placestats

Ethnic diversity vs. Infrastructure score

Holds up after adjusting for income

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

Ethnic diversity · index, 0 to 1 →Infrastructure score · typical gaps (standard deviations) →0.0190.889-1.011.72Afghanistan: 0.763 index, 0 to 1, 0.116 typical gaps (standard deviations)Angola: 0.779 index, 0 to 1, -0.152 typical gaps (standard deviations)Albania: 0.139 index, 0 to 1, 1.14 typical gaps (standard deviations)United Arab Emirates: 0.684 index, 0 to 1, 1.61 typical gaps (standard deviations)Argentina: 0.158 index, 0 to 1, 1.25 typical gaps (standard deviations)Armenia: 0.045 index, 0 to 1, 1.09 typical gaps (standard deviations)Australia: 0.276 index, 0 to 1, 1.5 typical gaps (standard deviations)Austria: 0.248 index, 0 to 1, 1.37 typical gaps (standard deviations)Azerbaijan: 0.122 index, 0 to 1, 1.19 typical gaps (standard deviations)Burundi: 0.308 index, 0 to 1, -0.835 typical gaps (standard deviations)Belgium: 0.592 index, 0 to 1, 1.64 typical gaps (standard deviations)Benin: 0.764 index, 0 to 1, 0.00537 typical gaps (standard deviations)Burkina Faso: 0.767 index, 0 to 1, -0.245 typical gaps (standard deviations)Bangladesh: 0.025 index, 0 to 1, 0.649 typical gaps (standard deviations)Bulgaria: 0.289 index, 0 to 1, 1.25 typical gaps (standard deviations)Bahrain: 0.582 index, 0 to 1, 1.17 typical gaps (standard deviations)Bosnia and Herzegovina: 0.637 index, 0 to 1, 1.29 typical gaps (standard deviations)Belarus: 0.309 index, 0 to 1, 1.48 typical gaps (standard deviations)Bolivia: 0.572 index, 0 to 1, 0.886 typical gaps (standard deviations)Brazil: 0.559 index, 0 to 1, 1.18 typical gaps (standard deviations)Bhutan: 0.651 index, 0 to 1, 0.847 typical gaps (standard deviations)Botswana: 0.395 index, 0 to 1, 0.5 typical gaps (standard deviations)Central African Republic: 0.79 index, 0 to 1, -1.01 typical gaps (standard deviations)Canada: 0.73 index, 0 to 1, 1.56 typical gaps (standard deviations)Switzerland: 0.367 index, 0 to 1, 1.7 typical gaps (standard deviations)Chile: 0.439 index, 0 to 1, 1.25 typical gaps (standard deviations)China: 0.19 index, 0 to 1, 1.66 typical gaps (standard deviations)Cote d'Ivoire: 0.731 index, 0 to 1, 0.207 typical gaps (standard deviations)Congo, Dem. Rep.: 0.7 index, 0 to 1, -0.672 typical gaps (standard deviations)Congo, Rep.: 0.704 index, 0 to 1, 0.312 typical gaps (standard deviations)Colombia: 0.639 index, 0 to 1, 1.01 typical gaps (standard deviations)Comoros: 0.054 index, 0 to 1, 0.259 typical gaps (standard deviations)Cabo Verde: 0.442 index, 0 to 1, 0.806 typical gaps (standard deviations)Costa Rica: 0.398 index, 0 to 1, 1.19 typical gaps (standard deviations)Cyprus: 0.347 index, 0 to 1, 1.47 typical gaps (standard deviations)Czechia: 0.262 index, 0 to 1, 1.48 typical gaps (standard deviations)Djibouti: 0.649 index, 0 to 1, 0.0105 typical gaps (standard deviations)Denmark: 0.177 index, 0 to 1, 1.65 typical gaps (standard deviations)Dominican Republic: 0.453 index, 0 to 1, 0.956 typical gaps (standard deviations)Algeria: 0.372 index, 0 to 1, 0.932 typical gaps (standard deviations)Ecuador: 0.528 index, 0 to 1, 0.989 typical gaps (standard deviations)Egypt, Arab Rep.: 0.041 index, 0 to 1, 0.864 typical gaps (standard deviations)Spain: 0.669 index, 0 to 1, 1.55 typical gaps (standard deviations)Estonia: 0.458 index, 0 to 1, 1.45 typical gaps (standard deviations)Ethiopia: 0.783 index, 0 to 1, -0.309 typical gaps (standard deviations)Finland: 0.138 index, 0 to 1, 1.48 typical gaps (standard deviations)Fiji: 0.528 index, 0 to 1, 0.665 typical gaps (standard