Across 186 countries, Control of corruption and Physicians rise and fall together at r = +0.59 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.07, which could easily be chance (q = 0.59, 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, 0.433 to 3.39 per 1,000 people, control of corruption is better by 1.02 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.141 points.
Link before adjusting (r)+0.585p = 1.8e-18 · 186 countries
After adjusting for national income+0.072p = 0.333 · likely range [-0.07, +0.21]
p, allowing for regions0.395standard error 0.078 · 7 regions
Check for luck (q)0.591across 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.
Control of corruption · index (approx. -2.5 to 2.5)link with income +0.73 · high
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
+0.585
1.8e-18
186
By rank (Spearman), not adjusted
+0.585
2.0e-18
186
Standard (Pearson), adjusted for national income
+0.072
0.333
186
By rank (Spearman), adjusted for national income
-0.052
0.485
186
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 186 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: +0.514 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from +0.047 (without GUY) to +0.095 (without RUS). 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: GUY (-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
29
+0.26
Europe & Central Asia
50
+0.04
Latin America & Caribbean
31
+0.11
Middle East, North Africa, Afghanistan & Pakistan
22
+0.23
Sub-Saharan Africa
46
+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
62
-0.06
Middle third
62
-0.13
Richest third
62
+0.25
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
108
+0.05
Muslims
49
-0.15
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
25
-0.26
Never a European colony
21
+0.05
Spain
20
+0.41
United Kingdom
63
+0.19
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 = +0.072 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 28years. 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 = +0.034, 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 control of corruption moved did not tend to see their physicians move with it.
Across countries (adjusted)+0.072for comparison
Within countries+0.029each country vs itself
…and vs the same year elsewhere+0.034q = 0.790 · 177 countries
Year-on-year changes+0.044q = 0.200
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.
2,868 country-years (one country in one year), 177 countries, 1996–2023, about 16 years each on average. Share of the variation that happens within countries over time: 8% for Control of corruption, 4% 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): +0.034. 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.
This curve peaks at the edge of the window (lag +10), so it is still drifting where the search stops. That pattern usually reflects a slow trend left over in the data, not a link with real timing. The peak value should not be read as the “true” link at the right lag.
The data
index (approx. -2.5 to 2.5)
per 1,000 people
Years
Denmark DNK
2.29
4.5
2024, 2021
Finland FIN
2.19
3.61
2024, 2021
Luxembourg LUX
2.02
2.98
2024, 2017
Switzerland CHE
2.01
4.48
2024, 2022
Singapore SGP
1.97
2.83
2024, 2022
Sweden SWE
1.97
4.41
2024, 2021
Norway NOR
1.96
4.97
2024, 2023
New Zealand NZL
1.93
3.61
2024, 2022
Netherlands NLD
1.91
3.88
2024, 2022
Australia AUS
1.85
4.09
2024, 2022
Iceland ISL
1.83
4.37
2024, 2023
Germany DEU
1.77
4.53
2024, 2022
Estonia EST
1.67
3.47
2024, 2022
Canada CAN
1.63
2.82
2024, 2023
Ireland IRL
1.57
3.88
2024, 2023
Kaufmann, D. and A. Kraay, Worldwide Governance Indicators, World Bank World Bank, World Development Indicators
Measures how much people believe public power is used for private gain. It is based on perceptions: it does not count corrupt acts. And perceptions themselves shift with press freedom and with prosecutions.
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 2012–2024. 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 12,497 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Control of corruption and Physicians
This page shows that two measures go together. It does not show that Control of corruption changes Physicians, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.
More links with Control of corruption or Physicians