placestats

Women in a union vs. Women's age at first marriage (census-based)

Holds up after adjusting for income

Across 173 countries, Women in a union and Women's age at first marriage (census-based) 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 -0.41 (q = 0.017, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a moderate negative link remains.

Comparing countries across the middle half of women in a union, 51.6% to 62.4% of women aged 15-49, women's age at first marriage (census-based) is 2.89 years lower at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 1.58 years.

Link before adjusting (r)-0.569p = 3.3e-16 · 173 countries
After adjusting for national income-0.407p = 3.0e-8 · likely range [-0.52, -0.27]
p, allowing for regions0.001standard error 0.065 · 7 regions
Check for luck (q)0.017across 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.

Women in a union · % of women aged 15-49link with income -0.44 · moderate
Women's age at first marriage (census-based) · yearslink with income +0.75 · high

“Link with income” is how closely each measure tracks GDP per person across countries, from −1 to +1.

Every country as a dot

Women in a union · % of women aged 15-49 →Women's age at first marriage (census-based) · years →33.6%81.3%1734Aruba: 46.8% of women aged 15-49, 28.5 yearsAfghanistan: 65.9% of women aged 15-49, 21.5 yearsAngola: 57.2% of women aged 15-49, 21.5 yearsAlbania: 65.7% of women aged 15-49, 24.2 yearsArgentina: 49.7% of women aged 15-49, 24.6 yearsArmenia: 61.5% of women aged 15-49, 24.2 yearsAustralia: 54% of women aged 15-49, 29.9 yearsAustria: 56.6% of women aged 15-49, 27.1 yearsAzerbaijan: 59.7% of women aged 15-49, 24.2 yearsBurundi: 58.2% of women aged 15-49, 22.8 yearsBelgium: 52.4% of women aged 15-49, 26.3 yearsBenin: 65.7% of women aged 15-49, 20.7 yearsBurkina Faso: 74.3% of women aged 15-49, 20 yearsBangladesh: 80.6% of women aged 15-49, 18.8 yearsBulgaria: 56.6% of women aged 15-49, 32.6 yearsBahrain: 56.4% of women aged 15-49, 26.9 yearsBahamas, The: 38.7% of women aged 15-49, 27.9 yearsBosnia and Herzegovina: 65.3% of women aged 15-49, 25.7 yearsBelarus: 62.4% of women aged 15-49, 22.5 yearsBelize: 58% of women aged 15-49, 20.5 yearsBermuda: 49.7% of women aged 15-49, 31.9 yearsBolivia: 50.2% of women aged 15-49, 24 yearsBrazil: 56.9% of women aged 15-49, 23.9 yearsBarbados: 57.2% of women aged 15-49, 17 yearsBrunei Darussalam: 54.2% of women aged 15-49, 26.1 yearsBhutan: 62% of women aged 15-49, 23.8 yearsBotswana: 41.5% of women aged 15-49, 22.9 yearsCentral African Republic: 65.4% of women aged 15-49, 17.3 yearsCanada: 51.6% of women aged 15-49, 27.2 yearsSwitzerland: 57.5% of women aged 15-49, 30.9 yearsChile: 48.5% of women aged 15-49, 27.4 yearsChina: 69.5% of women aged 15-49, 25.4 yearsCote d'Ivoire: 62.7% of women aged 15-49, 23 yearsCameroon: 58.1% of women aged 15-49, 22.2 yearsCongo, Dem. Rep.: 58.2% of women aged 15-49, 20.9 yearsCongo, Rep.: 55.3% of women aged 15-49, 21.9 yearsColombia: 50.9% of women aged 15-49, 22.7 yearsComoros: 63.1% of women aged 15-49, 22.6 yearsCosta Rica: 41.7% of women aged 15-49, 26.4 yearsCayman Islands: 53.6% of women aged 15-49, 