placestats

East Asian ancestry vs. Premature NCD mortality

Holds up 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, East Asian ancestry and Premature NCD mortality rise and fall together at r = +0.10 (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 = +0.16, q = 0.027, allowing for the many pairs tested). Caution: correlation moves sharply once outliers are trimmed.

Comparing countries across the middle half of east asian ancestry, 0% to 0.45% of population, premature ncd mortality is worse by 0.0102% probability, ages 30-70 at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.0125% probability, ages 30-70.

Link before adjusting (r)+0.101p = 0.210 · 157 countries
After adjusting for national income+0.165p = 0.040 · likely range [+0.01, +0.31]
p, allowing for regions0.002standard error 0.031 · 7 regions
Check for luck (q)0.027across 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.

East Asian ancestry · % of populationlink with income +0.24 · independent
Premature NCD mortality · % probability, ages 30-70link with income -0.70 · high

“Link with income” is how closely each measure tracks GDP per person across countries, from −1 to +1.

Every country as a dot

East Asian ancestry · % of population →Premature NCD mortality · % probability, ages 30-70 →0%100%6.9%37.9%Afghanistan: 0% of population, 32.7% probability, ages 30-70Angola: 0% of population, 24.7% probability, ages 30-70Albania: 0% of population, 10% probability, ages 30-70United Arab Emirates: 0% of population, 11.6% probability, ages 30-70Argentina: 0% of population, 13.4% probability, ages 30-70Armenia: 0% of population, 20.6% probability, ages 30-70Australia: 7.1% of population, 8.3% probability, ages 30-70Austria: 0% of population, 9.9% probability, ages 30-70Azerbaijan: 0% of population, 17.4% probability, ages 30-70Burundi: 0% of population, 24.8% probability, ages 30-70Belgium: 0% of population, 9.2% probability, ages 30-70Benin: 0% of population, 20.8% probability, ages 30-70Burkina Faso: 0% of population, 23.4% probability, ages 30-70Bangladesh: 0% of population, 18.1% probability, ages 30-70Bulgaria: 0% of population, 25.6% probability, ages 30-70Bahrain: 0% of population, 14.8% probability, ages 30-70Bosnia and Herzegovina: 0% of population, 17.2% probability, ages 30-70Belarus: 0% of population, 23.8% probability, ages 30-70Belize: 0.5% of population, 15.4% probability, ages 30-70Bolivia: 0% of population, 19.9% probability, ages 30-70Brazil: 0.8% of population, 14.5% probability, ages 30-70Bhutan: 0% of population, 15.9% probability, ages 30-70Botswana: 0% of population, 19% probability, ages 30-70Central African Republic: 0% of population, 30.8% probability, ages 30-70Canada: 5.59% of population, 9.7% probability, ages 30-70Switzerland: 0% of population, 7.5% probability, ages 30-70Chile: 0% of population, 9.4% probability, ages 30-70China: 100% of population, 15.9% probability, ages 30-70Cote d'Ivoire: 0% of population, 22.7% probability, ages 30-70Cameroon: 0% of population, 24.3% probability, ages 30-70Congo, Dem. Rep.: 0% of population, 25.1% probability, ages 30-70Congo, Rep.: 0% of population, 21.2% probability, ages 30-70Colombia: 0% of population, 10.3% probability, ages 30-70Comoros: 0% of population, 20.2% probability, ages 30-70Cabo Verde: 0% of population, 17.2% probability, ages 30-70Costa Rica: 1% of population, 9.9% probability, ages 30-70Cyprus: 0% of population, 8.5% probability, ages 30-70Czechia: 0% of population, 14.2% probability, ages 30-70Germany: 0% of population, 11.6% probability, ages 30-70Denmark: 0% of population, 10.7% probability, ages 30-70Dominican Republic: 0% of population, 17.4% probability, ages 30-70Algeria: 0% of population, 13.3% probability, ages 30-70Ecuador: 0% of population, 13.2% probability, ages 30-70Egypt, Arab Rep.: 0% of population, 26% probability, ages 30-70Spain: 0% of population, 9.3% probability, ages 30-70Estonia: 0% of population, 15.5% probability, ages 30-70Ethiopia: 0% of population, 16.8% probability, ages 30-70Finland: 0% of population, 9.6% probability, ages 30-70Fiji: 52.8% of population, 37.9% probability, ages 30-70France: 1% of population, 10.2% probability, ages 30-70Gabon: 0% of population, 18.1% probability, ages 30-70United Kingdom: 0% of population, 11% probability, ages 30-70Georgia: 0% of population, 22.1% probability, ages 30-70Ghana: 0% of population, 22.2% probability, ages 30-70Guinea: 0% of population, 23.6% probability, ages 30-70Gambia, The: 0% of population, 21.8% probability, ages 30-70Guinea-Bissau: 0% of population, 25.3% probability, ages 30-70Equatorial Guinea: 0% of population, 20.3% probability, ages 30-70Greece: 0% of population, 12% probability, ages 30-70Guatemala: 0% of population, 14.6% probability, ages 30-70Guyana: 0.7% of population, 25.4% probability, ages 30-70Honduras: 0% of population, 17.8% probability, ages 30-70Croatia: 0% of population, 15.9% probability, ages 30-70Haiti: 0% of population, 31.6% probability, ages 30-70Hungary: 0% of population, 21.7% probability, ages 30-70Indonesia: 100% of population, 21.9% probability, ages 30-70India: 0% of population, 23.6% probability, ages 30-70Ireland: 0% of population, 