placestats

African ancestry vs. Slum share of urban population

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 155 countries, African ancestry and Slum share of urban population rise and fall together at r = +0.70 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.32, which could easily be chance (q = 0.22, 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 african ancestry, 0% to 96.5% of population, slum share of urban population is worse by 38.6% of urban population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 14.7% of urban population.

Link before adjusting (r)+0.697p = 7.7e-24 · 155 countries
After adjusting for national income+0.315p = 6.8e-5 · likely range [+0.17, +0.45]
p, allowing for regions0.070standard error 0.143 · 7 regions
Check for luck (q)0.215across 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
Slum share of urban population · % of urban populationlink with income -0.78 · high

“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 →Slum share of urban population · % of urban population →0%100%0%92.5%Afghanistan: 0% of population, 71.6% of urban populationAngola: 98% of population, 62.7% of urban populationAlbania: 0% of population, 2.7% of urban populationUnited Arab Emirates: 0% of population, 0.113% of urban populationArgentina: 2% of population, 14.5% of urban populationArmenia: 0% of population, 8.39% of urban populationAustralia: 0% of population, 0.035% of urban populationAustria: 0% of population, 0.0466% of urban populationAzerbaijan: 0% of population, 26.9% of urban populationBurundi: 100% of population, 36.8% of urban populationBelgium: 0% of population, 0% of urban populationBenin: 100% of population, 64% of urban populationBurkina Faso: 100% of population, 87.9% of urban populationBangladesh: 0% of population, 51.5% of urban populationBulgaria: 0% of population, 0.197% of urban populationBahrain: 0% of population, 0% of urban populationBosnia and Herzegovina: 0% of population, 0.313% of urban populationBelarus: 0% of population, 0.004% of urban populationBelize: 17.4% of population, 15.7% of urban populationBolivia: 0.907% of population, 46.6% of urban populationBrazil: 15.7% of population, 14.9% of urban populationBhutan: 0% of population, 44.7% of urban populationBotswana: 98.5% of population, 39.6% of urban populationCentral African Republic: 99.4% of population, 68.9% of urban populationCanada: 1.53% of population, 1.1% of urban populationSwitzerland: 0% of population, 0% of urban populationChile: 1.4% of population, 7.32% of urban populationChina: 0% of population, 26.3% of urban populationCote d'Ivoire: 99.3% of population, 48.3% of urban populationCameroon: 100% of population, 32.7% of urban populationCongo, Dem. Rep.: 99.5% of population, 78.4% of urban populationCongo, Rep.: 100% of population, 75.3% of urban populationColombia: 17.3% of population, 9.7% of urban populationComoros: 100% of population, 48.5% of urban populationCabo Verde: 58.6% of population, 46.4% of urban populationCosta Rica: 8.91% of population, 3.55% of urban populationCyprus: 0% of population, 0.272% of urban populationCzechia: 0% of population, 0.0273% of urban populationGermany: 0% of population, 0% of urban populationDenmark: 0% of population, 0% of urban populationDominican Republic: 44.4% of population, 11.2% of urban populationAlgeria: 0% of population, 13.2% of urban populationEcuador: 6.97% of population, 57.8% of urban populationEgypt, Arab Rep.: 5.5% of population, 3.84% of urban populationSpain: 0% of population, 0.046% of urban populationEstonia: 0% of population, 0.345% of urban populationEthiopia: 100% of population, 64.3% of urban populationFinland: 0% of population, 0% of urban populationFiji: 0% of population, 9.4% of urban populationFrance: 0% of population, 0% of urban populationGabon: 99% of population, 38.8% of urban populationUnited Kingdom: 1.8% of population, 0.163% of urban populationGeorgia: 0% of population, 7.08% of urban populationGhana: 100% of population, 33.5% of urban populationGuinea: 100% of population, 44% of urban populationGambia, The: 99.5% of population, 37.1% of urban populationGuinea-Bissau: 96.5% of population, 59% of urban populationEquatorial Guinea: 94.3% of population, 64.7% of urban populationGreece: 0% of population, 0.05% of urban populationGuatemala: 4.14% of population, 37.6% of urban populationGuyana: 39% of population, 11.3% of urban populationHonduras: 2% of population, 31.5% of urban populationCroatia: 0% of population, 0.527% of urban populationHaiti: 97.5% of population, 51.1% of urban populationHungary: 0% of population, 7.2% of urban populationIndonesia: 0% of population, 19.4% of urban populationIndia: 0% of population, 5.41% of urban populationIreland: 0% of population, 8.5% of urban populationIran, Islamic Rep.: 0% of population, 44.7% of urban populationIraq: 0% of population, 49.3% of urban populationItaly: 0% of population, 0.02% of urban populationJamaica: 89% of population, 0.883% of urban populationJordan: 0% of population, 23.4% of urban populationJapan: 0% of population, 2% of urban populationKazakhstan: 0% of population, 0.794% of urban populationKenya: 100% of population, 40.5% of urban populationKyrgyz Republic: 0% of population, 2.44% of urban populationCambodia: 0% of population, 42.3% of urban populationKorea, Rep.: 0% of population, 4.5% of urban populationKuwait: 0% of population, 0% of urban populationLao PDR: 0% of population, 54.8% of urban populationLebanon: 0% of population, 4.53% of urban populationLiberia: 100% of population, 60.5% of urban populationLibya: 3.5% of population, 16.6% of urban populationSri Lanka: 0% of population, 44.7% of urban populationLesotho: 100% of population, 25.6% of urban populationLithuania: 0% of population, 0.509% of urban populationLuxembourg: 0% of population, 0.0173% of urban populationLatvia: 0% of population, 0.593% of urban populationMorocco: 1% of population, 10.9% of urban populationMoldova: 0% of population, 6.5% of urban populationMadagascar: 99.4% of population, 65.7% of urban populationMexico: 6.7% of population, 17.6% of urban populationNorth Macedonia: 0% of population, 0.278% of urban populationMali: 99.1% of population, 92.5% of urban populationMalta: 0% of population, 0.0375% of urban populationMyanmar: 0% of population, 58.3% of urban populationMongolia: 0% of population, 17.9% of urban populationMozambique: 100% of population, 55% of urban populationMauritania: 100% of population, 58.6% of urban populationMauritius: 13% of population, 48.7% of urban populationMalawi: 100% of population, 38% of urban populationMalaysia: 0% of population, 0.2% of urban populationNamibia: 89.2% of population, 41.4% of urban populationNiger: 98.1% of population, 70.4% of urban populationNigeria: 100% of population, 48.5% of urban populationNicaragua: 9% of population, 5.52% of urban populationNetherlands: 0.75% of population, 0% of urban populationNorway: 0% of population, 0% of urban populationNepal: 0% of population, 40.1% of urban populationNew Zealand: 0% of population, 0% of urban populationOman: 1.25% of population, 0% of urban populationPakistan: 0% of population, 56% of urban populationPanama: 13% of population, 16.3% of urban populationPeru: 5.72% of population, 45.1% of urban populationPhilippines: 0% of population, 35.9% of urban populationPapua New Guinea: 0% of population, 22.3% of urban populationPoland: 0% of population, 4.22% of urban populationPortugal: 0.61% of population, 0.059% of urban populationParaguay: 1% of population, 15.1% of urban populationQatar: 15.1% of population, 0% of urban populationRomania: 0% of population, 2.52% of urban populationRussian Federation: 0% of population, 2.62% of urban populationRwanda: 100% of population, 38.3% of urban populationSaudi Arabia: 1.5% of population, 0.55% of urban populationSudan: 98.8% of population, 73.7% of urban populationSenegal: 98.2% of population, 46.4% of urban populationSingapore: 0% of population, 0% of urban populationSierra Leone: 100% of population, 49.3% of urban populationEl Salvador: 0% of population, 16.5% of urban populationSomalia, Fed. Rep.: 98.9% of population, 48.7% of urban populationSlovak Republic: 0% of population, 0.066% of urban populationSlovenia: 0% of population, 0.527% of urban populationSweden: 0% of population, 0.336% of urban populationEswatini: 96.4% of population, 17% of urban populationSyrian Arab Republic: 0% of population, 41.1% of urban populationChad: 100% of population, 82% of urban populationTogo: 100% of population, 38.5% of urban populationThailand: 0% of population, 2% of urban populationTajikistan: 0% of population, 0.05% of urban populationTurkmenistan: 0% of population, 8.38% of urban populationTimor-Leste: 0% of population, 33.9% of urban populationTrinidad and Tobago: 46% of population, 8.63% of urban populationTunisia: 0% of population, 7.64% of urban populationTurkiye: 0% of population, 14.1% of urban populationTanzania: 100% of population, 70.1% of urban populationUganda: 100% of population, 52.7% of urban populationUkraine: 0% of population, 1.1% of urban populationUruguay: 4% of population, 1.3% of urban populationUnited States: 9.64% of population, 0.0932% of urban populationUzbekistan: 0% of population, 7.1% of urban populationViet Nam: 0% of population, 32.5% of urban populationSouth Africa: 78.7% of population, 24.2% of urban populationZambia: 99% of population, 48.3% of urban populationZimbabwe: 99% of population, 54.9% of urban populationAFGBELBFABHRCHEJAMLSOMLIMMRPAKSWZTCD