deviations)Gabon: 0.789 index, 0 to 1, 0.665 typical gaps (standard deviations)United Kingdom: 0.399 index, 0 to 1, 1.6 typical gaps (standard deviations)Georgia: 0.367 index, 0 to 1, 1.29 typical gaps (standard deviations)Ghana: 0.736 index, 0 to 1, 0.59 typical gaps (standard deviations)Guinea: 0.727 index, 0 to 1, 0.156 typical gaps (standard deviations)Gambia, The: 0.762 index, 0 to 1, 0.215 typical gaps (standard deviations)Guinea-Bissau: 0.808 index, 0 to 1, -0.158 typical gaps (standard deviations)Greece: 0.167 index, 0 to 1, 1.57 typical gaps (standard deviations)Guatemala: 0.511 index, 0 to 1, 0.576 typical gaps (standard deviations)Guyana: 0.695 index, 0 to 1, 0.809 typical gaps (standard deviations)Honduras: 0.229 index, 0 to 1, 0.417 typical gaps (standard deviations)Croatia: 0.171 index, 0 to 1, 1.28 typical gaps (standard deviations)Haiti: 0.105 index, 0 to 1, -0.312 typical gaps (standard deviations)Hungary: 0.232 index, 0 to 1, 1.5 typical gaps (standard deviations)Indonesia: 0.803 index, 0 to 1, 0.742 typical gaps (standard deviations)Ireland: 0.174 index, 0 to 1, 1.43 typical gaps (standard deviations)Iran, Islamic Rep.: 0.743 index, 0 to 1, 0.97 typical gaps (standard deviations)Iraq: 0.446 index, 0 to 1, 1.04 typical gaps (standard deviations)Israel: 0.376 index, 0 to 1, 1.31 typical gaps (standard deviations)Italy: 0.11 index, 0 to 1, 1.36 typical gaps (standard deviations)Jamaica: 0.21 index, 0 to 1, 1.06 typical gaps (standard deviations)Jordan: 0.044 index, 0 to 1, 0.776 typical gaps (standard deviations)Japan: 0.019 index, 0 to 1, 1.45 typical gaps (standard deviations)Kazakhstan: 0.538 index, 0 to 1, 1.09 typical gaps (standard deviations)Kenya: 0.855 index, 0 to 1, 0.22 typical gaps (standard deviations)Kyrgyz Republic: 0.487 index, 0 to 1, 0.943 typical gaps (standard deviations)Cambodia: 0.158 index, 0 to 1, 0.689 typical gaps (standard deviations)Korea, Rep.: 0.095 index, 0 to 1, 1.72 typical gaps (standard deviations)Kuwait: 0.765 index, 0 to 1, 0.878 typical gaps (standard deviations)Lao PDR: 0.634 index, 0 to 1, 0.399 typical gaps (standard deviations)Lebanon: 0.133 index, 0 to 1, 0.731 typical gaps (standard deviations)Liberia: 0.889 index, 0 to 1, -0.767 typical gaps (standard deviations)Libya: 0.259 index, 0 to 1, 0.512 typical gaps (standard deviations)Sri Lanka: 0.323 index, 0 to 1, 0.669 typical gaps (standard deviations)Lesotho: 0.313 index, 0 to 1, -0.0187 typical gaps (standard deviations)Lithuania: 0.284 index, 0 to 1, 1.29 typical gaps (standard deviations)Latvia: 0.547 index, 0 to 1, 1.28 typical gaps (standard deviations)Morocco: 0.566 index, 0 to 1, 0.928 typical gaps (standard deviations)Moldova: 0.427 index, 0 to 1, 1.23 typical gaps (standard deviations)Madagascar: 0.194 index, 0 to 1, -0.434 typical gaps (standard deviations)Mexico: 0.587 index, 0 to 1, 1.13 typical gaps (standard deviations)North Macedonia: 0.562 index, 0 to 1, 1.36 typical gaps (standard deviations)Mali: 0.852 index, 0 to 1, -0.495 typical gaps (standard deviations)Myanmar: 0.59 index, 0 to 1, 0.298 typical gaps (standard deviations)Mongolia: 0.315 index, 0 to 1, 1.03 typical gaps (standard deviations)Mauritania: 0.713 index, 0 to 1, 0.0018 typical gaps (standard deviations)Mauritius: 0.466 index, 0 to 1, 1.17 typical gaps (standard deviations)Malawi: 0.791 index, 0 to 1, -0.672 typical gaps (standard deviations)Malaysia: 0.57 index, 0 to 1, 1.09 typical gaps (standard deviations)Namibia: 0.76 index, 0 to 1, 0.203 typical gaps (standard deviations)Niger: 0.666 index, 0 to 1, -0.7 typical gaps (standard deviations)Nigeria: 