Do Primary enrollment and Women's age at first marriage go together across countries?

Across 40 countries the link before adjusting for anything is r = +0.41, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives +0.33.

Does the link hold within countries over time, not just across countries?

Comparing each country only with itself across 2001-2021, and setting aside what happened worldwide in each year, the link is -0.142 (41 countries, 655 country-years). Using year-to-year changes, it is -0.013.

Does Primary enrollment cause Women's age at first marriage?

This analysis cannot answer that. It shows only that the two go together. A link between two country measures, adjusted or not, does not show that either one changes the other. Country figures also say nothing about individual people.

Primary enrollment vs. Women's age at first marriage

Holds up after adjusting for income

Across 40 countries, Primary enrollment and Women's age at first marriage rise and fall together at r = +0.41 (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 = +0.48, q = 0.037, allowing for the many pairs tested). Caution: correlation moves sharply once outliers are trimmed.

Comparing countries across the middle half of women's age at first marriage, 28.9 to 32 years, primary enrollment is better by 2.49% gross at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 3.32% gross.

Link before adjusting (r)+0.408p = 0.009 · 40 countries

After adjusting for national income+0.482p = 0.002 · likely range [+0.20, +0.69]

p, allowing for regions0.008standard error 0.098 · 5 regions

Check for luck (q)0.037across 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.

Primary enrollment · % grosslink with income -0.03 · independent

Women's age at first marriage · yearslink with income +0.53 · moderate

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

Every country as a dot

best-fit line line after adjusting

Four ways to measure the link

Method	r	p	Countries
Standard (Pearson), not adjusted	+0.408	0.009	40
By rank (Spearman), not adjusted	+0.328	0.039	40
Standard (Pearson), adjusted for national income	+0.482	0.002	40
By rank (Spearman), adjusted for national income	+0.444	0.005	40

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 40 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: -0.074 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.401 (without SWE) to +0.534 (without TUR). 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: SWE (-0.081).

Fragile: this link may not hold up.

correlation moves sharply once outliers are trimmed

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.

	n	r
Europe & Central Asia	30	+0.46

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.

	n	r
Poorest third	14	+0.35
Middle third	13	+0.71
Richest third	13	+0.64

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.

	n	r
Christians	33	+0.55

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 = +0.317 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 21years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

Within countries over time, there is no link that is clearly more than chance (r = -0.142, comparing each country only with itself over the years, and each year only with the same year elsewhere). Whatever the cross-country picture shows, countries whose primary enrollment moved did not tend to see their women's age at first marriage move with it.

Across countries (adjusted)+0.482for comparison

Within countries-0.346each country vs itself

…and vs the same year elsewhere-0.142q = 0.170 · 41 countries

Year-on-year changes-0.013q = 0.904

Read with care.

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.

655 country-years (one country in one year), 41 countries, 2001–2021, about 16 years each on average. Share of the variation that happens within countries over time: 28% for Primary enrollment, 48% for Women's age at first marriage. 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

Same year (lag 0): -0.142. 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

	% gross	years	Years
Sweden SWE	115%	34.8	2024, 2020
Portugal PRT	111%	32.3	2023, 2020
Poland POL	105%	27.9	2024, 2020
Netherlands NLD	104%	31.8	2023, 2020
Italy ITA	104%	33.6	2023, 2020
France FRA	103%	33.1	2024, 2018
Spain ESP	103%	34.9	2024, 2020
Hungary HUN	103%	29.5	2024, 2020
Slovak Republic SVK	103%	28.8	2024, 2020
Germany DEU	103%	31.2	2024, 2017
Turkiye TUR	102%	25.2	2023, 2020
Mexico MEX	102%	27.5	2024, 2014
United Kingdom GBR	102%	32.1	2023, 2019
Belgium BEL	102%	32	2023, 2020
Greece GRC	102%	30.7	2023, 2020

World Bank, World Development Indicators
OECD databases as named on each indicator: PISA, Social Expenditure Database (SOCX), Income Distribution Database, Development Assistance Committee statistics, Family Database, Government at a Glance, Corporate Tax Statistics, employment and environment statistics

Sources

Primary enrollment

School enrollment, primary (% gross)

UNESCO Institute for Statistics · CC BY 4.0 · updated quarterly

Gross enrolment can be over 100% because it counts pupils older or younger than the usual age. A value above 100 is not an error, and it does not mean every child is enrolled.

Women's age at first marriage

Mean age of women at first marriage

OECD · via Our World in Data · CC BY 4.0 (OECD terms and conditions) · updated annual, except the snapshots noted per indicator

Published from 2001. How many countries report it varies from year to year: 25 in 2012, two in 2021. So the latest value in the time window used is from a different year for different countries. No "better" direction is set.

How this was computed

Data used: most recent value in 2010-2025; values come from 2011–2024. 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 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 Primary enrollment and Women's age at first marriage

This page shows that two measures go together. It does not show that Primary enrollment changes Women's age at first marriage, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.