28 yearsCyprus: 52.3% of women aged 15-49, 26.4 yearsCzechia: 53.7% of women aged 15-49, 32.4 yearsGermany: 54.4% of women aged 15-49, 31.7 yearsDjibouti: 46.5% of women aged 15-49, 29.9 yearsDenmark: 54.3% of women aged 15-49, 31.9 yearsDominican Republic: 53.2% of women aged 15-49, 21.5 yearsAlgeria: 55.5% of women aged 15-49, 28.2 yearsEcuador: 45.3% of women aged 15-49, 21.8 yearsEgypt, Arab Rep.: 65.6% of women aged 15-49, 22.3 yearsSpain: 50.2% of women aged 15-49, 27.7 yearsEstonia: 54.1% of women aged 15-49, 33.6 yearsEthiopia: 66.3% of women aged 15-49, 21.3 yearsFinland: 57.1% of women aged 15-49, 30.7 yearsFrance: 54.9% of women aged 15-49, 32 yearsFaroe Islands: 52.3% of women aged 15-49, 31 yearsGabon: 48.5% of women aged 15-49, 22.9 yearsUnited Kingdom: 49.7% of women aged 15-49, 27 yearsGeorgia: 65.3% of women aged 15-49, 22.8 yearsGhana: 53.4% of women aged 15-49, 23.7 yearsGuinea: 70.9% of women aged 15-49, 19.8 yearsGambia, The: 63% of women aged 15-49, 21.5 yearsGuinea-Bissau: 59% of women aged 15-49, 24.1 yearsEquatorial Guinea: 61.1% of women aged 15-49, 20.5 yearsGreece: 52.4% of women aged 15-49, 29.2 yearsGreenland: 38.8% of women aged 15-49, 32.9 yearsGuatemala: 56.2% of women aged 15-49, 22.7 yearsGuyana: 59% of women aged 15-49, 19.2 yearsHong Kong SAR, China: 49.8% of women aged 15-49, 29.9 yearsHonduras: 54.4% of women aged 15-49, 21.2 yearsCroatia: 50.1% of women aged 15-49, 27.4 yearsHaiti: 53.6% of women aged 15-49, 24.9 yearsHungary: 52.3% of women aged 15-49, 32.9 yearsIndonesia: 70.3% of women aged 15-49, 22.4 yearsIndia: 72.3% of women aged 15-49, 21.4 yearsIreland: 47.1% of women aged 15-49, 32.4 yearsIran, Islamic Rep.: 69.6% of women aged 15-49, 23.1 yearsIraq: 64.3% of women aged 15-49, 22.7 yearsIceland: 48.5% of women aged 15-49, 32.4 yearsIsrael: 50.4% of women aged 15-49, 26.2 yearsItaly: 51.8% of women aged 15-49, 32.2 yearsJamaica: 35.2% of women aged 15-49, 25.3 yearsJordan: 56% of women aged 15-49, 24.6 yearsJapan: 50% of women aged 15-49, 29.2 yearsKazakhstan: 63.8% of women aged 15-49, 22.4 yearsKenya: 53.2% of women aged 15-49, 21.9 yearsKyrgyz Republic: 57.4% of women aged 15-49, 21.2 yearsCambodia: 67.2% of women aged 15-49, 21.6 yearsKiribati: 67.4% of women aged 15-49, 21.6 yearsKorea, Rep.: 49.8% of women aged 15-49, 31.5 yearsKuwait: 59.5% of women aged 15-49, 25.7 yearsLao PDR: 61.6% of women aged 15-49, 20.7 yearsLiberia: 51.4% of women aged 15-49, 22.6 yearsSt. Lucia: 53.3% of women aged 15-49, 21.9 yearsSri Lanka: 66.3% of women aged 15-49, 23.9 yearsLesotho: 46.9% of women aged 15-49, 22.2 yearsLithuania: 51.8% of women aged 15-49, 32.7 yearsLuxembourg: 51.1% of women aged 15-49, 30.7 yearsLatvia: 49.6% of women aged 15-49, 30.4 yearsMacao SAR, China: 54.6% of women aged 15-49, 30.3 yearsMorocco: 58% of women aged 15-49, 24.4 yearsMoldova: 68.7% of women aged 15-49, 25 yearsMaldives: 68.4% of women aged 15-49, 22.5 yearsMexico: 53% of women aged 15-49, 23.2 yearsNorth Macedonia: 66.1% of women aged 15-49, 25.5 yearsMali: 79.2% of women aged 15-49, 18.8 yearsMalta: 52% of women aged 15-49, 28 yearsMyanmar: 58% of women aged 15-49, 22.9 yearsMontenegro: 57.6% of