Do Infant mortality and Tertiary enrollment go together across countries?

Across 171 countries the link before adjusting for anything is r = -0.72, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.79.

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

Comparing each country only with itself across 1990-2024, and setting aside what happened worldwide in each year, the link is +0.212 (175 countries, 3,684 country-years). Using year-to-year changes, it is +0.020.

Does Infant mortality cause Tertiary enrollment?

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.

Infant mortality vs. Tertiary enrollment

Fades after adjusting for income

Across 171 countries, Infant mortality and Tertiary enrollment rise and fall together at r = -0.72 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.29, which could easily be chance (q = 0.14, 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 infant mortality, 4.6 to 27.4 per 1,000 live births, tertiary enrollment is worse by 31.7% gross at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 15.1% gross.

Link before adjusting (r)-0.715p = 4.4e-28 · 171 countries

After adjusting for national income-0.289p = 0.000 · likely range [-0.42, -0.14]

p, allowing for regions0.031standard error 0.103 · 7 regions

Check for luck (q)0.137across 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.

Infant mortality · per 1,000 live birthslink with income -0.86 · proxy

Tertiary enrollment · % grosslink 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	-0.715	4.4e-28	171
By rank (Spearman), not adjusted	-0.790	9.4e-38	171
Standard (Pearson), adjusted for national income	-0.289	0.000	171
By rank (Spearman), adjusted for national income	-0.363	1.1e-6	171

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 171 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: +0.427 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.301 (without LKA) to -0.259 (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 (+0.029).

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	24	-0.08
Europe & Central Asia	50	-0.32
Latin America & Caribbean	28	-0.24
Middle East, North Africa, Afghanistan & Pakistan	20	-0.67
Sub-Saharan Africa	41	-0.23

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	57	-0.49
Middle third	57	-0.25
Richest third	57	-0.45

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	99	-0.28
Muslims	44	-0.59

By last european coloniser

	n	r
France	23	-0.52
Never a European colony	21	-0.29
Spain	17	-0.33
United Kingdom	54	-0.24

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 = -0.302 after dropping 5 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.

The link shows up within countries over time (r = +0.212) but mostly disappears in year-to-year changes (r = +0.020). That points to slow trends particular to each country, not the two measures moving together from year to year.

Across countries (adjusted)-0.289for comparison

Within countries-0.367each country vs itself

…and vs the same year elsewhere+0.212q = 0.000 · 175 countries

Year-on-year changes+0.020q = 0.561

The direction flips. Comparing each country only with itself gives -0.37. Also taking out what happened to the whole world each year gives +0.21. So the first figure was picking up a trend both measures share with worldwide progress, not a link between them.

3,684 country-years (one country in one year), 175 countries, 1990–2024, about 21 years each on average. Share of the variation that happens within countries over time: 19% for Infant mortality, 18% for Tertiary 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): +0.212. 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 (+0.33 at lag -8) is higher than the same-year one by 0.11. 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

	per 1,000 live births	% gross	Years
Nigeria NGA	69.8	9.74%	2024, 2011
Niger NER	65.6	4.15%	2024, 2019
Liberia LBR	63.2	10.8%	2024, 2012
Zimbabwe ZWE	62.4	7.75%	2024
Guinea GIN	59.8	10.9%	2024, 2014
Central African Republic CAF	58.8	2.78%	2024, 2012
Congo, Dem. Rep. COD	56.4	6.38%	2024, 2020
Chad TCD	56.3	4.4%	2024, 2020
Lesotho LSO	56.3	9.94%	2024, 2018
Afghanistan AFG	48.3	10.9%	2024, 2020
Pakistan PAK	48.2	10.9%	2024
Mali MLI	47.5	5.13%	2024, 2015
Benin BEN	45.2	10.2%	2024, 2022
Cote d'Ivoire CIV	44.8	12.3%	2024
Mozambique MOZ	43.7	7.46%	2024, 2018

World Bank, World Development Indicators
World Bank, World Development Indicators

Sources

Infant mortality

Mortality rate, infant (per 1,000 live births)

World Bank · CC BY 4.0 · updated quarterly

Tertiary enrollment

School enrollment, tertiary (% gross)

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 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 Infant mortality and Tertiary enrollment

This page shows that two measures go together. It does not show that Infant mortality changes Tertiary enrollment, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.