Do Ethnic diversity and Inequality-adjusted HDI go together across countries?

Across 138 countries the link before adjusting for anything is r = -0.50, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.50.

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

There are not enough years of data within countries to check this over time.

Does Ethnic diversity cause Inequality-adjusted HDI?

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.

Ethnic diversity vs. Inequality-adjusted HDI

Holds up after adjusting for income

Across 138 countries, Ethnic diversity and Inequality-adjusted HDI rise and fall together at r = -0.50 (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 = -0.36, q = 0.036, allowing for the many pairs tested).

Comparing countries across the middle half of ethnic diversity, 0.234 to 0.703 points, inequality-adjusted hdi is worse by 0.189 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.0479 points.

Link before adjusting (r)-0.503p = 3.3e-10 · 138 countries

After adjusting for national income-0.359p = 1.7e-5 · likely range [-0.50, -0.20]

p, allowing for regions0.003standard error 0.075 · 7 regions

Check for luck (q)0.036across 13,797 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.

Ethnic diversity · index, 0 to 1link with income -0.37 · moderate

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

“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	-0.503	3.3e-10	138
By rank (Spearman), not adjusted	-0.500	4.4e-10	138
Standard (Pearson), adjusted for national income	-0.359	1.7e-5	138
By rank (Spearman), adjusted for national income	-0.340	4.7e-5	138

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 138 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: +0.144 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.374 (without BGD) to -0.328 (without SLB). 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: SLB (+0.031).

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	17	-0.33
Europe & Central Asia	41	-0.15
Latin America & Caribbean	19	-0.32
Middle East, North Africa, Afghanistan & Pakistan	14	-0.27
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	46	-0.34
Middle third	46	-0.38
Richest third	46	-0.30

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	82	-0.36
Muslims	38	-0.37

By last european coloniser

	n	r
France	22	-0.61
Never a European colony	18	-0.57
Spain	18	-0.17
United Kingdom	38	-0.31

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 = -0.359 after dropping 0 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 0years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

Not enough history inside each country to look at this over time. That needs countries measured for several years in a row, and at least one of these measures is measured too rarely.

The data

	index, 0 to 1	index, 0 to 1	Years
Liberia LBR	0.889	0.326	2013, 2023
Uganda UGA	0.883	0.4	2013, 2023
Togo TGO	0.88	0.363	2013, 2023
Nepal NPL	0.86	0.437	2013, 2023
South Africa ZAF	0.856	0.476	2013, 2023
Chad TCD	0.855	0.252	2013, 2023
Kenya KEN	0.855	0.456	2013, 2023
Mali MLI	0.852	0.281	2013, 2023
Nigeria NGA	0.85	0.379	2013, 2023
Guinea-Bissau GNB	0.808	0.331	2013, 2023
Philippines PHL	0.807	0.597	2013, 2023
Indonesia IDN	0.803	0.608	2013, 2023
Timor-Leste TLS	0.802	0.451	2013, 2023
Sierra Leone SLE	0.801	0.281	2013, 2023
Malawi MWI	0.791	0.365	2013, 2023

Drazanova, L. (2019), Historical Index of Ethnic Fractionalization Dataset (HIEF), Harvard Dataverse, doi:10.7910/DVN/4JQRCL
United Nations Development Programme, Human Development Report 2025

Sources

Ethnic diversity

Ethnic fractionalization index

Lenka Drazanova · CC0 1.0 · updated one edition (2019), annual values 1945-2013

The chance that two people picked at random in a country belong to different ethnic groups, from 0 (everyone in one group) to close to 1 (many small groups). It measures how mixed a country is, not which groups live there. The groups are those each country's own sources use, so where the lines are drawn differs from country to country. Yearly from 1945 to 2013, so the latest value is from 2013. No "better" direction is set.

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.

How this was computed

Data used: most recent value in 2010-2025; values come from 2013–2023. 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 13,797 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Ethnic diversity and Inequality-adjusted HDI

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