placestats

Gender score vs. Marriage rate

Holds up after adjusting for income

Across 46 countries, Gender score and Marriage rate rise and fall together at r = -0.45 (on a scale from −1 to +1, where 0 means no link). It holds up almost unchanged when only countries with similar income (GDP per person) are compared (r = -0.44, q = 0.0033, allowing for the many pairs tested).

Comparing countries across the middle half of marriage rate, 3.7 to 5.33 marriages per 1,000 people, gender score is worse by 0.267 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.261 typical gaps.

Link before adjusting (r)-0.446p = 0.002 · 46 countries
After adjusting for national income-0.444p = 0.002 · likely range [-0.65, -0.17]
p, allowing for regions0.002standard error 0.064 · 5 regions
Check for luck (q)0.003across 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.

Gender score · typical gaps (standard deviations)link with income +0.37 · moderate
Marriage rate · marriages per 1,000 peoplelink with income -0.06 · independent

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

Every country as a dot

Gender score · typical gaps (standard deviations) →Marriage rate · marriages per 1,000 people →-0.5022.191.399.6Argentina: 1.08 typical gaps (standard deviations), 2.79 marriages per 1,000 peopleAustralia: 1.57 typical gaps (standard deviations), 4.89 marriages per 1,000 peopleAustria: 1.17 typical gaps (standard deviations), 5.24 marriages per 1,000 peopleBelgium: 1.45 typical gaps (standard deviations), 4.2 marriages per 1,000 peopleBulgaria: 0.273 typical gaps (standard deviations), 3.9 marriages per 1,000 peopleCanada: 1.38 typical gaps (standard deviations), 2.6 marriages per 1,000 peopleSwitzerland: 1.22 typical gaps (standard deviations), 4.7 marriages per 1,000 peopleChile: 1.72 typical gaps (standard deviations), 2.7 marriages per 1,000 peopleChina: -0.0629 typical gaps (standard deviations), 9.6 marriages per 1,000 peopleColombia: 0.908 typical gaps (standard deviations), 1.39 marriages per 1,000 peopleCosta Rica: 1.7 typical gaps (standard deviations), 4.39 marriages per 1,000 peopleCyprus: 0.593 typical gaps (standard deviations), 8.9 marriages per 1,000 peopleCzechia: 0.196 typical gaps (standard deviations), 5.1 marriages per 1,000 peopleGermany: 1.34 typical gaps (standard deviations), 4.7 marriages per 1,000 peopleDenmark: 1.1 typical gaps (standard deviations), 5.34 marriages per 1,000 peopleSpain: 1.69 typical gaps (standard deviations), 3.7 marriages per 1,000 peopleEstonia: 1.7 typical gaps (standard deviations), 5.3 marriages per 1,000 peopleFinland: 2.04 typical gaps (standard deviations), 3.9 marriages per 1,000 peopleFrance: 1.36 typical gaps (standard deviations), 3.6 marriages per 1,000 peopleUnited Kingdom: 1.23 typical gaps (standard deviations), 1.4 marriages per 1,000 peopleGreece: 0.861 typical gaps (standard deviations), 4.2 marriages per 1,000 peopleCroatia: 0.51 typical gaps (standard deviations), 4.7 marriages per 1,000 peopleHungary: -0.00646 typical gaps (standard deviations), 6.6 marriages per 1,000 peopleIreland: 0.727 typical gaps (standard deviations), 3.3 marriages per 1,000 peopleIceland: 2.19 typical gaps (standard deviations), 5 marriages per 1,000 peopleIsrael: 0.453 typical gaps (standard deviations), 5.46 marriages per 1,000 peopleItaly: 0.707 typical gaps (standard deviations), 3.2 marriages per 1,000 peopleJapan: -0.021 typical gaps (standard deviations), 4.1 marriages per 1,000 peopleKorea, Rep.: 0.412 typical gaps (standard deviations), 3.7 marriages per 1,000 peopleLithuania: 0.944 typical gaps (standard deviations), 5.7 marriages per 1,000 peopleLuxembourg: 0.927 typical gaps (standard deviations), 3.8 marriages per 1,000 peopleLatvia: 1.32 typical gaps (standard deviations), 6.3 marriages per 1,000 peopleMexico: 1.33 typical gaps (standard deviations), 3.9 marriages per 1,000 peopleMalta: 0.55 typical gaps (standard deviations), 4.8 marriages per 1,000 peopleNetherlands: 1.19 typical gaps (standard deviations), 4 marriages per 1,000 peopleNorway: 1.53 typical gaps (standard deviations), 3.8 marriages per 1,000 peopleNew Zealand: 1.62 typical gaps (standard deviations), 3.68 marriages per 1,000 peoplePoland: 1.13 typical gaps (standard deviations), 4.2 marriages per 1,000 peoplePortugal: 1.05 typical gaps (standard deviations), 3.5 marriages per 1,000 peopleRomania: 0.285 typical gaps (standard deviations), 6.2 marriages per 1,000 peopleRussian Federation: 0.137 typical gaps (standard deviations), 8.5 marriages per 1,000 peopleSlovak Republic: 0.374 typical gaps (standard deviations), 5.4 marriages per 1,000 peopleSlovenia: 1.26 typical gaps (standard deviations), 3.2 marriages per 1,000 peopleSweden: 1.79 typical gaps (standard deviations), 4.6 marriages per 1,000 peopleTurkiye: -0.502 typical gaps (standard deviations), 6.8 marriages per 1,000 peopleUnited States: 1.06 typical gaps (standard deviations), 6.2 marriages per 1,000 peopleBGRCHLCHNCOLCYPESTFINGBRISLJPNRUSTUR

