Do Ethnic diversity and Physicians go together across countries?

Across 148 countries the link before adjusting for anything is r = -0.49, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.50.

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

Comparing each country only with itself across 1990-2013, and setting aside what happened worldwide in each year, the link is +0.342 (146 countries, 2,178 country-years). Using year-to-year changes, it is +0.064.

Does Ethnic diversity cause Physicians?

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. Physicians

Holds up after adjusting for income

Across 148 countries, Ethnic diversity and Physicians rise and fall together at r = -0.49 (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 = 0.024, allowing for the many pairs tested).

Comparing countries across the middle half of ethnic diversity, 0.239 to 0.701 points, physicians is worse by 1.51 per 1,000 people at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.717 per 1,000 people.

Link before adjusting (r)-0.488p = 3.2e-10 · 148 countries

After adjusting for national income-0.352p = 1.2e-5 · likely range [-0.49, -0.20]

p, allowing for regions0.001standard error 0.064 · 7 regions

Check for luck (q)0.024across 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

Physicians · per 1,000 peoplelink with income +0.85 · 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	-0.488	3.2e-10	148
By rank (Spearman), not adjusted	-0.496	1.5e-10	148
Standard (Pearson), adjusted for national income	-0.352	1.2e-5	148
By rank (Spearman), adjusted for national income	-0.371	3.8e-6	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: +0.136 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.376 (without EGY) to -0.335 (without GUY). 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: EGY (-0.025).

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	-0.25
Europe & Central Asia	43	-0.18
Latin America & Caribbean	21	-0.47
Middle East, North Africa, Afghanistan & Pakistan	20	-0.33
Sub-Saharan Africa	41	-0.23

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	-0.23
Middle third	49	-0.37
Richest third	49	-0.51

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	-0.40
Muslims	46	-0.39

By last european coloniser

	n	r
France	23	-0.56
Never a European colony	21	-0.28
Spain	18	-0.67
United Kingdom	44	-0.16

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 24years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

The link also shows up within countries over time (r = +0.342). It holds even when looking only at changes from one year to the next (r = +0.064). This is the strongest kind of evidence available here: it holds when each country is compared only with itself, and it is not just a product of shared trends.

Across countries (adjusted)-0.352for comparison

Within countries+0.365each country vs itself

…and vs the same year elsewhere+0.342q = 5.0e-5 · 146 countries

Year-on-year changes+0.064q = 0.001

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.
The association is much weaker in year-on-year changes than in levels, which points to shared country-specific trends.

2,178 country-years (one country in one year), 146 countries, 1990–2013, about 15 years each on average. Share of the variation that happens within countries over time: 1% for Ethnic diversity, 6% for Physicians. 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): +0.342. 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	per 1,000 people	Years
Liberia LBR	0.889	0.178	2013, 2022
Uganda UGA	0.883	0.191	2013, 2022
Togo TGO	0.88	0.08	2013, 2022
Nepal NPL	0.86	1.01	2013, 2023
South Africa ZAF	0.856	0.794	2013, 2022
Chad TCD	0.855	0.085	2013, 2023
Kenya KEN	0.855	0.289	2013, 2023
Mali MLI	0.852	0.188	2013, 2023
Nigeria NGA	0.85	0.38	2013, 2023
Guinea-Bissau GNB	0.808	0.252	2013, 2022
Philippines PHL	0.807	0.792	2013, 2021
Indonesia IDN	0.803	0.524	2013, 2023
Timor-Leste TLS	0.802	0.752	2013, 2020
Sierra Leone SLE	0.801	0.134	2013, 2022
Malawi MWI	0.791	0.054	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.

Physicians

Physicians (per 1,000 people)

World Health Organization · CC BY 4.0 · updated quarterly

Reported irregularly, so the latest value in the time window used is from a different year for different countries. Hospital beds, already on the site, have no "better" direction, since more beds is not plainly better. More doctors is closer to plainly better, so a direction is set on that basis.

How this was computed

Data used: most recent value in 2010-2025; values come from 2013–2023. 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 Physicians

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