Do Ethnic diversity and Slum share of urban population go together across countries?

Across 147 countries the link before adjusting for anything is r = +0.50, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives +0.43.

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

Comparing each country only with itself across 2000-2012, and setting aside what happened worldwide in each year, the link is +0.082 (129 countries, 896 country-years). Using year-to-year changes, it is —.

Does Ethnic diversity cause Slum share of urban population?

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.

Ethnic diversity vs. Slum share of urban population

Holds up after adjusting for income

Across 147 countries, Ethnic diversity and Slum share of urban population rise and fall together at r = +0.50 (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 = +0.37, q = 0.027, allowing for the many pairs tested). Caution: direction is not consistent across regions.

Comparing countries across the middle half of ethnic diversity, 0.236 to 0.702 points, slum share of urban population is worse by 23.3% of urban population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 11.3% of urban population.

Link before adjusting (r)+0.497p = 1.6e-10 · 147 countries

After adjusting for national income+0.369p = 4.6e-6 · likely range [+0.22, +0.50]

p, allowing for regions0.002standard error 0.069 · 7 regions

Check for luck (q)0.027across 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

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

best-fit line line after adjusting

Four ways to measure the link

Method	r	p	Countries
Standard (Pearson), not adjusted	+0.497	1.6e-10	147
By rank (Spearman), not adjusted	+0.430	5.3e-8	147
Standard (Pearson), adjusted for national income	+0.369	4.6e-6	147
By rank (Spearman), adjusted for national income	+0.238	0.004	147

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 147 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: +0.128 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.336 (without SLB) to +0.393 (without BGD). 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: SLB (-0.033).

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	+0.33
Europe & Central Asia	43	-0.25
Latin America & Caribbean	21	+0.37
Middle East, North Africa, Afghanistan & Pakistan	19	+0.64
Sub-Saharan Africa	41	+0.30

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	49	+0.39
Middle third	49	+0.40
Richest third	49	+0.16

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	+0.39
Muslims	46	+0.44

By last european coloniser

	n	r
France	23	+0.60
Never a European colony	21	+0.34
Spain	18	+0.31
United Kingdom	43	+0.29

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 = +0.369 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 13years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

Within countries over time, there is no link that is clearly more than chance (r = +0.082, comparing each country only with itself over the years, and each year only with the same year elsewhere). Whatever the cross-country picture shows, countries whose ethnic diversity moved did not tend to see their slum share of urban population move with it.

Across countries (adjusted)+0.369for comparison

Within countries+0.002each country vs itself

…and vs the same year elsewhere+0.082q = 0.298 · 129 countries

Year-on-year changes—q = —

Read with care.

Almost all of this indicator's variation is between countries, not within them, so the within-country estimate rests on a small part of the data.

896 country-years (one country in one year), 129 countries, 2000–2012, about 7 years each on average. Share of the variation that happens within countries over time: 0% for Ethnic diversity, 5% for Slum share of urban population. The margins of error allow for each country appearing many times, and for each year’s value being close to the year before.

Year-to-year changes are a cautious test, not a final answer. Looking only at changes from one year to the next removes each country’s own trends. But it also makes measurement mistakes louder, which pulls the results toward zero. A near-zero result here is weak evidence of no link, not proof.

With one measure years behind the other

Same year (lag 0): +0.082. A peak away from zero does not show which comes first. These series change slowly — this year’s value is close to last year’s. That makes the curve smooth and nearly symmetric, whatever produced the data. Even a sharp peak at one lag (a gap of some years) would not prove that one measure changes the other.

The data

	index, 0 to 1	% of urban population	Years
Liberia LBR	0.889	60.5%	2013, 2022
Uganda UGA	0.883	52.7%	2013, 2022
Togo TGO	0.88	38.5%	2013, 2022
Nepal NPL	0.86	40.1%	2013, 2022
South Africa ZAF	0.856	24.2%	2013, 2022
Chad TCD	0.855	82%	2013, 2022
Kenya KEN	0.855	40.5%	2013, 2022
Mali MLI	0.852	92.5%	2013, 2022
Nigeria NGA	0.85	48.5%	2013, 2022
Guinea-Bissau GNB	0.808	59%	2013, 2022
Philippines PHL	0.807	35.9%	2013, 2022
Indonesia IDN	0.803	19.4%	2013, 2022
Timor-Leste TLS	0.802	33.9%	2013, 2022
Sierra Leone SLE	0.801	49.3%	2013, 2022
Malawi MWI	0.791	38%	2013, 2022

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

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.

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 2012–2022. 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 Ethnic diversity and Slum share of urban population

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