Across 187 countries, Adolescent fertility and Education spending rise and fall together at r = -0.19 (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 = -0.27, q = 0.012, allowing for the many pairs tested).
Comparing countries across the middle half of education spending, 3.09% to 5.28% of GDP, adolescent fertility is better by 7.42 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 6.59 births per 1,000 women aged 15-19.
Link before adjusting (r)-0.186p = 0.011 · 187 countries
After adjusting for national income-0.268p = 0.000 · likely range [-0.40, -0.13]
p, allowing for regions0.034standard error 0.098 · 7 regions
Check for luck (q)0.012across 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
Education spending · % of GDPlink with income +0.12 · 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
-0.186
0.011
187
By rank (Spearman), not adjusted
-0.224
0.002
187
Standard (Pearson), adjusted for national income
-0.268
0.000
187
By rank (Spearman), adjusted for national income
-0.223
0.002
187
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 187 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: -0.081 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from -0.285 (without HTI) to -0.243 (without TUV). 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: TUV (+0.025).
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
-0.20
Europe & Central Asia
48
-0.14
Latin America & Caribbean
37
-0.05
Middle East, North Africa, Afghanistan & Pakistan
18
-0.45
Sub-Saharan Africa
44
-0.21
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
63
-0.34
Middle third
62
-0.01
Richest third
62
-0.24
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
108
-0.34
Muslims
44
-0.25
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
-0.16
Never a European colony
21
-0.45
Spain
19
-0.59
United Kingdom
59
-0.30
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 = -0.209 after dropping 10 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 = -0.061, 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 adolescent fertility moved did not tend to see their education spending move with it.
Across countries (adjusted)-0.268for comparison
Within countries-0.141each country vs itself
…and vs the same year elsewhere-0.061q = 0.229 · 196 countries
Year-on-year changes+0.015q = 0.542
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.
4,038 country-years (one country in one year), 196 countries, 1990–2024, about 21 years each on average. Share of the variation that happens within countries over time: 23% for Adolescent fertility, 7% for Education spending. 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 (-0.11 at lag -6) is higher than the same-year one by 0.05. 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
births per 1,000 women aged 15-19
% of GDP
Years
Central African Republic CAF
161
1.83%
2024, 2023
Mozambique MOZ
152
5.98%
2024, 2022
Niger NER
142
4.07%
2024, 2023
Angola AGO
139
2.51%
2024, 2023
Mali MLI
136
4.18%
2024, 2023
Chad TCD
132
3.2%
2024, 2023
Madagascar MDG
129
3.05%
2024, 2023
Liberia LBR
125
2.27%
2024, 2023
Guinea GIN
117
1.72%
2024, 2023
Zambia ZMB
115
4.07%
2024, 2023
Somalia, Fed. Rep. SOM
115
0.000008%
2024, 2023
Malawi MWI
113
2.75%
2024, 2022
Tanzania TZA
113
3.16%
2024
Congo, Rep. COG
108
3.34%
2024, 2023
Cameroon CMR
105
2.84%
2024, 2023
World Bank, World Development Indicators World Bank, World Development Indicators
Government expenditure on education, total (% of GDP)
UNESCO Institute for Statistics · CC BY 4.0 · updated quarterly
How this was computed
Data used: most recent value in 2010-2025; values come from 2013–2025. 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 Education spending
This page shows that two measures go together. It does not show that Adolescent fertility changes Education spending, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.
More links with Adolescent fertility or Education spending