9.6% probability, ages 30-70Iran, Islamic Rep.: 0% of population, 13.9% probability, ages 30-70Iraq: 0% of population, 22.7% probability, ages 30-70Israel: 0% of population, 7.6% probability, ages 30-70Italy: 0% of population, 9.1% probability, ages 30-70Jamaica: 0.5% of population, 20.2% probability, ages 30-70Jordan: 0% of population, 11.6% probability, ages 30-70Japan: 100% of population, 8% probability, ages 30-70Kazakhstan: 2.1% of population, 21.2% probability, ages 30-70Kenya: 0% of population, 18% probability, ages 30-70Kyrgyz Republic: 2% of population, 18.8% probability, ages 30-70Cambodia: 100% of population, 22.6% probability, ages 30-70Korea, Rep.: 100% of population, 6.9% probability, ages 30-70Kuwait: 0% of population, 9.3% probability, ages 30-70Lao PDR: 100% of population, 26.5% probability, ages 30-70Lebanon: 0% of population, 11.9% probability, ages 30-70Liberia: 0% of population, 22% probability, ages 30-70Libya: 0% of population, 19.8% probability, ages 30-70Sri Lanka: 0% of population, 13.9% probability, ages 30-70Lesotho: 0% of population, 36.3% probability, ages 30-70Lithuania: 0% of population, 19.8% probability, ages 30-70Luxembourg: 0% of population, 7.8% probability, ages 30-70Latvia: 0% of population, 22.7% probability, ages 30-70Morocco: 0% of population, 22% probability, ages 30-70Moldova: 0% of population, 24.7% probability, ages 30-70Madagascar: 0% of population, 25.9% probability, ages 30-70Mexico: 0% of population, 16.3% probability, ages 30-70North Macedonia: 0% of population, 19.6% probability, ages 30-70Mali: 0% of population, 23% probability, ages 30-70Malta: 0% of population, 9.8% probability, ages 30-70Myanmar: 97.5% of population, 24% probability, ages 30-70Mongolia: 98.2% of population, 26.3% probability, ages 30-70Mozambique: 0% of population, 26.6% probability, ages 30-70Mauritania: 0% of population, 18.7% probability, ages 30-70Mauritius: 3% of population, 21.7% probability, ages 30-70Malawi: 0% of population, 24.9% probability, ages 30-70Malaysia: 92.5% of population, 19.9% probability, ages 30-70Namibia: 0% of population, 23.1% probability, ages 30-70Niger: 0% of population, 20.2% probability, ages 30-70Nigeria: 0% of population, 17.7% probability, ages 30-70Nicaragua: 0% of population, 12.6% probability, ages 30-70Netherlands: 0.95% of population, 9.9% probability, ages 30-70Norway: 0% of population, 7.8% probability, ages 30-70Nepal: 0% of population, 19.4% probability, ages 30-70New Zealand: 18.5% of population, 10.3% probability, ages 30-70Oman: 1.08% of population, 14.6% probability, ages 30-70Pakistan: 0% of population, 25.5% probability, ages 30-70Panama: 1.5% of population, 10.9% probability, ages 30-70Peru: 2% of population, 12.3% probability, ages 30-70Philippines: 100% of population, 31.9% probability, ages 30-70Papua New Guinea: 100% of population, 28.5% probability, ages 30-70Poland: 0% of population, 17% probability, ages 30-70Puerto Rico (US): 0% of population, 10.1% probability, ages 30-70Portugal: 0.9% of population, 10.5% probability, ages 30-70Paraguay: 0% of population, 16.1% probability, ages 30-70Qatar: 2.2% of population, 12.1% probability, ages 30-70Romania: 0% of population, 21.5% probability, ages 30-70Russian Federation: 0% of population, 22.4% probability, ages 30-70Rwanda: 0% of population, 20% probability, ages 30-70Saudi Arabia: 1.2% of population, 13.6% probability, ages 30-70Sudan: 0% of population, 21.2% probability, ages 30-70Senegal: 0% of population, 21.4% probability, ages 30-70Singapore: 92% of population, 10.3% probability, ages 30-70Sierra Leone: 0% of population, 23.9% probability, ages 30-70El Salvador: 0% of population, 12.6% probability, ages 30-70Somalia, Fed. Rep.: 0% of population, 27.6% probability, ages 30-70Slovak Republic: 0% of population, 16.6% probability, ages 30-70Slovenia: 0% of population, 11.6% probability, ages 30-70Sweden: 0.7% of population, 7.9% probability, ages 30-70Eswatini: 0% of population, 32.3% probability, ages 30-70Syrian Arab Republic: 0% of population, 21.3% probability, ages 30-70Chad: 0% of population, 23.4% probability, ages 30-70Togo: 0% of population, 25.4% probability, ages 30-70Thailand: 100% of population, 14.5% probability, ages 30-70Tajikistan: 0% of population, 18.1% probability, ages 30-70Turkmenistan: 0% of population, 25.7% probability, ages 30-70Timor-Leste: 100% of population, 20.1% probability, ages 30-70Trinidad and Tobago: 1.5% of population, 20.3% probability, ages 30-70Tunisia: 0% of population, 13% probability, ages 30-70Turkiye: 0% of population, 15.4% probability, ages 30-70Tanzania: 0% of population, 18.8% probability, ages 30-70Uganda: 0% of population, 21.5% probability, ages 30-70Ukraine: 0% of population, 24.8% probability, ages 30-70Uruguay: 0% of population, 17.4% probability, ages 30-70United States: 3.35% of population, 13.7% probability, ages 30-70Uzbekistan: 1% of population, 24.6% probability, ages 30-70Viet Nam: 100% of population, 20.1% probability, ages 30-70South Africa: 0.45% of population, 22.7% probability, ages 30-70Zambia: 0% of population, 23.6% probability, ages 30-70Zimbabwe: 0% of population, 31.2% probability, ages 30-70AFGCHEFJIISRJPNKORLSOLUXPHLPNGSGPSWZ