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.4460.00246
By rank (Spearman), not adjusted-0.3700.01146
Standard (Pearson), adjusted for national income-0.4440.00246
By rank (Spearman), adjusted for national income-0.3680.01346

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 46 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: +0.002 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.475 (without ISL) to -0.396 (without CHN). 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: CHN (+0.048).

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
Europe & Central Asia32-0.37

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 third16-0.57
Middle third15-0.35
Richest third15-0.06

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
Christians38-0.37

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 = -0.332 after dropping 3 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)
marriages per 1,000 people		Years
Iceland ISL2.1952024, 2020
Finland FIN2.043.92024, 2022
Sweden SWE1.794.62024, 2022
Chile CHL1.722.72024, 2021
Costa Rica CRI1.74.392025, 2022
Estonia EST1.75.32024, 2022
Spain ESP1.693.72024, 2022
New Zealand NZL1.623.682024, 2022
Australia AUS1.574.892024, 2022
Norway NOR1.533.82024, 2022
Belgium BEL1.454.22024, 2022
Canada CAN1.382.62024, 2020
France FRA1.363.62024, 2022
Germany DEU1.344.72024, 2022
Mexico MEX1.333.92025, 2022

vdem; wb_gender; wdi
OECD Family Database, indicators SF3.1 (marriage and divorce rates) and SF1.2 (children in families); US series before 1990 from the US Census Bureau and CDC/NCHS

Sources

Gender score

Gender score

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

The average of 5 measures from 3 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: Women's political empowerment, Women ministers, Women in parliament, Women in management, Married by 18.

Marriage rate

Crude marriage rate

OECD · via Our World in Data · CC BY 4.0 (OECD terms and conditions) · updated annual

Registered marriages in the year per 1,000 people (counted at mid-year), from official records. Couples living together and registered partnerships are left out. So the rate shows how often people marry, and nothing about how many live as couples. New Zealand counts civil unions. Same-sex marriages count in each country's figure from the year it made them legal. This is the only ready-made series that compares countries. It covers only OECD members, about forty countries, nearly all of them rich. So its links with other measures are worked out over only a third of the countries on the site, and adjusting for GDP has less to remove. The low points in 2020 are the pandemic, not a break in the series. But the British and Canadian series stop at 2020, so their latest value is that low point: 1.4 and 2.6, against 3.7 and 3.9 the year before. The links with the governance measures stay the same using pre-pandemic values, or with both countries removed. No "better" direction is set: it describes how people live, not how well a country is doing.

How this was computed

Data used: most recent value in 2010-2025; values come from 2012–2025. 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 5 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 Gender score and Marriage rate

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

More links with Gender score or Marriage rate