placestats

African ancestry vs. Physicians

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 156 countries, African ancestry and Physicians rise and fall together at r = -0.67 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.26, which could easily be chance (q = 0.29, 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 african ancestry, 0% to 96.4% of population, physicians is worse by 2.49 per 1,000 people at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.825 per 1,000 people.

Link before adjusting (r)-0.669p = 1.5e-21 · 156 countries
After adjusting for national income-0.258p = 0.001 · likely range [-0.40, -0.10]
p, allowing for regions0.115standard error 0.140 · 7 regions
Check for luck (q)0.287across 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.

African ancestry · % of populationlink with income -0.59 · moderate
Physicians · per 1,000 peoplelink with income +0.85 · proxy

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

Every country as a dot

African ancestry · % of population →Physicians · per 1,000 people →0%100%0.0386.58Afghanistan: 0% of population, 0.317 per 1,000 peopleAngola: 98% of population, 0.244 per 1,000 peopleAlbania: 0% of population, 1.88 per 1,000 peopleUnited Arab Emirates: 0% of population, 2.99 per 1,000 peopleArgentina: 2% of population, 5.11 per 1,000 peopleArmenia: 0% of population, 3.36 per 1,000 peopleAustralia: 0% of population, 4.09 per 1,000 peopleAustria: 0% of population, 5.51 per 1,000 peopleAzerbaijan: 0% of population, 3.19 per 1,000 peopleBurundi: 100% of population, 0.078 per 1,000 peopleBelgium: 0% of population, 3.57 per 1,000 peopleBenin: 100% of population, 0.216 per 1,000 peopleBurkina Faso: 100% of population, 0.148 per 1,000 peopleBangladesh: 0% of population, 0.722 per 1,000 peopleBulgaria: 0% of population, 4.33 per 1,000 peopleBahrain: 0% of population, 0.741 per 1,000 peopleBosnia and Herzegovina: 0% of population, 2.58 per 1,000 peopleBelarus: 0% of population, 4.72 per 1,000 peopleBelize: 17.4% of population, 1.09 per 1,000 peopleBolivia: 0.907% of population, 1.28 per 1,000 peopleBrazil: 15.7% of population, 2.36 per 1,000 peopleBhutan: 0% of population, 0.553 per 1,000 peopleBotswana: 98.5% of population, 0.379 per 1,000 peopleCentral African Republic: 99.4% of population, 0.074 per 1,000 peopleCanada: 1.53% of population, 2.82 per 1,000 peopleSwitzerland: 0% of population, 4.48 per 1,000 peopleChile: 1.4% of population, 3.33 per 1,000 peopleChina: 0% of population, 3.11 per 1,000 peopleCote d'Ivoire: 99.3% of population, 0.166 per 1,000 peopleCameroon: 100% of population, 0.136 per 1,000 peopleCongo, Dem. Rep.: 99.5% of population, 0.208 per 1,000 peopleCongo, Rep.: 100% of population, 0.173 per 1,000 peopleColombia: 17.3% of population, 2.54 per 1,000 peopleComoros: 100% of population, 0.424 per 1,000 peopleCabo Verde: 58.6% of population, 0.73 per 1,000 peopleCosta Rica: 8.91% of population, 2.69 per 1,000 peopleCyprus: 0% of population, 3.56 per 1,000 peopleCzechia: 0% of population, 4.35 per 1,000 peopleGermany: 0% of population, 4.53 per 1,000 peopleDenmark: 0% of population, 4.5 per 1,000 peopleDominican Republic: 44.4% of population, 2.43 per 1,000 peopleAlgeria: 0% of population, 1.66 per 1,000 peopleEcuador: 6.97% of population, 2.31 per 1,000 peopleEgypt, Arab Rep.: 5.5% of population, 0.671 per 1,000 peopleSpain: 0% of population, 4.29 per 1,000 peopleEstonia: 0% of population, 3.47 per 1,000 peopleEthiopia: 100% of population, 0.143 per 1,000 peopleFinland: 0% of population, 3.61 per 1,000 peopleFiji: 0% of population, 0.813 per 1,000 peopleFrance: 0% of population, 3.28 per 1,000 peopleGabon: 99% of population, 0.521 per 1,000 peopleUnited Kingdom: 1.8% of population, 3.3 per 1,000 peopleGeorgia: 0% of population, 5.64 per 1,000 peopleGhana: 100% of population, 0.266 per 1,000 peopleGuinea: 100% of population, 0.21 per 1,000 peopleGambia, The: 99.5% of population, 0.09 per 1,000 peopleGuinea-Bissau: 96.5% of population, 0.252 per 1,000 peopleEquatorial Guinea: 94.3% of population, 0.153 per 1,000 peopleGreece: 0% of population, 6.58 per 1,000 peopleGuatemala: 4.14% of population, 1.28 per 1,000 peopleGuyana: 39% of population, 1.39 per 1,000 peopleHonduras: 2% of population, 0.489 per 1,000 peopleCroatia: 0% of population, 3.91 per 1,000 peopleHaiti: 97.5% of population, 0.292 per 1,000 peopleHungary: 0% of population, 3.46 per 1,000 peopleIndonesia: 0% of population, 0.524 per 1,000 peopleIndia: 0% of population, 0.723 per 1,000 peopleIreland: 0% of