placestats

European ancestry vs. Gender Inequality Index

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 152 countries, European ancestry and Gender Inequality Index 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 -0.49, which could easily be chance (q = 0.12, 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 european ancestry, 0% to 98.1% of population, gender inequality index is better by 0.318 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.129 points.

Link before adjusting (r)-0.718p = 2.4e-25 · 152 countries
After adjusting for national income-0.485p = 2.6e-10 · likely range [-0.60, -0.35]
p, allowing for regions0.024standard error 0.162 · 7 regions
Check for luck (q)0.115across 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
Gender Inequality Index · index, 0 to 1link with income -0.89 · 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 →Gender Inequality Index · index, 0 to 1 →0%100%0.0030.677Afghanistan: 12% of population, 0.661 index, 0 to 1Angola: 2% of population, 0.515 index, 0 to 1Albania: 100% of population, 0.107 index, 0 to 1United Arab Emirates: 0% of population, 0.04 index, 0 to 1Argentina: 90% of population, 0.264 index, 0 to 1Armenia: 100% of population, 0.18 index, 0 to 1Australia: 89.6% of population, 0.056 index, 0 to 1Austria: 100% of population, 0.033 index, 0 to 1Azerbaijan: 99% of population, 0.315 index, 0 to 1Burundi: 0% of population, 0.501 index, 0 to 1Belgium: 98.4% of population, 0.031 index, 0 to 1Benin: 0% of population, 0.573 index, 0 to 1Burkina Faso: 0% of population, 0.555 index, 0 to 1Bangladesh: 0% of population, 0.487 index, 0 to 1Bulgaria: 100% of population, 0.208 index, 0 to 1Bahrain: 0% of population, 0.165 index, 0 to 1Bosnia and Herzegovina: 100% of population, 0.157 index, 0 to 1Belarus: 100% of population, 0.08 index, 0 to 1Belize: 40% of population, 0.428 index, 0 to 1Bolivia: 26.7% of population, 0.419 index, 0 to 1Brazil: 74.4% of population, 0.39 index, 0 to 1Bhutan: 0% of population, 0.278 index, 0 to 1Botswana: 1.5% of population, 0.49 index, 0 to 1Canada: 85.6% of population, 0.052 index, 0 to 1Switzerland: 99.5% of population, 0.01 index, 0 to 1Chile: 59.3% of population, 0.102 index, 0 to 1China: 0% of population, 0.132 index, 0 to 1Cote d'Ivoire: 0% of population, 0.589 index, 0 to 1Cameroon: 0% of population, 0.558 index, 0 to 1Congo, Dem. Rep.: 0.46% of population, 0.604 index, 0 to 1Congo, Rep.: 0% of population, 0.565 index, 0 to 1Colombia: 45.6% of population, 0.393 index, 0 to 1Comoros: 0% of population, 0.501 index, 0 to 1Cabo Verde: 41.4% of population, 0.298 index, 0 to 1Costa Rica: 60.4% of population, 0.217 index, 0 to 1Cyprus: 100% of population, 0.252 index, 0 to 1Czechia: 100% of population, 0.088 index, 0 to 1Germany: 100% of population, 0.057 index, 0 to 1Denmark: 100% of population, 0.003 index, 0 to 1Dominican Republic: 52% of population, 0.417 index, 0 to 1Algeria: 0% of population, 0.443 index, 0 to 1Ecuador: 32% of population, 0.358 index, 0 to 1Egypt, Arab Rep.: 0% of population, 0.398 index, 0 to 1Spain: 99.7% of population, 0.043 index, 0 to 1Estonia: 100% of population, 0.061 index, 0 to 1Ethiopia: 0% of population, 0.497 index, 0 to 1Finland: 100% of population, 0.021 index, 0 to 1Fiji: 2.2% of population, 0.35 index, 0 to 1France: 94.2% of population, 0.034 index, 0 to 1Gabon: 1% of population, 0.505 index, 0 to 1United Kingdom: 94.6% of population, 0.083 index, 0 to 1Georgia: 100% of population, 0.257 index, 0 to 1Ghana: 0% of population, 0.514 index, 0 to 1Guinea: 0% of population, 0.609 index, 0 to 1Gambia, The: 0.5% of population, 0.578 index, 0 to 1Guinea-Bissau: 3% of population, 0.632 index, 0 to 1Greece: 100% of population, 0.103 index, 0 to 1Guatemala: 22% of population, 0.48 index, 0 to 1Guyana: 1.3% of population, 0.427 index, 0 to 1Honduras: 46% of population, 0.437 index, 0 to 1Croatia: 100% of population, 0.074 index, 0 to 1Haiti: 2.5% of population, 0.618 index, 0 to 1Hungary: 100% of population, 0.213 index, 0 to 1Indonesia: 0% of population, 0.423 index, 0 to 1India: 0% of population, 0.403 index, 0 to 1Ireland: 100% of population, 0.054 index, 0 to 1Iran, Islamic Rep.: 