placestats

European ancestry vs. Inequality-adjusted HDI

Fades after adjusting for income

A country is not a person, and ancestry is not destiny. Countries where more people have ancestors from one part of the world also tend to differ on the other measure, and that is all. It does notfollow that people with that ancestry are the people with the different score. Making that leap is called the ecological fallacy: reading a pattern across places as a fact about individual people. Ancestry also goes very closely with region and with history, including colonisation and the Atlantic slave trade, and with national income. So much of what looks like ancestry here is really those. That is what the adjusted figures and the region-by-region view are for. The shares are one estimate, for around the year 2000, of where people’s ancestors lived in 1500, not how anyone identifies.

Across 145 countries, European ancestry and Inequality-adjusted HDI rise and fall together at r = +0.75 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.51, which could easily be chance (q = 0.17, 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. Caution: direction is not consistent across regions.

Comparing countries across the middle half of european ancestry, 0% to 98.4% of population, inequality-adjusted hdi is better by 0.338 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.0939 points.

Link before adjusting (r)+0.753p = 1.0e-27 · 145 countries
After adjusting for national income+0.509p = 7.3e-11 · likely range [+0.38, +0.62]
p, allowing for regions0.046standard error 0.203 · 7 regions
Check for luck (q)0.168across 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.

European ancestry · % of populationlink with income +0.57 · 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

