Across 177 countries, Adolescent fertility and Skilled birth attendance rise and fall together at r = -0.71 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.40, which could easily be chance (q = 0.063, 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 adolescent fertility, 10 to 64.4 births per 1,000 women aged 15-19, skilled birth attendance is worse by 15.7% of births at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 9.79% of births.
Link before adjusting (r)-0.708p = 3.3e-28 · 177 countries
After adjusting for national income-0.400p = 3.8e-8 · likely range [-0.52, -0.27]
p, allowing for regions0.008standard error 0.102 · 7 regions
Check for luck (q)0.063across 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
Skilled birth attendance · % of birthslink with income +0.69 · 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
-0.708
3.3e-28
177
By rank (Spearman), not adjusted
-0.714
7.7e-29
177
Standard (Pearson), adjusted for national income
-0.400
3.8e-8
177
By rank (Spearman), adjusted for national income
-0.374
3.1e-7
177
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 177 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: +0.308 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from -0.437 (without HTI) to -0.362 (without AGO). 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: AGO (+0.038).
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
27
-0.17
Europe & Central Asia
44
-0.52
Latin America & Caribbean
32
-0.02
Middle East, North Africa, Afghanistan & Pakistan
19
-0.34
Sub-Saharan Africa
46
-0.47
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
59
-0.34
Middle third
59
-0.64
Richest third
59
-0.34
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
102
-0.28
Muslims
48
-0.67
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
23
-0.36
Never a European colony
21
-0.03
Spain
19
-0.47
United Kingdom
61
-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 = -0.357 after dropping 20 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 33years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.
The link shows up within countries over time (r = -0.326) but mostly disappears in year-to-year changes (r = -0.010). That points to slow trends particular to each country, not the two measures moving together from year to year.
Across countries (adjusted)-0.400for comparison
Within countries-0.493each country vs itself
…and vs the same year elsewhere-0.326q = 0.000 · 168 countries
Year-on-year changes-0.010q = 0.847
Read with care.
Almost all of this indicator's variation is between countries, not within them, so the within-country estimate rests on a small part of the data.
The association is much weaker in year-on-year changes than in levels, which points to shared country-specific trends.
2,825 country-years (one country in one year), 168 countries, 1990–2022, about 17 years each on average. Share of the variation that happens within countries over time: 9% for Adolescent fertility, 13% for Skilled birth attendance. 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): -0.326. 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 data
births per 1,000 women aged 15-19
% of births
Years
Central African Republic CAF
161
40.3%
2024, 2019
Equatorial Guinea GNQ
153
68.3%
2024, 2011
Mozambique MOZ
152
73%
2024, 2015
Niger NER
142
43.7%
2024, 2021
Angola AGO
139
49.6%
2024, 2016
Mali MLI
136
67.3%
2024, 2018
Chad TCD
132
47.2%
2024, 2019
Madagascar MDG
129
45.8%
2024, 2021
Liberia LBR
125
84.4%
2024, 2020
Guinea GIN
117
55.3%
2024, 2018
Zambia ZMB
115
80.4%
2024, 2019
Somalia, Fed. Rep. SOM
115
31.9%
2024, 2019
Malawi MWI
113
96.4%
2024, 2020
Tanzania TZA
113
63.5%
2024, 2016
Congo, Rep. COG
108
91.2%
2024, 2015
World Bank, World Development Indicators World Bank, World Development Indicators
Births attended by skilled health staff (% of total)
UNICEF and WHO · CC BY 4.0 · updated quarterly
Based on surveys, so a country reports it every few years rather than yearly.
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 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 Skilled birth attendance
This page shows that two measures go together. It does not show that Adolescent fertility changes Skilled birth attendance, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.
More links with Adolescent fertility or Skilled birth attendance