Do Birth rate and Physicians go together across countries?

Across 186 countries the link before adjusting for anything is r = -0.76, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.84.

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

Comparing each country only with itself across 1990-2023, and setting aside what happened worldwide in each year, the link is +0.080 (189 countries, 3,841 country-years). Using year-to-year changes, it is +0.006.

Does Birth rate 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.

Birth rate vs. Physicians

Fades after adjusting for income

Across 186 countries, Birth rate and Physicians rise and fall together at r = -0.76 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.27, which could easily be chance (q = 0.094, 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 birth rate, 9.99 to 25.3 per 1,000 people, physicians is worse by 2.01 per 1,000 people at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.889 per 1,000 people.

Link before adjusting (r)-0.761p = 2.0e-36 · 186 countries

After adjusting for national income-0.274p = 0.000 · likely range [-0.40, -0.14]

p, allowing for regions0.016standard error 0.083 · 7 regions

Check for luck (q)0.094across 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.

Birth rate · per 1,000 peoplelink 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	-0.761	2.0e-36	186
By rank (Spearman), not adjusted	-0.843	2.3e-51	186
Standard (Pearson), adjusted for national income	-0.274	0.000	186
By rank (Spearman), adjusted for national income	-0.424	1.8e-9	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: +0.487 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.293 (without THA) to -0.245 (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: GUY (+0.030).

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	-0.08
Europe & Central Asia	50	-0.30
Latin America & Caribbean	31	-0.35
Middle East, North Africa, Afghanistan & Pakistan	22	-0.03
Sub-Saharan Africa	46	-0.32

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	-0.37
Middle third	62	-0.18
Richest third	62	-0.42

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	-0.43
Muslims	49	-0.25

By last european coloniser

	n	r
France	25	-0.44
Never a European colony	21	+0.07
Spain	20	-0.46
United Kingdom	63	-0.21

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 = -0.274 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 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.

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 birth rate moved did not tend to see their physicians move with it.

Across countries (adjusted)-0.274for comparison

Within countries-0.337each country vs itself

…and vs the same year elsewhere+0.080q = 0.307 · 189 countries

Year-on-year changes+0.006q = 0.924

The direction flips. Comparing each country only with itself gives -0.34. Also taking out what happened to the whole world each year gives +0.08. So the first figure was picking up a trend both measures share with worldwide progress, not a link between them.

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.
Much of the within-country association disappears once shared global year effects are removed, which points to a common worldwide trend rather than a country-level relationship.

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: 9% for Birth rate, 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): +0.080. 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

	per 1,000 people	per 1,000 people	Years
Central African Republic CAF	46.2	0.074	2024, 2023
Chad TCD	43.3	0.085	2024, 2023
Somalia, Fed. Rep. SOM	42.3	0.048	2024, 2014
Niger NER	41.4	0.038	2024, 2023
Congo, Dem. Rep. COD	40.9	0.208	2024, 2022
Mali MLI	39.5	0.188	2024, 2023
Angola AGO	37.2	0.244	2024, 2022
Mozambique MOZ	37	0.175	2024, 2022
Afghanistan AFG	35	0.317	2024, 2023
Tanzania TZA	34.8	0.134	2024, 2022
Uganda UGA	34.4	0.191	2024, 2022
Mauritania MRT	34	0.261	2024, 2022
Benin BEN	33.4	0.216	2024, 2023
Cameroon CMR	33.3	0.136	2024, 2022
Guinea GIN	33.3	0.21	2024, 2022

World Bank, World Development Indicators
World Bank, World Development Indicators

Sources

Birth rate

Birth rate, crude (per 1,000 people)

World Bank · CC BY 4.0 · updated quarterly

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 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 Birth rate and Physicians

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