European ancestry · % of population →Inequality-adjusted HDI · index, 0 to 1 →0%100%0.2290.909Afghanistan: 12% of population, 0.321 index, 0 to 1Angola: 2% of population, 0.36 index, 0 to 1Albania: 100% of population, 0.705 index, 0 to 1United Arab Emirates: 0% of population, 0.866 index, 0 to 1Argentina: 90% of population, 0.761 index, 0 to 1Armenia: 100% of population, 0.743 index, 0 to 1Australia: 89.6% of population, 0.873 index, 0 to 1Austria: 100% of population, 0.861 index, 0 to 1Azerbaijan: 99% of population, 0.735 index, 0 to 1Burundi: 0% of population, 0.286 index, 0 to 1Belgium: 98.4% of population, 0.891 index, 0 to 1Benin: 0% of population, 0.316 index, 0 to 1Burkina Faso: 0% of population, 0.273 index, 0 to 1Bangladesh: 0% of population, 0.482 index, 0 to 1Bulgaria: 100% of population, 0.748 index, 0 to 1Bosnia and Herzegovina: 100% of population, 0.689 index, 0 to 1Belarus: 100% of population, 0.771 index, 0 to 1Bolivia: 26.7% of population, 0.578 index, 0 to 1Brazil: 74.4% of population, 0.594 index, 0 to 1Bhutan: 0% of population, 0.478 index, 0 to 1Botswana: 1.5% of population, 0.509 index, 0 to 1Central African Republic: 0.6% of population, 0.253 index, 0 to 1Canada: 85.6% of population, 0.867 index, 0 to 1Switzerland: 99.5% of population, 0.894 index, 0 to 1Chile: 59.3% of population, 0.723 index, 0 to 1China: 0% of population, 0.67 index, 0 to 1Cote d'Ivoire: 0% of population, 0.35 index, 0 to 1Cameroon: 0% of population, 0.361 index, 0 to 1Congo, Dem. Rep.: 0.46% of population, 0.341 index, 0 to 1Congo, Rep.: 0% of population, 0.426 index, 0 to 1Colombia: 45.6% of population, 0.593 index, 0 to 1Comoros: 0% of population, 0.356 index, 0 to 1Cabo Verde: 41.4% of population, 0.478 index, 0 to 1Costa Rica: 60.4% of population, 0.678 index, 0 to 1Cyprus: 100% of population, 0.841 index, 0 to 1Czechia: 100% of population, 0.867 index, 0 to 1Germany: 100% of population, 0.89 index, 0 to 1Denmark: 100% of population, 0.909 index, 0 to 1Dominican Republic: 52% of population, 0.634 index, 0 to 1Algeria: 0% of population, 0.601 index, 0 to 1Ecuador: 32% of population, 0.64 index, 0 to 1Egypt, Arab Rep.: 0% of population, 0.582 index, 0 to 1Spain: 99.7% of population, 0.819 index, 0 to 1Estonia: 100% of population, 0.841 index, 0 to 1Ethiopia: 0% of population, 0.326 index, 0 to 1Finland: 100% of population, 0.891 index, 0 to 1Fiji: 2.2% of population, 0.626 index, 0 to 1France: 94.2% of population, 0.836 index, 0 to 1Gabon: 1% of population, 0.558 index, 0 to 1United Kingdom: 94.6% of population, 0.869 index, 0 to 1Georgia: 100% of population, 0.754 index, 0 to 1Ghana: 0% of population, 0.399 index, 0 to 1Guinea: 0% of population, 0.302 index, 0 to 1Gambia, The: 0.5% of population, 0.329 index, 0 to 1Guinea-Bissau: 3% of population, 0.331 index, 0 to 1Greece: 100% of population, 0.825 index, 0 to 1Guatemala: 22% of population, 0.479 index, 0 to 1Hong Kong SAR, China: 0% of population, 0.839 index, 0 to 1Honduras: 46% of population, 0.496 index, 0 to 1Croatia: 100% of population, 0.828 index, 0 to 1Haiti: 2.5% of population, 0.337 index, 0 to 1Hungary: 100% of population, 0.819 index, 0 to 1Indonesia: 0% of population, 0.608 index, 0 to 1India: 0% of population, 0.475 index, 0 to 1Ireland: 100% of population, 0.886 index, 0 to 1Iran, Islamic Rep.: 0% of population, 0.643 index, 0 to 1Iraq: 5.6% of population, 0.534 index, 0 to 1Israel: 35.6% of population, 0.813 index, 0 to 1Italy: 99.8% of population, 0.817 index, 0 to 1Jamaica: 8% of population, 0.59 index, 0 to 1Jordan: 2% of population, 0.637 index, 0 to 1Japan: 0% of population, 0.845 index, 0 to 1Kazakhstan: 97.9% of population, 0.766 index, 0 to 1Kenya: 0% of population, 0.456 index, 0 to 1Kyrgyz Republic: 98% of population, 0.649 index, 0 to 1Cambodia: 0% of population, 0.444 index, 0 to 1Korea, Rep.: 0% of population, 0.857 index, 0 to 1Lao PDR: 0% of population, 0.462 index, 0 to 1Liberia: 0% of population, 0.326 index, 0 to 1Sri Lanka: 0% of population, 0.63 index, 0 to 1Lesotho: 0% of population, 0.357 index, 0 to 1Lithuania: 100% of population, 0.812 index, 0 to 1Luxembourg: 99.2% of population, 0.838 index, 0 to 1Latvia: 100% of population, 0.812 index, 0 to 1Morocco: 8% of population, 0.517 index, 0 to 1Moldova: 100% of population, 0.719 index, 0 to 1Madagascar: 0.6% of population, 0.319 index, 0 to 1Mexico: 30% of population, 0.646 index, 0 to 1North Macedonia: 100% of population, 0.723 index, 0 to 1Mali: 0% of population, 0.281 index, 0 to 1Malta: 4.2% of population, 0.843 index, 0 to 1Myanmar: 0% of population, 0.477 index, 0 to 1Mongolia: 1.8% of population, 0.647 index, 0 to 1Mozambique: 0% of population, 0.297 index, 0 to 1Mauritania: 0% of population, 0.374 index, 0 to 1Mauritius: 16% of population, 0.669 index, 0 to 1Malawi: 0% of population, 0.365 index, 0 to 1Malaysia: 0% of population, 0.707 index, 0 to 1Namibia: 10.8% of population, 0.438 index, 0 to 1Niger: 0% of population, 0.265 index, 0 to 1Nigeria: 0% of population, 0.379 index, 0 to 1Nicaragua: 51% of population, 0.535 index, 0 to 1Netherlands: 96.6% of population, 0.892 index, 0 to 1Norway: 100% of population, 0.909 index, 0 to 1Nepal: 0% of population, 0.437 index, 0 to 1New Zealand: 80.6% of population, 0.853 index, 0 to 1Oman: 0% of population, 0.75 index, 0 to 1Pakistan: 0% of population, 0.364 index, 0 to 1Panama: 45.2% of population, 0.664 index, 0 to 1Peru: 28% of population, 0.633 index, 0 to 1Philippines: 0% of population, 0.597 index, 0 to 1Papua New Guinea: 0% of population, 0.423 index, 0 to 1Poland: 100% of population, 0.817 index, 0 to 1Portugal: 98.4% of population, 0.795 index, 0 to 1Paraguay: 52.9% of population, 0.599 index, 0 to 1Romania: 100% of population, 0.758 index, 0 to 1Russian Federation: 100% of population, 0.758 index, 0 to 1Rwanda: 0% of population, 0.399 index, 0 to 1Sudan: 0% of population, 0.328 index, 0 to 1Senegal: 1.3% of population, 0.34 index, 0 to 1Singapore: 0.5% of population, 0.823 index, 0 to 1Sierra Leone: 0% of population, 0.281 index, 0 to 1El Salvador: 50% of population, 0.555 index, 0 to 1Somalia, Fed. Rep.: 0% of population, 0.229 index, 0 to 1Slovak Republic: 100% of population, 0.833 index, 0 to 1Slovenia: 100% of population, 0.885 index, 0 to 1Sweden: 99.3% of population, 0.886 index, 0 to 1Eswatini: 2.8% of population, 0.431 index, 0 to 1Syrian Arab Republic: 5.2% of population, 0.419 index, 0 to 1Chad: 0% of population, 0.252 index, 0 to 1Togo: 0% of population, 0.363 index, 0 to 1Thailand: 0% of population, 0.677 index, 0 to 1Tajikistan: 100% of population, 0.594 index, 0 to 1Timor-Leste: 0% of population, 0.451 index, 0 to 1Tunisia: 0% of population, 0.595 index, 0 to 1Turkiye: 99.3% of population, 0.708 index, 0 to 1Tanzania: 0% of population, 0.391 index, 0 to 1Uganda: 0% of population, 0.4 index, 0 to 1Ukraine: 100% of population, 0.715 index, 0 to 1Uruguay: 92% of population, 0.747 index, 0 to 1United States: 76.2% of population, 0.832 index, 0 to 1Viet Nam: 0% of population, 0.641 index, 0 to 1South Africa: 18% of population, 0.476 index, 0 to 1Zambia: 1% of population, 0.361 index, 0 to 1Zimbabwe: 1% of population, 0.406 index, 0 to 1ARECAFCHEDNKJPNKGZKORNORSOMTCDTJK

