Across 148 countries, Ethnic diversity and Infrastructure score rise and fall together at r = -0.46 (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 = -0.35, q = 0.032, allowing for the many pairs tested). Caution: direction is not consistent across regions.
Comparing countries across the middle half of ethnic diversity, 0.239 to 0.701 points, infrastructure score is worse by 0.58 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.186 typical gaps.
Link before adjusting (r)-0.463p = 3.1e-9 · 148 countries
After adjusting for national income-0.352p = 1.2e-5 · likely range [-0.49, -0.20]
p, allowing for regions0.002standard error 0.070 · 7 regions
Check for luck (q)0.032across 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.
Ethnic diversity · index, 0 to 1link with income -0.37 · 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
-0.463
3.1e-9
148
By rank (Spearman), not adjusted
-0.465
2.5e-9
148
Standard (Pearson), adjusted for national income
-0.352
1.2e-5
148
By rank (Spearman), adjusted for national income
-0.340
2.5e-5
148
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 148 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: +0.111 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from -0.373 (without TKM) to -0.331 (without QAT). 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: QAT (+0.022).
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
East Asia & Pacific
17
-0.48
Europe & Central Asia
43
+0.17
Latin America & Caribbean
21
-0.17
Middle East, North Africa, Afghanistan & Pakistan
20
-0.31
Sub-Saharan Africa
41
-0.42
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
50
-0.39
Middle third
49
-0.18
Richest third
49
-0.49
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
84
-0.27
Muslims
46
-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.
n
r
France
23
-0.58
Never a European colony
21
-0.29
Spain
18
-0.18
United Kingdom
44
-0.43
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 = -0.352 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.
Loading…
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
index, 0 to 1
typical gaps (standard deviations)
Years
Liberia LBR
0.889
-0.767
2013, 2022
Uganda UGA
0.883
-0.319
2013, 2024
Togo TGO
0.88
-0.0274
2013, 2024
Nepal NPL
0.86
0.616
2013, 2024
South Africa ZAF
0.856
0.709
2013, 2024
Chad TCD
0.855
-0.72
2013, 2024
Kenya KEN
0.855
0.22
2013, 2024
Mali MLI
0.852
-0.495
2013, 2022
Nigeria NGA
0.85
-0.0861
2013, 2024
Guinea-Bissau GNB
0.808
-0.158
2013, 2024
Philippines PHL
0.807
0.696
2013, 2024
Indonesia IDN
0.803
0.742
2013, 2024
Timor-Leste TLS
0.802
0.389
2013, 2024
Sierra Leone SLE
0.801
-0.336
2013, 2022
Malawi MWI
0.791
-0.672
2013, 2024
Drazanova, L. (2019), Historical Index of Ethnic Fractionalization Dataset (HIEF), Harvard Dataverse, doi:10.7910/DVN/4JQRCL wdi
The chance that two people picked at random in a country belong to different ethnic groups, from 0 (everyone in one group) to close to 1 (many small groups). It measures how mixed a country is, not which groups live there. The groups are those each country's own sources use, so where the lines are drawn differs from country to country. Yearly from 1945 to 2013, so the latest value is from 2013. No "better" direction is set.
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 2013–2024. 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 Ethnic diversity and Infrastructure score
This page shows that two measures go together. It does not show that Ethnic diversity changes Infrastructure score, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.
More links with Ethnic diversity or Infrastructure score