Across 184 countries, Education score and Physicians rise and fall together at r = +0.72 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.23, which could easily be chance (q = 0.075, allowing for the many pairs tested, and allowing for countries in the same region being alike). So the first figure mostly shows richer countries differing from poorer ones, not a link between these two things.
Comparing countries across the middle half of physicians, 0.451 to 3.4 per 1,000 people, education score is better by 0.811 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.241 typical gaps.
Link before adjusting (r)+0.718p = 1.8e-30 · 184 countries
After adjusting for national income+0.229p = 0.002 · likely range [+0.09, +0.36]
p, allowing for regions0.074standard error 0.106 · 7 regions
Check for luck (q)0.075across 12,497 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.
Education score · typical gaps (standard deviations)link with income +0.81 · proxy
Physicians · per 1,000 peoplelink with income +0.85 · proxy
Both of these rise and fall closely with national income, so a strong link between them before adjusting was close to certain. Read the adjusted figure, not the first one.
“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
+0.718
1.8e-30
184
By rank (Spearman), not adjusted
+0.824
1.0e-46
184
Standard (Pearson), adjusted for national income
+0.229
0.002
184
By rank (Spearman), adjusted for national income
+0.426
1.8e-9
184
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 184 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: +0.490 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from +0.206 (without GUY) to +0.254 (without SOM). 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: SOM (+0.026).
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
29
+0.25
Europe & Central Asia
50
+0.11
Latin America & Caribbean
30
+0.42
Middle East, North Africa, Afghanistan & Pakistan
21
+0.45
Sub-Saharan Africa
46
+0.10
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
62
+0.41
Middle third
61
+0.27
Richest third
61
+0.47
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
107
+0.25
Muslims
48
+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
24
+0.21
Never a European colony
21
+0.19
Spain
20
+0.57
United Kingdom
62
+0.13
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 = +0.313 after dropping 6 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
typical gaps (standard deviations)
per 1,000 people
Years
New Zealand NZL
1.44
3.61
2023, 2022
Greece GRC
1.31
6.58
2023, 2022
Netherlands NLD
1.27
3.88
2023, 2022
Iceland ISL
1.26
4.37
2023
Singapore SGP
1.26
2.83
2023, 2022
Belgium BEL
1.25
3.57
2023, 2022
United Kingdom GBR
1.19
3.3
2023
Ireland IRL
1.18
3.88
2022, 2023
Slovenia SVN
1.18
3.37
2023, 2022
St. Kitts and Nevis KNA
1.13
3.08
2016, 2018
United States USA
1.11
3.68
2022
Czechia CZE
1.08
4.35
2023, 2022
Canada CAN
1.06
2.82
2023
Japan JPN
1.04
2.65
2023, 2022
Argentina ARG
1.03
5.11
2023
uis; undp_hdr; wdi World Bank, World Development Indicators
PlaceStats · via PlaceStats · Derived work; each member keeps its own licence · updated recomputed with the panel
The average of 10 measures from 3 publishers. 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: Primary completion, Expected schooling, Mean years of schooling, Adult literacy, Pupil-teacher ratio, Primary enrollment, Out of school, primary, Secondary enrollment, Tertiary enrollment, Researchers.
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 2010–2025. 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 12,497 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Education score and Physicians
This page shows that two measures go together. It does not show that Education score changes Physicians, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.