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.7531.0e-27145
By rank (Spearman), not adjusted+0.6871.3e-21145
Standard (Pearson), adjusted for national income+0.5097.3e-11145
By rank (Spearman), adjusted for national income+0.3501.7e-5145

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 145 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: +0.244 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.484 (without TJK) to +0.530 (without JPN). 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: TJK (-0.025).

Fragile: this link may not hold up.
  • direction is not consistent across regions
  • much weaker within regions than across them (largely a between-region difference)

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.

nr
East Asia & Pacific19+0.45
Europe & Central Asia43-0.08
Latin America & Caribbean19+0.31
Middle East, North Africa, Afghanistan & Pakistan14+0.21
Sub-Saharan Africa43+0.22

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.

nr
Poorest third49+0.72
Middle third48+0.40
Richest third48+0.33

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.

nr
Christians87+0.64
Muslims37+0.54

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.

nr
France23+0.23
Never a European colony18+0.54
Spain18+0.37
United Kingdom39+0.46

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 = +0.509 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


% of population
index, 0 to 1		Years
Ukraine UKR100%0.7152000, 2023
Tajikistan TJK100%0.5942000, 2023
Slovenia SVN100%0.8852000, 2023
Slovak Republic SVK100%0.8332000, 2023
Russian Federation RUS100%0.7582000, 2023
Romania ROU100%0.7582000, 2023
Poland POL100%0.8172000, 2023
Norway NOR100%0.9092000, 2023
North Macedonia MKD100%0.7232000, 2023
Moldova MDA100%0.7192000, 2023
Latvia LVA100%0.8122000, 2023
Lithuania LTU100%0.8122000, 2023
Ireland IRL100%0.8862000, 2023
Hungary HUN100%0.8192000, 2023
Croatia HRV100%0.8282000, 2023

Putterman, L. and Weil, D. N. (2010), 'Post-1500 Population Flows and the Long-Run Determinants of Economic Growth and Inequality', Quarterly Journal of Economics 125(4); World Migration Matrix version 1.1 (2009).
United Nations Development Programme, Human Development Report 2025

Sources

European ancestry

European and Central Asian ancestry (share of the population)

Louis Putterman and David N. Weil, Brown University · No licence stated; used with citation · updated one edition (version 1.1, 2009)

The estimated share of today's population whose ancestors in the year 1500 lived in Europe or Central Asia (the World Bank's Europe & Central Asia region, which includes Turkey, the Caucasus and Central Asia). This is the closest thing to race shares that can be compared across countries: national censuses each use their own race and ethnic categories, and most countries record none. It is about where people's ancestors lived, not how anyone identifies, and it is one estimate for around the year 2000, built from history, migration records and genetic studies. Every country's six ancestry shares add up to 100%, so they are linked to each other by arithmetic. 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 2000–2023. 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 European ancestry and Inequality-adjusted HDI

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

More links with European ancestry or Inequality-adjusted HDI