Across 186 countries, Maternal mortality and Physicians rise and fall together at r = -0.61 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.12, which could easily be chance (q = 0.24, 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. Caution: correlation moves sharply once outliers are trimmed.
Comparing countries across the middle half of maternal mortality, 11 to 148 per 100,000 live births, physicians is worse by 0.866 per 1,000 people at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.155 per 1,000 people.
Link before adjusting (r)-0.606p = 4.8e-20 · 186 countries
After adjusting for national income-0.124p = 0.094 · likely range [-0.26, +0.02]
p, allowing for regions0.081standard error 0.059 · 7 regions
Check for luck (q)0.240across 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.
Maternal mortality · per 100,000 live birthslink with income -0.85 · 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
-0.606
4.8e-20
186
By rank (Spearman), not adjusted
-0.855
2.8e-54
186
Standard (Pearson), adjusted for national income
-0.124
0.094
186
By rank (Spearman), adjusted for national income
-0.482
3.6e-12
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: +0.483 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from -0.143 (without CAF) to -0.105 (without NGA). 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: CAF (-0.019).
Fragile: this link may not hold up.
correlation moves sharply once outliers are trimmed
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
-0.19
Europe & Central Asia
50
-0.03
Latin America & Caribbean
31
-0.19
Middle East, North Africa, Afghanistan & Pakistan
22
-0.22
Sub-Saharan Africa
46
-0.08
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
-0.38
Middle third
62
-0.39
Richest third
62
-0.38
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
-0.08
Muslims
49
-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
25
-0.42
Never a European colony
21
-0.15
Spain
20
-0.35
United Kingdom
63
-0.12
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 = -0.288 after dropping 17 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 34years. 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 shows up within countries over time (r = +0.208) but mostly disappears in year-to-year changes (r = +0.043). That points to slow trends particular to each country, not the two measures moving together from year to year.
Across countries (adjusted)-0.124for comparison
Within countries-0.077each country vs itself
…and vs the same year elsewhere+0.208q = 0.000 · 189 countries
Year-on-year changes+0.043q = 0.137
3,841 country-years (one country in one year), 189 countries, 1990–2023, about 20 years each on average. Share of the variation that happens within countries over time: 15% for Maternal mortality, 11% 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): +0.208. 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
per 100,000 live births
per 1,000 people
Years
Nigeria NGA
993
0.38
2023
Chad TCD
748
0.085
2023
Central African Republic CAF
692
0.074
2023
Liberia LBR
628
0.178
2023, 2022
Somalia, Fed. Rep. SOM
563
0.048
2023, 2014
Afghanistan AFG
521
0.317
2023
Benin BEN
518
0.216
2023
Guinea-Bissau GNB
505
0.252
2023, 2022
Guinea GIN
494
0.21
2023, 2022
Lesotho LSO
478
0.235
2023, 2022
Madagascar MDG
445
0.172
2023, 2022
Congo, Dem. Rep. COD
427
0.208
2023, 2022
Burundi BDI
392
0.078
2023, 2022
Mauritania MRT
381
0.261
2023, 2022
Mali MLI
367
0.188
2023
World Bank, World Development Indicators World Bank, World Development Indicators
Maternal mortality ratio, modelled estimate (per 100,000 live births)
WHO and the UN Maternal Mortality Estimation Inter-Agency Group · CC BY 4.0 · updated quarterly
Estimated with a model, like stunting, and for the same reason. Deaths in childbirth are rare in small countries with weak death records, so the agencies estimate rather than count.
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 2012–2023. 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 12,497 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Maternal mortality and Physicians
This page shows that two measures go together. It does not show that Maternal mortality changes Physicians, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.