population, 3.88 per 1,000 peopleIran, Islamic Rep.: 0% of population, 1.81 per 1,000 peopleIraq: 0% of population, 1.02 per 1,000 peopleIsrael: 0.982% of population, 3.8 per 1,000 peopleItaly: 0% of population, 4.19 per 1,000 peopleJamaica: 89% of population, 0.46 per 1,000 peopleJordan: 0% of population, 2.85 per 1,000 peopleJapan: 0% of population, 2.65 per 1,000 peopleKazakhstan: 0% of population, 3.75 per 1,000 peopleKenya: 100% of population, 0.289 per 1,000 peopleKyrgyz Republic: 0% of population, 1.57 per 1,000 peopleCambodia: 0% of population, 0.211 per 1,000 peopleKorea, Rep.: 0% of population, 2.61 per 1,000 peopleKuwait: 0% of population, 2.27 per 1,000 peopleLao PDR: 0% of population, 0.331 per 1,000 peopleLebanon: 0% of population, 2.68 per 1,000 peopleLiberia: 100% of population, 0.178 per 1,000 peopleLibya: 3.5% of population, 2.04 per 1,000 peopleSri Lanka: 0% of population, 1.14 per 1,000 peopleLesotho: 100% of population, 0.235 per 1,000 peopleLithuania: 0% of population, 4.47 per 1,000 peopleLuxembourg: 0% of population, 2.98 per 1,000 peopleLatvia: 0% of population, 3.4 per 1,000 peopleMorocco: 1% of population, 0.74 per 1,000 peopleMoldova: 0% of population, 4.02 per 1,000 peopleMadagascar: 99.4% of population, 0.172 per 1,000 peopleMexico: 6.7% of population, 2.59 per 1,000 peopleNorth Macedonia: 0% of population, 2.94 per 1,000 peopleMali: 99.1% of population, 0.188 per 1,000 peopleMalta: 0% of population, 4.51 per 1,000 peopleMyanmar: 0% of population, 0.757 per 1,000 peopleMongolia: 0% of population, 4.13 per 1,000 peopleMozambique: 100% of population, 0.175 per 1,000 peopleMauritania: 100% of population, 0.261 per 1,000 peopleMauritius: 13% of population, 1.44 per 1,000 peopleMalawi: 100% of population, 0.054 per 1,000 peopleMalaysia: 0% of population, 2.34 per 1,000 peopleNamibia: 89.2% of population, 0.553 per 1,000 peopleNiger: 98.1% of population, 0.038 per 1,000 peopleNigeria: 100% of population, 0.38 per 1,000 peopleNicaragua: 9% of population, 0.682 per 1,000 peopleNetherlands: 0.75% of population, 3.88 per 1,000 peopleNorway: 0% of population, 4.97 per 1,000 peopleNepal: 0% of population, 1.01 per 1,000 peopleNew Zealand: 0% of population, 3.61 per 1,000 peopleOman: 1.25% of population, 1.99 per 1,000 peoplePakistan: 0% of population, 1.16 per 1,000 peoplePanama: 13% of population, 1.63 per 1,000 peoplePeru: 5.72% of population, 1.69 per 1,000 peoplePhilippines: 0% of population, 0.792 per 1,000 peoplePapua New Guinea: 0% of population, 0.061 per 1,000 peoplePoland: 0% of population, 4.03 per 1,000 peoplePortugal: 0.61% of population, 5.85 per 1,000 peopleParaguay: 1% of population, 3.89 per 1,000 peopleQatar: 15.1% of population, 3.02 per 1,000 peopleRomania: 0% of population, 3.63 per 1,000 peopleRussian Federation: 0% of population, 5.11 per 1,000 peopleRwanda: 100% of population, 0.09 per 1,000 peopleSaudi Arabia: 1.5% of population, 3.41 per 1,000 peopleSudan: 98.8% of population, 0.25 per 1,000 peopleSenegal: 98.2% of population, 0.109 per 1,000 peopleSingapore: 0% of population, 2.83 per 1,000 peopleSierra Leone: 100% of population, 0.134 per 1,000 peopleEl Salvador: 0% of population, 1.62 per 1,000 peopleSomalia, Fed. Rep.: 98.9% of population, 0.048 per 1,000 peopleSlovak Republic: 0% of population, 3.7 per 1,000 peopleSlovenia: 0% of population, 3.37 per 1,000 peopleSweden: 0% of population, 4.41 per 1,000 peopleEswatini: 96.4% of population, 0.557 per 1,000 peopleSyrian Arab Republic: 0% of population, 1.52 per 1,000 peopleChad: 100% of population, 0.085 per 1,000 peopleTogo: 100% of population, 0.08 per 1,000 peopleThailand: 0% of population, 0.541 per 1,000 peopleTajikistan: 0% of population, 1.87 per 1,000 peopleTurkmenistan: 0% of population, 1.93 per 1,000 peopleTimor-Leste: 0% of population, 0.752 per 1,000 peopleTrinidad and Tobago: 46% of population, 4.16 per 1,000 peopleTunisia: 0% of population, 1.31 per 1,000 peopleTurkiye: 0% of population, 2.24 per 1,000 peopleTanzania: 100% of population, 0.134 per 1,000 peopleUganda: 100% of population, 0.191 per 1,000 peopleUkraine: 0% of population, 3.53 per 1,000 peopleUruguay: 4% of population, 4.67 per 1,000 peopleUnited States: 9.64% of population, 3.68 per 1,000 peopleUzbekistan: 0% of population, 2.81 per 1,000 peopleViet Nam: 0% of population, 1.11 per 1,000 peopleSouth Africa: 78.7% of population, 0.794 per 1,000 peopleZambia: 99% of population, 0.324 per 1,000 peopleZimbabwe: 99% of population, 0.136 per 1,000 peopleAFGBDIGEOGRCKHMMWIPNGPRTTGOTTO