0% of population, 0.482 index, 0 to 1Iraq: 5.6% of population, 0.558 index, 0 to 1Israel: 35.6% of population, 0.08 index, 0 to 1Italy: 99.8% of population, 0.043 index, 0 to 1Jamaica: 8% of population, 0.358 index, 0 to 1Jordan: 2% of population, 0.433 index, 0 to 1Japan: 0% of population, 0.059 index, 0 to 1Kazakhstan: 97.9% of population, 0.182 index, 0 to 1Kenya: 0% of population, 0.526 index, 0 to 1Kyrgyz Republic: 98% of population, 0.34 index, 0 to 1Cambodia: 0% of population, 0.506 index, 0 to 1Korea, Rep.: 0% of population, 0.038 index, 0 to 1Kuwait: 0% of population, 0.188 index, 0 to 1Lao PDR: 0% of population, 0.475 index, 0 to 1Lebanon: 6.9% of population, 0.36 index, 0 to 1Liberia: 0% of population, 0.646 index, 0 to 1Libya: 0% of population, 0.253 index, 0 to 1Sri Lanka: 0% of population, 0.367 index, 0 to 1Lesotho: 0% of population, 0.534 index, 0 to 1Lithuania: 100% of population, 0.07 index, 0 to 1Luxembourg: 99.2% of population, 0.044 index, 0 to 1Latvia: 100% of population, 0.117 index, 0 to 1Morocco: 8% of population, 0.438 index, 0 to 1Moldova: 100% of population, 0.146 index, 0 to 1Madagascar: 0.6% of population, 0.584 index, 0 to 1Mexico: 30% of population, 0.358 index, 0 to 1North Macedonia: 100% of population, 0.112 index, 0 to 1Mali: 0% of population, 0.612 index, 0 to 1Malta: 4.2% of population, 0.111 index, 0 to 1Myanmar: 0% of population, 0.478 index, 0 to 1Mongolia: 1.8% of population, 0.284 index, 0 to 1Mozambique: 0% of population, 0.479 index, 0 to 1Mauritania: 0% of population, 0.603 index, 0 to 1Mauritius: 16% of population, 0.352 index, 0 to 1Malawi: 0% of population, 0.581 index, 0 to 1Malaysia: 0% of population, 0.172 index, 0 to 1Namibia: 10.8% of population, 0.448 index, 0 to 1Niger: 0% of population, 0.591 index, 0 to 1Nigeria: 0% of population, 0.677 index, 0 to 1Nicaragua: 51% of population, 0.408 index, 0 to 1Netherlands: 96.6% of population, 0.013 index, 0 to 1Norway: 100% of population, 0.004 index, 0 to 1Nepal: 0% of population, 0.487 index, 0 to 1New Zealand: 80.6% of population, 0.082 index, 0 to 1Oman: 0% of population, 0.222 index, 0 to 1Pakistan: 0% of population, 0.536 index, 0 to 1Panama: 45.2% of population, 0.374 index, 0 to 1Peru: 28% of population, 0.34 index, 0 to 1Philippines: 0% of population, 0.351 index, 0 to 1Papua New Guinea: 0% of population, 0.584 index, 0 to 1Poland: 100% of population, 0.081 index, 0 to 1Portugal: 98.4% of population, 0.076 index, 0 to 1Paraguay: 52.9% of population, 0.412 index, 0 to 1Qatar: 0.9% of population, 0.195 index, 0 to 1Romania: 100% of population, 0.227 index, 0 to 1Russian Federation: 100% of population, 0.169 index, 0 to 1Rwanda: 0% of population, 0.394 index, 0 to 1Saudi Arabia: 0% of population, 0.228 index, 0 to 1Sudan: 0% of population, 0.588 index, 0 to 1Senegal: 1.3% of population, 0.49 index, 0 to 1Singapore: 0.5% of population, 0.031 index, 0 to 1Sierra Leone: 0% of population, 0.566 index, 0 to 1El Salvador: 50% of population, 0.362 index, 0 to 1Somalia, Fed. Rep.: 0% of population, 0.675 index, 0 to 1Slovak Republic: 100% of population, 0.176 index, 0 to 1Slovenia: 100% of population, 0.042 index, 0 to 1Sweden: 99.3% of population, 0.007 index, 0 to 1Eswatini: 2.8% of population, 0.484 index, 0 to 1Syrian Arab Republic: 5.2% of population, 0.49 index, 0 to 1Chad: 0% of population, 0.67 index, 0 to 1Togo: 0% of population, 0.564 index, 0 to 1Thailand: 0% of population, 0.288 index, 0 to 1Tajikistan: 100% of population, 0.258 index, 0 to 1Timor-Leste: 0% of population, 0.394 index, 0 to 1Trinidad and Tobago: 7.1% of population, 0.262 index, 0 to 1Tunisia: 0% of population, 0.238 index, 0 to 1Turkiye: 99.3% of population, 0.227 index, 0 to 1Tanzania: 0% of population, 0.504 index, 0 to 1Uganda: 0% of population, 0.524 index, 0 to 1Uruguay: 92% of population, 0.218 index, 0 to 1United States: 76.2% of population, 0.169 index, 0 to 1Uzbekistan: 99% of population, 0.291 index, 0 to 1Viet Nam: 0% of population, 0.299 index, 0 to 1South Africa: 18% of population, 0.388 index, 0 to 1Zambia: 1% of population, 0.524 index, 0 to 1Zimbabwe: 1% of population, 0.519 index, 0 to 1AREAZEDNKKGZKORNGANORSGPSOMSWETCDUZB

