Do African ancestry and Infrastructure score go together across countries?

Across 158 countries the link before adjusting for anything is r = -0.78, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.65.

Does the link hold within countries over time, not just across countries?

There are not enough years of data within countries to check this over time.

Does African ancestry cause Infrastructure score?

This analysis cannot answer that. It shows only that the two go together. A link between two country measures, adjusted or not, does not show that either one changes the other. Country figures also say nothing about individual people.

African ancestry vs. Infrastructure score

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 158 countries, African ancestry and Infrastructure score rise and fall together at r = -0.78 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.46 (q = 0.016, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a moderate negative link remains. Caution: direction is not consistent across regions.

Comparing countries across the middle half of african ancestry, 0% to 95.9% of population, infrastructure score is worse by 1.18 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.401 typical gaps.

Link before adjusting (r)-0.777p = 4.1e-33 · 158 countries

After adjusting for national income-0.462p = 1.1e-9 · likely range [-0.58, -0.33]

p, allowing for regions0.001standard error 0.072 · 7 regions

Check for luck (q)0.016across 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

Infrastructure score · typical gaps (standard deviations)link with income +0.90 · proxy

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

Every country as a dot

best-fit line line after adjusting

Four ways to measure the link

Method	r	p	Countries
Standard (Pearson), not adjusted	-0.777	4.1e-33	158
By rank (Spearman), not adjusted	-0.655	1.0e-20	158
Standard (Pearson), adjusted for national income	-0.462	1.1e-9	158
By rank (Spearman), adjusted for national income	-0.355	4.9e-6	158

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 158 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: +0.314 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.514 (without PNG) to -0.441 (without GNQ). 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: PNG (-0.052).

Fragile: this link may not hold up.

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.

	n	r
Europe & Central Asia	45	+0.16
Latin America & Caribbean	23	-0.30
Middle East, North Africa, Afghanistan & Pakistan	20	-0.54
Sub-Saharan Africa	44	-0.21

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.

	n	r
Poorest third	53	-0.38
Middle third	52	-0.46
Richest third	53	-0.58

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.

	n	r
Christians	92	-0.39
Muslims	45	-0.57

By last european coloniser

	n	r
France	24	-0.29
Never a European colony	21	-0.56
Spain	19	-0.86
United Kingdom	46	-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 = -0.462 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
Uganda UGA	100%	-0.319	2000, 2024
Tanzania TZA	100%	-0.0741	2000, 2024
Togo TGO	100%	-0.0274	2000, 2024
Chad TCD	100%	-0.72	2000, 2024
Sierra Leone SLE	100%	-0.336	2000, 2022
Rwanda RWA	100%	0.0662	2000, 2024
Nigeria NGA	100%	-0.0861	2000, 2024
Malawi MWI	100%	-0.672	2000, 2024
Mauritania MRT	100%	0.0018	2000, 2024
Mozambique MOZ	100%	-0.592	2000, 2024
Lesotho LSO	100%	-0.0187	2000, 2024
Liberia LBR	100%	-0.767	2000, 2022
Kenya KEN	100%	0.22	2000, 2024
Guinea GIN	100%	0.156	2000, 2023
Ghana GHA	100%	0.59	2000, 2024

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).
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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.

Infrastructure score

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

The average of 5 measures from 1 publisher. 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: Electricity access, Slum share of urban population, Mobile subscriptions, Fixed broadband, Internet users.

How this was computed

Data used: most recent value in 2010-2025; values come from 2000–2024. 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 Infrastructure score

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