0.85 index, 0 to 1, -0.0861 typical gaps (standard deviations)Nicaragua: 0.544 index, 0 to 1, 0.574 typical gaps (standard deviations)Netherlands: 0.354 index, 0 to 1, 1.63 typical gaps (standard deviations)Norway: 0.151 index, 0 to 1, 1.68 typical gaps (standard deviations)Nepal: 0.86 index, 0 to 1, 0.616 typical gaps (standard deviations)New Zealand: 0.485 index, 0 to 1, 1.51 typical gaps (standard deviations)Oman: 0.597 index, 0 to 1, 1.03 typical gaps (standard deviations)Pakistan: 0.748 index, 0 to 1, 0.386 typical gaps (standard deviations)Panama: 0.612 index, 0 to 1, 0.927 typical gaps (standard deviations)Peru: 0.618 index, 0 to 1, 0.907 typical gaps (standard deviations)Philippines: 0.807 index, 0 to 1, 0.696 typical gaps (standard deviations)Poland: 0.069 index, 0 to 1, 1.24 typical gaps (standard deviations)Portugal: 0.22 index, 0 to 1, 1.6 typical gaps (standard deviations)Paraguay: 0.179 index, 0 to 1, 0.928 typical gaps (standard deviations)Qatar: 0.708 index, 0 to 1, 1.06 typical gaps (standard deviations)Romania: 0.207 index, 0 to 1, 1.45 typical gaps (standard deviations)Russian Federation: 0.376 index, 0 to 1, 1.32 typical gaps (standard deviations)Rwanda: 0.28 index, 0 to 1, 0.0662 typical gaps (standard deviations)Saudi Arabia: 0.224 index, 0 to 1, 1.67 typical gaps (standard deviations)Sudan: 0.746 index, 0 to 1, -0.572 typical gaps (standard deviations)Senegal: 0.747 index, 0 to 1, 0.482 typical gaps (standard deviations)Singapore: 0.395 index, 0 to 1, 1.33 typical gaps (standard deviations)Solomon Islands: 0.097 index, 0 to 1, 0.0941 typical gaps (standard deviations)Sierra Leone: 0.801 index, 0 to 1, -0.336 typical gaps (standard deviations)El Salvador: 0.165 index, 0 to 1, 0.822 typical gaps (standard deviations)Somalia, Fed. Rep.: 0.222 index, 0 to 1, -0.395 typical gaps (standard deviations)Serbia: 0.396 index, 0 to 1, 1.38 typical gaps (standard deviations)Slovak Republic: 0.241 index, 0 to 1, 1.4 typical gaps (standard deviations)Slovenia: 0.258 index, 0 to 1, 1.42 typical gaps (standard deviations)Sweden: 0.219 index, 0 to 1, 1.57 typical gaps (standard deviations)Eswatini: 0.39 index, 0 to 1, 0.534 typical gaps (standard deviations)Syrian Arab Republic: 0.213 index, 0 to 1, 0.27 typical gaps (standard deviations)Chad: 0.855 index, 0 to 1, -0.72 typical gaps (standard deviations)Togo: 0.88 index, 0 to 1, -0.0274 typical gaps (standard deviations)Thailand: 0.352 index, 0 to 1, 1.06 typical gaps (standard deviations)Tajikistan: 0.367 index, 0 to 1, 0.389 typical gaps (standard deviations)Turkmenistan: 0.255 index, 0 to 1, 0.294 typical gaps (standard deviations)Timor-Leste: 0.802 index, 0 to 1, 0.389 typical gaps (standard deviations)Trinidad and Tobago: 0.655 index, 0 to 1, 1.22 typical gaps (standard deviations)Tunisia: 0.034 index, 0 to 1, 0.956 typical gaps (standard deviations)Turkiye: 0.521 index, 0 to 1, 1.19 typical gaps (standard deviations)Tanzania: 0.591 index, 0 to 1, -0.0741 typical gaps (standard deviations)Uganda: 0.883 index, 0 to 1, -0.319 typical gaps (standard deviations)Ukraine: 0.37 index, 0 to 1, 0.97 typical gaps (standard deviations)Uruguay: 0.176 index, 0 to 1, 1.41 typical gaps (standard deviations)United States: 0.527 index, 0 to 1, 1.53 typical gaps (standard deviations)Uzbekistan: 0.313 index, 0 to 1, 1.42 typical gaps (standard deviations)South Africa: 0.856 index, 0 to 1, 0.709 typical gaps (standard deviations)Zambia: 0.706 index, 0 to 1, -0.161 typical gaps (standard deviations)Zimbabwe: 0.415 index, 0 to 1, 0.0863 typical gaps (standard deviations)AREBDICAFCANESPHTIJPNKORLBRMDGNORTCD

