Do Conflict deaths and Tertiary enrollment go together across countries?

Across 170 countries the link before adjusting for anything is r = +0.01, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.31.

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

Comparing each country only with itself across 1990-2025, and setting aside what happened worldwide in each year, the link is +0.021 (174 countries, 3,686 country-years). Using year-to-year changes, it is -0.002.

Does Conflict deaths 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.

Conflict deaths vs. Tertiary enrollment

Holds up after adjusting for income

Across 170 countries, Conflict deaths and Tertiary enrollment rise and fall together at r = +0.01 (on a scale from −1 to +1, where 0 means no link). There is almost no link to explain.

Comparing countries across the middle half of conflict deaths, 0 to 0.0126 per 100,000 people, tertiary enrollment is better by 0.00016% gross at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.00168% gross.

Link before adjusting (r)+0.011p = 0.884 · 170 countries

After adjusting for national income+0.178p = 0.021 · likely range [+0.03, +0.32]

p, allowing for regions0.000standard error 0.026 · 7 regions

Check for luck (q)0.013across 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.

Conflict deaths · per 100,000 peoplelink with income -0.42 · moderate

Tertiary enrollment · % grosslink with income +0.75 · high

“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	+0.011	0.884	170
By rank (Spearman), not adjusted	-0.307	4.7e-5	170
Standard (Pearson), adjusted for national income	+0.178	0.021	170
By rank (Spearman), adjusted for national income	+0.050	0.522	170

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 170 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: -0.166 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.113 (without UKR) to +0.192 (without GRC). 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: UKR (-0.065).

Fragile: this link may not hold up.

direction is not consistent across regions

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	-0.07
Europe & Central Asia	50	+0.15
Latin America & Caribbean	27	+0.05
Middle East, North Africa, Afghanistan & Pakistan	20	+0.29
Sub-Saharan Africa	41	+0.25

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	+0.37
Middle third	56	+0.24
Richest third	57	-0.10

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	+0.17
Muslims	44	+0.22

By last european coloniser

	n	r
France	23	+0.41
Never a European colony	21	-0.14
Spain	17	-0.23
United Kingdom	54	+0.12

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 = +0.031 after dropping 43 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 36years. 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 = +0.021, 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 conflict deaths moved did not tend to see their tertiary enrollment move with it.

Across countries (adjusted)+0.178for comparison

Within countries+0.014each country vs itself

…and vs the same year elsewhere+0.021q = 0.079 · 174 countries

Year-on-year changes-0.002q = 0.796

3,686 country-years (one country in one year), 174 countries, 1990–2025, about 21 years each on average. Share of the variation that happens within countries over time: 84% for Conflict deaths, 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): +0.021. 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

	per 100,000 people	% gross	Years
West Bank and Gaza PSE	259	44.5%	2025, 2023
Ukraine UKR	232	87.2%	2025, 2024
Sudan SDN	145	15.9%	2025, 2015
Burkina Faso BFA	18.6	10%	2025, 2024
Syrian Arab Republic SYR	13.4	43.8%	2025, 2016
Mali MLI	8.54	5.13%	2025, 2015
Congo, Dem. Rep. COD	7.85	6.38%	2025, 2020
Central African Republic CAF	6.28	2.78%	2025, 2012
Mexico MEX	5.94	48.2%	2025, 2024
Lebanon LBN	5.93	54.4%	2025, 2023
Congo, Rep. COG	4.64	10.4%	2025, 2023
Myanmar MMR	4.21	20.4%	2025, 2018
Ethiopia ETH	4	5.92%	2025, 2014
Niger NER	3.98	4.15%	2025, 2019
Russian Federation RUS	3.07	60.4%	2025, 2024

Uppsala Conflict Data Program, Georeferenced Event Dataset; rate per population from Our World in Data
World Bank, World Development Indicators

Sources

Conflict deaths

Deaths in armed conflicts, per 100,000 people

Uppsala Conflict Data Program, Uppsala University · via Our World in Data · CC BY 4.0 · updated annual

Deaths from organised violence on a country's territory, per 100,000 of its people. That covers fighting involving a government, fighting between other armed groups, and attacks on civilians. Deaths are counted where the fighting happens, not by who was fighting. So a war fought on one country's soil by another country's army counts here. It is zero for most countries in most years, and in the hundreds per hundred thousand for a few at war. That is why it is put on a log scale (which squeezes very large values together) where it is used for adjusting. It is also why the "leave one out" check matters on every pair involving it: a handful of countries carry the whole link. The site's only other measure of violence is the homicide rate, which counts something different. A peaceful country can have a high homicide rate, and a country at war can have a low one. It is kept out of every topic score. In 2023, 143 of the 197 countries reporting it report exactly zero. A log scale, used here since a few values are huge, cannot handle zero, so every zero becomes a blank. A safety score built on it covered 79 countries instead of 172. It silently dropped the peaceful ones, the opposite of what such a score should say. A score needs parts that vary across nearly every country. This one is a rare event almost everywhere and the biggest fact in a dozen places. That makes it worth its own page, and useless inside an average.

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 Conflict deaths and Tertiary enrollment

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