women aged 15-49, 26.3 yearsMongolia: 60.2% of women aged 15-49, 23 yearsMozambique: 64.8% of women aged 15-49, 18.7 yearsMauritania: 66.1% of women aged 15-49, 21.8 yearsMauritius: 51.6% of women aged 15-49, 23.9 yearsMalawi: 62.1% of women aged 15-49, 20.4 yearsMalaysia: 57.1% of women aged 15-49, 25.7 yearsNamibia: 33.7% of women aged 15-49, 30.3 yearsNiger: 81.3% of women aged 15-49, 17.2 yearsNigeria: 67.6% of women aged 15-49, 21.2 yearsNicaragua: 56% of women aged 15-49, 21.1 yearsNetherlands: 53.5% of women aged 15-49, 32.4 yearsNorway: 57.4% of women aged 15-49, 32.1 yearsNepal: 75.1% of women aged 15-49, 20.1 yearsOman: 55.8% of women aged 15-49, 25.6 yearsPakistan: 63.4% of women aged 15-49, 23.2 yearsPanama: 53.2% of women aged 15-49, 21 yearsPeru: 52.7% of women aged 15-49, 23.3 yearsPhilippines: 55.6% of women aged 15-49, 23.4 yearsPalau: 48.3% of women aged 15-49, 28.5 yearsPapua New Guinea: 66.9% of women aged 15-49, 21.4 yearsPoland: 58.6% of women aged 15-49, 26.6 yearsPuerto Rico (US): 40.9% of women aged 15-49, 29.2 yearsPortugal: 50.6% of women aged 15-49, 30.1 yearsParaguay: 54.8% of women aged 15-49, 21.9 yearsWest Bank and Gaza: 62.4% of women aged 15-49, 22.1 yearsRomania: 56% of women aged 15-49, 25.9 yearsRussian Federation: 57.6% of women aged 15-49, 24.4 yearsRwanda: 49.9% of women aged 15-49, 24.3 yearsSaudi Arabia: 55.3% of women aged 15-49, 26.6 yearsSudan: 62.6% of women aged 15-49, 21.9 yearsSenegal: 65.5% of women aged 15-49, 22.2 yearsSingapore: 43.8% of women aged 15-49, 27.9 yearsSolomon Islands: 64.5% of women aged 15-49, 22.4 yearsSierra Leone: 61.5% of women aged 15-49, 23.1 yearsEl Salvador: 50.7% of women aged 15-49, 22.6 yearsSan Marino: 55.7% of women aged 15-49, 31.3 yearsSerbia: 54.3% of women aged 15-49, 26.6 yearsSao Tome and Principe: 53.1% of women aged 15-49, 21 yearsSuriname: 52.2% of women aged 15-49, 29.3 yearsSlovak Republic: 49.2% of women aged 15-49, 31 yearsSlovenia: 47.2% of women aged 15-49, 34 yearsSweden: 53.6% of women aged 15-49, 31 yearsEswatini: 36.4% of women aged 15-49, 26.5 yearsChad: 72.6% of women aged 15-49, 18.8 yearsTogo: 63.7% of women aged 15-49, 21.3 yearsThailand: 52.4% of women aged 15-49, 22.5 yearsTajikistan: 68% of women aged 15-49, 20.7 yearsTurkmenistan: 65% of women aged 15-49, 23.5 yearsTimor-Leste: 56.4% of women aged 15-49, 23 yearsTonga: 50.7% of women aged 15-49, 25.3 yearsTrinidad and Tobago: 42.7% of women aged 15-49, 29.2 yearsTunisia: 52.5% of women aged 15-49, 28.2 yearsTurkiye: 59.6% of women aged 15-49, 23.4 yearsTanzania: 59.2% of women aged 15-49, 21 yearsUganda: 57.8% of women aged 15-49, 20.7 yearsUkraine: 61.9% of women aged 15-49, 23 yearsUruguay: 53.6% of women aged 15-49, 23 yearsUnited States: 52.1% of women aged 15-49, 27.5 yearsSt. Vincent and the Grenadines: 51.1% of women aged 15-49, 21.2 yearsVirgin Islands (U.S.): 48.5% of women aged 15-49, 30.9 yearsViet Nam: 72.8% of women aged 15-49, 22.8 yearsSamoa: 62% of women aged 15-49, 23.8 yearsSouth Africa: 33.6% of women aged 15-49, 27.2 yearsZambia: 55.7% of women aged 15-49, 21.8 yearsZimbabwe: 62.2% of women aged 15-49, 21.1 yearsBGDBGRBWAESTGRLJAMMLINAMNERNORSVNZAF