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.4633.1e-9148
By rank (Spearman), not adjusted-0.4652.5e-9148
Standard (Pearson), adjusted for national income-0.3521.2e-5148
By rank (Spearman), adjusted for national income-0.3402.5e-5148

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.

nr
East Asia & Pacific17-0.48
Europe & Central Asia43+0.17
Latin America & Caribbean21-0.17
Middle East, North Africa, Afghanistan & Pakistan20-0.31
Sub-Saharan Africa41-0.42

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.

nr
Poorest third50-0.39
Middle third49-0.18
Richest third49-0.49

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.

nr
Christians84-0.27
Muslims46-0.43

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
France23-0.58
Never a European colony21-0.29
Spain18-0.18
United Kingdom44-0.43

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 LBR0.889-0.7672013, 2022
Uganda UGA0.883-0.3192013, 2024
Togo TGO0.88-0.02742013, 2024
Nepal NPL0.860.6162013, 2024
South Africa ZAF0.8560.7092013, 2024
Chad TCD0.855-0.722013, 2024
Kenya KEN0.8550.222013, 2024
Mali MLI0.852-0.4952013, 2022
Nigeria NGA0.85-0.08612013, 2024
Guinea-Bissau GNB0.808-0.1582013, 2024
Philippines PHL0.8070.6962013, 2024
Indonesia IDN0.8030.7422013, 2024
Timor-Leste TLS0.8020.3892013, 2024
Sierra Leone SLE0.801-0.3362013, 2022
Malawi MWI0.791-0.6722013, 2024

Drazanova, L. (2019), Historical Index of Ethnic Fractionalization Dataset (HIEF), Harvard Dataverse, doi:10.7910/DVN/4JQRCL
wdi

Sources

Ethnic diversity

Ethnic fractionalization index

Lenka Drazanova · CC0 1.0 · updated one edition (2019), annual values 1945-2013

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.

Infrastructure score

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 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