Across 187 countries, Control of corruption and Infant mortality rise and fall together at r = -0.60 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.08, which could easily be chance (q = 0.67, 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 infant mortality, 4.75 to 28.8 per 1,000 live births, control of corruption is worse by 0.849 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.132 points.
Link before adjusting (r)-0.601p = 9.9e-20 · 187 countries
After adjusting for national income-0.079p = 0.286 · likely range [-0.22, +0.07]
p, allowing for regions0.580standard error 0.135 · 7 regions
Check for luck (q)0.672across 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
Infant mortality · per 1,000 live birthslink with income -0.86 · 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
-0.601
9.9e-20
187
By rank (Spearman), not adjusted
-0.678
1.6e-26
187
Standard (Pearson), adjusted for national income
-0.079
0.286
187
By rank (Spearman), adjusted for national income
-0.189
0.010
187
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 187 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: +0.522 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from -0.093 (without LBY) to -0.050 (without GNQ). 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: GNQ (+0.029).
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
-0.14
Europe & Central Asia
51
-0.10
Latin America & Caribbean
31
-0.15
Middle East, North Africa, Afghanistan & Pakistan
22
+0.03
Sub-Saharan Africa
46
-0.47
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
63
-0.28
Middle third
62
-0.06
Richest third
62
-0.37
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
-0.13
Muslims
50
+0.08
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
+0.15
Never a European colony
21
-0.00
Spain
20
-0.30
United Kingdom
63
-0.32
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 = -0.068 after dropping 3 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 29years. 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 = -0.080, 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 infant mortality move with it.
Across countries (adjusted)-0.079for comparison
Within countries-0.016each country vs itself
…and vs the same year elsewhere-0.080q = 0.242 · 194 countries
Year-on-year changes+0.001q = 0.944
Read with care.
Almost all of the second indicator's variation is between countries, so the within-country estimate rests on a small part of the data.
4,937 country-years (one country in one year), 194 countries, 1996–2024, about 25 years each on average. Share of the variation that happens within countries over time: 17% for Control of corruption, 5% for Infant mortality. 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): -0.080. 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 (approx. -2.5 to 2.5)
per 1,000 live births
Years
Denmark DNK
2.29
3.5
2024
Finland FIN
2.19
1.9
2024
Luxembourg LUX
2.02
1.9
2024
Switzerland CHE
2.01
3.4
2024
Singapore SGP
1.97
2.2
2024
Sweden SWE
1.97
2
2024
Norway NOR
1.96
2
2024
New Zealand NZL
1.93
4
2024
Netherlands NLD
1.91
3.4
2024
Australia AUS
1.85
3.1
2024
Iceland ISL
1.83
2.1
2024
Germany DEU
1.77
3.1
2024
Estonia EST
1.67
1.5
2024
Canada CAN
1.63
4.7
2024
Ireland IRL
1.57
3.4
2024
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.
Data used: most recent value in 2010-2025; values come from 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 Infant mortality
This page shows that two measures go together. It does not show that Control of corruption changes Infant mortality, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.
More links with Control of corruption or Infant mortality