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.7182.4e-25152
By rank (Spearman), not adjusted-0.6481.7e-19152
Standard (Pearson), adjusted for national income-0.4852.6e-10152
By rank (Spearman), adjusted for national income-0.3362.5e-5152

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 152 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: +0.232 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.506 (without KOR) to -0.461 (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 (+0.025).

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.

nr
East Asia & Pacific18-0.18
Europe & Central Asia43+0.04
Latin America & Caribbean22-0.46
Middle East, North Africa, Afghanistan & Pakistan20-0.29
Sub-Saharan Africa42-0.58

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 third51-0.59
Middle third50-0.44
Richest third51-0.40

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
Christians88-0.65
Muslims44-0.53

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
France23-0.26
Never a European colony20-0.29
Spain18-0.30
United Kingdom46-0.28

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 = -0.485 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
Tajikistan TJK100%0.2582000, 2023
Slovenia SVN100%0.0422000, 2023
Slovak Republic SVK100%0.1762000, 2023
Russian Federation RUS100%0.1692000, 2023
Romania ROU100%0.2272000, 2023
Poland POL100%0.0812000, 2023
Norway NOR100%0.0042000, 2023
North Macedonia MKD100%0.1122000, 2023
Moldova MDA100%0.1462000, 2023
Latvia LVA100%0.1172000, 2023
Lithuania LTU100%0.072000, 2023
Ireland IRL100%0.0542000, 2023
Hungary HUN100%0.2132000, 2023
Croatia HRV100%0.0742000, 2023
Greece GRC100%0.1032000, 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.

Gender Inequality Index

Gender Inequality Index

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

Built from deaths in childbirth, births to teenagers, seats in parliament, secondary schooling, and the share of women and men in the workforce. Five of those inputs are on the site and flagged. It is kept out of the gender score, for the same reason the HDI is kept out of the economy score.

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 Gender Inequality Index

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

More links with European ancestry or Gender Inequality Index