placestats

European ancestry vs. Governance score

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 157 countries, European ancestry and Governance score rise and fall together at r = +0.51 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.22, which could easily be chance (q = 0.52, 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 98.4% of population, governance score is better by 0.976 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.404 typical gaps.

Link before adjusting (r)+0.514p = 5.6e-12 · 157 countries
After adjusting for national income+0.222p = 0.005 · likely range [+0.07, +0.37]
p, allowing for regions0.316standard error 0.203 · 7 regions
Check for luck (q)0.516across 17,292 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
Governance score · typical gaps (standard deviations)link with income +0.63 · high

“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 →Governance score · typical gaps (standard deviations) →0%100%-1.531.57Afghanistan: 12% of population, -1.53 typical gaps (standard deviations)Angola: 2% of population, -0.602 typical gaps (standard deviations)Albania: 100% of population, 0.259 typical gaps (standard deviations)United Arab Emirates: 0% of population, -0.438 typical gaps (standard deviations)Argentina: 90% of population, 0.568 typical gaps (standard deviations)Armenia: 100% of population, 0.127 typical gaps (standard deviations)Australia: 89.6% of population, 1.42 typical gaps (standard deviations)Austria: 100% of population, 1.31 typical gaps (standard deviations)Azerbaijan: 99% of population, -1.03 typical gaps (standard deviations)Burundi: 0% of population, -1.16 typical gaps (standard deviations)Belgium: 98.4% of population, 1.32 typical gaps (standard deviations)Benin: 0% of population, 0.0936 typical gaps (standard deviations)Burkina Faso: 0% of population, -0.825 typical gaps (standard deviations)Bangladesh: 0% of population, -0.507 typical gaps (standard deviations)Bulgaria: 100% of population, 0.537 typical gaps (standard deviations)Bahrain: 0% of population, -0.69 typical gaps (standard deviations)Bosnia and Herzegovina: 100% of population, -0.0604 typical gaps (standard deviations)Belarus: 100% of population, -1.15 typical gaps (standard deviations)Belize: 40% of population, 0.087 typical gaps (standard deviations)Bolivia: 26.7% of population, 0.114 typical gaps (standard deviations)Brazil: 74.4% of population, 0.502 typical gaps (standard deviations)Bhutan: 0% of population, 0.659 typical gaps (standard deviations)Botswana: 1.5% of population, 0.763 typical gaps (standard deviations)Central African Republic: 0.6% of population, -1.17 typical gaps (standard deviations)Canada: 85.6% of population, 1.39 typical gaps (standard deviations)Switzerland: 99.5% of population, 1.54 typical gaps (standard deviations)Chile: 59.3% of population, 1.15 typical gaps (standard deviations)China: 0% of population, -0.875 typical gaps (standard deviations)Cote d'Ivoire: 0% of population, -0.208 typical gaps (standard deviations)Cameroon: 0% of population, -0.934 typical gaps (standard deviations)Congo, Dem. Rep.: 0.46% of population, -1.07 typical gaps (standard deviations)Congo, Rep.: 0% of population, -0.987 typical gaps (standard deviations)Colombia: 45.6% of population, 0.377 typical gaps (standard deviations)Comoros: 0% of population, -0.604 typical gaps (standard deviations)Cabo Verde: 41.4% of population, 0.977 typical gaps (standard deviations)Costa Rica: 60.4% of population, 1.15 typical gaps (standard deviations)Cyprus: 100% of population, 0.98 typical gaps (standard deviations)Czechia: 100% of population, 1.24 typical gaps (standard deviations)Germany: 100% of population, 1.35 typical gaps (standard deviations)Denmark: 100% of population, 1.57 typical gaps (standard deviations)Dominican Republic: 52% of population, 0.48 typical gaps (standard deviations)Algeria: 0% of population, -0.674 typical gaps (standard deviations)Ecuador: 32% of population, 0.126 typical gaps (standard deviations)Egypt, Arab Rep.: 0% of population, -0.9 typical gaps (standard deviations)Spain: 99.7% of population, 1.13 typical gaps (standard deviations)Estonia: 100% of population, 1.4 typical gaps (standard deviations)Ethiopia: 0% of population, -0.809 typical gaps (standard deviations)Finland: 100% of population, 1.51 typical gaps (standard deviations)Fiji: 2.2% of population, 0.538 typical gaps (standard deviations)France: 94.2% of population, 1.14 typical gaps (standard deviations)Gabon: 1% of population, -0.556 typical gaps (standard deviations)United Kingdom: 94.6% of population, 1.25 typical gaps (standard deviations)Georgia: 100% of population, 0.0236 typical gaps (standard deviations)Ghana: 0% of population, 0.586 typical gaps (standard deviations)Guinea: 0% of population, -0.779 typical gaps (standard deviations)Gambia, The: 0.5% of population, 0.128 typical gaps (standard deviations)Guinea-Bissau: 3% of population, -0.69 typical gaps (standard deviations)Equatorial Guinea: 5.75% of population, -1.3 typical gaps (standard deviations)Greece: 100% of population, 0.751 typical gaps (standard deviations)Guatemala: 22% of population, 0.000403 typical gaps (standard deviations)Guyana: 1.3% of population, 0.328 typical gaps (standard deviations)Hong Kong SAR, China: 0% of population, -0.017 typical gaps (standard deviations)Honduras: 46% of population, -0.154 typical gaps (standard deviations)Croatia: 100% of population, 0.761 typical gaps (standard deviations)Haiti: 2.5% of population, -0.951 typical gaps (standard deviations)Hungary: 100% of population, 0.237 typical gaps (standard deviations)Indonesia: 0% of population, 0.0177 typical gaps (standard deviations)India: 0% of population, 0.0581 typical gaps (standard deviations)Ireland: 100% of population, 1.43 typical gaps (standard deviations)Iran, Islamic Rep.: 0% of population, -1.28 typical gaps (standard deviations)Iraq: 5.6% of population, -0.727 typical gaps (standard deviations)Israel: 35.6% of population, 