Do Pupil-teacher ratio and Women's age at first marriage (census-based) go together across countries?

Across 162 countries the link before adjusting for anything is r = -0.61, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.69.

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

Comparing each country only with itself across 1990-2018, and setting aside what happened worldwide in each year, the link is +0.061 (81 countries, 689 country-years). Using year-to-year changes, it is -0.112.

Does Pupil-teacher ratio cause Women's age at first marriage (census-based)?

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.

Pupil-teacher ratio vs. Women's age at first marriage (census-based)

Fades after adjusting for income

Across 162 countries, Pupil-teacher ratio and Women's age at first marriage (census-based) 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 -0.08, which could easily be chance (q = 0.52, 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 women's age at first marriage (census-based), 21.8 to 27.6 years, pupil-teacher ratio is better by 11.6 pupils per teacher at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 1.2 pupils per teacher.

Link before adjusting (r)-0.613p = 4.3e-18 · 162 countries

After adjusting for national income-0.075p = 0.342 · likely range [-0.23, +0.08]

p, allowing for regions0.118standard error 0.041 · 7 regions

Check for luck (q)0.523across 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.

Pupil-teacher ratio · pupils per teacherlink with income -0.83 · proxy

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

best-fit line line after adjusting

Four ways to measure the link

Method	r	p	Countries
Standard (Pearson), not adjusted	-0.613	4.3e-18	162
By rank (Spearman), not adjusted	-0.693	1.8e-24	162
Standard (Pearson), adjusted for national income	-0.075	0.342	162
By rank (Spearman), adjusted for national income	-0.172	0.030	162

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 162 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: +0.538 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.100 (without RWA) to -0.036 (without GUY). 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 (+0.040).

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
East Asia & Pacific	23	-0.23
Europe & Central Asia	46	-0.14
Latin America & Caribbean	28	-0.28
Middle East, North Africa, Afghanistan & Pakistan	16	-0.17
Sub-Saharan Africa	41	-0.24

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	54	-0.24
Middle third	54	-0.01
Richest third	54	-0.40

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	98	-0.14
Muslims	39	-0.09

By last european coloniser

	n	r
France	21	-0.13
Never a European colony	19	+0.10
Spain	19	-0.47
United Kingdom	45	-0.10

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 = -0.107 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.

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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 29years. 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 = +0.061, 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 pupil-teacher ratio moved did not tend to see their women's age at first marriage (census-based) move with it.

Across countries (adjusted)-0.075for comparison

Within countries-0.150each country vs itself

…and vs the same year elsewhere+0.061q = 0.558 · 81 countries

Year-on-year changes-0.112q = 0.409

The direction flips. Comparing each country only with itself gives -0.15. Also taking out what happened to the whole world each year gives +0.06. So the first figure was picking up a trend both measures share with worldwide progress, not a link between them.

Read with care.

Almost all of the second indicator's variation is between countries, so the within-country estimate rests on a small part of the data.

689 country-years (one country in one year), 81 countries, 1990–2018, about 9 years each on average. Share of the variation that happens within countries over time: 12% for Pupil-teacher ratio, 7% 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

Same year (lag 0): +0.061. 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 largest value on this curve (+0.22 at lag -8) is higher than the same-year one by 0.16. That gap is not a finding. It is the largest of 21 closely related estimates, and picking the biggest of many tests always makes it look bigger. That is exactly why nothing on this site is ranked or chosen by lag.

The data

	pupils per teacher	years	Years
Central African Republic CAF	83.4	17.3	2016, 2010
Rwanda RWA	59.5	24.3	2018, 2015
Malawi MWI	58.7	20.4	2018
Chad TCD	56.9	18.8	2016, 2015
Mozambique MOZ	55.3	18.7	2018, 2017
Ethiopia ETH	55.1	21.3	2011, 2016
Guinea-Bissau GNB	51.9	24.1	2010, 2014
Tanzania TZA	50.6	21	2018, 2016
Angola AGO	50	21.5	2015, 2016
Afghanistan AFG	48.8	21.5	2018, 2015
Guinea GIN	47.1	19.8	2016, 2014
Cameroon CMR	44.8	22.2	2018, 2014
Congo, Rep. COG	44.4	21.9	2012, 2015
Pakistan PAK	44.1	23.2	2018, 2017
Uganda UGA	42.7	20.7	2017, 2016

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

Sources

Pupil-teacher ratio

Pupil-teacher ratio, primary

UNESCO Institute for Statistics · CC BY 4.0 · updated quarterly

The World Bank's copy stops at 2019.

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 2010–2019. 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 Pupil-teacher ratio and Women's age at first marriage (census-based)

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