Do Inequality-adjusted HDI and Secondary enrollment go together across countries?

Across 162 countries the link before adjusting for anything is r = +0.83, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives +0.82.

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

Comparing each country only with itself across 2010-2023, and setting aside what happened worldwide in each year, the link is +0.145 (138 countries, 1,558 country-years). Using year-to-year changes, it is +0.116.

Does Inequality-adjusted HDI cause Secondary 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.

Inequality-adjusted HDI vs. Secondary enrollment

Fades after adjusting for income

Across 162 countries, Inequality-adjusted HDI and Secondary enrollment rise and fall together at r = +0.83 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.43, which could easily be chance (q = 0.065, 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 inequality-adjusted hdi, 0.446 to 0.772 points, secondary enrollment is better by 38.5% gross at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 34.5% gross.

Link before adjusting (r)+0.829p = 3.0e-42 · 162 countries

After adjusting for national income+0.431p = 1.1e-8 · likely range [+0.30, +0.55]

p, allowing for regions0.008standard error 0.111 · 7 regions

Check for luck (q)0.065across 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.

Inequality-adjusted HDI · index, 0 to 1link with income +0.94 · proxy

Secondary enrollment · % grosslink with income +0.71 · 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	+0.829	3.0e-42	162
By rank (Spearman), not adjusted	+0.822	4.9e-41	162
Standard (Pearson), adjusted for national income	+0.431	1.1e-8	162
By rank (Spearman), adjusted for national income	+0.421	2.6e-8	162

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 162 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: +0.398 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.413 (without VUT) to +0.456 (without GMB). 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: GMB (+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	29	+0.55
Europe & Central Asia	47	+0.30
Latin America & Caribbean	21	+0.55
Middle East, North Africa, Afghanistan & Pakistan	14	+0.56
Sub-Saharan Africa	43	+0.15

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	54	+0.57
Middle third	54	+0.35
Richest third	54	+0.51

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	98	+0.38
Muslims	38	+0.51

By last european coloniser

	n	r
France	22	+0.56
Never a European colony	18	+0.30
Spain	18	+0.63
United Kingdom	49	+0.29

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 = +0.435 after dropping 1 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 14years. 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 also shows up within countries over time (r = +0.145). It holds even when looking only at changes from one year to the next (r = +0.116). This is the strongest kind of evidence available here: it holds when each country is compared only with itself, and it is not just a product of shared trends.

Across countries (adjusted)+0.431for comparison

Within countries+0.337each country vs itself

…and vs the same year elsewhere+0.145q = 0.046 · 138 countries

Year-on-year changes+0.116q = 0.032

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.

1,558 country-years (one country in one year), 138 countries, 2010–2023, about 11 years each on average. Share of the variation that happens within countries over time: 1% for Inequality-adjusted HDI, 5% for Secondary 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): +0.145. 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

	index, 0 to 1	% gross	Years
Iceland ISL	0.923	109%	2023
Norway NOR	0.909	116%	2023, 2024
Denmark DNK	0.909	126%	2023, 2024
Switzerland CHE	0.894	101%	2023, 2024
Netherlands NLD	0.892	138%	2023
Finland FIN	0.891	142%	2023, 2024
Belgium BEL	0.891	143%	2023
Germany DEU	0.89	100%	2023, 2024
Sweden SWE	0.886	132%	2023, 2024
Ireland IRL	0.886	131%	2023, 2022
Slovenia SVN	0.885	108%	2023
Australia AUS	0.873	132%	2023, 2024
United Kingdom GBR	0.869	112%	2023
Czechia CZE	0.867	97.6%	2023
Canada CAN	0.867	104%	2023

United Nations Development Programme, Human Development Report 2025
World Bank, World Development Indicators

Sources

Inequality-adjusted HDI

Inequality-adjusted Human Development Index

United Nations Development Programme, Human Development Report Office · via Our World in Data · CC BY 3.0 IGO · updated annual · comparable from 2010 only

The HDI marked down for how unequally each of its three parts is spread within the country. The gap between this and the HDI is what inequality costs, and that is the number worth reading. The level itself is the HDI again, with its parts flagged the same way.

Secondary enrollment

School enrollment, secondary (% 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 2012–2025. 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 Inequality-adjusted HDI and Secondary enrollment

This page shows that two measures go together. It does not show that Inequality-adjusted HDI changes Secondary enrollment, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.