0.72 typical gaps (standard deviations)Italy: 99.8% of population, 0.911 typical gaps (standard deviations)Jamaica: 8% of population, 0.748 typical gaps (standard deviations)Jordan: 2% of population, -0.18 typical gaps (standard deviations)Japan: 0% of population, 1.34 typical gaps (standard deviations)Kazakhstan: 97.9% of population, -0.619 typical gaps (standard deviations)Kenya: 0% of population, -0.0288 typical gaps (standard deviations)Kyrgyz Republic: 98% of population, -0.706 typical gaps (standard deviations)Cambodia: 0% of population, -0.921 typical gaps (standard deviations)Korea, Rep.: 0% of population, 1.06 typical gaps (standard deviations)Kuwait: 0% of population, -0.424 typical gaps (standard deviations)Lao PDR: 0% of population, -0.976 typical gaps (standard deviations)Lebanon: 6.9% of population, -0.406 typical gaps (standard deviations)Liberia: 0% of population, 0.064 typical gaps (standard deviations)Libya: 0% of population, -1.12 typical gaps (standard deviations)Sri Lanka: 0% of population, 0.356 typical gaps (standard deviations)Lesotho: 0% of population, 0.356 typical gaps (standard deviations)Lithuania: 100% of population, 1.16 typical gaps (standard deviations)Luxembourg: 99.2% of population, 1.48 typical gaps (standard deviations)Latvia: 100% of population, 1.13 typical gaps (standard deviations)Morocco: 8% of population, -0.333 typical gaps (standard deviations)Moldova: 100% of population, 0.335 typical gaps (standard deviations)Madagascar: 0.6% of population, -0.392 typical gaps (standard deviations)Mexico: 30% of population, -0.0935 typical gaps (standard deviations)North Macedonia: 100% of population, 0.285 typical gaps (standard deviations)Mali: 0% of population, -0.943 typical gaps (standard deviations)Malta: 4.2% of population, 0.978 typical gaps (standard deviations)Myanmar: 0% of population, -1.49 typical gaps (standard deviations)Mongolia: 1.8% of population, 0.484 typical gaps (standard deviations)Mozambique: 0% of population, -0.547 typical gaps (standard deviations)Mauritania: 0% of population, -0.54 typical gaps (standard deviations)Mauritius: 16% of population, 0.962 typical gaps (standard deviations)Malawi: 0% of population, 0.271 typical gaps (standard deviations)Malaysia: 0% of population, 0.278 typical gaps (standard deviations)Namibia: 10.8% of population, 0.568 typical gaps (standard deviations)Niger: 0% of population, -0.715 typical gaps (standard deviations)Nigeria: 0% of population, -0.334 typical gaps (standard deviations)Nicaragua: 51% of population, -1.19 typical gaps (standard deviations)Netherlands: 96.6% of population, 1.38 typical gaps (standard deviations)Norway: 100% of population, 1.53 typical gaps (standard deviations)Nepal: 0% of population, 0.128 typical gaps (standard deviations)New Zealand: 80.6% of population, 1.45 typical gaps (standard deviations)Oman: 0% of population, -0.349 typical gaps (standard deviations)Pakistan: 0% of population, -0.658 typical gaps (standard deviations)Panama: 45.2% of population, 0.565 typical gaps (standard deviations)Peru: 28% of population, 0.244 typical gaps (standard deviations)Philippines: 0% of population, -0.052 typical gaps (standard deviations)Papua New Guinea: 0% of population, -0.0334 typical gaps (standard deviations)Poland: 100% of population, 0.922 typical gaps (standard deviations)Portugal: 98.4% of population, 1.15 typical gaps (standard deviations)Paraguay: 52.9% of population, 0.135 typical gaps (standard deviations)Qatar: 0.9% of population, -0.421 typical gaps (standard deviations)Romania: 100% of population, 0.595 typical gaps (standard deviations)Russian Federation: 100% of population, -1.11 typical gaps (standard deviations)Rwanda: 0% of population, -0.532 typical gaps (standard deviations)Saudi Arabia: 0% of population, -0.712 typical gaps (standard deviations)Sudan: 0% of population, -1.43 typical gaps (standard deviations)Senegal: 1.3% of population, 0.459 typical gaps (standard deviations)Singapore: 0.5% of population, 0.528 typical gaps (standard deviations)Sierra Leone: 0% of population, -0.0448 typical gaps (standard deviations)El Salvador: 50% of population, -0.354 typical gaps (standard deviations)Somalia, Fed. Rep.: 0% of population, -1.29 typical gaps (standard deviations)Slovak Republic: 100% of population, 0.851 typical gaps (standard deviations)Slovenia: 100% of population, 1.1 typical gaps (standard deviations)Sweden: 99.3% of population, 1.49 typical gaps (standard deviations)Eswatini: 2.8% of population, -0.976 typical gaps (standard deviations)Syrian Arab Republic: 5.2% of population, -1.29 typical gaps (standard deviations)Chad: 0% of population, -1.1 typical gaps (standard deviations)Togo: 0% of population, -0.492 typical gaps (standard deviations)Thailand: 0% of population, -0.321 typical gaps (standard deviations)Tajikistan: 100% of population, -1.22 typical gaps (standard deviations)Turkmenistan: 100% of population, -1.33 typical gaps (standard deviations)Timor-Leste: 0% of population, 0.458 typical gaps (standard deviations)Trinidad and Tobago: 7.1% of population, 0.718 typical gaps (standard deviations)Tunisia: 0% of population, -0.317 typical gaps (standard deviations)Turkiye: 99.3% of population, -0.647 typical gaps (standard deviations)Tanzania: 0% of population, -0.365 typical gaps (standard deviations)Uganda: 0% of population, -0.676 typical gaps (standard deviations)Ukraine: 100% of population, -0.182 typical gaps (standard deviations)Uruguay: 92% of population, 1.29 typical gaps (standard deviations)United States: 76.2% of population, 0.906 typical gaps (standard deviations)Uzbekistan: 99% of population, -0.864 typical gaps (standard deviations)Viet Nam: 0% of population, -0.692 typical gaps (standard deviations)South Africa: 18% of population, 0.602 typical gaps (standard deviations)Zambia: 1% of population, 0.0286 typical gaps (standard deviations)Zimbabwe: 1% of population, -0.779 typical gaps (standard deviations)AFGBLRCHEDNKJPNKORMLTMMRNORSDNTJKTKM

