, 18.7 yearsMauritania: 58.7% of population, 21.8 yearsMauritius: 97.4% of population, 23.9 yearsMalawi: 49.2% of population, 20.4 yearsMalaysia: 96% of population, 25.7 yearsNamibia: 38.7% of population, 30.3 yearsNiger: 16.3% of population, 17.2 yearsNigeria: 47.9% of population, 21.2 yearsNetherlands: 97.7% of population, 32.4 yearsNorway: 98% of population, 32.1 yearsNepal: 86% of population, 20.1 yearsOman: 99.3% of population, 25.6 yearsPakistan: 71.9% of population, 23.2 yearsPanama: 89.1% of population, 21 yearsPeru: 78.8% of population, 23.3 yearsPhilippines: 87.2% of population, 23.4 yearsPalau: 99% of population, 28.5 yearsPapua New Guinea: 23.6% of population, 21.4 yearsPoland: 99% of population, 26.6 yearsPuerto Rico (US): 100% of population, 29.2 yearsPortugal: 99.7% of population, 30.1 yearsParaguay: 95.9% of population, 21.9 yearsWest Bank and Gaza: 99.4% of population, 22.1 yearsRomania: 90.5% of population, 25.9 yearsRussian Federation: 92.2% of population, 24.4 yearsRwanda: 81.3% of population, 24.3 yearsSaudi Arabia: 97.6% of population, 26.6 yearsSudan: 65.7% of population, 21.9 yearsSenegal: 62.5% of population, 22.2 yearsSingapore: 100% of population, 27.9 yearsSolomon Islands: 34.6% of population, 22.4 yearsSierra Leone: 24.9% of population, 23.1 yearsEl Salvador: 88% of population, 22.6 yearsSan Marino: 100% of population, 31.3 yearsSerbia: 97.9% of population, 26.6 yearsSao Tome and Principe: 48.7% of population, 21 yearsSuriname: 90% of population, 29.3 yearsSlovak Republic: 97.5% of population, 31 yearsSlovenia: 98.3% of population, 34 yearsSweden: 98.7% of population, 31 yearsEswatini: 64.5% of population, 26.5 yearsSint Maarten (Dutch part): 98.8% of population, 28 yearsChad: 13.1% of population, 18.8 yearsTogo: 25.2% of population, 21.3 yearsThailand: 99.4% of population, 22.5 yearsTajikistan: 96% of population, 20.7 yearsTurkmenistan: 99.7% of population, 23.5 yearsTimor-Leste: 72.9% of population, 23 yearsTonga: 93.9% of population, 25.3 yearsTrinidad and Tobago: 94.2% of population, 29.2 yearsTunisia: 98.5% of population, 28.2 yearsTurkiye: 99.3% of population, 23.4 yearsTanzania: 37.2% of population, 21 yearsUganda: 23.9% of population, 20.7 yearsUkraine: 97.7% of population, 23 yearsUruguay: 99.2% of population, 23 yearsUnited States: 99.6% of population, 27.5 yearsSt. Vincent and the Grenadines: 89.7% of population, 21.2 yearsVirgin Islands (U.S.): 99.3% of population, 30.9 yearsViet Nam: 95.4% of population, 22.8 yearsSamoa: 97.1% of population, 23.8 yearsSouth Africa: 77.4% of population, 27.2 yearsZambia: 37.5% of population, 21.8 yearsZimbabwe: 34.6% of population, 21.1 yearsBRBCAFESTGRLGUYHTIHUNNAMNERSVNTCDTJK

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5304.6e-14175
By rank (Spearman), not adjusted+0.6093.8e-19175
Standard (Pearson), adjusted for national income-0.0900.238175
By rank (Spearman), adjusted for national income+0.0420.578175

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 175 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: +0.440 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.116 (without GUY) to -0.064 (without NAM). 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: GUY (-0.026).

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 & Pacific26+0.09
Europe & Central Asia50-0.13
Latin America & Caribbean31-0.10
Middle East, North Africa, Afghanistan & Pakistan17-0.36
Sub-Saharan Africa42+0.20

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 third59+0.09
Middle third58-0.01
Richest third58+0.03

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
Christians104-0.02
Muslims42+0.14

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+0.18
Never a European colony20-0.36
Spain19+0.40
United Kingdom50-0.06

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 = -0.109 after dropping 5 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 20years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

The link shows up within countries over time (r = -0.160) but mostly disappears in year-to-year changes (r = -0.022). That points to slow trends particular to each country, not the two measures moving together from year to year.

Across countries (adjusted)-0.090for comparison
Within countries+0.114each country vs itself
…and vs the same year elsewhere-0.160q = 0.040 · 82 countries
Year-on-year changes-0.022q = 0.837
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.

690 country-years (one country in one year), 82 countries, 2000–2019, about 8 years each on average. Share of the variation that happens within countries over time: 9% for Basic sanitation, 2% for Women's age at first marriage (census-based). 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+9years Women's age at first marriage (census-based) trails Basic sanitation →lag -10: r = -0.199 (n = 937)lag -9: r = -0.190 (n = 940)lag -8: r = -0.169 (n = 940)lag -7: r = -0.178 (n = 953)lag -6: r = -0.191 (n = 957)lag -5: r = -0.182 (n = 918)lag -4: r = -0.160 (n = 876)lag -3: r = -0.162 (n = 823)lag -2: r = -0.160 (n = 781)lag -1: r = -0.152 (n = 747)lag +0: r = -0.160 (n = 690)lag +1: r = -0.146 (n = 588)lag +2: r = -0.121 (n = 528)lag +3: r = -0.126 (n = 472)lag +4: r = -0.124 (n = 433)lag +5: r = -0.104 (n = 380)lag +6: r = -0.086 (n = 327)lag +7: r = -0.095 (n = 278)lag +8: r = -0.131 (n = 249)lag +9: r = -0.095 (n = 207)

Same year (lag 0): -0.160. 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


% of population
years		Years
San Marino SMR100%31.32024, 2018
Singapore SGP100%27.92024, 2010
Puerto Rico (US) PRI100%29.22024, 2010
Malta MLT100%282024, 2011
Macao SAR, China MAC100%30.32024, 2011
Kuwait KWT100%25.72024, 2011
Brunei Darussalam BRN100%26.12024, 2011
Bahrain BHR100%26.92024, 2010
Australia AUS100%29.92024, 2016
Chile CHL100%27.42024, 2011
Austria AUT100%27.12024, 2011
Japan JPN99.9%29.22024, 2015
Bermuda BMU99.9%31.92024, 2016
Israel ISR99.9%26.22024, 2016
Spain ESP99.9%27.72024, 2011

World Bank, World Development Indicators
World Bank Gender Statistics, from UN Population Division, World Marriage Data

Sources

Basic sanitation

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

World Bank · CC BY 4.0 · updated quarterly

Women's age at first marriage (census-based)

Age at first marriage, female

World Bank · CC BY 4.0 · updated annual

The average age at which women first marry. It is worked out from census and survey counts of how many women in each age group have never married, by the UN Population Division (World Marriage Data), so it covers far more countries than registered marriages do. Most countries have a figure only every few years, and the series ends in 2019. No "better" direction is set.

How this was computed

Data used: most recent value in 2010-2025; values come from 2010–2024. 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 14,952 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Basic sanitation and Women's age at first marriage (census-based)

This page shows that two measures go together. It does not show that Basic sanitation changes Women's age at first marriage (census-based), or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with Basic sanitation or Women's age at first marriage (census-based)