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.1010.210157
By rank (Spearman), not adjusted-0.0700.385157
Standard (Pearson), adjusted for national income+0.1650.040157
By rank (Spearman), adjusted for national income+0.1200.137157

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: -0.064 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.121 (without PHL) to +0.198 (without KOR). 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: PHL (-0.044).

Fragile: this link may not hold up.
  • correlation moves sharply once outliers are trimmed
  • sign flips once outliers are trimmed
  • direction is not consistent across regions

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 & Pacific18-0.07
Europe & Central Asia45-0.12
Latin America & Caribbean23+0.03
Middle East, North Africa, Afghanistan & Pakistan20+0.28
Sub-Saharan Africa44+0.10

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+0.21
Middle third52+0.29
Richest third52-0.10

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
Christians92+0.25
Muslims45+0.23

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+0.51
Never a European colony21-0.25
Spain19+0.72
United Kingdom46+0.23

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 = -0.027 after dropping 28 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
% probability, ages 30-70		Years
Viet Nam VNM100%20.1%2000, 2021
Timor-Leste TLS100%20.1%2000, 2021
Thailand THA100%14.5%2000, 2021
Papua New Guinea PNG100%28.5%2000, 2021
Philippines PHL100%31.9%2000, 2021
Lao PDR LAO100%26.5%2000, 2021
Korea, Rep. KOR100%6.9%2000, 2021
Cambodia KHM100%22.6%2000, 2021
Japan JPN100%8%2000, 2021
Indonesia IDN100%21.9%2000, 2021
China CHN100%15.9%2000, 2021
Mongolia MNG98.2%26.3%2000, 2021
Myanmar MMR97.5%24%2000, 2021
Malaysia MYS92.5%19.9%2000, 2021
Singapore SGP92%10.3%2000, 2021

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).
World Bank, World Development Indicators

Sources

East Asian ancestry

East Asian and Pacific 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 East Asia, Southeast Asia or the Pacific. 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.

Premature NCD mortality

Mortality from cardiovascular disease, cancer, diabetes or chronic respiratory disease between ages 30 and 70 (%)

World Health Organization · CC BY 4.0 · updated quarterly

The series ends in 2021.

How this was computed

Data used: most recent value in 2010-2025; values come from 2000–2021. 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 East Asian ancestry and Premature NCD mortality

This page shows that two measures go together. It does not show that East Asian ancestry changes Premature NCD mortality, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with East Asian ancestry or Premature NCD mortality