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5145.6e-12157
By rank (Spearman), not adjusted+0.4916.6e-11157
Standard (Pearson), adjusted for national income+0.2220.005157
By rank (Spearman), adjusted for national income+0.2060.010157

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 157 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: +0.292 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.197 (without QAT) to +0.252 (without TKM). 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: TKM (+0.030).

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 Asia45-0.04
Latin America & Caribbean22+0.28
Middle East, North Africa, Afghanistan & Pakistan20+0.52
Sub-Saharan Africa44+0.37

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 third53-0.25
Middle third52-0.04
Richest third52+0.57

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
Christians91+0.07
Muslims45-0.17

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
France24-0.07
Never a European colony21-0.37
Spain19+0.32
United Kingdom46+0.51

By official ideology

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
Nationalist25-0.33
No strong official ideology106+0.26

By party system

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
A few parties banned25-0.16
No parties banned110+0.10

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 = +0.222 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
typical gaps (standard deviations)		Years
Ukraine UKR100%-0.1822000, 2025
Turkmenistan TKM100%-1.332000, 2025
Tajikistan TJK100%-1.222000, 2025
Slovenia SVN100%1.12000, 2025
Slovak Republic SVK100%0.8512000, 2025
Russian Federation RUS100%-1.112000, 2025
Romania ROU100%0.5952000, 2025
Poland POL100%0.9222000, 2025
Norway NOR100%1.532000, 2025
North Macedonia MKD100%0.2852000, 2025
Moldova MDA100%0.3352000, 2025
Latvia LVA100%1.132000, 2025
Lithuania LTU100%1.162000, 2025
Ireland IRL100%1.432000, 2025
Hungary HUN100%0.2372000, 2025

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).
fh; un_sdg; vdem; wdi; wgi

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.

Governance score

Governance score

PlaceStats · via PlaceStats · Derived work; each member keeps its own licence · updated recomputed with the panel

The average of 19 measures from 5 publishers. Each measure is first put on a common scale: 0 is the average country in an average year, and 1 is a typical gap between countries (a standard deviation). Each is turned so that higher is better. Measures are averaged within each publisher first, so a publisher with many measures does not count for more. Members: Civil liberties (FH), Political rights (FH), Pre-trial detainee share, Civil society participation, Political corruption, Electoral democracy, Freedom of association, Freedom of expression, Free and fair elections, Liberal democracy, Participatory democracy, Political civil liberties, Birth registration, Control of corruption, Government effectiveness, Political stability, Rule of law, Regulatory quality, Voice and accountability.

How this was computed

Data used: most recent value in 2010-2025; values come from 2000–2025. 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 17,292 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated V-Dem v16; V-Party v2. How the country data works · explore European ancestry and Governance score

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

More links with European ancestry or Governance score