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.6977.7e-24155
By rank (Spearman), not adjusted+0.6094.3e-17155
Standard (Pearson), adjusted for national income+0.3156.8e-5155
By rank (Spearman), adjusted for national income+0.2470.002155

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 155 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: +0.381 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.292 (without TJK) to +0.343 (without JAM). 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: JAM (+0.027).

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
Europe & Central Asia45-0.09
Latin America & Caribbean22-0.20
Middle East, North Africa, Afghanistan & Pakistan19+0.05
Sub-Saharan Africa44-0.07

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+0.33
Middle third51+0.24
Richest third52+0.50

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+0.41
Muslims45+0.40

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+0.36
Never a European colony21+0.32
Spain19+0.44
United Kingdom45+0.04

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 = +0.315 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
% of urban population		Years
Uganda UGA100%52.7%2000, 2022
Tanzania TZA100%70.1%2000, 2022
Togo TGO100%38.5%2000, 2022
Chad TCD100%82%2000, 2022
Sierra Leone SLE100%49.3%2000, 2022
Rwanda RWA100%38.3%2000, 2022
Nigeria NGA100%48.5%2000, 2022
Malawi MWI100%38%2000, 2022
Mauritania MRT100%58.6%2000, 2022
Mozambique MOZ100%55%2000, 2022
Lesotho LSO100%25.6%2000, 2022
Liberia LBR100%60.5%2000, 2022
Kenya KEN100%40.5%2000, 2022
Guinea GIN100%44%2000, 2022
Ghana GHA100%33.5%2000, 2022

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.

Slum share of urban population

Population living in slums (% of urban population)

UN-Habitat · CC BY 4.0 · updated quarterly

How this was computed

Data used: most recent value in 2010-2025; values come from 2000–2022. 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 Slum share of urban population

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

More links with African ancestry or Slum share of urban population