Across 192 countries, Adolescent fertility and Primary enrollment rise and fall together at r = -0.11 (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 = -0.21, q = 0.032, allowing for the many pairs tested). Caution: correlation moves sharply once outliers are trimmed.
Comparing countries across the middle half of primary enrollment, 95.2% to 106% gross, adolescent fertility is better by 3.05 births per 1,000 women aged 15-19 at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 3.55 births per 1,000 women aged 15-19.
Link before adjusting (r)-0.115p = 0.113 · 192 countries
After adjusting for national income-0.209p = 0.004 · likely range [-0.34, -0.07]
p, allowing for regions0.015standard error 0.062 · 7 regions
Check for luck (q)0.032across 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.
Adolescent fertility · births per 1,000 women aged 15-19link with income -0.81 · proxy
Primary enrollment · % grosslink with income -0.03 · independent
“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
-0.115
0.113
192
By rank (Spearman), not adjusted
-0.041
0.575
192
Standard (Pearson), adjusted for national income
-0.209
0.004
192
By rank (Spearman), adjusted for national income
-0.110
0.131
192
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 192 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: -0.094 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from -0.228 (without SLE) to -0.140 (without GNQ). 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: GNQ (+0.070).
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
East Asia & Pacific
31
-0.13
Europe & Central Asia
50
-0.17
Latin America & Caribbean
37
-0.32
Middle East, North Africa, Afghanistan & Pakistan
19
+0.04
Sub-Saharan Africa
46
-0.27
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
64
-0.17
Middle third
64
-0.38
Richest third
64
-0.09
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
111
-0.28
Muslims
46
-0.15
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.
n
r
France
24
-0.32
Never a European colony
21
-0.32
Spain
20
-0.66
United Kingdom
61
+0.03
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 = -0.032 after dropping 18 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 35years. 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 = -0.106, 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 adolescent fertility moved did not tend to see their primary enrollment move with it.
Across countries (adjusted)-0.209for comparison
Within countries-0.224each country vs itself
…and vs the same year elsewhere-0.106q = 0.266 · 201 countries
Year-on-year changes-0.001q = 0.976
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.
5,225 country-years (one country in one year), 201 countries, 1990–2024, about 26 years each on average. Share of the variation that happens within countries over time: 42% for Adolescent fertility, 9% for Primary enrollment. 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): -0.106. 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
births per 1,000 women aged 15-19
% gross
Years
Central African Republic CAF
161
96.8%
2024, 2016
Equatorial Guinea GNQ
153
47.5%
2024, 2015
Mozambique MOZ
152
119%
2024, 2022
Niger NER
142
64.7%
2024
Angola AGO
139
86.7%
2024, 2023
Mali MLI
136
72.8%
2024, 2023
Chad TCD
132
88.5%
2024
Madagascar MDG
129
133%
2024
Liberia LBR
125
66.6%
2024, 2022
Guinea GIN
117
100%
2024, 2020
Zambia ZMB
115
116%
2024
Somalia, Fed. Rep. SOM
115
20.9%
2024, 2023
Malawi MWI
113
139%
2024
Tanzania TZA
113
92.7%
2024
Congo, Rep. COG
108
87.9%
2024, 2023
World Bank, World Development Indicators World Bank, World Development Indicators
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.
How this was computed
Data used: most recent value in 2010-2025; values come from 2010–2025. 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 7 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 Adolescent fertility and Primary enrollment
This page shows that two measures go together. It does not show that Adolescent fertility changes Primary enrollment, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.
More links with Adolescent fertility or Primary enrollment