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.6691.5e-21156
By rank (Spearman), not adjusted-0.6711.0e-21156
Standard (Pearson), adjusted for national income-0.2580.001156
By rank (Spearman), adjusted for national income-0.3924.5e-7156

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 156 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: +0.411 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.271 (without KHM) to -0.237 (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 (+0.021).

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
Europe & Central Asia45-0.04
Latin America & Caribbean22-0.25
Middle East, North Africa, Afghanistan & Pakistan20-0.15
Sub-Saharan Africa44-0.83

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 third52-0.64
Middle third52-0.42
Richest third52-0.21

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
Christians91-0.42
Muslims45-0.43

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
France24-0.61
Never a European colony21-0.19
Spain19-0.48
United Kingdom46-0.21

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 = -0.258 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
per 1,000 people		Years
Uganda UGA100%0.1912000, 2022
Tanzania TZA100%0.1342000, 2022
Togo TGO100%0.082000, 2022
Chad TCD100%0.0852000, 2023
Sierra Leone SLE100%0.1342000, 2022
Rwanda RWA100%0.092000, 2022
Nigeria NGA100%0.382000, 2023
Malawi MWI100%0.0542000, 2022
Mauritania MRT100%0.2612000, 2022
Mozambique MOZ100%0.1752000, 2022
Lesotho LSO100%0.2352000, 2022
Liberia LBR100%0.1782000, 2022
Kenya KEN100%0.2892000, 2023
Guinea GIN100%0.212000, 2022
Ghana GHA100%0.2662000, 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).
World Bank, World Development Indicators

Sources

African ancestry

Sub-Saharan African 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 sub-Saharan Africa. 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.

Physicians

Physicians (per 1,000 people)

World Health Organization · CC BY 4.0 · updated quarterly

Reported irregularly, so the latest value in the time window used is from a different year for different countries. Hospital beds, already on the site, have no "better" direction, since more beds is not plainly better. More doctors is closer to plainly better, so a direction is set on that basis.

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 African ancestry and Physicians

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

More links with African ancestry or Physicians