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.5693.3e-16173
By rank (Spearman), not adjusted-0.5993.2e-18173
Standard (Pearson), adjusted for national income-0.4073.0e-8173
By rank (Spearman), adjusted for national income-0.4218.6e-9173

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 173 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: +0.162 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.422 (without BWA) to -0.381 (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: NAM (+0.026).

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 & Pacific24-0.48
Europe & Central Asia50-0.45
Latin America & Caribbean31-0.61
Middle East, North Africa, Afghanistan & Pakistan17-0.87
Sub-Saharan Africa42-0.76

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 third58-0.74
Middle third57-0.30
Richest third58-0.37

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
Christians104-0.29
Muslims42-0.85

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
France22-0.68
Never a European colony20-0.56
Spain19-0.48
United Kingdom50-0.58

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 = -0.352 after dropping 6 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 30years. 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 = -0.306) but mostly disappears in year-to-year changes (r = -0.038). That points to slow trends particular to each country, not the two measures moving together from year to year.

Across countries (adjusted)-0.407for comparison
Within countries-0.551each country vs itself
…and vs the same year elsewhere-0.306q = 0.001 · 122 countries
Year-on-year changes-0.038q = 0.478
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.
  • The association is much weaker in year-on-year changes than in levels, which points to shared country-specific trends.

1,172 country-years (one country in one year), 122 countries, 1990–2019, about 10 years each on average. Share of the variation that happens within countries over time: 9% for Women in a union, 14% 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

0.40−0.4-100+10years Women's age at first marriage (census-based) trails Women in a union →lag -10: r = -0.096 (n = 738)lag -9: r = -0.130 (n = 805)lag -8: r = -0.144 (n = 866)lag -7: r = -0.174 (n = 921)lag -6: r = -0.192 (n = 998)lag -5: r = -0.215 (n = 1,064)lag -4: r = -0.223 (n = 1,108)lag -3: r = -0.232 (n = 1,132)lag -2: r = -0.251 (n = 1,147)lag -1: r = -0.271 (n = 1,151)lag +0: r = -0.306 (n = 1,172)lag +1: r = -0.330 (n = 1,125)lag +2: r = -0.322 (n = 1,055)lag +3: r = -0.333 (n = 1,019)lag +4: r = -0.328 (n = 985)lag +5: r = -0.329 (n = 946)lag +6: r = -0.331 (n = 902)lag +7: r = -0.328 (n = 850)lag +8: r = -0.341 (n = 808)lag +9: r = -0.336 (n = 773)lag +10: r = -0.338 (n = 708)

Same year (lag 0): -0.306. 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 women aged 15-49

years
Years
Niger NER81.3%17.22021, 2015
Bangladesh BGD80.6%18.82022, 2014
Mali MLI79.2%18.82018
Nepal NPL75.1%20.12022, 2016
Burkina Faso BFA74.3%202021, 2017
Viet Nam VNM72.8%22.82021, 2015
Chad TCD72.6%18.82019, 2015
India IND72.3%21.42020, 2016
Guinea GIN70.9%19.82018, 2014
Indonesia IDN70.3%22.42022, 2017
Iran, Islamic Rep. IRN69.6%23.12022, 2016
China CHN69.5%25.42021, 2016
Moldova MDA68.7%252020, 2014
Maldives MDV68.4%22.52022, 2017
Tajikistan TJK68%20.72020, 2017

United Nations, Department of Economic and Social Affairs, Population Division: World Population Prospects 2024 and Estimates and Projections of Women of Reproductive Age Who Are Married or in a Union
World Bank Gender Statistics, from UN Population Division, World Marriage Data

Sources

Women in a union

Share of women aged 15 to 49 who are married or in a union

United Nations Department of Economic and Social Affairs, Population Division · via Our World in Data · CC BY 3.0 IGO · updated biennial

A snapshot of how many women are in a union at one time, not a count of new unions each year. It counts couples living together as a union, which the OECD marriage rate does not. So the two can disagree without either being wrong, and their pair is worth reading for exactly that. The UN estimates it with a statistical model from survey and census data, for over two hundred countries. No "better" direction is set.

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 Women in a union and Women's age at first marriage (census-based)

This page shows that two measures go together. It does not show that Women in a union 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 Women in a union or Women's age at first marriage (census-based)